The Organizational Tension between Static and Dynamic Efficiency

Efficiency has been defined in at least two different ways: in terms of the refinement of existing products, processes or capabilities (static efficiency) and as the development of new ones (dynamic efficiency). This paper analyzes the organizational trade-off between these two forms of efficiency. It shows that there is a tendency towards extremes, and that the irreversibility of efficiency orientations tends to tip the balance to be struck between static and dynamic efficiency toward the latter. The paper also advances hypotheses about industry, business and corporate factors that mediate between the choice of a particular efficiency orientation and organizational performance. We are grateful to Chris Argyris, Joseph Bower, Clayton Christensen, David Collis, Anita McGahan, Nitin Nohria, Gary Pisano, Richard Rosenbloom, Arthur Schleifer, Jr., Josep Valor, and David Yoffie and the editors for helpful comments on an earlier draft of this paper. This paper was prepared while Ricart was a Research Fellow at the Harvard Business School during the 1992-1993 academic year, when he was supported in part by the Secretary of State for Universities and Research at the Ministry of Education and Science in Madrid. Ghemawat's research was supported by the Division of Research at the Harvard Business School. THE ORGANIZATIONAL TENSION BETWEEN STATIC AND DYNAMIC EFFICIENCY This paper builds on the growing literature on the firm as an informationprocessing entity. It focuses on a basic tension in processing information: using it to search for improvements within a framework of fixed beliefs about how the environment behaves and responds to organizational actions versus using it to reconsider the beliefs themselves. Implicit in this tension are two understandings of efficiency (or actually, efficiency-oriented search processes): static efficiency, which involves continuous search for improvements within a fixed set of initial conditions, and dynamic efficiency, which involves continuous reconsideration of initial conditions. In this paper, we seek to advance the analysis of the trade-off between static and dynamic efficiency. Section 1 touches upon some related literature. Section 2 models the single-period allocation of a fixed information-processing resource between the imperatives of static and dynamic efficiency and relates the model’s identification of a possible tendency toward extremes –toward an exclusive focus on either static or dynamic efficiency– to practical considerations. Section 3 analyzes a multi-period extension of the basic model and emphasizes that, in most circunstances, once a commitment is made to a particular efficiency orientation, this tends to tip optima toward dynamic efficiency relative to the single-period benchmark. Section 4 sketches some hypotheses about how the performance of an organization of a particular type may be affected by its industry environment and strategic choices. Section 5 concludes. 1. Some related literature The distinction between static and dynamic efficiency has not passed unnoticed. We cite just a few prominent sightings here and apologize in advance to the authors of other important studies which are omitted. The distinction between static and dynamic efficiency is evident in Ashby’s [1956] seminal work on systems analysis, which distinguishes between a single-loop system, rather like a thermostat, which adjusts to environmental changes in preprogrammed ways, and a more complicated double-loop system, in which the lower-level feedback loop can be reprogrammed by a second, higherlevel loop. In organization theory, Burns and Stalker [1961] crystallize similar considerations into two ideal organizational types, mechanistic and organic, that are suited to stable and changing environments respectively; and Argyris and Schon [1974] distinguish between single-loop and double-loop organizational learning in a way that evokes Ashby’s loops and influences our definitions of the two types of efficiency. In technology and operations management, the distinction between static and dynamic efficiency is implicit in Abernathy’s [1978] productivity dilemma: the idea that a productive unit cannot be both highly efficient (in a static sense) and support a high rate of innovation. In terms of strategic management, and from a more pedagogical perspective, it is worth noting that until quite recently an important linchpin of the strategy formulationimplementation sequence at the Harvard Business School was a note that emphasized the distinction between “Type A” organizations, developed for current efficiency and regularity, and “Type B” organizations, developed for innovation and flexibility (Heskett [1987]). The distinction between static and dynamic efficiency has also been examined by economists, mostly under the heading of flexibility. We discuss their work in slightly more detail because of its connections to our own. Stigler [1939] and Hart [1942] initiated economic research in this area by analyzing the cost penalties incurred in adapting production technologies to changes in demand by adjusting output levels, an inverse measure of flexibility. Marschak and Nelson [1962] broadened the analysis to consider adaptation to all forms of environmental turbulence, not just to changes in demand. Jones and Ostroy [1984] apply option-theoretic concepts to analyze the value of flexibility. The two most interesting economic treatments for our purposes, however, are those of Klein [1984] and Carlsson [1989]. Klein [1984] begins by distinguishing between static and dynamic efficiency, defining the former as “fine-tuning whose objective is to make the best use of existing information” (p. 50). In other words, he defines static efficiency as the optimal combination of given inputs, subject to the constraints imposed by a fixed production function. Dynamic efficiency, in contrast, is defined as “changing the production function in profitable directions” (p. 46). Klein goes on to differentiate dynamic efficiency into two types of flexibility (p. 47). Type I flexibility is associated with anticipated uncertainty (or “risk” in the sense of Knight [1921]) and is “built into production processes so they can produce quite dissimilar existing products on the same production line... It is aimed at rapid short-term response to changes in market conditions by permitting very significant shifts in the composition of output without the usual penalties involved in closing down entire production lines.” Type II flexibility, in contrast, is associated with unanticipated uncertainty (“true” Knightian uncertainty) and “is concerned with the ability to make good use of newly disclosed opportunities, be they opportunities for improving the production process or developing and producing new products”. Carlsson [1989] builds on Klein’s work but comes up with a somewhat different scheme for classifying flexibility: into its operational, tactical and strategic aspects. Operational flexibility is associated with the short run and is defined as “built-in procedures which permit a high degree of variation in sequencing, scheduling, etc.” (p. 47). Tactical flexibility is associated with the medium run and “is built into the technology, i.e., the organization and the production equipment, of the firm and enables it to deal e.g. with changes in the rate of production or in product mix over the course of the business cycle, as well as moderate changes in design.” Finally, strategic flexibility is associated with the long run and “reflects how the firm positions itself with respect to a menu of choices for the future”. Although Klein’s and Carlsson’s classification schemes are obviously related to each other, it should be noted that they have different bases: the type of uncertainty and the length of the time horizon, respectively. We shall offer our own definitions of what we mean by orientations toward static and dynamic efficiency in the next section and in the context of a specific model. What needs to be explained here are the ways in which our definitions differ from Klein’s and Carlsson’s, 2