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Spare Parts Logistics and Installed Base Information
Abstract
Many of the challenges in spare parts logistics emerge due to the combination of large service networks, and sporadic/slow-moving demand. Customer heterogeneity and stringent service deadlines entail further challenges. Meanwhile, high revenues rates in service operations motivate companies to invest and optimize the service logistics function. An important aspect of the spare parts logistics function is its ability to support customer-specific requirements with respect to service deadlines. To support customer specific operations, many companies are actively maintaining and utilizing installed base data during forecasting, planning and execution stages. In this paper, we highlight the potential economic value of installed base data for spare parts logistics. We also discuss various data quality issues that are associated with the use of installed base data and show that planning performance depends on the quality dimensions.
... We refer to those characteristics as the installed base information (IBI). In the literature the following sources of IBI have been introduced: the size of the installed base (e.g., Jalil et al., 2011;Stormi et al., 2018) and its evolution over time (e.g., Jin and Liao, 2009;Kim et al., 2017), the age of the installed base (e.g., Kim et al., 2017; Van der Auweraer and Boute, 2019), the age of the parts in the products/machines (e.g., Deshpande et al., 2006;Minner, 2011), the part reliability (e.g., Ritchie and Wilcox, 1977;Hong et al., 2008), which can be impacted by environmental factors (e.g., Ghodrati and Kumar, 2005a,b), and the maintenance policy in place (e.g., Wang and Syntetos, 2011;Zhu et al., 2020). ...
... Kim et al. (2017) and Van der Auweraer and Boute (2019), for example, explicitly consider information on such historical machine discards and consider the size of the active installed base as a source of IBI. Other authors implicitly assume that the true size of the installed base active at a certain point in time is known and use this information to predict demand (see for example, Ghodrati and Kumar, 2005b;Jalil et al., 2011;Lin et al., 2017;Stormi et al., 2018). Some works (additionally) consider the anticipation of potential future discards, decreasing the number of active machines (see for example Chou et al., 2015;Kim et al., 2017;Minner, 2011;Stormi et al., 2018;, or expiring service contracts (Pince et al., 2015) as a source of IBI. ...
... Only these products/machines are maintained and generate spare part demands. The size of the active installed base can either directly be taken into account (for example, Ghodrati and Kumar, 2005a;Jalil et al., 2011;Stormi et al., 2018), or indirectly by tracking the number of machine installs and machine discards. The latter indicate when machines are no longer maintained and cease to generate demand (e.g., Kim et al., 2017;. ...
This paper analyzes the value of different sources of installed base information for spare part demand forecasting and inventory control. The installed base is defined as the set of products (or machines) in use where the part is installed. Information on the number of products still in use, the age of the products, the age of their parts, as well as the part reliability may indicate when a part will fail and trigger a demand for a new spare part. The current literature is unclear which of this installed base information adds most value – and should thus be collected – for inventory control purposes. For this reason, we evaluate the inventory performance of eight methods that include different sets of installed base information in their demand forecasts. Using a comparative simulation study we identify that knowing the size of the active installed base is most valuable, especially when the installed base changes over time. We also find that when a failure-based prediction model is used, it is important to work with the part age itself, rather than the machine age. When one is not able to collect information on the part age, a logistic regression on the machine age might be a valuable alternative to a failure-based prediction model. Our findings may support the prioritization of data collection for spare part demand forecasting and inventory control.
... The fleet size often expands rapidly during the introduction stage and shrinks in the decline stage [7] . Compared with traditional resource-based contracting, manufacturers are currently highly conscious of the importance of installation forecasting for service-oriented operational practices, especially in capitalintensive industries, since the fleet size and the failure rate are the determinants of repair demand [5,14] . There is limited but growing recent literature on installation forecasting and spare parts provisioning. ...
... As a result, the expected backorder quantity at the central depot in interval k can be determined by (14) as follows: ...
... The mathematics involved in the proofs is straightforward but tedious. The convexity of the functions for the expected backorder quantities (14) and (24) and the objective function of Model P1 (40) provide a theoretical basis for implementing the greedy algorithm based on marginal analysis that sequentially converges to the global maxima. The marginal benefit at the central depot is defined as the decrease in the expected backorders divided by the change in the expected profit by increasing the inventory level by one unit, as shown in (44) below: ...
Performance-based service is becoming a dominant business strategy, especially in the capital-intensive equipment industry. When new equipment is released into multiregional markets, managing repairable spare parts provisioning so as to maximize the service profit from an increasing number of installations becomes challenging since the demand for spare parts varies over time across multiregional markets. A two-echelon repairable spare parts service network is studied to support performance-based service under multiregional fleet expansions. This study proposes an adaptive multi-phase basestock policy that dynamically adjusts the basestock levels in the two-echelon warehouses and the repair capacity in the central depot to meet the nonstationary demands. A profit-centric model is formulated that considers linear-step revenue and availability bands. Using the structural properties of the optimization model, a greedy algorithm is developed based on marginal analysis to solve the problem. Numerical experiements are conducted using a case study of a real business problem and its variants to demonstrate the effectiveness and applicability of the proposed model and the greedy algorithm.
... Third, Ghodrati & Kumar (2005a,b) additionally identify the operating environment in which the installed base operates (e.g., the humidity or the dust) as a factor that affects part reliability and as such spare part demand. Similarly, Jalil et al. (2011) and Andersson & Jonsson (2018) use information on the regional distribution of the installed products (e.g., the installed base may be concentrated in certain regions). ...
... A different installed base approach is used by Jalil et al. (2011), who study where to place the spare part inventories throughout a service network. They make an aggregate forecast for an entire region, where they sum up the historical spare part demand observed at each location. ...
... Table 5 classifies the causal based forecast methods that make use of installed base information into three categories (as in Andersson & Jonsson (2018)): reliability based forecasting, regression based forecasting, and condition-based maintenance (forecasting using sensor data). The method of Jalil et al. (2011) is not included in this classification, as it uses Category Papers Reliability based Shaunty & Hare Jr (1960); Ritchie & Wilcox (1977); Fortuin (1984); Yamashina (1989); Petrovic & Petrovic (1992) Andersson & Jonsson (2018) the installed base information merely as an allocation method, nor is the work of Romeijnders et al. (2012), who apply a time series based forecasting, rather than a causal method. ...
The classical spare part demand forecasting literature studies methods for forecasting intermittent demand. However, the majority of these methods do not consider the underlying demand-generating factors. The demand for spare parts originates from the replacement of parts in the installed base of machines, either preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast the future demand for spare parts. This paper reviews the literature on the use of such installed base information for spare part demand forecasting in order to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) the value of using installed base information to improve forecasting; and (4) the limits of the existing methods. This serves as motivation for future research.
... Third, Ghodrati & Kumar (2005a,b) additionally identify the operating environment in which the installed base operates (e.g., the humidity or the dust) as a factor that affects part reliability and as such spare part demand. Similarly, Jalil et al. (2011) and Andersson & Jonsson (2018) use information on the regional distribution of the installed products (e.g., the installed base may be concentrated in certain regions). ...
... A different installed base approach is used by Jalil et al. (2011), who study where to place the spare part inventories throughout a service network. They make an aggregate forecast for an entire region, where they sum up the historical spare part demand observed at each location. ...
... Table 5 classifies the causal based forecast methods that make use of installed base information into three categories (as in Andersson & Jonsson (2018)): reliability based forecasting, regression based forecasting, and condition-based maintenance (forecasting using sensor data). The method of Jalil et al. (2011) is not included in this classification, as it uses Category Papers Reliability based Shaunty & Hare Jr (1960); Ritchie & Wilcox (1977); Fortuin (1984); Yamashina (1989); Petrovic & Petrovic (1992) Andersson & Jonsson (2018) the installed base information merely as an allocation method, nor is the work of Romeijnders et al. (2012), who apply a time series based forecasting, rather than a causal method. ...
... Hence the location and number of products in use, also called the Installed Base (IB), is of primary interest as generator of spare parts demand. Several authors, including Jalil, Zuidwijk, and Fleischmann (2011) and Dekker, Pince, Zuidwijk, and Jalil (2013), have therefore proposed to use IB as the causal variable in forecasting spare parts demand. This kind of approach requires that companies keep track of their IB. ...
... IB forecasting then consists of establishing the demand per product and forecasting the future development of IB. This two-step forecasting procedure does require some work in practice because products may have been adapted for customers and demand may also be influenced by local conditions (Jalil et al., 2011). ...
... Jin and Liao (2009) use IB within a simulation context for inventory control to satisfy maintenance demand for spare parts and assume that the IB is known. Thereafter, Jalil et al. (2011) describe further experience with IBM and highlight the value of the IB concept. Dekker et al. (2013) review the use of this concept and its application at several companies. ...
