Forrester’s theory of industrial dynamics provides the theoretical foundation of this research. This section reviews its origins, core tenets, and theoretical implications in order to provide a clear understanding of the theory.
2.1.1 Origins
Jay W. Forrester published the seminal article (1958) “Industrial Dynamics - a Major Breakthrough for Decision Makers” and book (1961) “Industrial Dynamics”
following insights gained from a serendipitous project he conducted with the General Electric Corporation (Lane 2007).
According to Lane (2007), managers at GE’s Kentucky appliance plant had
observed oscillations with a three-year period in their component inventories and workforce numbers. Although the oscillations were attributed to various exogenous effects such as business cycles and general “noise” from the market, efforts to eradicate the oscillations were unsuccessful. Forrester saw the situation as a system of multiple feedback loops through interviews with managers. Each feedback loop consisted of an inventory level, a manager’s collecting information on that level, the decision he then made and the subsequent effects on the level (Lane 2007). This insight was based on Forrester’s prior experience in systems engineering in the military, where his work with servomechanisms (a mechanical device that uses negative feedback systems to self-regulate its actions) established a foundation for understanding how interconnected feedback control systems could result in counterintuitive effects (Lane 2007).
When Forrester mapped the various inventory levels, actions, and therefore feedback loops, he confirmed that GE’s policies actually amplified existing oscillations.
He observed that subjected to a single small change, the system was capable of generating endogenously large and sustained oscillations without complex external explanations (Lane 2007). Based on his model, Forrester was then able to design policies that GE’s managers could use successfully to calm the oscillations (Lane 2007).
2.1.2 Core Elements
“Industrial dynamics is the study of the information-feedback characteristics of industrial activity to show how organizational structure, amplification (in policies), and time delays (in decisions and actions) interact to influence the success of the enterprise”
(Forrester 1961, p 13).
The core tenet of industrial dynamics stems from the idea that economic and
industrial activities are closed-loop, information-feedback systems (Forrester 1961). In an information-feedback system, conditions are converted to information that is a basis for decisions that control action to alter the surrounding conditions (Forrester 1961).
Information-feedback systems owe their overall behavior to three characteristics:
structure, delays, and amplification (Forrester 1961).
The structure of a system identifies the component parts and tells how the parts are related to one another. This describes the feedback loops relevant to the system of interest. These feedback loops can be related to the six types of interconnected flows (materials, orders, money, personnel, capital equipment, and information) that represent industrial activity. Forrester (1961) recommended that the inclusion of any specific flow into the system to be studied should be based on the management question to be
answered. However, information flow is considered to be most important for inclusion as it is not only a stand-alone flow, but it is also the interconnecting tissue between all of the other flows. For example, in studying a production-distribution system such as GE’s appliance system, Forrester (1961) focused on flows and feedback loops of information (orders) as well as of physical goods at the factory, distributor, and retailer echelons.
Delays refer to the time relationships between parts of the system (Forrester 1961). Delays occur through the availability of information, in making decisions based on the information, and in taking action on the decisions. This is represented by time lags in the flows between component parts of the system. Within Forrester’s (1961) production-distribution system example, delays of one week or more exist between various parts of the system, such as when a customer places an order and when goods are delivered.
Amplification also exists throughout information-feedback systems, particularly in
the decision policies that control the rates of flow within these systems (Forrester 1961).
Amplification is “a response from some part of a system which is greater than would at first seem to be justified by the causes of that response” (Forrester 1961, p 62). Order and inventory policies in the production-distribution system example are good examples of amplification forces. At any given component part of the system, policies to replace goods sold and orders to adjust inventories upward or downward as the level of business activity changes, all impact the rates of flow of information and goods throughout this system (Forrester 1961).
2.1.2 Theoretical Implications
Taken together, the pattern of system interconnection (structure), the time relationships between parts of the system (delays), and the impact of decisions and policies (amplification) all combine to determine the stability and behavior of
information-feedback systems (Forrester 1961). A central premise of Industrial Dynamics (1961) is that these systems exhibit behavior as a whole that may not be evident from their individual parts. In addition, it is the interaction of these parts that impact the system’s behavior over time. Small changes from a single exogenous force may set into motion long-term effects from endogenous elements of the system.
For example, within Forrester’s (1961) production-distribution system illustration, he demonstrated through simulation (tracing step by step the actual flow of orders, goods, and information and observing the series of new decisions that take place) that a simple 10% increase in retail sales results in cycles of oscillation for order rates, factory output, factory warehouse inventory, and unfilled orders. Moreover, this model of a production-distribution system showed increasing variance of orders between retailer, distributor,
and factory echelons after the 10% increase in retail sales. Over a year was required before all ordering and manufacturing rates stabilize to levels prior to the 10% increase in retail sales (Forrester 1961).
Managerially, Forrester (1961) advocated the use of controlled simulation experiments to understand the industrial dynamics of a given system. By holding all conditions but one constant, managers have the ability to determine changes to endogenous elements that will positively impact the system of study. For example, additional experimental variations to the production-distribution system such as speeding up time for order handling, eliminating the distributor echelon, or changing inventory policies show that in the system under study, adjustments in inventory ordering policies led to greater system stability over time relative to managerial changes in order handling times or distribution network structure (Forrester 1961). Although Forrester’s example of a production-distribution system was most detailed, he also discussed how industrial dynamics could be applied to product life cycles, commodity industries, and the research and development process. In each case, the system of study should include mechanisms of interaction relevant to the management problem being investigated. In addition, Forrester also highlighted the importance of considering competition among firms in an industry because “the factors interlocking their behavior are sufficiently strong” (1961, p336) such that each firm is impacted by the actions of similar firms.
2.1.3 Summary
In summary, as the theoretical foundation of this research, industrial dynamics can explain the behavior of an interconnected system of information-feedback loops through interactions of its structure, time relationship delays, and amplification relevant
decision policies. In order to understand industrial dynamics, it is necessary to identify feedback loops that are relevant to the phenomena under study, which can encompass a wide range of business activity.
Following, section 2.2 discusses the application of industrial dynamics within supply chain management research in order to identify the literature gaps that this research seeks to fill.
2.2 INDUSTRIAL DYNAMICS IN SUPPLY CHAIN MANAGEMENT RESEARCH