12,288 research outputs found
Decision support for production capacity planning under uncertainty: A case study of TINE SA
Confidential until 21 May 201
A comparison between the order and the volume fill rates for a base-stock inventory control system under a compound renewal demand process
The order fill rate is less commonly used than the volume fill rate (most often just denoted fill rate) as a performance measure for inventory control systems. However, in settings where the focus is on filling customer orders rather than total quantities, the order fill rate should be the preferred measure. In this paper we consider a continuous review, base-stock policy, where all replenishment orders have the same constant lead time and all unfilled demands are backordered. We develop exact mathematical expressions for the two fill-rate measures when demand follows a compound renewal process. We also elaborate on when the order fill rate can be interpreted as the (extended) ready rate. Furthermore, for the case when customer orders are generated by a negative binomial distribution, we show that it is the size of the shape parameter of this distribution that determines the relative magnitude of the two fill rates. In particular, we show that when customer orders are generated by a geometric distribution, the order fill rate and the volume fill rate are equal (though not equivalent when considering sample paths). For the case when customer inter-arrival times follow an Erlang distribution, we show how to compute the two fill rates.Backordering; continuous review; compound renewal process; inventory control; negative binomial distribution; service levels
A tri-level optimization model for inventory control with uncertain demand and lead time
We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings
A Review and Analysis of Service Level Agreements and Chargebacks in the Retail Industry
Purpose: This study examines service level agreements (SLAs) in the retail industry and uses empirical data to draw conclusions on relationships between SLA parameters and retailer financial performance. Design/methodology/approach: Based on prior SLA theories, hypotheses about the impacts of SLA confidentiality, choice of chargeback mechanisms, and chargeback penalty on retailer inventory turnover are tested. Findings: Retailer inventory turnover could vary by the level of SLA confidentiality, and the variation of retailer inventory turnovers could be explained by chargeback penalty. Research limitations/implications – The research findings may not be readily applicable to SLAs outside of the retail industry. Also, most conclusions were drawn from publicly available SLAs. Practical implications: The significant relationships between SLA parameters and retailer inventory turnover imply that a retailer could improve its financial performance by leveraging its SLA design. Originality/value: Not only does this study contribute to the understanding of retail SLA design in practice, but it also extends prior theories by investigating the implications of SLA design on retailer inventory turnover
Optimizing Inventory for Profitability and Order Fulfillment Improvement
Despite the extensive research on inventory management, few studies have investigated the optimization of inventory classification and control policies for maximizing the net present value of profit and order fulfillment performance. This dissertation aims to fill the gaps, and consists of two main essays. Essay One (Chapter 1) presents a new multi-period optimization model to explicitly address nonstationary demand, arbitrary review periods, and SKU-specific lead times, with the objective of maximizing the net present value of profit. A real-world application and computational experiments show that the optimal dynamic inventory classification and control decisions obtained from the model significantly reduce both safety stock and base stock levels compared to a multi-criteria inventory classification scheme and the traditional ABC approach. Essay Two (Chapter 2) examines two order-based fulfillment performance measures: the order fill rate, defined as the percentage of orders that are completely filled from available inventory; and the average customer-order fill rate, defined as the mean percentage of total units in a customer order that can be filled from on-hand inventory. Novel optimization models are developed to maximize the order fulfillment performance. Computational results indicate that a commonly used item-based measure in general does not adequately indicate order-based performance, and the tradeoffs between profit and order-based measures vary with inventory investment. This research contributes to the existing literature by providing new approaches to optimize inventory classification and control policies with various performance criteria. It also provides practitioners with a viable way to manage inventory with nonstationary demand, general review periods and lead times, and further allows companies to quantity the tradeoffs of different performance measures
A numerical study of expressions for fill rate for single stage inventory system with periodic review.
Fill rate is one of the most important measurements for inventory systems in the supply chain management. The primary goal of this thesis is to give a comprehensive review of existing analytical expressions for the system fill rate, and provide numerical comparison for all relevant expressions in terms of their accuracy against (simulated) fill rate from the Monte Carlo simulation. We prove relationships between several expressions. Although majority of the expressions discussed herein are designed for standard periodic review system, we conduct numerical simulations for the general periodic review system. Under this general periodic review setting, numerical results indicate that all else being equal, replenishment lead time has larger effect on the system\u27s fill rate than does the review interval. In addition, numerical comparison suggests that Johnson et al.\u27s approach, Zhang and Zhang\u27s approach, Hadley and Whitin\u27s approach dominate the traditional approach, exponential approximation and Silver\u27s modified approach. The dominance is especially true for cases with high demand variability. For general periodic review system, our numerical results indicate that scaling is necessary for Silver\u27s modified, Johnson et al.\u27s and Johnson et al.\u27s modified approaches
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