When stopping production, the manufacturer has to decide on the lot size in the final production run to cover spare part demand during the end-of-life phase. This decision can be supported by forecasting how much demand is expected in the future. Forecasts can be obtained from the installed base of the product, that is, the number of products still in use. This type of information is relatively easily available in case of B2B maintenance contracts, but it is more complicated in B2C spare parts supply management. Consumer decisions on whether or not to repair a malfunctioning product depend on the specific product and spare part. Further, consumers may differ in their decisions, for example, for products with fast innovations and changing social trends. Consumer behavior can be accounted for by using appropriate types of installed base, for example, lifetime installed base for essential spare parts of expensive products with ling lifecycle, and warranty installed base for products with short lifecycle. This paper proposes a set of installed base concepts with associated simple empirical forecasting methodologies that can be applied in practice for B2C spare parts supply management during the end-of-life phase of consumer products. The methodology is illustrated by case studies for eighteen spare parts of six products from a consumer electronics company. The research hypotheses on which installed base type performs best under which conditions are supported in the majority of cases, and forecasts obtained from installed base are substantially better than simple black box forecasts. Incorporating past sales via installed base therefore supports final production decisions to cover future consumer demand for spare parts.
... Dekker et al. [8] studied the use of IBI in forecasting spare parts demand and return, and conclude that these forecasts can be made timelier and more accurate using IBI, compared to using only historical demand. Jalil et al. [16] analyzed the value of installed base data in spare parts planning. They report cost savings of 1% to 58% using machine location data to derive transportation costs, travel times, and demand forecasts. ...
... Jalil et al. [16] studied the effect of data quality in spare parts planning. They observed that the gains of using IBI deteriorated because of systematic data errors: all installed base items reported at the headquarters location or at the primary stock location due to incomplete or missing location data, and data communication error due to mismatch in communication with the vendor of the installed base items. ...
... It is seen of importance for manufacturers' service delivery process, for the performance measurement, and the development of user's operations [6]. The relevance of IBI is reported for the forecasting of spare parts demand [16,22]. Equipment location information can be utilized for optimizing the traveling routes of field engineers [9,16]. ...
Installed base information (IBI) is used in industrial service operations, but currently there are challenges with maintaining and utilizing this information to its full potential. The purpose of this paper is to aid in improving the realized value of IBI. We conducted two case studies of IBI collection and utilization with two industrial product-service system suppliers. From this material, we identified four elements contributing to the value of IBI and constructed a framework for managing this value. Furthermore, we identified the sources of the difference between the potential value and the realized value of IBI. The elements contributing to the value of IBI are its management, scope, utilization and quality. These elements form interconnected leaves in the proposed value clover framework. Each element potentially contributes to the difference between the theoretical maximum value and the realized value of IBI. Future research should look into the different elements and their relationships in more detail. Our framework helps managers in identifying and balancing the different elements of IBI value and in deciding on investments in this area. Previous literature has recognized the need for IBI, but not analyzed the different elements affecting its value. Our research offers industrial service operations a novel framework of IBI value.
... Jin and Liao (2009) discuss inventory control in case of a stochastic growing installed base and assume the data is available. Jalil et al. (2010) highlight the potential economic value of installed base data for spare parts logistics. They also discuss various data quality issues that are associated with the use of installed base data and show that planning performance depends on the quality dimensions. ...
... We should clarify that in addition to demand forecasting, installed base data is also used to derive other input parameters (such as transportation costs and possible delivery options) for spare parts inventory planning at IBM. During spare parts logistics execution, installed base data is used to determine customer entitlements for service delivery (Jalil et al. 2010;Draper and Suanet, 2005). Fleischmann et al. (2003) study the interaction of spare parts logistics and asset recovery at IBM and highlight the benefits of combining installed base data with extrapolation methods for product return forecasting. ...
... The postal code level forecast is obtained by disaggregating the country level -contract type demand forecast according to each contract type installs present at each postal code. The details of this forecasting method are discussed in Jalil et al. (2010). The derived demand forecasts are used as an input for the spare parts inventory planning application that determines base stock levels for forward stock locations (Erke et al. 2003). ...
Demand for spare parts is often difficult to forecast using historical data only. In this paper, we give an overview of installed based information and provide several ways in which installed base forecasting can be used. We discuss cases of installed based forecasting at four companies and list the issues involved. Moreover, we provide some models to assess the value of installed base information and conclude that forecasts of spare parts demand and return can be made considerably more timely and accurate by using installed base information.
... To gain more insight, we also added 15 fictional SKUs. First, we used the ten SKUs from Table 4 and doubled the shipment lead times between central warehouse and local warehouses (SKU [11][12][13][14][15][16][17][18][19][20]. Our hypothesis H1 is that interventions in the downstream part of the supply chain are more effective if these lead times are longer. ...
... Experimental results for doubled shipment lead-time between central warehouse and local warehouse (SKUs[11][12][13][14][15][16][17][18][19][20]. Numbers in bold indicate a significant difference compared to no interventions (alpha = 0.05). ...
... We further analyze the impact of these data quality errors on spare parts inventory planning and highlight the role of an error's structural and ontological characteristics. This chapter is based on the paper Spare Parts Logistics and Installed Base Information (Jalil et al., 2010b). We should note that this chapter is inspired by spare parts inventory planning situation at IBM. ...
... This chapter is based on the paper Spare Parts Logistics and Installed Base Information(Jalil et al., 2010b). ...
Over the years, after sales service business in capital goods and high tech sectors has experienced significant growth. The drivers for growth are higher service profits, increased competitions, and primary market contractions. The enablers for growth include information driven service processes and a move from one size fit all oriented warranty contracts to service level agreement offerings that differ in service prices and response guarantees. Although, these trends provide an opportunity to the service providers to match their service resources to the time varying service requirements of a heterogeneous customer base, the tools and techniques to support decision makers are lacking as of to date. In this thesis, we aim to make a small contribution in closing this gap. We gain business environment related insights of after sales service by studying it at a major computer equipment manufacturer. After sales service is a complex task that is accomplished by making a series of strategic, tactical, and operational decisions in maintenance services management, spare parts logistics management and spare part returns management. We exclusively focus on operational and tactical decisions in spare parts logistics management. We identify that customer information, or more specifically installed base information is a valuable source to support spare parts logistics decisions at the operational and tactical levels. We present an execution technique for spare parts logistics that uses installed base information to provide differentiated service to a heterogeneous customer base and results in additional profits for the service provider. Finally, we study execution decisions in spare parts logistics and spare part returns management for their interrelation. We highlight that explicit consideration of this interrelation yields additional benefits.
... The installed base constitutes a rich compilation of data about the customer, the equipment, and the service history. Although it is a significant challenge, capturing, cultivating, and distributing these data can be very rewarding for the OEM (Främling, Ala-Risku, Karkainen, & Holmstorm, 2006;Jalil, Zuidwijk, Fleischmann, & van Nunen, 2011;Johnson & Mena, 2008). ...
... The conventional way to forecast spare part demand is to use historical sales data (Teunter & Duncan, 2009), but there is a growing body of researchers that considers spare part demand as dependent on factors such as the size of the installed base or the failure rate of the equipment (Hong, Koo, Lee, & Ahn, 2008). With reliable forecasts, the OEM can anticipate spare part demand by adapting its inventory allocation and procurement practices accordingly (Jalil et al., 2011). ...
Increasingly, original equipment manufacturers (OEMs) are offering integrated solutions of products and services (product service systems (PSS)). To be successful in a service strategy, the OEM has to be able to provide services at a lower cost than the do-it-yourself alternative of the customer. We identify five service operations guidelines to achieve operational excellence in the field of PSS. The guidelines are based on a structured return-on-invested-capital analysis in collaboration with an OEM in the compressed air and generator industry (due to confidentiality reasons the OEM is referred to as AirGen). Each of the guidelines is cascaded into operational practices.
... Several studies have been carried out using the exponential smoothing method produce accurate forecasting values making writer interested in conducting similar research, such as Supriatin and Rohman (2020), Navalina et al. (2020), Jalil et al. (2011). Then the writer will predict the amount Pangasius production in South Sumatra uses the exponential method smoothing. ...
The product of inland waters in South Sumatra which is very potential, namely Patin fish (Pangasius). South Sumatra is working on the export potential of pangasius because production has penetrated the first rank in Indonesia. Sumatra Island is the largest contributor, namely 68.07% of the total national pangasius production, while South Sumatra is recorded as the largest pangasius aquaculture producer in Indonesia, which is around 47.4% of the total national production. As one of the production centers for pangasius in Indonesia, every year it is hoped that there will be an increase in production to meet local and national demand. This study aims to predict pangasius production in South Sumatra on 2023 using the Single Exponential Smoothing Method. The data used is secondary data from the Ministry of Maritime Affairs and Fisheries and the Central Statistics Agency for South Sumatra in the form of pangasius production data in South Sumatra from 2006 to 2022. Forecasting results are carried out by calculating the average error value using the MAPE method. The results showed that the MAPE value of the pangasius production forecasting model in South Sumatra was 43,58%. Forecasting pangasius production in South Sumatra in 2023 is 53.111,15 tons.
... The methods employed to predict spare parts consumption in the literature are the statistical and probabilistic models that use only historical consumption data or mix them with their corresponding IB information to predict the consumption in the following periods at the EOL of the spare part. These models are elaborated for a short horizon, providing more certainty to the calculated forecast (Kim et al., 2017;Van der Auweraer and Boute, 2019;Jalil et al., 2011). ...
