Article

The Influence of Quality Inspections on the Optimal Safety Stock Level

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Due to yields of less than 50% during the production of curved glass for the displays on their new cell phone series, Samsung has to deal with higher than expected production costs of several million dollars. Where there is random yield, production costs as well as holding costs can be reduced by introducing quality inspections, in which defective items are discarded before further production. To achieve the greatest cost savings, it is important to determine the optimal number and positions of these inspections across the production process which, due to several influencing parameters, is not simple. We show how the positions of inspection within a production process influence the safety stock level that is required to buffer against uncertainties due to demand and yield randomness. Our approach is the first one, combining decisions about the number and positions of inspections with inventory control strategies in a warehouse. We achieve a maximum safety stock reduction of more than 30% in our examples, which can be even larger depending on the parameter setting. For a company like Intel, reporting inventories for finished goods of nearly 1.5 billion dollars in the 2014 annual report, this allows for significant savings.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Graves & Schoenmeyr (2016) generalized the guaranteed-service model for safety stock placement incorporating the yield (capacity constraints) and demand uncertainties. Other relevant studies regarding the problem of safety stock placement under multiple uncertainties/risks can be found in Sonntag & Kiesmüller (2017), Woerner et al. (2018b) and De Smet et al. (2019). Sonntag & Kiesmüller (2017) proposed a model of in-house multi-stage serial production systems with random yield and demand, in order to calculate the optimal safety stock and positions of quality inspections through the production stages and optimize the position of inspections. ...
... Other relevant studies regarding the problem of safety stock placement under multiple uncertainties/risks can be found in Sonntag & Kiesmüller (2017), Woerner et al. (2018b) and De Smet et al. (2019). Sonntag & Kiesmüller (2017) proposed a model of in-house multi-stage serial production systems with random yield and demand, in order to calculate the optimal safety stock and positions of quality inspections through the production stages and optimize the position of inspections. Woerner et al. (2018b) developed a simulation-based optimization model for determining the optimal base stock level of a multi-echelon assembly system under capacity constraints (yield uncertainty) and uncertain demand. ...
Article
This paper analyses literature contributions in the search for safety stock problem under uncertainties and risks in the procurement process, focusing on the dimensioning problem (determination of the safety stock level). We perform a systematic literature review (SLR) from 1995 to 2019 in relevant journals, covering 193 selected articles. These selected articles were classified into three safety stock main issues: safety stock dimensioning, safety stock management, and safety stock positioning, allocation or placement. The SLR analysis allowed the identification of literature gaps and research opportunities, thus providing a road map to guide future research on this topic.
... For example, Samsung is in charge of the production of curved displays on a new cell phone series, but its final yields are less than 50% due to an imperfect production process. In this case, Samsung introduced quality inspection to successfully save production and holding costs of several million dollars (Sonntag and Kiesmüller, 2017). ...
Article
In an integrated inventory system with an imperfect production process, the manufacturer usually devotes itself to inspecting and reworking defective items. However, inspecting and reworking processes consume additional time, thereby possibly delaying the manufacturer’s timely delivery and further failing to satisfy the retailer’s order requirement on time. Thus, this paper builds joint economic lot-size problem (JELP) models that consider defective items, where the manufacturer is responsible for production, inspection and reworking processes. Based on the relationship between the inspection rate and production rate, this paper incorporates both the inspection-rate-insufficient (IRI) and inspection-rate-sufficient (IRS) scenarios. Under each scenario, two rework policies are proposed based on the order of priority between reworking process and delivery process: rework-priority policy (RPP) and delivery-priority policy (DPP). In addition, by incorporating the inspection/production/rework rate, effective-capacity-utilization (ECU) is first defined to consider the feasibility of joint production-inventory solutions, thus resulting in the following several interesting findings. (1) The integrated inventory system under IRI (IRS) scenario is suggested to be based on the system’s inspection rate (production rate) to quantify its ECU to ensure the feasibility of derived solutions. (2) Furthermore, ECU under RPP (DPP) is related (unrelated) to the number of shipments, thus implying that the corresponding integrated system can (cannot) initiatively manage its ECU by adjusting the number of shipments. (3) Additionally, the integrated system can be based on the defect rate to evaluate applications of RPP and DPP. That is, under the middle-defect-rate, RPP can be theoretically proven to be the preferred rework policy, while under the lower-defect-rate, the integrated system performs better under DPP via numerical results.
