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This article has been withdrawn as it was published elsewhere and accidentally duplicated. The original article can be seen here: 10.1108/eb014522. When citing the article, please cite: Ronald H. Ballou, (1981), “Reformulating a Logistics Strategy: A Concern for the Past, Present and Future”, International Journal of Physical Distribution & Materials Management, Vol. 11 Iss: 8, pp. 71 - 83.

Visualise a company with 10,000 product items and several warehousing levels in its physical distribution channel. If this company is typical, it is not likely to stock all 10,000 items at all warehouses. But which items should be stocked at which warehouses? Fortunately, there are at least some generalised concepts for dealing with this question. Unfortunately, concepts alone do not make a workable methodology. This article is directed toward extending these generalised concepts into an easy, efficient procedure that a firm can use to provide optimum or near optimum solutions to its stock location problems.

This article provides an overview of the practical application of modeling to the business logistics network design problem. Various location models are categorized and selected ones are illustrated that represent an example of the class and/or that have been used extensively in practice. Suggestions are made as to how data can be aggregated to facilitate the modeling process. Numerous examples are given as to how and where these location models have been applied.

In logistics strategy, it is now being recognized that revenue generation is as important as cost reduction. Although it has long been known that logistics customer service has a positive effect on customer satisfaction, little research has been conducted to determine precisely the degree to which logistics customer service affects sales and generates the revenues for the firm. The purpose of this paper is to identify, categorize, and illustrate methods for estimating revenues associated with various levels of logistics customer service offerings.

The purpose is achieved through simulation, statistics, and case studies.

When the revenue effects of logistics customer service can be approximated, logistics strategies can be formulated that optimize profits and/or return on investment. Otherwise, logistics strategy is in danger of being sub‐optimized when logistics customer service levels are treated as planning constraints and costs minimized to meet them.

The paper sets forth a number of methods by which this sales‐service relationship can be generated and the effect of service on sales quantified. This provides an important basis for optimizing logistics strategy.

Deciding where to locate stocking points and plants within a logistics network, which ports or vendor locations to use, and how the product should flow through this network are common strategic planning problems for many businesses. Management scientists have laboured for many years to create models that can assist businesses with this type of problem. Numerous such models are described in the management literature. Much attention is given to improving their scope, robustness, speed of solution, and mathematical sophistication. Very little attention is given to the information that drives these models and so strongly affects their accuracy and utility, yet management should have more concern about the input data to these models than about the way in which a particular model solves the problem.

Computerising inventory control procedures is usually an attempt to gain better control over stock availability. The effectiveness of the procedures depends on the time delays imparted by such events as order processing and delivery. Through these time delays, much of a finished goods physical distribution system is linked together through the inventory control procedures. Changing the length of any one time element through changes in inventory stocking rules, order processing methods or selected transportation services impacts on the economics of the entire physical distribution system. Little is understood about the effects of time change in such complex systems. In this article, the actual computer inventory control procedures of a chemical company were computer simulated. Physical distribution system design decisions and their associated time delay effects were explored by interrogating the model. Surprising effects were discovered, some of them being counter‐intuitive to what simple theory would predict. Management guidelines were provided as to the system‐wide economic consequences of change in individual elements of a physical distribution system.

The design of logistical systems historically has been responsive to the economic climate and market requirements of the times. When the cost of money rose dramatically during the 1960s, companies reacted by reducing the number of stocking points in their distribution systems. This was a proper response considering that capital costs represent a high proportion of inventory carrying costs. On the other hand, warehouse storage and handling, transportation, and production costs had much smaller relative increases. Consider just how dramatic the warehousing change was for two companies.

This monograph progresses from a consideration of definitional issues to the development of a conceptual model for marketing‐logistics interaction and finally to a discussion of the issues of implementation of the model within the context of marketing strategy. Thus, following an introduction, Part II begins with definition of the field and examines the position of physical distribution in relation to marketing. Part III discusses the relationship of physical distribution and macro‐marketing, and is thus concerned about the social, aggregative goals of logistics systems, including the costs of distribution. Part IV continues this argument, examining specifically the influence of physical distribution on channel structure. Part V then focuses on the assumptions underlying the customer service function, asking how physical distribution can influence final demand in the market place. Part VI presents a conceptual model of marketing‐logistics demand stimulation. The operational issues concerned with its implementation are shown in Part VII; and a summary of the relevant points is presented in Part VIII. The concern has been not with presenting either new computational models nor empirical data but with presenting a new perspective on the marketing‐logistics interface. There is a need to reduce the barriers between these fields and to present more useful ways for co‐operation.

Managing inventory levels in the aggregate is a common concern of senior management. A generalized formula (turnover curve) developed in previous research that mimics practical inventory control is used to audit inventory control performance of inventories in the aggregate and at multiple stocking points. The same turnover curve is used to estimate the impact of changing the inventory control procedures or to set new targets for inventory levels. It is a simple yet powerful tool for evaluating inventory managerial performance that can be developed from readily available company data. This research provides additional examples to further validate the practical usefulness of the turnover curve.

As the president of a large cosmetics manufacturing firm put it, “All of management's mistakes end up in inventory”. This seems to be an accurate description of the 1977 situation at Matheson Foods. Finished goods inventories had ballooned to $40 million, which represented 16 weeks demand compared with a planned 8 weeks demand. This is a turnover ratio of a little over 3, where a ratio of 7 to 10 might be more typical for a food manufacturer. Rejected raw materials have also contributed to elevated inventory levels and the need for external storage space. Although the exact rejection rate was not known, a fair estimate seems to be a range of 7 to 15% of all raw material purchases. A rough evaluation can be made of what this means in terms of inventory value and carrying costs. With sales of approximately $100,000,000 per year and purchased materials amounting to 50% of sales, a 7% rejection rate results in $100,000 × 0.50 × 0.07 = $3,500,000 in rejected raw materials. Disposition of these materials takes 90 days or 4 turns per year. The average value of rejected materials is $3,500,000÷4 = $875,000. At an average carrying cost of 25% per year, $875,000 × 0.25 = $218,750. A rejection rate of 15% would result in a cost of $468,750 per year. It was also estimated that these materials were absorbing 7% of the internal storage space.