This paper addresses the complexity in spare parts supply chains, emphasizing the importance of
predicting spare parts demand in the long term. Conventional forecasting methods relying on expert
knowledge, statistical approaches, and consumer/market research face limitations, especially in the
absence of sufficient predictive data. The paper introduces a novel approach employing Transfer
Learning for predicting installed base (IB) information during different phases of healthcare
product life cycles. A two-stage Transfer Deep Learning framework, trained on data from previous
product generations, is employed to predict IB information and subsequently forecast spare parts
consumption. The study explores the correlation between spare part consumption, characteristics,
and IB information, showcasing the impact of IB information on consumption. Comparative
analysis of four deep learning models—RNN, LSTM, a combination of RNN and LSTM, and GRU
reveals that GRU and RNN are the most effective models, demonstrated through two use cases
based on GEHC data. This research contributes a valuable framework for predicting spare parts
consumption in the healthcare industry, bridging the gap between product installed base prediction
and spare parts demand forecasting.
Keywords: healthcare industry; closed-loop supply chain; spare parts consumption prediction; product
IB prediction; Deep learning, Transfer learning.
... (7) and (8) effectively capture the nonstationary behavior of spares demand during the lead time, they can serve as the theoretical basis for planning spares inventory during the new product introduction. Jalil et al. (2011) examined where to place the spare parts inventories throughout a service supply chain network. An aggregate forecast is performed for an entire region by summing up the historical spare parts demand observed at individual locations. ...
Recently, firms have begun to handle the design, manufacturing, and maintenance of capital goods through a consolidated mechanism called the integrated product-service system. This new paradigm enables firms to deliver high-reliability products while lowering the ownership cost. Hence, holistic optimization models must be proposed for jointly allocating reliability, maintenance, and spare parts inventory across the entire value chain. In the existing literature, these decisions are often made fragmentally, thus resulting in local optimality. This study reviews the extant works pertaining to reliability-redundancy allocation, preventative maintenance, and spare parts logistics models. We discuss the challenges and opportunities of consolidating these decisions under an integrated reliability-maintenance-inventory framework for attaining superior system availability. Specific interest is focused on the new product introduction phase in which firms face a variety of uncertainties, including installed base, usage, reliability, and trade policy. The goal is to call for tackling the integrated reliability-maintenance-inventory allocation model under a nonstationary operating condition. Finally, we place the integrated allocation model in the semiconductor equipment industry and show how the firm deploys reliability initiatives and after-sale support logistics to ensure the fleet uptime for its global customers.
... In the context of spare parts inventory, the installed base corresponds to the set of all components supported by the inventory system (Dekker et al., 2013). The contributions in this research stream investigate the impact of incorporating information on the installed base such as quantity, failure rate, hours of operation, and geographic distribution of operating components/machines supported by the inventory system (Jalil et al., 2011). A recent review on the use of IBI for spare parts demand prediction was reported by . ...
The performance of any inventory control model depends on the quality of future demand forecasting. The better the accuracy of future demand predictions is, the lower is the safety inventory level needed to fulfill demands and meet fill rate requirements. In most real-world applications, historical demand profiles are commonly used to predict future demands. In the specific case of spare parts inventory, one way to improve the accuracy of demand prediction is to use information obtained by monitoring the degradation level of components. This approach may be especially important when demand behavior can vary over time, for instance, due to a change in the operational conditions. Thus, this paper aims at presenting a novel spare parts inventory control model for non-repairable items with periodic review. In the proposed model, Remaining Useful Life (RUL) predictions of monitored components obtained from a Prognostics and Health Monitoring (PHM) system are used to predict future demands for spare parts. It allows the reorder point, s, and the order-up-to level, S, to be dynamically adjusted as new PHM data become available. It is assumed that PHM data are updated periodically, with period R. The proposed model minimizes the total inventory cost subject to a fill rate constraint. Numerical experiments are carried out to compare the performance of the proposed [R, s, S] model with the classical [s, S] model in terms of average total cost per period and average inventory level. The results show that the proposed model yields a reduction in both the average total cost per period and the average inventory level.
... Taking this variable into account is discussed by various authors, e.g. Jalil et al. (2011), Minner (2011, Dekker et al. (2013), and Kim et al. (2017), and is referred to as installed base forecasting. A recent paper by van der Auweraer et al. important requirement for this approach is that companies keep track of their installed base (i.e. the location, number of machines, and machine types in use). ...
For advanced capital goods with high system availability requirements, it is common that all customers have service contracts with the Original Equipment Manufacturer (OEM). These service contracts include service level agreements on spare parts supply. The OEM operates a service network to support these logistic contracts. To determine spare parts stock levels the OEM needs to forecast spare parts demand. An important input for this forecast is the service Bill Of Material (BOM) per installed machine in the field, which specifies the applicable spare parts for a machine, and is usually derived from the machine configuration. Because of a growing installed base, increasing machine complexity, and an increasing number of machine variants, companies face a challenge in defining and maintaining machine configurations, which is why the service BOM is not always in line with the actual installed machine. An incorrect service BOM results in either a too low or a too high forecast for spare parts demand, and will result in under- or overstock.
In this paper we study the service BOMs at ASML, a large OEM in the semiconductor industry. We develop a method to generate alerts for possible errors. This method builds on multiple sources of machine information. Our method was tested in a pilot study, and found to be very effective. 95% of the generated alerts were correctly triggered and did result in actions that improved the service BOM. As a result, the method has been implemented by ASML. By this method, ASML reduced spare part non-availabilities by approximately 4–5 percent per year.
... There are a number of papers that discuss the use of installed base information for spare part demand forecasting (see Van der Auweraer et al., 2019). The demand drivers that are most commonly discussed in literature are the maintenance policy, which depicts when a part is replaced (e.g., Hu et al., 2015;Romeijnders et al., 2012), the size and age of the installed base (e.g., Jalil et al., 2011;Kim et al., 2017;Stormi et al., 2018), and the part failure probability (e.g., Barabadi, 2012;Si et al., 2017;Ritchie & Wilcox, 1977). Those works tend to focus on only a specific subset of installed base information and the combination of these different demand drivers is generally lacking. ...
We focus on the inventory management of critical spare parts that are used for service maintenance. These parts are commonly characterised by a large variety, an intermittent demand pattern and oftentimes a high shortage cost. Specialized service parts models focus on improving the availability of parts whilst limiting the investment in inventories. We develop a method to forecast the demand of these spare parts by linking it to the service maintenance policy. The demand of these parts originates from the maintenance activities that require their use, and is thus related to the number of machines in the field that make use of this part (known as the active installed base), in combination with the part's failure behaviour and the maintenance plan. We use this information to predict future demand. By tracking the active installed base and estimating the part failure behaviour, we provide a forecast of the distribution of the future spare parts demand during the upcoming lead time. This forecast is in turn used to manage inventories using a base-stock policy. Through a simulation experiment, we show that our method has the potential to improve the inventory-service trade-off, i.e., it can achieve a certain cycle service level with lower inventory levels compared to the traditional forecasting techniques for intermittent spare part demand. The magnitude of the improvement increases for spare parts that have a large installed base and for parts with longer replenishment lead times.
... Condition monitoring data have a high impact when combined with installed base data [i.e. product master data, service data and contractual data (Jalil et al. 2011)]. To date, this has rarely been recognized by researchers. ...
Services play an important role in the manufacturing industry. A shift in emphasis from selling physical products to offering product–service systems is perceived. Detailed knowledge of machines, components and subcomponents in whole plants must be provided. Installed base management contributes to this and enables services in manufacturing to maintain high machine availability and reduce downtimes. Installed base management assists in data structuring and management. By combining installed base data with sensor data, a digital twin of the installed base results. Following the action design research approach, an integrated installed base management system for manufacturing is presented and implemented in practice. An engineering and manufacturing company is involved in the research process and ensures practical relevance. Requirements are not only deduced from the literature but also identified in focus group discussions. A detailed test run with real data is performed for evaluation purpose using a demonstration machine. To enable a generalization, design principles for the development and implementation of such an integrated installed base management system are created.
... The installed base of a product, that is, the number of products still in use can also be utilized to obtain forecasts (van der Heijden and Iskandar, 2013;Jalil et al., 2011;Dekker et al., 2013) An interesting installed-based approach to spare part demand modeling was provided by Kim et al. (2017) with the ability to capture the turning point of the purchase life-cycle curve. The primary aim of this paper is to develop a time series method that is applicable to long-term forecasting and takes into account the uncertainty present in the time series under investigation. ...
In order to provide high service levels, companies competing in
the electronics manufacturing sector need to ensure the availability of
spare parts for repair and maintenance operations. This paper examines
the purchase life-cycles of electronic spare parts and presents a new way
of modeling and forecasting spare part demand for electronic commodities
in the spare parts logistics services. The presented modeling methodology
is founded on the assumption that the purchase life-cycles of spare parts
can be described by a curve with short term fluctuations around it. For
this purpose, a flexible Demand Model Function is introduced. The
proposed forecasting method uses a knowledge discovery-based approach
that is built upon the combined application of analytic and soft
computational techniques and is able to indicate the turning points of
the purchase life-cycle curve. The novelty lies in the fact that the
model function has certain characteristics which support describing and
interpreting the demand trend as a function of time. The application of
our methodology is mainly advantageous in long-term forecasting, it can
be especially useful in supporting purchase planning decisions in the
ramp-up and declining phases of purchase life-cycles of product specific
spare parts. A demonstrative example is used to illustrate the
applicability of the proposed methodology. Its forecasting capability is
compared to those of some widely applied methods in business practice.
