The influence of demand variability on the performance of a make-to-stock queue

Variability, in general, has a deteriorating effect on the performance of stochastic inventory systems. In particular, previous results indicate that demand variability causes a performance degradation in terms of inventory related costs when production capacity is unlimited. In order to investigate the effects of demand variability in capacitated production settings, we analyze a make-to-stock queue with general demand arrival times operated according to a basestock policy. We show that when demand inter-arrival distributions are ordered in a stochastic sense, increased arrival time variability indeed leads to an augmentation of optimal base-stock levels and to a corresponding increase in optimal inventory related costs. We quantify these effects through several numerical examplesproduction/inventory; make-to-stock; base-stock; stochastic comparisons; GI/M/1, POLICIES; COSTS; SYSTEMS; LEAD

Time bucket size and lot-splitting approach

We address the problem of lot splitting for various time bucket lengths in MRP systems. Two approaches for lot splitting can be applied: either use the same (equal) or a variable number of subbatches. Equal subbatching strategies have logistical and computational advantages. Literature states that variable batching strategies are only marginal better. However, these results do not take into account the sensitivity for changes in time bucket length. Managers have reduced time bucket lengths in planning systems. We examine the sensitivity of lot splitting for these changes. Our study reveals that it is not cost-effective to disregard time bucket length when deciding on the number of subbatches. Using the same number of subbatches per time bucket for all products results in substantial cost-differences, where the magnitude is affected by the discontinuity of the total cost curve. For a given time bucket length, a cost difference with a variable number of subbatches per operation of only 2.1% can be obtained if an appropriate, equal number of subbatches for each product can be found. Other equal subbatching strategies show much larger cost differences on average, ranging from 4-11%. In order to obtain these results, a new variable subbatch heuristic has been designed.

Inventory control with multiple setup costs

We consider an infinite-horizon, periodic-review, single-item production/inventory system with random demand and backordering, where multiple setups are allowed in any period and a separate fixed cost is associated for each setup. Contrary to the majority of the literature on this topic, we do not restrict the order quantities to be integer multiples of the exogenously given batch size and instead allow the possibility of partial batches, in which case the fixed cost for ordering the batch is still fully charged. We build a model that particularly takes the batch-ordering cost structure into account. We introduce an alternative cost-accounting scheme to analyze the problem, which we use to develop a computationally efficient optimal solution method and several properties of the optimal solution. In addition, we propose two heuristic policies, both of which perform extremely well computationally

Approximation algorithms for capacitated stochastic inventory systems with setup costs

We develop the first approximation algorithm with worst-case performance guarantee for capacitated stochastic periodic-review inventory systems with setup costs. The structure of the optimal control policy for such systems is extremely complicated, and indeed, only some partial characterization is available. Thus, finding provably near-optimal control policies has been an open challenge. In this article, we construct computationally efficient approximate optimal policies for these systems whose demands can be nonstationary and/or correlated over time, and show that these policies have a worst-case performance guarantee of 4. We demonstrate through extensive numerical studies that the policies empirically perform well, and they are significantly better than the theoretical worst-case guarantees. We also extend the analyses and results to the case with batch ordering constraints, where the order size has to be an integer multiple of a base load.National Science Foundation (U.S.) (CMMI-1362619)National Science Foundation (U.S.) (CMMI-1131249)National Science Foundation (U.S.) (DMS-0732175)National Science Foundation (U.S.) (CMMI-0846554)National Science Foundation (U.S.) (FA9550-08-1–0369

Managing the trade-off implications of global supply

The cost versus response trade-off is a growing logistics issue due to many markets being increasingly characterized by demand uncertainty and shorter product life cycles. This is exacerbated further with supply increasingly moving to low cost global sources. However, the poor response implications of global supply are often not addressed or even acknowledged when undertaking such decisions. Consequently, various practical approaches to minimising, postponing or otherwise managing the impact of the demand uncertainty are often only adopted retrospectively. Even though such generic solutions are documented through case examples we lack effective tools and concepts to support the proactive identification and resolution of such trade-offs. This paper reports on case-based theory building research, involving three cases from the UK and USA used in developing a conceptual model with associated tools, in support of such a process

