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An Approach to Simulation model Development for Improved Planning
An Approach to Sim Development for Im This. paper proposes an approach to planning which relies on involving the manager in the development of a simulation model. The purpose of this involvement is to improve the manager's understanding of his environment; and therefore, the appropriateness of his plans. The approach is based upon the premise that active planning is a learning process. This premise is the result of an experience in several modeling projects and a clinical study of a manager developing a simulation model. James L. McKenney Professor, Harvard Massachusetts.
University,
Cambridge,
A manager in a continuouscausalsearchfactors and
CTIVE PLANNING INVOLVES THE
analysis of the significant influencing the future of his business. This search process is normally conducted by creating comprehensive plans which commit resources of the business for three to five years. These plans are then revised and extended as the environment changes, in accordance with some periodic schedule. The function of the simulation model in the planning process per se is to serve as an experimental device to allow the manager to evaluate alternative plans and in the process consider different concepts of his business. By concept we mean an articulation of the significant factors influencing the profitability of a firm and how they seem to relate to the firm. A simulation model is an explicit representation of how these influences could affect the firm. As such it provides a manipulatable structure with which to represent alternative actions the firm could take and define the economic outcome of such responses. We suggest the model can also serve as a stimulus to create a broader range of testable concepts which improve as to their pertinance in explaining and identifying forces influencing the future of the firm through time, thus, a stimulus to learning. It is for this reason that simulation model development is a unique and powerful approach to planning.1 Prior to the present era, planning structures or models were often rigorous economic models concerned with longterm predictability rather than specific This article is based on a paper presented to the Fall 1968 Joint Computer Conference of the American Federation of Information Processing Societies.
56
opportunities. In addition, the model was typically characterized in f.ymbolic terms foreign to the experience of the manager and difficult to relate directly to decision problems. He typically dealt with the model and the model builder as most other staff activities within a business by gauging their usefulness in resolving planning issues. If the staff man was persuasive, the manager over time, would rely upon the modeller and his model to help him commit future resources. It would seem, we are approaching a stage of conceptual expertise wherein computer programmes can be flexible enough to adapt to a manager's concepts of his business, and of equal importance a growing number of modellers have the capacity to operationally represent managerial concepts. An operational model can serve as a dynamic set of hypotheses with which to develop a consistent codified set of concepts about a business. The model allows prompt evaluation with data or judgment and then easy modification of the hypotheses for further testing. The richness of the vocabulary available to the modeller and the broad spectrum of alternative methods of representing data, allow a full range of tools to be utilized in distilling the essence of the manager's concepts. Further, they can be programmed to generate output statements pertinent to decision problems. In addition, programmes allow detailed documentation including flow~charts so that they can be understandable to the manager. We propose this opportunity to explicitly define and test business concepts can be a powerful process to improve planning if the manager becomes involved in developing the model. In brief, we see a
great advantage in bringing the power of the scientific method to the manager directly, by allowing him to assume an active role in the design and evolution of simulation models. An appropriate modelling project can combine the insight of the manager with the analytic capacity of the scientist. GUIDELINES FOR MODEL DEVELOPMENT The following guidelines outline one approach to model development which has been successful in obtaining the active involvement of the manager in a project. The guidelines are as follows: 1. Simulation model development should be conducted as a project to aid the planning process. 2. An important characteristic of the project is the evolution of goals, uses and specification of the model, as it relates to the planning process. 3. The manager's intuition is typically the operational reference of the pertinent environment; therefore, one role of the model is to improve the consistency of the manager's intuition and to make him aware of new information requirements. 4. A prime function of the model is to amplify the intuition of the manager generating a spectrum of analyses for a range of codifiable conditions. 5. The entire project should be considered in part a learning experience for both modellers to communicate and define adaptable concepts and for the manager to consider how explicit statements and concise definition can improve his understanding of the world.