Article
A retailer places orders periodically for items that are shipped by a wholesaler. Items that are not sold perish randomly and independently of one another, with the perish probability depending on the age class. We consider a first‐in first‐out policy for depleting items. We model this problem as a Markov Decision Process with stochastic demand, unit holding, outdating and ordering costs, plus unit penalty costs for lost sales. We prove convexity for the penultimate period, and show convexity may not hold any earlier. A dynamic program can be solved optimally for small instances. We introduce both a one‐stage‐lookahead heuristic and a heuristic which is a combination of two existing standard approaches, the newsvendor and periodic review models. For simulated data, we compare these heuristics to the optimal solution for small problem instances and to further lookahead policies for larger problem instances. We show that the two new heuristics achieve results close to optimal. Our numerical study, which includes real data from a large European retail chain, highlights that products perishing independently from each other strongly affects model behaviour compared to existing approaches from the literature. This article is protected by copyright. All rights reserved
Article
This paper considers a single‐stage make‐to‐stock production‐inventory system under random demand and random yield, where defective units are reworked. We examine how to set cost‐minimizing production/order quantities in such imperfect systems, which is challenging because a random yield implies an uncertain arrival time of outstanding units and the possibility of them crossing each other in the pipeline. To determine the order/production quantity in each period, we extend the unit tracking/decomposition approach, taking into account the possibility of order‐crossing, which is new to the literature and relevant to other planning problems. The extended unit tracking/decomposition approach allows us to determine the optimal base‐stock level and to formulate the exact and an approximate expression of the per‐period cost of a base‐stock policy. The same approach is also used to develop a state‐dependent ordering policy. The numerical study reveals that our state‐dependent policy can reduce inventory‐related costs compared to the base‐stock policy by up to 6% and compared to an existing approach from the literature by up to 4.5%. From a managerial perspective, the most interesting finding is that a high mean production yield does not necessarily lead to lower expected inventory‐related costs. This counter‐intuitive finding, which can be observed for the most commonly used yield model, is driven by an increased probability that all the units in a batch are either of good or unacceptable quality. This article is protected by copyright. All rights reserved
Article
We consider an assembly system for a final product consisting of multiple components. The assembler orders each component from its primary supplier, and one of the components is subject to supply uncertainty in which the actual available quantity for assembly is only equal to a random fraction of the order quantity. After this actual available quantity is realized but before assembly for meeting final demand, the assembler can procure additional components from some backup suppliers. We derive the optimal ordering policy under this backup sourcing strategy and analyze the benefits of using backup suppliers in mitigating the adverse effect of supply uncertainty in component procurement from the primary suppliers. Our result shows that the availability of backup supplier for the component with supply uncertainty can provide very substantial benefits to the assembler, especially when the unit cost of this component from the primary supplier is high relative to the extra unit component cost from the backup supplier for high-margin products under an operating environment of high supply uncertainty and low demand uncertainty. We further show that the availability of backup suppliers for the other components with no supply uncertainty can provide additional significant benefits to the assembler when extra unit component cost of backup supplier for the unreliable component supplier is high for low-margin products with high demand uncertainty.
Article
The coronavirus pandemic (COVID-19) threatens people’s health. During the COVID-19 outbreak, people are encouraged to wear masks to reduce the spread of the virus. With the strong demand for masks, it has come a boom in counterfeit production. Combating counterfeit masks is vital and urgent to reduce the risks for public health. Motivated by the actual practices during the COVID-19, we examine how quality inspection and blockchain adoption help combat counterfeit masks. We find that quality inspection may not be always effective, as the government will tolerate the presence of counterfeit masks if the presence of the counterfeits is not significant. Comparing quality inspection with blockchain adoption, when the spread of COVID-19 is mild, authentic mask sellers may be encouraged to use the blockchain technology, which can increase their profits and reduce the social health risk. Furthermore, we extend our model to investigate the impacts of endogenous quality. Both quality inspection and blockchain adoption can induce low-quality mask sellers to enhance thequality level. When the number of counterfeit masks is increasing, encouraging the high-quality mask sellers to adopt the blockchain technology is effective to reduce social health risk when the spread of the coronavirus is rapid.
