Chandrasekhar Das

WHOLESALER: A Decision Support System for Wholesale Procurement and Distribution

Develops a decision‐making framework for the design and operation of a wholesaling system. Considers the new roles of wholesaling and proposes an optimization model to satisfy best the service requirements at the minimum cost. Using this model, a prototype decision‐support system is developed to build various scenarios and suitable models for these scenarios. A number of management reports are prepared to help managers negotiate prices or volumes, consolidate warehouses, select transport modes, and test the effectiveness of proposed service packages.

A Vendor′s View of the JIT Manufacturing System

Just‐in‐time (JIT) is a Japanese concept which has revolutionsed manufacturing management. It has reduced product cost, and improved productivity, product quality and delivery time by as much as 80 per cent in some companies. Manufacturers are anxious to implement this system but it requires a high level of co‐operation and support from the vendors. Rooted in a tradition of adversarial game‐playing with the manufacturer, vendors are prone to thinking that much of the benefits of JIT to the manufacturers accrue at their expense. This mode of thinking has proved to be counterproductive and needs to be changed. The role of vendors in a JIT system must be properly evaluated and efforts should be made to make them more co‐operative.

Grouping customers for better allocation of resources to serve correlated demands

Abstract In this paper, we discuss a common decision-making problem arising in the allocation and decentralization of resources under uncertain demand. The total resource requirements for a given service level equals the sum of mean demands plus a safety factor multiplied by the standard deviations of demands. Since the demand means are unaffected by any customer groupings, we attempt to exploit demand correlations for developing customer groups such that the sum of the standard deviations over all groups is minimized. A concave minimization model with binary variables is developed for this purpose and a heuristic partitioning method is proposed to efficiently solve the model. The model is appropriate for both manufacturing and service management with potential applications in salesforce allocation, grouping of machines in job shops, and allocation of plant capacities. Scope and purpose In this paper, it is shown that when demands are correlated, complete aggregation of all customers in a single-service center may require more resources than is necessary to provide a given service level. A model and a solution technique are proposed to optimally aggregate/disaggregate customers into groups such that total resource requirements are minimized. Many potential applications of the proposed technique in centralization/decentralization of resources are discussed.

Selection and evaluation of most profitable process targets for the control of canning quality

Abstract A noniterative numerical method is proposed for solving the Hunter and Kartha model to determine the most profitable target for a canning process. Given a specified lower limit which determines the acceptability of the product, the method produces an almost exact estimate of the optimal process mean. Only a pair of estimating constants and percentiles of the distribution of fill are needed for this purpose. Simple formulas are also derived to compute the optimal expected profit and the average contents of underfilled and overfilled cans. Using these formulas the effects of adopting a target on process outputs and the benefit of reducing the process variability or improving the salvage value of the product can be examined to validate the model.

Manufacturer selection and price negotiation for competitive wholesale distribution operations

This paper considers a new class of problems arising in the distribution side of supply chains. It considers cooperative market expansion and price negotiation by wholesalers and manufacturers in a class of franchise‐type distribution markets. For wholesalers, expanding the coverage of geographically dispersed markets while retaining profitability of business requires new levels of price discounts from manufacturers. Manufacturers, on the other hand, attempt to limit such discounts to maintain a resale price to retailers. A successful negotiation results in a set of spatial markets with appropriate wholesale prices for each wholesaler. A sequential method is proposed for this purpose, assuming that the manufacturers and wholesalers are co‐operative and they exchange relevant information. The suggested process uses two optimisation models and is illustrated with a case study.

A generalized discount structure and some dominance rules for selecting the price-break EOQ

Abstract This paper introduces a generalized discount structure that combines the features of incremental and all-units quantity discount policies. General properties of the EOQ model under this discount structure are studied, and dominance rules for comparing order quantity intervals under either type of discount policy are established. In addition, procedures for developing an iso-cost function and the minimal feasible set for optimal order quantity are proposed.

A Model for the Optimal Timing and Sizing of Capacity Increments for Single-Location Facilities

Abstract This paper develops and tests a mixed-integer programming model for finding the optimal timing and sizing of plant capacities, The object of the model is to minimize the total discounted costs over a planning horizon. The factors that are considered for this purpose are the current and future values of the following: discount rates, construction and setup costs, operating costs, subcontracting costs, idle costs of excess capacities, and growth rates of demand. Actual data from a set of electrical power generating facilities are utilized to test the model. Several variations in time paths of demand, and the cost factors are considered to assess the effects of each factor on the timing and sizing decisions

Decision making by classical test procedures using an optimal level of significance

Abstract This paper expands the decision space of a hypothesis testing situation by including the significance level as a decision variable along with the sample size and test statistic. It suggests that in cases where hypothesis testing is tantamount to decision making the significance level should be optimally determined based on the consequences of errors of both kinds. To achieve this goal it develops a three stage procedure that minimizes the total costs of sampling and incorrect decisions as a function of the sample size, significance level and the test statistic. The procedure is applied to test the mean of a normal distribution and a bivariate table is prepared to routinise the process. The advantages of using this table instead of the standard normal table are discussed and the benefits of the optimal as opposed to a predetermined significance level are illustrated with the help of a numerical exmaple.