LONG RANGE PLANNING
ulation Mod el proved Plan ning The sole criterion of the success of a simulation model for planning is the utilization of the model to allocate resources. An unused model, no matter how elegant, is a failure. However, it may be that the most significant use a manager makes of a simulation model is during its development, is to refine his own concept of his business. The discussion below deals with how these guidelines serve to encourage the manager to contribute to the development of a model and what the modeller should have in mind to facilitate this contribution. USING THE GUIDELINES TO DEVELOP A PLANNING MODEL How these guidelines might aid the development of a simulation model is presented in the following synopsis of a simulation project in an industrial firm. The firm, a consumer goods producer, with sales in excess of $200 million was planning to enter the European market. This was its first venture overseas and the executives felt a need for improved decision-making procedures to cope with the unknown seemingly more complex situation. The staff aids to the executive committee had suggested that a simulation model might assist them in allocating capital overseas to assure an orderly and profitable entry into the new market. A series of seminars conducted by the staff with outside consultants was initiated to explore methods of planning in general and the potential of simulation models in particular. A topic of one of the seminars was the evolutionary nature of simulation models with the expectant change in understanding of their environment by the individuals using the model. This session MARCH, 1970
produced a lively interest in developing a model tailored to the needs of the executive committee. The eventual result was a tentative five-year programme for the improvement of the firms strategic planning. A three-year capital budget of approximately $70,000 per year was allocated to a project for the development of a simulation model. Progress review periods were to be held every six months by the executive vice president of sales who was responsible for long-range planning. All vice presidents of the firm and members of the executive committee were to be the active managers of the project and they agreed to spend up to four hours per week to develop improved planning procedures. The initial proposal developed in the seminar cailed for a tentative model to be operational in one calendar year at which time a redefinition of project goals and model specifications would take place. The initial planning problem the model was to aid was the allocation of capital resources in order that the company could most effectively enter and become established in the European market. The initial model, as specified by the executive committee, called for a representation of the necessary resources measured in required dollars for specific future calendar months. The model would simulate the production of goods in European countries to meet simulated demand for the company's products identified by price and type of product. It was the responsibility of the managers to identify the sequence in which products would be introduced, probable competitive actions, estimates of total market growth and available capital. The model was
expected to derive a profit figure for the defined conditions and allow conditions to be easily changed to observe the impact of alternatives. The modellers began the project by attempting to define what factors the managers considered in their planning decisions and which of these seemed best to include on the model. To accomplish this definition a series of meetings with each manager was held to identify the critical elements which influence his operation. Further, how these influences might be formulated in the simulation model. For example, the marketing vice president thought total market, market share, price level of products, and pattern of growth were the basic factors in describing a market. Research was then started on how one could create a statement that would describe the impact of these factors in the European market. A series of definition papers were developed for each country on how these factors would be combined to describe the total European market for the next ten years. In addition, the papers identified what data would be required and what reports would be generated from the model. A series of memoranda on the production, distribution and financial systems were generated to serve as a basis for seminars with all modellers and managers to discuss what should be included in the simulation model to generate more appropriate plans. After each seminar small groups would often discuss a specific aspect such as how a new product should be represented in the production process or how the time lags in the distribution system could be measured and made dynamic. The seminars were followed by additional two- to three-hour individual manager 57
conferences with two or three members of the modelling group to insure the manager's ideas were accurately represented in the model and to explain how the model was functioning with other manager's definitions. A continual effort was made by the modellers to define all terms as clearly as possible for inclusion in a glossary that all participants received. The glossary established a common understanding on words all too often not well-defined such as: assumptions, sensitivity, programming, and planning horizon. The glossary also identified all time lags assumed in the model such as two weeks delay between order receipt and delivery. The glossary aided in educating the manager in a bit of modelling jargon and preventing the modellers from using terms without defining them. It was invaluable in documentation of the model. Concurrent with the manager conferences, data were collected to define the specific form of the relations. Thus, total market data was obtained for each country in Europe and various schemes tested to resolve how to represent growth utilizing historical data. This required the modellers to work with the staff assistants of the managers in an analysis of present measures of what the managers felt to be important. The available accounting data often did not prove sufficient and, therefore, new information had to be created and stated in accessible format. For example, data were collected and distilled to develop a productive capacity model which related the total cost and elapsed time of producing a given quantity of product to the mix of products and the level of production. The elapsed time required to acquire additional productive capacity or change product mix was defined in accordance with how the manufacturing vice president thought capacity responded. It was not until after six months of discussion during which time data was being collected to formulate the manager's concept into a model that the programming of the model for computer manipulation was started. Simultaneous with the programming effort, a second series of meetings were held with the managers on how they might utilize the simulation in their on-going planning procedures. It was felt important to maintain the managers' interest in model development as it was conjectured that during the programming process several revisions in the managers' model would be necessary. The individual meetings soon became formalized into bi-monthly planning meetings to disucss the state of the model and how it might be used to evaluate alternative resource 58