From the results, the new method may be viewed as a viable alternative
spare part demand forecasting technique in spare part logistics sector.
... Industry case studies have shown such spare parts supply chains to be effective (Cohen et al. 2000;Lee et al. 2005) from the perspective of the original equipment manufacturer. More specifically, centralizing control enables the original equipment manufacturer to use an installed database in planning more efficient responses concerning spare parts with low demand rates (Jalil et al. 2010). ...
Purpose
The paper studies the adoption and motivation to adopt global spare parts practices in autonomous units servicing the products of an original equipment manufacturer.
Design/methodology/approach
The methodological approach is case study investigating the reasons for different levels of use and the perceptions regarding the benefits of a centralized supply chain management in four representative service units.
Findings
Autonomous spare part units often source locally because local suppliers are easy to work with in terms of purchasing processes and have no requirements for systematic planning and control of spare parts purchases and inventory management. However, increasing the share of centrally sourced and managed spare parts in the supply chain brings advantages in terms of lower total cost and higher availability. From the perspective of individual subunits engaged in providing product support services, this advantage of relying on a centrally managed spare parts supply chain of an original equipment manufacturer is not self-evident. Autonomous units frequently choose to continue sourcing spare parts from alternative sources, undermining the economies of scale attainable through the original equipment manufacturer’s supply chain. Higher levels of use are facilitated by back-office purchasing management at the unit level. The positive perceptions of centralized supply management in general – including the relationship between the supply unit and the service unit – further facilitate adoption, while local requirements and practices inhibit it.
Research limitations/implications
The study is a single case study and presents proposals requiring further study of the reasons for the observed differences in use of centralized supply chain management.
Practical implications
Centralized spare parts management service requires investment in back-office resources at the service unit level.
Originality/value
The research increases the practical relevance of existing research through an empirical investigation on the autonomous units’ motivations for and perceived benefits of centralized spare parts supply.
... Iyoob and Kutanoglu (2013) and Yang et al. (2013) present models with the objective similar to that of Kutanoglu and Mahajan (2009) while considering demand-facility allocation decision and stocks in replenishment pipeline respectively. Jalil et al. (2011), adapting the model by Kutanoglu and Mahajan (2009), analyse the planning performance in relation to the quality of installed-base data. ...
... Iyoob and Kutanoglu (2013) and Yang et al. (2013) present models with the objective similar to that of Kutanoglu and Mahajan (2009) while considering demand-facility allocation decision and stocks in replenishment pipeline respectively. Jalil et al. (2011), adapting the model by Kutanoglu and Mahajan (2009), analyse the planning performance in relation to the quality of installed-base data. ...
... Other forecasting methods supplement historical demand data with additional information. The use of installed base information is discussed in Jalil et al. (2011) and . Information on component repairs is first considered in Romeijnders et al. (2012). ...
Inventory control for spare parts is essential for many organizations due to the trade-off between preventing high holding cost and stockouts. The lead time demand distribution plays a central role in inventory control. The estimation of this distribution is problematic as the spare part demand is often intermittent, and as a consequence often only a limited number of non-zero data points are available in practice. The well-known empirical method uses historical demand data to construct the lead time demand distribution. Although it performs reasonably well when service requirements are relatively low, it has difficulties in achieving high target service levels. In this paper, we improve the empirical method by applying extreme value theory to model the tail of the lead time demand distribution. To make the most out of a limited number of demand observations, we establish that extreme value theory can be applied to lead time demand periods computed over overlapping intervals. We consider two service levels: the expected waiting time and cycle service level. Our experiments show that our method improves the inventory performance compared to the empirical method and is competitive with the WSS method, Croston’s method and SBA for a range of demand distributions.
... Among the limited research published on the configuration of after-sales logistics networks, quantitative methods that involve the determination of facility locations and flows among them are proposed in Persson and Saccani (2009), Jalil et al (2011), Wu et al (2011 and Landrieux and Vandaele (2012). Persson and Saccani (2009) utilize simulation over different demand scenarios to calculate transportation costs in order to determine the allocation of suppliers and parts for a new second warehouse. ...
After-sales services have become high-margin businesses that account for larger portions of corporate profits. Delivering the after-sales services is challenging as after-sales services supply chains are significantly different than production–distribution supply chains. The literature provides little guidance on the use of quantitative methods for after-sales services network design. We present a mixed integer linear programming problem formulation to determine warehouse locations, assign repair vendors to facilities and choose mode of transportation while minimizing the total network cost. We transform the large-scale real-life problem of a household appliances manufacturer into a smaller scale to solve optimally in reasonable time. Through a scenario-based approach, we evaluate different configurations of a decentralized network with choices of transportation mode. We test the sensitivity of the solutions. The total cost decreases with additional choices of transportation mode and only slightly increases with the next-day delivery policy while the network solution may change significantly.
... In industry, IBM, IHC Merwede (a worldwide leading shipyard for dredging ships) and Voestalpine Railpro (a railway service parts provider in the Netherlands) leverage the installed fleet information to forecast the spare parts demand and define the service capacity (Jalil et al, 2011;Dekker et al, 2013). They conclude that the forecasting of spare parts demand could be made in a more timely and accurate manner if the real-time fleet size information were available. ...
Performance-based contracting (PBC) is envisioned to lower the asset ownership cost while ensuring desired system performance. System availability, widely used as a performance metric in such contracts, is affected by multiple factors such as equipment reliability, spares stock, fleet size, and service capacity. Prior studies have either focussed on ensuring parts availability or advocating the reliability allocation during design. This paper investigates a single echelon repairable inventory model in PBC. We focus on reliability improvement and its interaction with decisions affecting service time, taking into account the operating fleet size. The study shows that component reliability in a repairable inventory system is a function of the operating fleet size and service rate. A principal-agent model is further developed to evaluate the impact of the fleet size on the incentive mechanism design. The numerical study confirms that the fleet size plays a critical role in determining the penalty and cost sharing rates when the number of backorders is used as the negative incentive scheme.
... arch design. The empirical findings are then presented, including the manual data collection framework. Thereafter, we discuss our findings in relation to the existing body of knowledge and present managerial implications and limitations of our research. In the last section, we draw conclusions of the study and present avenues for further research.Jalil et al. (2011)studied the effect of data quality in spare parts planning. They observed that the gains of using asset data deteriorated because of systematic data errors: all installed base items reported at the headquarters location or at the primary stock location due to incomplete or missing location data, and data communication error due to mismat ...
Sensors provide plenty of data about assets in use. However, efficient service operations require asset data that cannot be acquired through sensors. For example, maintenance actions must be manually reported. We have observed many challenges with the quality of this manually gathered data, such as missing or inaccurate data. We conducted two case studies to find out the factors influencing manual data gathering. We combined the results of these case studies with a literature review to create a framework of manual data gathering. The framework describes how the quality of manually collected asset data is affected by the organization and culture, the tools used, the tasks and competences, and, most importantly, the people and their motivation for collecting the data. This framework helps managers in organizing the data collection work by visualizing the aspects that need to be considered. Further work should test the framework in an industrial context.
... Internally in the network, batching per part type makes little sense, since every couple of days usually a shipment of various parts is sent from the central depot to each of the local warehouses. Batching or, Our experience (e.g., [36]) IBM [37][38][39] ...
Stocks of spare parts, located at appropriate locations, can prevent long downtimes of technical systems that are used in the primary processes of their users. Since such downtimes are typically very expensive, generally system-oriented service measures are used in spare parts inventory control. Examples of such measures are system availability and the expected number of backorders over all spare parts. This is one of the key characteristics that distinguishes such inventory control from other fields of inventory control. In this paper, we survey models for spare parts inventory control under system-oriented service constraints. We link those models to two archetypical types of spare parts networks: networks of users who maintain their own systems, for instance in the military world, and networks of original equipment manufacturers who service the installed base of products that they have sold. We describe the characteristics of these networks and refer back to them throughout the survey. Our aim is to bring structure into the large body of related literature and to refer to the most important papers. We discuss both the single location and multi-echelon models. We further focus on the use of lateral and emergency shipments, and we refer to other extensions and the coupling of spare parts inventory control models to related problems, such as repair shop capacity planning. We conclude with a short discussion of application of these models in practice.
... We are interested in learning whether (1) using aggregate carbon footprint drives decisions so self-imposed carbon reduction targets are reached, and (2) whether actual lot-sizing decisions are different under different aggregation levels of carbon footprinting. Cost aggregation has been studied before in the operations research literature in Jalil et al. (2011) who found that errors can be significant in real-life spare parts planning. Unlike prices or costs, carbon emission information is not currently shared among supply chain members. ...
A variety of activity-based methods exist for estimating the carbon footprint in transportation. For instance, the greenhouse gas protocol suggests a more aggregate estimation method than the Network for Transport and Environment (NTM) method. In this study, we implement a detailed estimation method based on NTM and different aggregate approaches for transportation carbon emissions in the dynamic lot sizing model. Analytical results show the limitations of aggregate models for both accurate estimation of real emissions and risks of compliance with carbon constraints (e.g., carbon caps). Extensive numerical experimentation shows that the magnitude of errors can be substantial. We provide insights under which limited conditions aggregate estimations can be used safely and when more detailed estimates are appropriate.
