James R. Bradley

Improved Base-Stock Approximations for Independent Stochastic Lead Times with Order Crossover

Order crossovers occur when replenishment orders arrive in a sequence that is different than the one in which they were placed. Order crossovers require that optimal reorder levels be set with regard to the inventory shortfall distribution rather than the lead-time demand distribution. Assuming periodic review and independent lead times, this paper suggests simple approximations of the shortfall distribution by showing that the variance of the number of orders outstanding is bounded above by the standard deviation of lead time divided by âx88x9a3. Using this bound in a normal approximation improves significantly upon the common practice of basing policies on the lead-time demand distribution. A negative binomial approximation of the shortfall, based on its exact variance, offers even greater improvement, at the cost of some additional informational and computational requirements.

An Alternative FMEA Method for Simple and Accurate Ranking of Failure Modes

Failure modes and effects analysis (FMEA) is a methodology for prioritizing actions to mitigate the effects of failures in products and processes. Although originally used by product designers, FMEA is currently more widely used in industry in Six Sigma quality improvement efforts. Two prominent criticisms of the traditional application of FMEA are that the risk priority number (RPN) used to rank failure modes is an invalid measure according to measurement theory, and that the RPN does not weight the three decision criteria used in FMEA. Various methods have been proposed to mitigate these concerns, including many using fuzzy logic. We develop a new ranking method in this article using a data-elicitation technique. Furthermore, we develop an efficient means of eliciting data to reduce the effort associated with the new method. Subsequently, we conduct an experimental study to evaluate that proposed method against the traditional method using RPN and against an approach using fuzzy logic.

Analyzing the effect of inventory policies on the nonstationary performance with transfer functions

In this paper we apply transfer function methods to analyze the performance of supply chains in response to nonstationary demand and, in particular, we investigate how various inventory policies and demand forecasting parameters affect supply chain responsiveness. In a single-echelon inventory system we investigate the performance of a common base stock policy. Specifically, we describe the order and inventory trajectories using discrete transfer functions, and we derive closed-form analytical expressions for the transient behavior in response to a step change in demand. We introduce performance measures commonly used to analyze nonstationary performance and derive closed-form expressions for these measures. Next, we study the performance of a two-echelon supply chain under installation stock and echelon stock policies. We explicate the performance tradeoff in response to stationary versus nonstationary demand, and show that the transient response of orders and inventory levels can be either underdamped or overdamped depending on the exponential smoothing parameter. We show that the echelon stock policy is more responsive than the installation stock policy when both policies have similar stationary performances.

Note---Further Improvements on Base-Stock Approximations for Independent Stochastic Lead Times with Order Crossover

When inventory replenishments can arrive in a different sequence than the one in which they were placed, it is important to use the shortfall distribution to set the base-stock level. Because the exact shortfall distribution is quite difficult to compute, heuristics are commonly used in its stead. Bradley and Robinson [Bradley, J. R., L. W. Robinson. 2005. Improved base-stock approximations for independent stochastic lead times with order crossover. Manufacturing Service Oper. Management7 319--329.] developed an upper bound on the variance of the number of outstanding orders that they used within a normal approximation of the shortfall distribution. In this short note, we tighten their upper bound and use it within a beta approximation of the shortfall distribution to derive a policy whose costs average only 0.05% above that of the optimal.

A Framework for RFID Deployment in Supply Chains

The proposed framework shows how the granularity of tagging and deployment scope affect RFID implementation, helping managers identify RFID benefits and barriers and align deployment with their companys business strategy and return-on-investment goals.

An Evaluation of Capacity and Inventory Buffers as Mitigation for Catastrophic Supply Chain Disruptions

By some accounts supply chains are increasingly being affected by catastrophic events that disrupt goods flow for prolonged periods. This may be because the occurrence of catastrophic events has, indeed, increased or because we are simply more attuned to such events because global supply chains are exposed to a greater number of catastrophic risks. Regardless of which is true, arguments have been made in the popular press that the impacts of catastrophic events are more severe than in past years because supply chains have less inventory which reduces the amount of time before deliveries to customers are affected. These same accounts argue for managers to return to past practices where more inventory was held, which motivated the analysis in this article of whether such inventory buffers are financially feasible. To broaden the discussion, we also analyzed whether the alternative type of buffer, manufacturing capacity, is feasible. We characterize the feasibility of these two buffer tactics by measuring their effect on manufacturing companies’ net incomes and credit worthiness. We also discuss nonfinancial factors that determine whether capacity and inventory buffers are effective as well as provide some ideas for restructuring supply chains so that less capacity is needed to mitigate the effect of catastrophic disruptions.