Ricardo Ernst

Operations related groups (ORGs): A clustering procedure for production/inventory systems

Abstract Many production/inventory systems contain thousands of stock keeping units (SKUs). In general, it is not computationally (or conceptually) feasible to consider every one of these items individually in the development of control polices and strategies. Our objective here is to develop a methodology for defining groups to support strategic planning for the operations function. Accordingly, such groups should take into consideration all product characteristics which have a significant impact on the particular operations management problem of interest. These characteristics can include many of the attributes which are used in other functional groupings and will most certainly go beyond the cost and volume attributes used in ABC analysis. The ORG methodology is based on statistical clustering and can utilize a full range of operationally significant item attributes. It considers both statistical measures of discrimination and the operational consequences associated with implementing policies derived on the basis of group membership. The main departure of this analysis from earlier work is: 1) the approach can handle any combination of item attribute information that is important for strategy purposes, 2) managements interest in defining groups on the basis of operational factors can be accommodated, 3) statistical discrimination is considered directly, 4) group definition reflects the performance of management policies which are based (in part) on group membership, and 5) the method can be applied successfully to systems with a large number of SKUs. The specific application which motivated development of the ORG methodology was an analysis of distribution strategy for the service parts division of a major automobile manufacturer. The manufacturer was interested in developing optimal inventory stocking policies, which took into account the complexities of its multiechelon distribution network, supplier relationships and customer service targets for each market segment. This manufacturer stocked over 300,000 part numbers in an extensive network with approximately 50 distribution centers and thousands of dealer locations (i.e., 1.5 million SKU/ location combinations). The results of this application indicated that the advantage of using operationally relevant data for grouping and for defining generic, group-based policies for controlling inventory can be substantial. The ORG methodology can be of value to operations managers in industries with a large number of diverse items.

Optimal base stock policies and truck capacity in a two-echelon system

With the recent trend toward just-in-time deliveries and reduction of inventories, many firms are reexamining their inventory and logistics policies. Some firms have dramatically altered their inventory, production, and shipping policies with the goal of reducing costs and improving service. Part of this restructuring may involve a specific contract with a trucking company, or it may entail establishing in-house shipping capabilities. This restructuring, however, raises new questions regarding the choice of optimal trucking capacity, shipping frequency, and inventory levels. In this study, we examine a two-level distribution system composed of a warehouse and a retailer. We assume that demand at the retailer is random. Since the warehouse has no advance notice of the size of the retailer order, inventory must be held there as well as at the retailer. We examine inventory policies at both the warehouse and the retailer, and we explicitly consider the trucking capacity, and the frequency of deliveries from the warehouse to the retailer. Both linear and concave fixed transportation costs are examined. We find the optimal base stock policies at both locations, the optimal in-house or contracted regular truck capacity, and the optimal review period (or, equivalently, delivery frequency). For the case of normally distributed demand we provide analytical results and numerical examples that yield insight into systems of this type. Some of our results are counterintuitive. For instance, we find some cases in which the optimal truck capacity decreases as the variability of demand increases. In other cases the truck capacity increases with variability of demand.

An Economic Model for Evaluating Mining and Manufacturing Ventures with Output Yield Uncertainty

This paper develops an operational risk management model for evaluating production efforts in manufacturing and mining industries where the resource to be exploited is nonhomogenous. Using acontingent claims methodology now commonly encountered in financial applications, we formulate a production control model in an environment characterized bymarket andprocess uncertainty. In our analysis, market risk is depicted by the output price while process uncertainty is captured by the random variability inherent in the outputs yield. In this light, adjustments to the rate of production are viewed as a sequence of (nested) real options affording operating flexibility. We account for an optimal sequence of production adjustments, over a preestablished production horizon, by taking the production rate as an adapted positive real-valued process. Accordingly, techniques of stochastic control theory and contingent claims analysis (CCA) are employed to ensure value maximizing production policies are rendered in a manner consistent with an equilibrium price structure.

Manufacturer incentives to improve retail service levels

In this paper we provide a solution to the problem of how a manufacturer can provide an incentive to a separately-owned retailer to raise its service level above the level it would choose on its own. We analyze the effects the manufacturers incentive has on the individual profits of the retailer and manufacturer, as well as their joint profits. Our work is an application of game theory in which we tackle the problem posed by separate ownership of the two firms and the resulting conflict over the gains from cooperation. We utilize a plausible model of service-sensitive demand, one which is relatively free of ad hoc assumptions. Our approach has the strength that it proposes a credible solution to the problem of manufacturer-retailer cooperation when profits are sensitive to retail service levels. Our results indicate that the optimal level of incentive for the manufacturer and the resulting shares of the manufacturer and retailer in increased profits are particularly sensitive to the underlying variability of demand and to the relative variability of additional demand induced by higher service levels.

