Estelle A. Shale

Pitfalls and protocols in DEA

Abstract The practical application of data envelopment analysis (DEA) presents a range of procedural issues to be examined and resolved including those relating to the homogeneity of the units under assessment, the input/output set selected, the measurement of those selected variables and the weights attributed to them. Each of these issues can present difficulties in practice. The purpose of this paper is to highlight some of the pitfalls that have been identified in application papers under each of these headings and to suggest protocols to avoid the pitfalls and guide the application of the methodology.

Data envelopment analysis, operational research and uncertainty

This paper discusses a number of applications of data envelopment analysis and the nature of uncertainty in those applications. It then reviews the key approaches to handling uncertainty in data envelopment analysis (DEA) (imprecise DEA, bootstrapping, Monte Carlo simulation and chance constrained DEA) and considers their suitability for modelling the applications. The paper concludes with suggestions about the challenges facing an operational research analyst in applying DEA in real-world situations.

An examination of the size of orders from customers, their characterisation and the implications for inventory control of slow moving items

This paper examines half a million observations of the size of orders from customers at an electrical wholesaler. It notes: the distribution of the size of customer orders for a single item (stock keeping unit or SKU) is very skewed and resembles a geometric distribution; while the average size of an order is different for different items, for one SKU the mean order size is effectively the same at different branches even when the branches have very different demand rates; across a range of SKUs there is a strong relationship linking the mean and the variance of order size. The general results above are shown to apply to even the slowest movers. This extension is important because for items with intermittent demand the size of customer orders is required to produce an unbiased estimate of demand. Also a knowledge of the distribution of demand is important for setting maximum and minimum stock levels and the scheme employed is described.

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.

Forecasting for intermittent demand : the estimation of an unbiased average

The majority of the range of items held by many stockists exhibit intermittent demand. Accurate forecasting of the issue rate for such items is important and several methods have been developed, but all produce biased forecasts to a greater or lesser degree. This paper derives the bias expected when the order arrivals follows a Poisson process, which leads to a correction factor for application in practice. Extensions to some other arrival processes are briefly considered.

Including the customer in efficiency analysis:evidence of a hybrid relational-transactional approach

Purpose – The purpose of this paper is to illustrate the effect of including the customer as a resource in efficiency measurement. Variations in counting the customer illustrate the different impacts on efficiency between a transactional and a relational approach to bank branch marketing.Design/methodology/approach – The paper uses data envelopment analysis to analyse the efficiency of the branch network under consideration. This technique, while well established in the bank branch efficiency literature, is used here to gain insight into how relationship and transactional paradigms are affecting performance.Findings – Although the average profile of the efficiency scores was similar, the scores of the individual branches differed greatly depending on how customers were counted. Some branches then can be typified as relationship oriented while others as transactions oriented bearing in mind that all branches have both remits.Practical implications – Future research in efficiency measurement should include ...

Teaching OR/MS to MBAs at Warwick business school: a turnaround story

OR/MS is under pressure in MBA programs in the United Kingdom as it is in the United States. To ensure its continuance, members of the operational research group at Warwick Business School redesigned the core OR/MS module. The first attempt at a redesign was a failure. As a result, the module was redesigned again. The second version was a success. Through this process, we have learned many lessons about teaching OR/MS to MBA students, among them the need to have a guiding philosophy, adopt a critical-consumer approach, stress managerial relevance, discuss the context and process of modeling as well as content, and use spreadsheets for analytical work.

Breadth of range and depth of stock: forecasting and inventory management at Euro Car Parts Ltd.

This paper investigates inventory management issues in a distribution network. The study is motivated by examining the operation of a wholesaling car parts company. Customer service requirements are of paramount importance in this market sector. The nature of the demand facing the company is characterised. The breadth of range of stock keeping units (SKUs) held at a stocking location and the quantity of each SKU held are normally treated in isolation but in this case, the rule developed to select the range of SKU was extended to determine the level of stock to hold. It is intuitively obvious that these two factors should be linked, yet the authors have not found any other literature developing the connection in a practical context. Forecasting issues are explored as the rule on stock range depends on a forecast of the number of orders received for each SKU at each stocking unit. Some implementation issues and extensions are indicated.

The Effect of Different Inputs to Factor Analysis: an Example using Service Quality in UK Branch Banking

This Conference Paper is brought to you for free and open access by theSchool of Marketing at ARROW@DIT. It has been accepted for inclusionin Conference papers by an authorized administrator of ARROW@DIT.For more information, please contactyvonne.desmond@dit.ie,arrow.admin@dit.ie.This work is licensed under aCreative Commons Attribution-Noncommercial-Share Alike 3.0 License