... Some authors have also considered to use types of information other than historic demand, such as installed base information (Song and Zipkin (1996); Jalil et al. (2011)), reliability information (Petrovic and Petrovic (1992)) and expert judgment (Syntetos et al. (2009)). ...
Forecasting spare parts demand is notoriously difficult, as demand is typically intermittent and lumpy. Specialized methods such as that by Croston are available, but these are not based on the repair operations that cause the intermittency and lumpiness of demand. In this paper, we do propose a method that, in addition to the demand for spare parts, considers the type of component repaired. This two-step forecasting method separately updates the average number of parts needed per repair and the number of repairs for each type of component. The method is tested in an empirical, comparative study for a service provider in the aviation industry. Our results show that the two-step method is one of the most accurate methods, and that it performs considerably better than Croston’s method. Moreover, contrary to other methods, the two-step method can use information on planned maintenance and repair operations to reduce forecasts errors by up to 20%. We derive further analytical and simulation results that help explain the empirical findings.
... Companies realizing these facts start keeping track of the changes in their own or customers' base of installed products (installed base) to trace customers and operating units more closely and to react to the changes in demand rate as early as possible. A recent study by Jalil et al. (2009) revealed that at IBM, tracking of the installed base for spare parts can lead to savings up to 58% in transportation and inventory holding costs. ...
In this study, we develop and analyze models incorporating some of the dynamic aspects of inventory systems. In particular, we focus on two major themes to be analyzed separately: nonstationarity in demand rate and unfixed purchasing prices.
In the first part of the study, we consider an inventory system with a nonstationary demand rate. In particular, we consider critical service parts subject to obsolescence. Inventory management of such items is notoriously difficult due to their slow moving character and the high risks involved when they are not available or no more needed.
In practice, there is a need for policies tailored for service parts taking these aspects into account and easy to implement. We propose an obsolescence based control policy and investigate its performance and impact on costs. We find that ignoring obsolescence in the control policy increases costs significantly and early adaptation of base stock levels can lead to important savings.
In the second part of the study, we consider an inventory system where the supplier offers price discounts at random points in time. We extend the literature by assuming a more general backordering structure. That is, when the system is out of stock, an arriving customer either decides to be backlogged with a certain probability or leaves the system and becomes a lost sale. We derive equations to calculate optimal policy parameters and demonstrate that allowing backorders in face of random deal offerings can result in considerable savings.
... esearchers relate forecasting methods to this information. Existing models use data about historical spare parts demand in relation to the operating machines, so called installed base data, for subsequent planning (cf. Dekker et al. (2010)). However, the varying quality of the generated data is still an open issue (cf. Dekker et. al. (2010), p. 2; Jalil et. al (2009), p 2.). Consequently, the integration of status data into planning and forecasting methods is still an open research topic. ...
Effective and efficient maintenance is of major importance for the operators of complex production systems. Insufficient maintenance may induce downtimes with significant effects on operators costs, profits and customer service perception. However, guaranteeing a high maintenance service-level may lead to immoderate running costs exceeding the intended benefits. Thus, our research aims at balancing the dimensions of an adequate maintenance service level and moderate costs. The proposed concept approaches this issue by integrating intelligent maintenance systems, i.e. technical information about a systems maintenance needs, with the coordination and efficient management of the respective spare parts supply chain.
... Companies realizing these facts start keeping track of the changes in their own or customers' base of installed products (installed base) to trace customers and operating units more closely and to react to the changes in demand rate as early as possible. A recent study by Jalil et al. (2009) revealed that at IBM, tracking of the installed base for spare parts can lead to savings up to 58% in transportation and inventory holding costs. ...
In this paper, we consider a continuous review inventory system of a slow moving item for which the demand rate drops to a lower level at a pre-determined time. Inventory system is controlled according to one-for-one replenishment policy with fixed lead time. Adaptation to the lower demand rate is achieved by changing the control policy in advance and letting the demand take away the excess stocks. We show that the timing of the control policy change primarily determines the tradeoff between backordering penalties and obsolescence costs. We propose an approximate solution for the optimal time to shift to the new control policy minimizing the expected total cost during the transient period. We find that the advance policy change results in significant cost savings and the approximation yields near optimal expected total costs.
... Companies realizing these facts start keeping track of the changes in their own or customers' base of installed products (installed base) to trace customers and operating units more closely and to react to the changes in demand rate as early as possible. A recent study by Jalil et al. (2009) revealed that at IBM, tracking of the installed base for spare parts can lead to savings up to 58% in transportation and inventory holding costs. ...
In this paper, we consider a continuous review inventory system of a slow moving item for which the demand rate drops to a lower level at a pre-determined time. Inventory system is controlled according to one-for-one replenishment policy with fixed lead time. Adaptation to the lower demand rate is achieved by changing the control policy in advance and letting the demand take away the excess stocks. We showed that the timing of the control policy change primarily determines the tradeoff between backordering penalties and obsolescence costs. We propose an approximate solution for the optimal time to shift to the new control policy minimizing the expected total cost during the transient period. We found that the advance policy change results in significant cost savings and our model yields near optimal expected total costs.
In the context of Industry 4.0, the manufacturing sector is increasingly facing the challenge of data usability, which is becoming a widespread phenomenon and a new contemporary concern. In response, Data Governance (DG) emerges as a viable avenue to address data challenges. This study aims to call attention on DG research in the field of operations and supply chain management (OSCM). Based on literature research, we investigate research gaps in academia. Built upon three case studies, we exanimated and analyzed real life data issues in the industry. Four types of cause related to data issues were found: 1) human factors, 2) lack of written rules and regulations, 3) ineffective technological hardware and software, and 4) lack of resources. Subsequently, a three-pronged research framework was suggested. This paper highlights the urgency for research on DG in OSCM, outlines a research pathway for fellow scholars, and offers guidance to industry in the design and implementation of DG strategies.
In the dynamic landscape of contemporary business, the wave in data and technological advancements has directed companies toward embracing data-driven decision-making processes. Despite the vast potential that data holds for strategic insights and operational efficiencies, substantial challenges arise in the form of data issues. Recognizing these obstacles, the imperative for effective data governance (DG) becomes increasingly apparent. This research endeavors to bridge the gap in DG research within the Operations and Supply Chain Management (OSCM) domain through a comprehensive literature review. Initially, we redefine DG through a synthesis of existing definitions, complemented by insights gained from DG practices. Subsequently, we delineate the constituent elements of DG. Building upon this foundation, we develop an analytical framework to scrutinize the collected literature from the perspectives of both OSCM and DG. Beyond a retrospective analysis, this study provides insights for future research directions. Moreover, this study also makes a valuable contribution to the industry, as the insights gained from the literature are directly applicable to real-world scenarios.
We study performance improvement in multi-echelon, closed loop spare part supply chains using operational interventions based on real-time status information. Our objective is to minimize the total cost relevant costs, consisting of intervention costs and the backorder costs. In this paper, we focus on proactive interventions, aiming to avoid stockouts. We assume that all reactive interventions are fixed. Proactive interventions that we study include lateral transshipments, emergency shipments, stock reservations, expediting part repairs, and early new buys of parts. These interventions are invoked by using alert generation, when the supply chain status deviates from the plan. We propose heuristic rules to generate alerts. We also develop heuristic rules for the choice of operational interventions. We model and test our heuristics in a simulation test bed, based on data of a global IT company by using the case data in Germany. Numerical experiments reveal the following key insights: (i) downstream interventions – proactive lateral and emergency shipments – have most impact in reducing costs, (ii) communicating losses in the supply chain (no returns, failed repairs) for early new buys has positive impact on fill rates at negligible costs, and (iii) expedite repair and stock reservations using the proposed rules is not profitable.
Despite the criticality and expense of spare parts, many firms lack a coherent strategy for ensuring needed supply of spare parts. Moreover, scientific research regarding a comprehensive spare parts strategy is sparse in comparison with direct material. Our research identifies and tests three literature‐based, theoretically anchored attributes that influence a buyer's preference for inventory ownership and inventory placement when managing the stock of a critical spare part. Our findings indicate that item specificity and item supply uncertainty are useful in predicting a buyer's preference for managing the inventory of a critical spare part. Furthermore, we find that buyers have (1) a strong preference for consignment‐based inventory management approaches, (2) a bias against inventory speculation despite its use in practice and analytical models, and (3) a strong preference for inventory postponement when the level of supply uncertainty is low.
Lead time plays a critical role in stochastic inventory management because it is directly correlated with inventory performance, such as the probability of stockout, fill rate, and expected backorders. This chapter introduces the basic inventory planning and optimization models under both continuous and periodic review policy. It overviews the inventory models based on review policy and demand patterns. The chapter presents the economic order quantity (EOQ) and newsvendor models as both form the foundation of inventory theory. It introduces the continuous review model with stochastic demand and presents an approximation solution to the periodic review model. The chapter introduces the concept of an installed base driven inventory planning model and discusses spare parts demand forecasting with the focus on the installed base concept. Inventory systems prevail in process manufacturing, discrete/repetitive production, service parts logistics, product and goods distributors, and repair and maintenance services.