Full-text available
Article
Because of environmental and economic benefits, remanufacturing has become an integral part of a large number of manufacturing companies. This new reality forces them to focus on production planning and control (PPC) to minimize costs and reduce the environmental impact by decreasing waste and resource consumption. This paper deals with a PPC problem within a hybrid manufacturing-remanufacturing system (HMRS), which evolves in a constrained dynamic and stochastic context. The problem considers jointly three central decisions to optimize the long-term total cost. The main objective is to find both remanufacturing and manufacturing production rates as well as the policy used to switch between the remanufacturing modes of returns with different quality conditions. The optimal control policy characterization is performed combining optimal control theory and numerical techniques. Such policies consist of hedging point ones and stock-based switching decisions. Additional relevant control policies adapted from the literature are also considered and compared to the developed policies through a combined simulation-optimization approach, which optimize the policies control parameters. Both illustrative examples and a comparative analysis bring out the practical aspects of the proposed control policies and the interdependence between the quality conditions of returns and the production control settings. They also bring effective solutions to assist decision makers controlling simultaneously remanufacturing, manufacturing and switching operations for proper management of resource utilization, inventories and productivity.
Article
This paper investigates a retailer’s quality information acquisition and ordering decisions in the presence of risk aversion. We formulate a newsvendor model and derive the retailer’s optimal order quantity under two scenarios, one with and one without quality information acquisition, by minimising expected risk based on the CVaR criterion. Then, we compare the optimal ordering quantities and the profits under the two scenarios and discuss the quality information acquisition decision. Our results show that it will be more effective for the retailer to acquire quality information if the retailer exhibits greater risk aversion or the information is more precise; the retailer would order less when she is more risk-averse and the impact of risk aversion would be reduced by quality information acquisition. Interestingly, the retailer’s profits may decrease even when the quality information is more precise.
Article
A manufacturer places orders periodically for products that are shipped from a supplier. During transit, orders get damaged with some probability, that is, the order is subject to random yield. The manufacturer has the option to track orders to receive information on damages and to potentially place additional orders. Without tracking, the manufacturer identifies potential damages after the order has arrived. With tracking, the manufacturer is informed about the damage when it occurs and can respond to this information. We model the problem as a dynamic program with stochastic demand, tracking cost, and random yield. For small problem sizes, we provide an adjusted value iteration algorithm that finds the optimal solution. For moderate problem sizes, we propose a novel aggregation-based approximate dynamic programming (ADP) algorithm and provide solutions for instances for which it is not possible to obtain optimal solutions. For large problem sizes, we develop a heuristic that takes tracking costs into account. In a computational study, we analyze the performance of our approaches. We observe that our ADP algorithm achieves savings of up to 16% compared to existing heuristics. Our heuristic outperforms existing ones by up to 8.1%. We show that dynamic tracking reduces costs compared to tracking always or never and identify savings of up to 3.2%.
Article
In a production environment where random yield plays a fairly significant role, a decision has to be made on how to handle products that do not satisfy given quality requirements. We consider a single-stage production system with a positive production time and random yield. To ensure that only high quality items are sold to the customer, a post-production quality control system has been put in place. We compare two different strategies for defective items: disposal or rework. Disposal is possible without any time delay whereas the rework process requires a positive rework time. While disposed-of items leave the production process, reworked products stay in the process and are assumed to be as good as products that are perfect when they are initially produced. The end products are stored in a warehouse to satisfy stochastic demand. We show how to determine the optimal base-stock level, which is very difficult because of unknown covariances between orders. Subsequently, an optimization model is proposed to support the planner’s decision on which strategy to choose when it comes to whether to dispose of or rework defective items. By means of a sensitivity analysis we show which parameters directly affect this decision and give managerial insights. The analysis indicates that significant cost reductions can be obtained by choosing the best strategy for defective products.
Full-text available
Article
Methodologies, modeling approaches, and the interactions between various system elements involved in inspection-allocation and sensor-distribution problems, that influence operational quality decisions, are discussed. The surveyed papers fall into two broad categories: inspection-oriented quality-assurance strategies and diagnosis-oriented sensor-distribution strategies. Within each subarea, individual papers are further classified according to the system characteristics of the physical processes being investigated and the modeling characteristics of the approaches being used. As evident from nearly 100 journal articles published in the past four decades, these two problems have received considerable attention from researchers in quality engineering, management science, operations research as well as robotic vision arenas. We find that the inspection-allocation problem has been studied rather comprehensively whereas the relatively new sensor-distribution problem has plenty of opportunities for researchers. Discussions are also presented to summarize our observations based on the classifications along with some thoughts on future research.