Optimal inventory policies under service-sensitive demand

The service level provided by a retailer influences demand and sales in several ways. In the short run, sales can fall short of demand when customers experience stockouts and choose not to backorder. In the long run, demand itself may decline as customers who experience excessive stockouts shift permanently to more reliable sources. The traditional approach to determining inventory policies assumes a fixed stockout cost attributable to each stockout occasion. In practice, stockout costs are difficult to determine, at least in part because they reflect the long-term loss of demand due to inadequate service. In this paper we model the response of long-run demand to the service level of the retailer, and determine optimal order-up-to inventory policies in the presence of service-sensitive demand. We show under what conditions inventory policy is sensitive to both the mean and the variance of long-run demand.

Allocation of warehouse inventory with electronic data interchange and fixed order intervals

This paper considers the problem of allocating warehouse inventory to retailers where retailer orders and the replenishment of warehouse inventory occur periodically on a fixed schedule. We assume that the warehouse and the retailers have the opportunity to exchange demand information through Electronic Data Interchange (EDI). At the warehouse level, for instance, the available information on the retailers demand may be utilized in determining the shipment quantities needed to meet the desired service level to the retailers. Unlike similar models focusing primarily on optimizing systems wide performance measures, in this paper we focus on the service level furnished to the retailers by the warehouse. To this end, three different allocation policies are considered: static, myopic, and dynamic rules characterizing the impact of available demand information on the resulting service levels. Numerical illustrations exemplify the allocation rules considered. An interesting though counter intuitive observation is that the existence of additional demand information cannot, a prior, be assumed superior.

Estimating demand by using sales information: inaccuracies encountered

Abstract Demand data is integral to a company’s overall information requirement. This is particularly true for manufacturers and retailers with regard to capacity, production, and inventory planning. Notwithstanding the implicit inaccuracies encountered, companies are predisposed to employ sales data as a primary source of information for estimating future demand. In this paper, by adopting a two-product setting, we measure inventory cost inaccuracies that arise from using sales data in estimating demand. By analyzing these costs, we also explore the conditions under which the resulting inaccuracies are either “lessened” or become “acute.” In this context, the determining rule of an induced substitution structure between the two products during stockout occasions is explicitly analyzed. We use a newsboy framework, in a two product environment, wherein one product may be taken as a direct substitute for the other. We provide necessary and sufficient optimality conditions and an extensive computational study to illustrate and support our findings and to provide additional insights on the conditions characterizing optimal stocking policies.

Customer Prioritization Strategies for Distribution Management

Analyses the operational implications of marketing strategies that try to distinguish between high‐ and low‐priority customers. For a high‐priority customer, the distributor is willing to expedite an order from any emergency source. For a low‐priority customer, on the other hand, distributors will back‐order demand. Bases results on a normative model of dealer behaviour developed by the authors. The distributors are assumed to follow a periodic‐review, stochastic‐demand (s, S) inventory control policy. The principal constraint is a minimum level of service (fill rate) which reflects the objectives of the manufacturer. Bases the analysis on a complete experimental design where a distinction is made between exogenous (replenishment lead time and expedite cost) and endogenous (achieved service level and customer prioritization) variables. In addition, identifies small and large distributors as a function of the demand parameters. Results highlight policy options available to the manufacturer to increase the ...

OM Forum -- Benchmarking Global Production Sourcing Decisions: Where and Why Firms Offshore and Reshore

Problem definition: Manufacturing firms are undergoing restructuring defined by a collection of adjustments and decisions, which affect the source and destination of manufactured products throughout the firm’s global supply chain network. We report on a comprehensive picture of manufacturing sourcing on a global basis. Academic/practical relevance: With dynamic changes in global economic, political, and technological conditions, the design of global supply chain strategies has become critically important for executives and has great potential for operations management research. Methodology: Our work is based on a global field study conducted in 2014 and 2015 among leading manufacturers from a wide range of industries. The data set has the distinguishing feature of reflecting actual decisions that the firms made recently (during the last three years). Results: Companies are currently restructuring their global production footprints. The majority of firms engage in offshoring. Reshoring does occur but seldo...

The Use of Longitudinal Mediation Models for Testing Causal Effects and Measuring Direct and Indirect Effects

Mediation variables are those intervening variables ( M ) that affect the relationship between the independent variable ( X ) and the dependent variable ( Y ). The causal relation between X and Y through the mediation variable M must be established to analyze mediation. M is a mediator, if it involves indirect effects among the set of original variables X and Y . A requirement for a variable to have a causal effect on another is that the cause must precede the outcome in time. This paper argues that the right way to prove the mediation effect is by analyzing different moments in time to support the relations among the variables, which can be done using longitudinal models. The use of traditional analysis in mediation models without the time effect (assumed to occur instantaneously) biases the parameter estimation and could create the possible cancelation of effects, if the paths have opposite signs. Mediation models (without the time effect) are quite frequently used in social sciences. In contrast, the longitudinal mediation model is a very uncommon methodology within the social science community. The aim of this paper is to shed some light about how to analyze the time framework and to provide a methodology to solve the possible limitations that could hinder the robustness of the mediation analysis.