Michael E. Ketzenberg

Value of information in remanufacturing complex products

Remanufacturing facilities usually face a trade-off between limited information about remanufacturing yields and potentially long supplier lead times. To improve production performance, these firms may attempt to acquire more timely and accurate information about remanufacturing yields or alternatively, to reduce the lead times of purchased parts. We develop four decision-making models to evaluate the impact of yield information and supplier lead time on manufacturing costs. We identify the operating conditions under which these capabilities are valuable, along with their relative impact on facility performance. Each model is formulated as an infinite horizon, stochastic dynamic program (Markov decision process). Our results indicate that the yield information is generally quite valuable, while investments in supplier responsiveness provide trivial returns to products with few parts. However, as product complexity increases with large number of target parts, the value of short lead times increases.

A framework for the value of information in inventory replenishment

Abstract Technological advances and changes in supply chain management practices have combined to draw attention to the value of information sharing in inventory replenishment. Academic research has produced seemingly conflicting results due to differences in the type of information that is shared, the supply chain structure, and the selection and parameterization of performance goals. This research provides a framework to help explain apparent differences in the extant literature. Our purpose is to understand what determines the value of information. With this specific view, we establish a set of research questions and suggest directions for future research. We introduce a research framework organized around the current literature and established theory. This framework is then evaluated by collectively classifying and coding results from the literature. Using a split-sample, least squares regression analysis, our results provide tentative empirical evidence that supports the framework, but also indicate that there are additional complex relationships among modeling parameters and assumptions that present opportunities for future research.

Two-stage make-to-order remanufacturing with service-level constraints

We consider a firm that meets demand for an order with remanufactured products, new products or a mix of both. There are constraints on the service level. We use a stylized two-stage GI/G/ 1 queuing network model to study the problem. The first stage is unique to each product, whereas remanufactured and new products share the second stage. The objective was to find the optimal, long run production mix that maximizes profit subject to a service-level constraint that restricts the average order lead-time. There is yield in the remanufacturing process, where yield is the per cent of returned used products that result in a good part after remanufacturing. In the analytic model, we make the simplifying assumption that the producer always gets enough return of used products to meet its remanufacturing needs in the production mix. However, we relax this assumption in a simulation. We find that the optimal solution is generally non-trivial, i.e. the firm generally uses a mix of remanufactured and new products to meet demand. When the new product is less profitable than the remanufactured product, then it is optimal to remanufacture 100%, provided that there is enough supply of used products. When the new product is more profitable, however, the proportion of remanufacturing increases as service level increases. We use simulation to test the robustness of the analytic model by including complexities such as stochastic product returns and stochastic production yield.

The value of information in a capacitated closed loop supply chain

We explore the value of information in the context of a firm that faces uncertainty with respect to demand, product returns, recovery yield, and capacity utilization. Capacity is finite and shared between new production and recovery operations. The operational decisions of interest are the quantity of new product to produce, the quantity of returns to recover, and the quantity of returns to dispose. Product recovery is uncertain in that each returned unit can be successfully recovered with a known probability, and otherwise it is discarded at a cost. Demand in a period is satisfied with new production, recovered returns, or a mix of both types. We measure and evaluate the value of information through three information cases that separately address different types of information: demand, recovery yield, and capacity utilization. We find that there is no dominance in value amongst the different types of information, although information on capacity utilization provides the highest average value and exceeds the value of the other two types of information in 55% of the cases studied. We also identify the operating conditions in which each type of information is most valuable, compare the value of information to other types of investments, and also assess robustness with respect to the accuracy of information.

A heuristic for multi-item production with seasonal demand

A heuristic is developed for a common production/inventory problem characterized by multiple products, stochastic seasonal demand, lost sales, and a constraint on overall production. Heuristics are needed since the calculation of optimal policies is impractical for real-world instances of this problem. The proposed heuristic is compared with those in current use as well as optimal solutions under a variety of conditions. The proposed heuristic is both near optimal and superior to existing heuristics. The heuristic deviated from optimality by an average of 1.7% in testing using dynamic programming as a benchmark. This compares favorably against linear-programming-based heuristics and practitioner heuristics, which deviated from optimality by 4.5 to 10.6%.

Quantifying the benefits of breaking bulk in retail operations

Abstract We explore the benefits of ‘breaking bulk’ in retail operations. Here, breaking bulk refers to delivering single units from distribution centers to retail outlets rather than the multiple units bundled together by manufacturers termed ‘case-packs’. The focus is largely on the benefits to space management at the retail level, rather than the more obvious reduction in inventory costs. Using data from the grocery industry, results indicate that retail unit profitability can be increased substantially by breaking bulk—but only if current inventory replenishment practices are changed. In essence, breaking bulk allows for either higher product variety within a store or identical variety in smaller stores. This work seeks to quantify the order of magnitude of that benefit.

Expiration dates and order quantities for perishables

We address the problem of how to set expiration dates for perishable products in the context of a retailer that sells a random lifetime product under periodic review. Although the lifetime is random, the retailer, in addition to deciding when and how much to order, must also determine an expiration date that corresponds to the maximum number of periods that inventory may remain available for sale before it must be discarded. Unlike other models of perishable inventory, but similar to real-world processes, it is possible to unknowingly sell products that have reached the limit of their retail shelf life, or to discard products that are still good for sale. As a result, retailers must balance hazard costs and perishing costs by setting a remaining shelf life that provides customers with a reasonable time for storage at home before the product actually spoils.