High demand variability and uncertainty that is driven by different phases of a product’s and its critical components life-cycles make spare parts demand forecasting and safety inventory management a major challenge. Mainstream stochastic inventory management approaches for spare parts make use of
distributional assumptions for demands. In reality, distributions and their parameters are hardly known and need to be estimated. In this paper we pursue a causal
demand modeling approach that combines theoretical models from reliability and inventory theory to derive improved service parts demand forecasts. Based on a small simulation experiment we illustrate the benefits of this accurate but more complex approach over simple time series based forecasting techniques and forecast error driven safety stock approaches for different life-cycle patterns and different phases of a product life-cycle.
The aim of this chapter is to combine two types of problems: a facility location problem, that determines where a facility should be set up in order to serve a set of customers, and a EOQ-based spare parts inventory problem, which seeks to determine the optimal stocking levels of a collection of spare parts. The theoretical findings are practiced on a case for projectors of a Belgian Digital Cinema Projector producer, serving customers located all over the world. The goal is to minimize the total costs under four scenarios, which differ mainly in terms of SLA’s and modes of transport. The most remarkable difference between these total costs is related to the number of opened facilities, the safety stock that is required, and the transportation modes that can be used in the considered scenario.
The service industry has become an engine of regional and global economic development, and the employment and revenue of the service industry are increasing dramatically in recent years. This chapter discusses product-service supply chain (PSSC) design by outlining the PSSC implications and focusing on the crucial elements of PSSC design. Viewing the subject from the perspective of manufacturers, several presentations are used in practice, such as the supply chain for after-sales service; maintenance, repair, and operations; and product-service systems providing. It first explains what is the PSSC and then moves on to the prominent features and the structure of the PSSC. In a general sense, PSSC design is designed toward the value of all supply chain members by configuring the service resource reasonably. From the viewpoint of supply chain design
, five elements are elaborated in this chapter, which include the PSSC strategy, service facility location, outsourced service supplier selection, service network configuration, and process design for PSSC. The approaches for the PSSC design are reviewed, suggested, and elaborated.
Line Replaceable Units (LRUs), which can be quickly replaced at a first-level maintenance facility, are widely deployed on capital-intensive systems in order to maintain high system availability. Failed LRU are repaired after replacement and reused as fully serviceable spare units. Demand for spare LRUs depends on factors such as the time-varying installed base, reliability deterioration or growth over maintenance cycles, procurement leadtime of new LRUs, turn-around leadtime of repaired LRUs, etc. In this article, we propose an integrated framework for both reliability analysis and spares provisioning for LRUs with a time-varying installed base. We assume that each system consists of multiple types of LRUs and associated with each type of LRU is a non-stationary sub-failure process. The failure of a system is triggered by sub-failure processes that are statistically dependent. A hierarchical probability model is developed for the demand forecasting of LRUs. Based on the forecasted demand, the optimum inventory level is found through dynamic programming. An application example is presented. A computer program, called the Integrated Platform for Reliability Analysis and Spare Provision, is available that makes the proposed methods readily applicable.
The majority of after-sales service providers manage their service parts inventory by focusing on the availability of service parts. This approach, combined with automatic replenishment systems, leads to reactive inventory control policies where base stock levels are adjusted only after a service contract expires. Consequently, service providers often face excess stock of critical service parts that are difficult to dispose due to their specificity. In this paper, we address this problem by developing inventory control policies taking into account contract expirations. Our key idea is to reduce the base stock level of the one-for-one policy before obsolescence (a full or partial drop in demand rate) occurs and let demand take away excess stock. We refer to this policy as the single-adjustment policy. We benchmark the single-adjustment policy with the multiple-adjustment policy (allowing multiple base stock adjustments) formulated as a dynamic program and verify that for a wide range of instances the single-adjustment policy is an effective heuristic for the multiple-adjustment policy. We also compare the single-adjustment policy with world-dependent base stock policy offered by Song and Zipkin (1993) and identify the parameter combinations where both policies yield similar costs. We consider two special cases of the single-adjustment policy where the base stock level is kept fixed or the base stock adjustment is postponed to the contract expiration time. We find that the initial demand rate, contract expiration time, and size of the drop in demand rate are the three key parameters driving the choice between the single-adjustment policy and its special cases.This article is protected by copyright. All rights reserved.
Performance based contracting (PBC) emerges as a new after-sales service practice to support the operation and maintenance of capital equipment or systems. Under the PBC framework, the goal of the study is to increase the system operational availability while minimizing the logistics footprint through the design for reliability. We consider the situation where the number of installed systems randomly increases over the planning horizon, resulting in a non-stationary maintenance and repair demand. Renewal equation and Poisson process are used to estimate the aggregate fleet failures. We propose a dynamic stocking policy that adaptively replenishes the inventory to meet the time-varying parts demand. An optimization model is formulated and solved under a multi-phase adaptive inventory control policy. The study provides theoretical insights into the performance-driven service operation in the context of changing system fleet size due to new installations. Trade-offs between reliability design and inventory level are examined and compared in various shipment scenarios. Numerical examples drawn from semiconductor equipment industry are used to demonstrate the applicability and the performance of the proposed method.
A fundamental issue in the use of optimization models is the tradeoff between the level of detail and ease of using and solving the model. Aggregation and disaggregation techniques have proven to be valuable tools for manipulating data and determining the appropriate policies to employ for this tradeoff. Furthermore, aggregation and disaggregation techniques offer promise for solving large-scale optimization models, supply a set of promising methodologies for studying the underlying structure of both univariate and multivariate data sets, and provide a set of tools for manipulating data for differrent levels of decision makers. In this paper, we develop a general framework for aggregation and disaggregation methodology, survey previous work regarding aggregation and disaggregation techniques for optimzation problems, illuminate the appropriate role of aggregation and disaggregation methodology for optimization applications, and propose future research directions.
This paper considers efforts to improve in-patient flows, a particularly urgent issue in the National Health Service (NHS). The context is described and related to reasons why OR has been making relatively little contribution. The paper argues that large complex models may often be unnecessary and even get in the way of providing clear insight and guidance for problem owners. The importance of understanding the generic working of systems to lead to improvement, and the limitations of simply describing them, is stressed. It is demonstrated that some very simple models can be of significant practical value in understanding and managing complex systems, changing mindsets and driving collection and use of operationally valuable data. Recommendations for more effective engagement with the NHS are offered.
Information Management has a critical role as an enabler and contributor to successful business strategy implementation. At Nokia Mobile Phones this has been highlighted especially in the context of Global Demand Chain Management. In this paper basic principles and differences of Supply Chain Management and Demand Chain Management are explained. The role of Information Management and Technology in Demand Chain Management is discussed. Finally, key drivers and criteria for Information Management system design are outlined.
IBM recently implemented Optimizer, a system for flexible and optimal control of service levels and spare parts inventory , in its US network for service support. It is based upon recent research in multi-echelon inventory theory to address the IBM network. The inherent complexity and very large scale of the basic problem required IBM to develop suitable algorithms and sophisticated data structures and required large-scale systems integration. Optimizer has greatly improved IBM's US service business. The implementation of Optimizer has made it possible to make strategic changes to the configuration and control of the parts distribution network. It resulted in simultaneously reducing inventory investment and operating costs and improving service levels. Most important, however. Optimizer has proven to be a highly flexible planning and operational control system.
From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.
Management literature is almost unanimous in suggesting to manufacturers that they should integrate services into their core product offering. The literature, however, is surprisingly sparse in describing to what extent services should be integrated, how this integration should be carried out, or in detailing the challenges inherent in the transition to services. Reports on a study of 11 capital equipment manufacturers developing service offerings for their products. Focuses on identifying the dimensions considered when creating a service organization in the context of a manufacturing firm, and successful strategies to navigate the transition. Analysis of qualitative data suggests that the transition involves a deliberate developmental process to build capabilities as firms shift the nature of the relationship with the product end-users and the focus of the service offering. The report concludes identifying implications of our findings for further research and practitioners.
From the Publisher:Decision Support and Data Warehouse Systems ties the more traditional view of decision support to the rapidly evolving topics of database management and data warehouse. As organizations move quickly into networked-based environments,the nature of decision support tools has become increasingly complex. These tools are now used collaboratively and the use of data warehousing mechanisms will be a critical success factor for the survival of many organizations. This new book provides a strong foundation for the use of models within the context of building and using decision support systems,and it will focus on multi-dimensional databases and client/server computing.
The purpose of this paper is to take stock of what is known and to suggest some conceptual foundations for future progress in the areas of postoptimality analysis and parametric optimization techniques for integer programming.
This paper summarizes the results of a benchmark study focusing on after-sales service logistics systems for technologically complex high-value products, i.e., in the computer industry. Current industrial practices and trends in service logistics operations are reported on. Specific data on costs, revenues, service measures, control policies, and distribution strategies were collected and used to define best practice performance. Analysis methodologies to support cross-sectional comparisons and causal factors are discussed.