Full-text available
Article
Purpose – This paper aims to present a survey of published literature about various quality costing approaches and reports of their success in order to provide a better understanding of cost of quality (CoQ) methods. Design/methodology/approach – The paper's approach is a literature review and discussion of the issues surrounding quality costing approaches. Findings – Even though the literature review shows an interest by the academic community, a CoQ approach is not utilized in most quality management programs. The evidence presented shows that companies that do adopt CoQ methods are successful in reducing quality costs and improving quality for their customers. The survey shows that the method most commonly implemented is the classical prevention‐appraisal‐failure model; however, other quality cost models are used with success as well. Originality/value – The paper shows that the selected CoQ model must suit the situation, the environment, the purpose and the needs of the company in order to have a chance to become a successful systematic tool in a quality management program.
Full-text available
Article
The allocation of quality control stations (AQCS) in multistage manufacturing systems has been studied extensively over the decades. This paper reviews the existing approaches, models comparison and solution techniques applied in AQCS. The relevance of the models and the effectiveness of the inspection strategies are examined by developing a generalised model. The conducting simulation experiments show that as the number of workstation increases the processing time to solve the problem increases significantly. This led to the development of a heuristic algorithm with local search. The performance the heuristic was compared with the optimization method based on complete enumeration method (CEM). It was found that the heuristic method can derive an acceptable solution significantly faster than the CEM. The review has shown that the most common techniques used are dynamic programming and non-linear programming. The paper suggests some biologically inspired optimisation algorithms can be of interest for further study.
Full-text available
Article
We are studying the manufacturing performance of semiconductor wafer fabrication plants in the US, Asia, and Europe. There are great similarities in production equipment, manufacturing processes, and products produced at semiconductor fabs around the world. However, detailed comparisons over multi-year intervals show that important quantitative indicators of productivity, including defect density (yield), major equipment production rates, wafer throughput time, and effective new process introduction to manufacturing, vary by factors of 3 to as much as 5 across an international sample of 28 fabs. We conduct on-site observations, and interviews with manufacturing personnel at all levels from operator to general manager, to better understand reasons for the observed wide variations in performance. We have identified important factors in the areas of information systems, organizational practices, process and technology improvements, and production control that correlate strongly with high...
Article
We investigate a periodic-review inventory system for a single item with stochastic demand and random yield. Since the optimal policy for such a system is complicated, we study the class of stationary linear-inflation policies where orders are only placed if the inventory position is below a critical stock level, and where the order quantity is controlled by a yield inflation factor. We consider two different models for the uncertain supply, namely binomial and stochastically proportional yield, and we allow positive and constant lead times as well as asymmetric demand and yield distributions. In this paper we propose two novel approaches to derive optimal and near-optimal numerical values for the critical stock level, minimizing the average holding and backorder cost for a given inflation factor. First, we present a Markov chain approach which is exact in case of negligible lead time. Second, we provide a steady-state analysis to derive approximate closed-form expressions for the optimal critical stock level. We conduct an extensive numerical study to test the performance of our approaches. The numerical experiments reveal an excellent performance of both approaches. Since our derived formulas are easily implementable and highly accurate they are very valuable for practical application.
Article
This paper reviews the literature on quantitatively-oriented approaches for determining lot sizes when production or procurement yields are random. We discuss issues related to the modelling of costs, yield uncertainty, and performance in the context of systems with random yields. We provide a review of the existing literature, concentrating on descriptions of the types of problems that have been solved and important structural results. We identity a variety of shortcomings of the literature in addressing problems encountered in practice, and suggest directions for future research.
Article
We consider a random yield inventory system, where a company has access to real time information about the actual yield realizations. To contribute to a better understanding of the value of this information, we develop a mathematical model of the inventory system and derive structural properties. We build on these properties to develop an optimal solution approach that can be used to solve small to medium sized problems. To solve large problems, we develop two heuristics. We conduct numerical experiments to test the performances of our approaches and to identify conditions under which real time yield information is particularly beneficial. Our research provides the approaches that are necessary to implement inventory control policies that utilize real time yield information. The results can also be used to estimate the cost savings that can be achieved by using real time yield information. The cost savings can then be compared against the required investments to decide if such an investment is profitable. http://www.sciencedirect.com/science/article/pii/S0377221714005311
Book
Everything about inventory management in discrete time (this is the reality) and much about stochastic lotsizing models.
