This article is about research and its problems. It considers one area of management research and explores its particular difficulties. The article begins with the special character of logistics, and highlights the tension between technical and strategic, and the problem of finding appropriate causal models. This leads to a discussion of a particular investigation which deployed an unusual mix of methods. The article concludes with a summary of lessons for future research.
Logistics: why research is so difficult
Logistics. one of the sub-fields of management which likes to wallow in its own obscurity. Conferences and trade journals tend to reiterate the commonly held view that logistics is a vital business area which has been too long neglected, and that the wider world is just now beginning to appreciate the importance of the field. This rallying cry becomes less persuasive when it is pointed out that the same rhetoric crops up in other managerial disciplines. It seems a common strategy is to re-designate a field and then expand its horizons: thus personnel has become the "more trendy" human resource management; quality and inspection becomes total quality; maintenance becomes total preventive maintenance, and so on. Logistics follows this pattern by evolving into integrated logistics or strategic supply chain management, or any other label which can be generated by combining managerial buzzwords.
This continual relabelling (or strategic discipline title re-engineering, as we now call it) seems harmless enough, but it does reveal a more serious problem. We have argued elsewhere that the use of supply chain terminology may in some cases be unhelpful, in that a useful metaphor may be stretched beyond the evidence and become established as an accepted wisdom. Furthermore, the problem is that as the scope of the domain is stretched to include more business functions and strategic decisions, it becomes less clear what differentiates the subject as a distinctive field, and what constitutes valid re&arch questions and investigative strategies.
To explore these issues, we propose two simple frameworks which help to illustrate the problems of research in logistics. Here we take logistics in the broadest sense, concerning the holistic or integrative system along the supply chain (e.g. [31). The first highlights a fundamental problem which reflects the fact that the process of research is itself socially constituted. This is a feature which is shared by other areas of management inquiry but which is reflected particularly sharply in logistics. The second framework concerns the problem of assumed causality.
Rigour or relevance?
Managerial research relies on a complex web of conventions and rules which determine what counts as research and what does not. In practice this means that unless certain patterns are followed, research contributions are considered to be inadequate. This helps explain why intellectual "disciplines" are called such. Logically, fulfilling the conventional criteria should be a necessary but not sufficient step in determining the value of a piece of research. However, research is often complicated and difficult to understand, and it could be argued that in many cases the "adherence to a set of rules is assumed to be itself the guarantor of a piece of research. In other words, an editor or referee might not really know (or know enough to have an opinion) about the value of a piece of work in terms of its usefulness or the soundness of its assumptions, but might accept it nevertheless because it has lots of references, contains difficult mathematics, has used a statistical test to a 5 per cent degree of significance, and follows a logical structure. All these things may be useful suggestions of "good research", but they may not a wholly reliable guide. However, in the socially constructed domain of management science, they are often the only indicators available. "Progress" is technical, incremental, and corresponds to the gradual extension of models: a pattern which corresponds to Kuhnian "normal science".
The result is that it is possible to have academic research which scores high on "rigour" and "cleverness" but low on connection to "real" problems[5,6]. However, in management more than-,any other discipline (and operations management at that) there is fundamental commitment to an encounter with that which managers and workers do. If this" is not the case, then research could perhaps be more accurately labelled under a different heading. This problem has caused considerable debate in the operations research/management science community, where many authors have argued that the accepted paradigm of mathematical model building and technique-driven research has resulted in a terminal sclerosis of the discipline. Rosenhead, following Schon, has argued that management scientists should retreat from the lofty hills of the artificial but tractable and descend into the "swamp" of real-life complexity and real-life problems. For Rosenhead and others this means a reformulation of operations research into a portfolio of participative and "soft" problem-solving techniques[9-11]. The difficulty is that these approaches are less easy to cast into the research conventions which dominate the field, and so it is less easy to get recognition in some quarters of the academic community.
This problem appears to be endemic in management research, and is illustrated crudely in Figure 1. The mechanisms of academia offer a trade-off: one can pursue artificial and abstract problems with the rigour necessary to play the research game, or one can pursue more interesting and real issues and be lost in the extraordinary complexity and ambiguity of the real world. The broader the question and the issues involved (e.g. the emergence of "value-adding partnerships"), the more difficult rigorous research becomes. This choice appears to be particularly salient for research in logistics, in which the real world is very complicated indeed. Indeed, the very label "supply chain" is a rather heroic attempt to simplify reality to a level which enables some sort of analysis.
Some examples illustrate this problem. For example, there is now a substantial body of work on the effects on inter-organizational operations and the effect of electronic data interchange. Much of this is based on theoretical inventory models based on developments from the economic order quantity (EOQ), and is based on some principle of joint optimality. Articles are written and research conducted to the highest standards of mathematical rigour and often tested with fiendishly clever simulations. Yet this work is founded on assumptions that make it unlikely …