Designing a supply chain network provides the basic structure for supply chain operations, where the network is a major element in a firm's competitiveness and a significant area of capital investment. With the boundary-spanning scope encouraged by supply chain management, the question is whether existing analyses used for network design are adequate. The general answer is yes, because the number of echelons that the analysis must span is self-limiting to a relative few. However, there remain unresolved issues in network design analysis and in the models used to support the analysis that can enhance its accuracy, appropriateness, and acceptability to practitioners. Issues discussed relate to the development of better models, the treatment of the data needed by them, and the need for comparing the various models for network design.
The diversity of the operational control characteristics of spare parts is taken as a basis for supporting the planning and designing of a spare parts logistics system. Four control characteristics of maintenance spare parts – criticality, specificity, demand pattern, and value of parts – are discussed in terms of their effects on logistics system elements – network structure, positioning of materials, responsibility of control, and control principles. Distinct operating policies for different types of parts in the spare parts supply chain are illustrated.
An important challenge arising in reverse logistics supply chains is the effective use of returns so as to maximize the value of this resource. To this end, decisions at strategic, tactical, and operational levels should explicitly incorporate information about return flow characteristics, primarily quantity and quality. In this chapter, we review informational issues concerning product returns. We then investigate the value of accurate forecasting at an operational level by focusing on inventory control.
This monograph, viewing logistics systems from an integrated standpoint, aims to describe, and also to demonstrate how to find, rational structures for logistics systems, including their operation and organization. The work departs from traditional operations research procedures in that it avoids detailed descriptions of problems or theiH solutions. For a typical problem, for example, the book demonstrates how to achieve reasonable solutions (described in terms of their properties) based on minimum information and avoiding detailed data handling and complex numerical analyses. The book also makes the point that detailed solutions can often be improved if preceded by the kind of exploratory analysis described here. This monograph on operations research and transportation is intended for researchers.
In the field of logistics, a variable that is to be predicted (e. g. cost) often varies in a nonsmooth, irregular, but known manner, with various factors (e. g. , distances, quantity, and density of material to be carried, etc. ). This paper identifies conditions, where given approximate input factors, a prediction of the variable is less error prone if one uses a smooth approximation to the exact function of the factors. This phenomenon, which is quite prevalent, may enhance the appeal of continuous approximation models in some instances.
Despite all the data that retailers and e-tailers can now gather about point-of-purchase information, buying patterns, and customers' tastes, they still haven't figured out how to offer the right product, in the right place, at the right time, for the right price. Most retailers largely ignore the billions of bytes of customer data stored in their databases-or they handle that information incorrectly. As a result, they don"t adequately supply what consumers demand. But some retailers are moving profitably toward what the authors call "rocket science retailing"- a blend of traditional forecasting systems, which are largely based on the gut feel of employees, with the prowess of information technology. Marshall Fisher, Ananth Raman, and Anna Sheen McClelland recently finished surveying 32 retail companies in which they tracked their practices and progress in four areas critical to rocket science retailing: demand forecasting, supply-chain speed, inventory planning, and data gathering and organization. In this article, the authors look at some companies that have excelled in those four areas and offer some valuable advice for other businesses seeking retailing perfection. In particular, the authors emphasize the need to monitor crucial metrics such as forecast accuracy, early sales data, and stockouts-information that will help retailers determine when to tweak their supply-chain processes to get the right products to stores at just the right time. The authors discuss the information technologies now available for tracking that information. They point out the flaws in some reporting and planning systems and suggest alternate methods for measuring stockouts, inventory, and losses.
Over the past 4 years, the Hospice Palliative Care Network Project, co-led by the Temmy Latner Centre for Palliative Care, Mount Sinai Hospital, and the Toronto Community Care Access Centre, has been working toward developing an innovative model of home palliative care coordination and service delivery. After a successful completion of stage one involving data collection of approximately 400 variables to a common Linux database repository, the current stage of the Project is to compare various modes of care delivery and disseminate the results to internal and external evaluation stakeholders. The Temmy Latner Centre's customized Panacea Information Management System (PIMS) had been linked to the Linux repository in order to customize reports to determine the optimal model of coordination and service delivery that could serve as a template for home palliative care delivery in Ontario. The objective of this paper is to outline the development and functionality of the PIMS and to evaluate its contribution in terms of improved data quality and health outcomes; in other words, to justify the information systems investment by demonstrating the relationship between this investment and improved health system delivery and effectiveness.
When applying an aggregate view to production planning, the production system is given a hierarchical structure. The problem description at the upper level (which is less detailed) concerns product groups and machine groups rather than individual products and machines. This means that product structures and capacity requirements also must be expressed in terms of product groups and machine groups. In this study it is shown under what conditions a perfect aggregation of product data can be obtained. If a perfect aggregation is not possible, the problem is to find a good approximate solution. A mathematical formulation of this approximation problem and its general solution is also given.
Service parts are needed for maintenance of industrial systems as well as for consumer products. Their logistics has an inherent difficulty: common models for inventory management are invalid, as the demand process is different and demand data scarce. The paper discusses experiences gained in case studies of practical stock control techniques. New concepts aimed to reduce the problem of slow moving parts are described, for example: suppliers leasing service parts; standardisation of parts for the group of machines in a factory or over a complete sector of industry; a “broker” between suppliers and customers who makes the service parts inventories transparent and facilitates pooling of parts.
The selection of a method for policy assessment in a particular industry varies according to the characteristics of the issues involved. In the natural gas industry, each component has its own specific features and, when analysed as a single whole, a synthesized modelling approach may turn appropriate. This paper shows that in some instances, the integration of modelling methodologies might be of great value for understanding, evaluating and formulating energy policy. Here we address methodological issues that have been considered for the assessment of policy options in the natural gas industry in Colombia. We focus on both modelling and policy, specifically with respect to industry sustainability, and also on environmental impacts.Journal of the Operational Research Society (2005) 56, 11221131. doi:10.1057/palgrave.jors.2601895 Published online 8 December 2004
In this paper, we describe an alogirthm for the parametric solution of MINLP models in the context of process synthesis problems under uncertainty. The procedure, based on the outer-approximation/equation relaxation algorithm, involves the iterative solution of NLP subproblems and a parametric MILP master problem, with which an ε-approximate parametric solution profile can be obtained which corresponds to the set of optimal structures/designs as a function of a scalar uncertain parameter varying within a closed range. Three example problems are presented in detail to illustrate the steps of the proposed algorithm; its applicability to address general process synthesis problems under uncertainty is also briefly discussed.
A systematic framework is developed to solve the parametric mixed integer linear programming (pMILP) problems where uncertain parameters are present on the righthand side (RHS) of the constraints. For the case of multiple uncertain parameters, a new algorithm of multiparametric linear programming (mpLP) is proposed, which solves a number of nonlinear problems (NLP) iteratively. At each iteration, a point at which the objective value cannot be represented by the current optimal functions is found, and the new optimal function is included in the next iteration. Given the range of uncertain parameters in a MILP problem, the output of this proposed framework is a set of optimal integer solutions and their corresponding critical regions and optimal functions. A number of examples are presented to illustrate the applicabilities of the proposed approach and comparison with existing techniques. © 2006 American Institute of Chemical Engineers AIChE J, 2006
In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. We study the value of sharing these data in a model with one supplier, N identical retailers, and stationary stochastic consumer demand. There are inventory holding costs and back-order penalty costs. We compare a traditional information policy that does not use shared information with a full information policy that does exploit shared information. In a numerical study we find that supply chain costs are 2.2% lower on average with the full information policy than with the traditional information policy, and the maximum difference is 12.1%. We also develop a simulation-based lower bound over all feasible policies. The cost difference between the traditional information policy and the lower bound is an upper bound on the value of information sharing: In the same study, that difference is 3.4% on average, and no more than 13.8%. We contrast the value of information sharing with two other benefits of information technology, faster and cheaper order processing, which lead to shorter lead times and smaller batch sizes, respectively. In our sample, cutting lead times nearly in half reduces costs by 21% on average, and cutting batches in half reduces costs by 22% on average. For the settings we study, we conclude that implementing information technology to accelerate and smooth the physical flow of goods through a supply chain is significantly more valuable than using information technology to expand the flow of information.
This paper presents a general model to assess the impact of data and process quality upon the outputs of multi-user information-decision systems. The data flow/data processing quality control model is designed to address several dimensions of data quality at the collection, input, processing and output stages.
Starting from a data flow diagram of the type used in structured analysis, the model yields a representation of possible errors in multiple intermediate and final outputs in terms of input and process error functions. The model generates expressions for the possible magnitudes of errors in selected outputs. This is accomplished using a recursive-type algorithm which traces systematically the propagation and alteration of various errors. These error expressions can be used to analyze the impact that alternative quality control procedures would have on the selected outputs.
The paper concludes with a discussion of the tractability of the model for various types of information systems as well as an application to a representative scenario.
In contrast to methods of parametric linear programming which were developed soon after the invention of the simplex algorithm and are easily included as an extension of that method, techniques for parametric analysis on integer programs are not well known and require considerable effort to append them to an integer programming solution algorithm.
The paper reviews some of the theory employed in parametric integer programming, then discusses algorithmic work in this area over the last 15 years when integer programs are solved by different methods. A summary of applications is included and the article concludes that parametric integer programming is a valuable tool of analysis awaiting further popularization.