Article
We determine the value of monitoring perishable freight in-transit for a single vehicle traveling from an origin to a destination. We develop a computationally practical approach for determining the optimal expected cost function and an optimal policy, based on an infinite horizon partially observed Markov decision process model. Structural properties of the optimal expected cost function and optimal policy are determined. These results can lend insight when deciding whether to acquire the capacity to monitor freight status in transit and what actions to take, based on the data from the in-transit monitoring, that optimally increase expected supply chain productivity.
Article
In this paper, we develop integrated inventory inspection models with and without replacement of nonconforming items. Inspection policies include no inspection, sampling inspection, and 100% inspection. We consider a buyer who places an order from a supplier when his inventory level drops to a certain point, due to demand which is stochastic in nature. When a lot is received, the buyer uses some type of inspection policy. The fraction nonconforming is assumed to be a random variable following a beta distribution. The order quantity, reorder point and the inspection policy are decision variables. In the inspection policy involving determining sampling plan parameters, constraints on the buyer and manufacturer risks is set in order to obtain a fair plan for both parties. A solution procedure for determining the operating policies for inventory and inspection consisting of order quantity, sample size, and acceptance number is proposed. Numerical examples are presented to conduct a sensitivity analysis for important model parameters and to illustrate important issues about the developed models.
Article
This work provides a comprehensive analysis of a general periodic review production/inventory model with random (variable) yield. Existence of a order point whose value does not depend on yield being random is proved in the single period case without specifying the yield model and using a very general cost structure. When yield is a random multiple of lot size, the nonorder-up-to optimal policy is characterized for a finite-horizon model. The finite-horizon value functions are shown to converge to the solution of an infinite-horizon functional equation, and the infinite-horizon order point is shown to be no smaller than when yield is certain.
Article
The use of wafer inspection systems in managing semiconductor manufacturing yields is described. These systems now detect defects of size as small as 40 nm. Some high-speed systems have achieved 200-mm diameter wafer throughputs of 150 wafers per hour. The particular technologies involved are presented. Extensions of these technologies to meet the requirements of manufacturing integrated circuits with smaller structures on larger wafers are discussed.
Article
We consider a single item periodic review inventory problem with random yield and stochastic demand. The yield is proportional to the quantity ordered, with the multiplicative factor being a random variable. The demands are stochastic and are independent across the periods, but they need not be stationary. The holding, penalty, and ordering costs are linear. Any unsatisfied demands are backlogged. Two cases for the ordering cost are considered: The ordering cost can be proportional to either the quantity ordered (e.g., in house production) or the quantity received (e.g., delivery by an external supplier). Random yield problems have been addressed previously in the literature, but no constructive solutions or algorithms are presented except for simple heuristics that are far from optimal. In this paper, we present a novel analysis of the problem in terms of the inventory position at the end of a period. This analysis provides interesting insights into the problem and leads to easily implementable and highly accurate myopic heuristics. A detailed computational study is done to evaluate the heuristics. The study is done for the infinite horizon case, with stationary yields and demands and for the finite horizon case with a 26-period seasonal demand pattern. The best of our heuristics has worst-case errors of 3.0% and 5.0% and average errors of 0.6% and 1.2% for the infinite and finite horizon cases, respectively.
Article
Are there economic benefits to improving customer satisfaction? Many firms that are frustrated in their efforts to improve quality and customer satisfaction are beginning to question the link between customer satisfaction and economic returns. The authors investigate the nature and strength of this link. They discuss how expectations, quality, and price should affect customer satisfaction and why customer satisfaction, in turn, should affect profitability; this results in a set of hypotheses that are tested using a national customer satisfaction index and traditional accounting measures of economic returns, such as return on investment. The findings support a positive impact of quality on customer satisfaction, and, in tum, profitability. The authors demonstrate the economic benefits of increasing customer satisfaction using both an empirical forecast and a new analytical model. In addition, they discuss why increasing market share actually might lead to lower customer satisfaction and provide preliminary empirical support for this hypothesis. Finally, two new findings emerge: First, the market's expectations of the quality of a firm's output positively affects customers' overall satisfaction with the firm; and second, these expectations are largely rational, albeit with a small adaptive component.