Lost sales inventory models with batch ordering and handling costs

In this paper, we integrate inventory and handling into a single model for analysis and optimization of inventory replenishment decisions for a grocery retail store. We consider a retailer who periodically manages his inventory of a single item facing stochastic demand. The retailer may only order in multiples of a xed batch size, the lead time is less than the review period length and all unmet demand is lost, which is a realistic situation for a large part of the assortment of grocery retailers. The replenishment cost includes both xed and variable components, dependent on the number of batches and units in the order. This structure captures the shelf-stacking costs in retail stores. We investigate the optimal policy structure under the long-run average cost criterion. Our results show that it is worthwhile to explicitly take handling costs into account when making inventory decisions. We use parameter values typical for rocery retail environments. For an important subset of the retail assortment, we show that signicant cost reductions exist by explicitly considering handling in the inventory policy. Keywords: Retail inventory control, Handling, Lost sales, Periodic review, Fixed batch size

Performance Evaluation of Stochastic Multi-Echelon Inventory Systems: A Survey

Globalization, product proliferation, and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling inventory in general supply networks under uncertainty and their widefspread applications to industry. These developments are based on three generic methods: the queueing-inventory method, the lead-time demand method and the flow-unit method. In this paper, we compare and contrast these methods by discussing their strengths and weaknesses, their differences and connections, and showing how to apply them systematically to characterize and evaluate various supply networks with different supply processes, inventory policies, and demand processes. Our objective is to forge links among research strands on different methods and various network topologies so as to develop unified methodologies.Masdar Institute of Science and TechnologyNational Science Foundation (U.S.) (NSF Contract CMMI-0758069)National Science Foundation (U.S.) (Career Award CMMI-0747779)Bayer Business ServicesSAP A

Analysis of simple inventory control systems with execution errors: Economic impact under correction opportunities

Cataloged from PDF version of article.Motivated by recent empirical evidence, we study the economic impact of inventory record inaccuracies that arise due to execution errors. We model a set of probable events regarding the erroneous registering of sales at each demand arrival. We define correction opportunities that can be used to (at least partially) correct inventory records. We analyze a simple inventory control model with execution errors and correction opportunities, and demonstrate that decisions that consider the existence of recording errors and the mechanisms with which they are corrected can be quite complicated and exhibit complex tradeoffs. To evaluate the economic impact of inventory record inaccuracies, we use a simulation model of a (Q,r) inventory control system and evaluate suboptimalities in cost and customer service that arise as a result of untimely triggering of orders due to inventory record inaccuracies. We show that the economic impact of inventory record inaccuracies can be significant, particularly in systems with small order sizes and low reorder levels. (C) 2010 Elsevier BM. All rights reserved

Inventory control with partial batch ordering

In an in¯nite-horizon, periodic-review, single-item production/inventory system with random demand and back-ordering, we study the feature of batch ordering, where a separate ¯xed cost is associated for each batch ordered. Contrary to majority of the literature on this topic, we do not restrict the order quantities to be integer multiples of the batch size and instead allow the possibility of partial batches, in which case the ¯xed cost for ordering the batch is still fully charged. We build a model that particu- larly takes the batch ordering cost structure into account. We introduce an alternative cost accounting scheme to analyze the problem, and we discuss several properties of the optimal solution. Based on the analysis of a single-period problem and a multi-period lower-bound problem, we study two heuristic policies for the original partial batch or- dering problem, both of which perform very well computationally for a wide range of problem parameters. Finally, we compare the performance of the optimal policy to the performance of the best full-batch-size ordering policy to quantify the value of partial ordering °exibility