Article

Spare Parts Logistics and Installed Base Information

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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.

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... 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;. ...
Article
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: ...
Article
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. ...
Article
Full-text available
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. ...
Article
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]. ...
Conference Paper
Full-text available
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). ...
Article
Full-text available
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). ...
Article
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). ...
Article
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.
... 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 . ...
Article
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). ...
Article
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. ...
Article
Full-text available
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. ...
Article
Full-text available
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. ...
Article
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). ...
Article
Full-text available
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). ...
Article
Full-text available
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. ...
Article
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. ...
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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 ...
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... 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. ...
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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)). ...
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... 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. ...
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... 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. ...
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... 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. ...
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... 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. ...
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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.
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Chapter
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Chapter
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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.
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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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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...
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