In this paper, we analyze how sharing advance demand information (ADI) can improve supply-chain performance. We consider two types of ADI, aggregated ADI (A-ADI) and detailed ADI (D-ADI). With A-ADI, customers share with manufacturers information about whether they will place an order for some product in the next time period, but do not share information about which product they will order and which of several potential manufacturers will receive the order. With D-ADI, customers additionally share information about which product they will order, but which manufacturer will receive the order remains uncertain. We develop and solve mathematical models of supply chains where ADI is shared. We derive exact expressions and closed-form approximations for expected costs, expected base-stock levels, and variations of the production quantities. We show that both the manufacturer and the customers benefit from sharing ADI, but that sharing ADI increases the bullwhip effect. We also show that under certain conditions it is optimal to collect ADI from either none or all of the customers. We study two supply chains in detail: a supply chain with an arbitrary number of products that have identical demand rates, and a supply chain with two products that have arbitrary demand rates. For these two supply chains, we analyze how the values of A-ADI and D-ADI depend on the characteristics of the supply chain and on the quality of the shared information, and we identify conditions under which sharing A-ADI and D-ADI can significantly reduce cost. Our results can be used by decision makers to analyze the cost savings that can be achieved by sharing ADI and help them to determine if sharing ADI is beneficial for their supply chains.
In this paper we address the complexity of postoptimality analysis of programs with a linear objective function. After an optimal solution has been determined for a given cost vector, one may want to know how much each cost coefficient can vary individually without affecting the optimality of the solution. We show that, under mild conditions, the existence of a polynomial method to calculate these maximal ranges implies a polynomial method to solve the program itself. As a consequence, postoptimality analysis of many well-known NP-hard problems cannot be performed by polynomial methods, unless P =NP. A natural question that arises with respect to these problems is whether it is possible to calculate in polynomial time reasonable approximations of the maximal ranges. We show that it is equally unlikely that there exists a polynomial method that calculates conservative ranges for which the relative deviation from the true ranges is guaranteed to be at most some constant. Finally, we address the issue of postoptimality analysis ofε-optimal solutions of NP-hard problems. It is shown that for an ε-optimal solution that has been determined in polynomial time, it is not possible to calculate in polynomial time the maximal amount by which a cost coefficient can be increased such that the solution remains ε-optimal, unless P = NP.
This paper surveys the recent results in stability analysis for discrete optimization problems, such as a traveling salesman problem, an assignment problem, a shortest path problem, a Steiner problem, a scheduling problem and so on. The terms “stability”, “sensitivity” or “postoptimal analysis” are generally used for the phase of an algorithm at which a solution (or solutions) of the problem has been already found, and additional calculations are also performed in order to investigate how this solution depends on changes in the problem data.In this paper, the main attention is paid to the stability region and to the stability ball of optimal or approximate solutions. A short sketch of some other close results has been added to emphasize the differences in approach surveyed.
Service parts logistics systems are usually characterized by very low random demand, high part cost, and target time-based service levels, all of which lead inventory planners to utilize inventory sharing or “emergency lateral transshipments” across multiple stocking locations. Using a stylized model with one central warehouse and multiple local stocking locations, we seek insights into the behavior of an inventory sharing system with time-based service level considerations. The goal of inventory sharing is to satisfy a demand in case a local facility is out of stock, by meeting the demand with a direct delivery from another location that can provide the service within the time window necessary for the target service level. We adapt an existing model from the literature for such a system and study its performance in terms of cost and service level. In addition, we evaluate two-location and three-location scenarios to study the effect of having additional stocking locations.
Information quality (IQ) is critical in organizations. Yet, despite a decade of active research and practice, the field lacks comprehensive methodologies for its assessment and improvement. Here, we develop such a methodology, which we call AIM quality (AIMQ) to form a basis for IQ assessment and benchmarking. The methodology is illustrated through its application to five major organizations. The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures. We develop and validate the questionnaire and use it to collect data on the status of organizational IQ. These data are used to assess and benchmark IQ for four quadrants of the model. These analysis techniques are applied to analyze the gap between an organization and best practices. They are also applied to analyze gaps between IS professionals and information consumers. The results of the techniques are useful for determining the best area for IQ improvement activities.
Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
Companies realize the importance of providing spare parts and after-sales services, but most could make far more money in the aftermarket than they do. Here's how.
Manufacturing companies are increasingly offering services related to the products that they supply and building capabilities to exploit better the installed base. This move downstream requires new skills to understand and manage the demand and supply networks of industrial services. However, not much research has been published on demand and supply management of such services. As it appears that leading edge companies are ahead of the academic work on industrial service provision, we approached the topic through an explorative study and interviewed representatives from five companies to determine their conceptions of the demand–supply network management of industrial services. The interviews showed that, although companies are providing industrial services, many aspects related to the supply and demand of the services are still poorly understood. This paper presents the issues raised by the company representatives that should be addressed with further research.
We study the integrated logistics network design and inventory stocking problem as characterized by the interdependency of the design and stocking decisions in service parts logistics. These two sets of decisions are usually considered sequentially in practice, and the associated problems are tackled separately in the research literature. The overall problem is typically further complicated due to time-based service constraints that provide lower limits on the percentage of demand satisfied within specified time windows. We introduce an optimization model that explicitly captures the interdependency between network design (location of facilities, and allocation of demands to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates), and present computational results from our extensive experiments that investigate the effects of several factors including demand levels, time-based service levels and costs. We show that the integrated approach can provide significant cost savings over the decoupled approach (solving the network design first and inventory stocking next), shifting the whole efficient frontier curve between cost and service level to superior regions. We also show that the decoupled and integrated approaches may generate totally different solutions, even in the number of located facilities and in their locations, magnifying the importance of considering inventory as part of the network design models.
We consider a model to allocate stock levels at warehouses in a service parts logistics network. The network is a two-echelon distribution system with one central warehouse with infinite capacity and a number of local warehouses, each facing Poisson demands from geographically dispersed customers. Each local warehouse uses a potentially different base stock policy. The warehouses are collectively required to satisfy time-based service targets: Certain percentages of overall demand need to be satisfied from facilities within specified time windows. These service levels not only depend on the distance between customers and the warehouses, but also depend on the part availabilities at the warehouses. Moreover, the warehouses share their inventory as a way to increase achieved service levels, i.e., when a local warehouse is out of stock, demand is satisfied with an emergency shipment from another close-by warehouse. Observing that the problem of finding minimum-cost stock levels is an integer non-linear program, we develop an implicit enumeration-based method which adapts an existing inventory sharing model from the literature, prioritizes the warehouses for emergency shipments, and makes use of a lower bound. The results show that the proposed inventory sharing strategy results in considerable cost reduction when compared to the no-sharing case and the method is quite efficient for the considered test problems.
Ever more companies are recognizing the benefits of closed-loop supply chains that integrate product returns into business operations. IBM has been among the pioneers seeking to unlock the value dormant in these resources. We report on a project exploiting product returns as a source of spare parts. Key decisions include the choice of recovery opportunities to use, the channel design, and the coordination of alternative supply sources. We developed an analytic inventory control model and a simulation model to address these issues. Our results show that procurement cost savings largely outweigh reverse logistics costs and that information management is key to an efficient solution. Our recommendations provide a basis for significantly expanding the usage of the novel parts supply source, which allows for cutting procurement costs.
This annotated bibliography focuses on what has been published since the 1977 Geoffrion-Nauss survey, and it is in BibT E X format, so it can be searched on the World Wide Web. In addition to postoptimal sensitivity analysis, this survey includes debugging a run, such as when the integer program is unbounded, anomalous or infeasible. Keywords: integer programming, combinatorial optimization, sensitivity analysis, postoptimal analysis, parametric programming, infeasibility diagnosis, computational economics, computer-assisted analysis. Contents 1 Introduction 1 2 Terms and Concepts 3 3 Some Observations and Avenues for Research 9 Acknowledgments 15 References 16 1 INTRODUCTION 1 1 Introduction A primary concern of sensitivity analysis is how optimal solution values change when the data changes. There are, however, at least four types of postoptimal sensitivity analyses. The induced change, called an impulse, can be either a data object or a decision object. The resulting change that...
Method of determining inventory levels
- Y Erke
- Christina Ma
- Yh Booth
Logistics system design The Distribution Handbook
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Best practices in service chain performance management: measuring the value of a profit-centric service operation
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Modern Logistics Management: Integrating Marketing Manufacturing and Physical Distribution
- Jf Magee
- Wc Copacino
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Service parts management: unlocking value and profit in the service chain
- M Vigoroso
Sensitivity analysis in combinatorial optimization
- A Wagelmans
FortuinLMartinHControl of service partsInt J Opns Prod
AcevedoJPistikopoulosENA parametric MINLP algorithm for process synthesis problems under uncertaintyInd Eng Chem
AxsaterSAggregation of product data for hierarchical production planningOpns
CohenMKamesamPVKleindorferPLeeHTekerianAOptimizer: IBM's multi-echelon inventory system for managing service
United States Patent Publication 0061126A1
- Y Erke
- Christina Ma
- Y H Booth