Article
New developments in corporate information technology such as enterprise resource planning systems have significantly increased the flow of information among members of supply chains. However, the benefits of sharing information can vary depending on the supply chain structure and its operational characteristics. Most of the existing research has studied the impact of sharing downstream information (e.g., a manufacturer sharing information with its suppliers). We evaluate the benefits of sharing upstream yield information (e.g., a supplier sharing information with the manufacturer) in a two-stage serial supply chain in which the supplier has multiple internal processes and is faced with uncertain output due to yield losses. We are interested in determining when the sharing of the supplier's information is most beneficial to the manufacturer. After proposing an order-up-to type heuristic policy, we perform a detailed computational study and observe that this information is most beneficial when the supplier's yield variance is high and when end-customer demand variance is low. We also find that the manufacturer's backorder-to-holding cost ratio has little, if any, impact on the usefulness of information.
Article
Typically the operating policy for the inventory control system for a commodity is developed independent of the operating policy for the quality control system for that commodity and vice versa. In many circumstances, these systems are dependent on one another. A cost model that combines a fixed order quantity inventory control system with a Bayesian quality control system for a lot-by-lot attribute acceptance sampling plan is presented along with an algorithm to obtain the operating parameters for the combined systems. Behavior of the combined systems is investigated, and the costs and operating policy determined using this model are compared with the costs and operating policy determined when there is no integration of the two systems and when there is partial integration of the two systems. There are significant cost savings obtained by using the operating policy developed under the combined systems over the operating policy derived from the separate systems.
Article
A method for determining where to locate the inspection stations in a multistage production process with imperfect inspection is presented. Dynamic programming is used to establish that the optimal expected total cost function at every stage is piecewise linear and concave. While the optimal policy at every stage usually consists of one "inspect" region and one "do not inspect" region, this policy structure is found not to hold in general. A computation scheme based directly on the dynamic programming formulation is proposed, a time-sharing computer program is discussed, and the results of an example problem are presented.
Article
A general screening inspection program is developed in which inspection levels and the locations of inspection points are treated as variables. It is shown that the function representing the total of inspection and scrap costs will be minimized by an extreme-point solution, thus the minimum-cost inspection program will lie in a relatively restricted subset of all possible allocations. Application of a computational procedure based on dynamic-programming allows the minimum-cost program to be readily determined for instances in which the requirement for inspection is the maintenance of a specified quality level or when a linear cost may be associated with outgoing defective material.
Article
The economic performance of many modern production processes is substantially influenced by process yields. Their first effect is on product cost — in some cases, low-yields can cause costs to double or worse. Yet measuring only costs can substantially underestimate the importance of yield improvement. We show that yields are especially important in periods of constrained capacity, such as new product ramp-up. Our analysis is illustrated with numerical examples taken from hard disk drive manufacturing. A three percentage point increase in yields can be worth about 6% of gross revenue and 17% of contribution. In fact, an eight percentage point improvement in process yields can outweigh a US$20/h increase in direct labor wages. Therefore, yields, in addition to or instead of labor costs, should be a focus of attention when making decisions such as new factory siting and type of automation. The paper also provides rules for when to rework, and shows that cost minimization logic can again give wrong answers.
Article
Service quality and customer satisfaction are widely recognized as key influences in the formation of consumers' purchase intentions in service environments. However, a review of the existing literature suggests that the specific nature of the relationship between these important constructs in the determination of consumers' purchase intentions continues to elude marketing scholars (c.f. Bitner and Hubbert 1994; Bolton and Drew 1994; Gronroos 1993; Rust and Oliver 1994). The study reported here was designed to aid in the understanding of these relationships by empirically assessing the nature of the relationship between service quality and consumer satisfaction in the formation of consumers' purchase intentions across four unique service industries. The results of the current research, coupled with the weight of the evidence in the emerging services literature, suggest that consumer satisfaction is best described as moderating the service quality/purchase intention relationship. The managerial and research implications of the reported study are also discussed.
Article
In this paper, we propose a simple heuristic approach for the inventory control problem with stochastic demand and multiplicative random yield. Our heuristic tries to find the best candidate within a class of policies that are referred to in the literature as the linear inflation rule (LIR) policies. Our approach is computationally fast, easy to implement, and intuitive to understand. Moreover, we find that in a significant number of instances our heuristic performs better than several other well-known heuristics that are available in the literature.
2012 Intel's battle with ARM is about making its future fabs viable http
  • T Foremski
Rumor: Low yield 3D glass forcing Samsung to look for additional suppliers for Galaxy
  • C Mcnutt
Intel's battle with ARM is about making its future fabs viable
  • T Foremski
Inventory Control Volume 90
  • S. Axsäter