allocations in the European market. One question of burning interest was the capital requirements for establishing a plant in Belgium or Germany including cash flow characteristics. This initiated a project on rules for funds flow and how to represent legal delays. These discussion& aided the modellers in defining appropriate time units, ranges of accuracy, specific output requirements and potential changes in the input variables. They served to keep the managers informed on the state of the model and its limitations. As the model entered the final debugging stage the meetings focussed more onto methods of testing the model for validity and formulating plans for evaluation by the model. It was decided to run the model one country at a time as each one seemed unique. In these later meetings the managers began to develop expertise in explicity defining a feasible range of circumstances which could be tested on the model. A test set of data was developed representing the past two years and a set of parameters for generating the next eight years for Italy. This, in turn, caused the modellers to improve the model's ability to accurately represent a set of conditions. The results of this iterative process was an awareness of the importance of experimental design and new insight to the evolutionary aspect of the simulation project. Most individuals were convinced it was a rewarding experience. The strong commitment was fortunate as early simulation runs proved to generate quantities of useless output. The first simulations were intended to represent the ten years of sales experience in the Italian market. The simulations on the average produced bizarre sales and production demands after the first or second simulated year. The one bright side was that the cash flows resulting from the sales were consistent with past experience. The managers were not dismayed and suggested procedures which the modeller could incorporate which would aid in the understanding of simulation results. Typical error prevention procedures called for the managers to estimate for the next two, five, and eight years feasible product price ranges, and estimates of production capacity, given the present base of the company. These estimates served as minimum and maximum limits on capacity and sales. The model operated within these bounds to evaluate the proposed price structure, time of product introduction and other aspects of their plan. They then considered the output of the simulation in terms of these limits. If the output indicated the simulation results hit an upper limit and remained there, the planners discounted the answer, because of model deficiencies
but would judge that the plan might be a better one than a plan which drove the model to the lower limits. These procedures have afforded a basis for jointly testing plans and their assumptions while evaluating the sensitivity of the simulation model to a variety of inputs in order to investigate the model's validity. THE SIMULATION PROJECT TODAY There has been an obvious growth in the attitude of the modellers and managers as to what should be in the model and what should be excluded. A few of the original factors included as determinants of demand have been tested and found unimportant. But of more interest is the number of new factors that seem to be of a more basic and casual nature than the original factors. Originally, population had been considered as a basic determinant of demand; now age distribution, wealth distribution, geographical distribution, and other factors of the economy in a given country are being considered as determinants of market potential. Continual evaluation of factors in the model including the definition of assumptions and defense or explanation of these assumptions is now accepted by modellers and managers alike. Finally, measures specified at the start have been superseded by new ones. Specific dollar requirements and time specifications originally desired as outputs have been replaced by requirements of rate of market penetration or equity growth and likely range of profits. In general, most measures of performance are more sophisticated than when the project began. The managers seem to be evaluating alternative plans with the model to support their intuition. They suggest that the model has improved their judgment by testing some variables which heretofore were thought very important and found wanting as indicators of future influential environmental forces. The model development in part has forced the managers to define their time assumptions explicitly and to codify cost assumptions to accommodate manipulation. This has resulted in an expansion of the accounting system to allow an evaluation of future plans rather than only a reporting of the accumulated costs of past activities. For example, costs are recorded by product in accordance with length of time since introduction. This change has improved the firm's planning procedures and given a better data for developing an improved model. At present the model can almost be considered a pn?fessional goal for the management of the company as they are LONG RANGE PLANNING
committed to its future development. They do not rely upon it for specific decisions, but seem to feel it a useful tool for improving their planning procedures. Perhaps at some future date they will rely upon it as a partner in decision making as well as process improvement. DISCUSSION OF SIMULATION MODEL PROJECTS A conclusion of this experience and other reported simulation developments would suggest that constituting the project as a research and development venture on the managing process provides a useful orientation 3 • This orientation is important as it not only develops a useful model but it can engender an open attitude. This learning attitude is helpful when exploring how one can formulate heretofore nonexplicit relations. In addition, a development project by nature should commit a management to a sizeable budget over an extended period of time. The results of this expenditure are uncertain and, therefore, the project should regularly be appraised as to its effectiveness. This appraisal process is especially important in regard to simulations intended to aid the planning process. The definition of criteria to evaluate improvement of the planning process is a difficult art and requires experimentation and attention. However, focussing on this aspect of the model's impact provides an appropriate perspective for considering the effectiveness of the model. The appraisal should allow adequate elapsed time for the development of a series of plans concurrent with the implementation of the model. During this time the defense for continuing financial support for the model probably rests on the degree to which it stimulates the management to consider their planning process. After the model is being utilized as an active aid, support should be judged on documental evidence produced by the managers involved. The model should not be judged solely on appropriateness of results, number of plans evaluated, or mechanics of operation. These can be modified by utilizing different resources to develop the model. It should be judged on how effective it is in improving the planning process. The prime reason for a preordained ~xtended life of a planning model project is that the only constant characteristic of a simulation model is change. The product of this evolutionary process is assisted if the changing nature of the model is understood by all associated with the model from the very start of the project. Models with a tradition of change will encourage the managers to attempt to define hazy ideas and to experiment with MARCH, 1970
formulating relationships, as conjectures can be changed if desired. It will also induce the modellers to design their procedures to accommodate changing definitions and specifications. A tactic for developing useful simulation models which are adaptable is to start by working with the responsible managers to model the aspects of the business they feel important. A manager with significant budgetary responsibility is assumed to have had an adequate involvement with his environment to have developed an understanding of what elements are critical to the success of his operation. His measures of these elements often range from precise dollar figures to vague intuitive impressions, but all are important and real to him. It seems reasonable to accept the planner's notion of his business as fact and to attempt to substantiate his concept by programming a model to imitate the concept. Normally, it is impossible to explicitly define all the factors a planner considers. In addition, individual managers will not be consistent with each other or emphasize the same aspects of the environment. To cope with these concomitant ambiguities, the model should be programmed to codify as many factors as possible with freedom for the manager to modify the impact and range of each variable. Where variables cannot be defined, provision should be made for direct planner influence as he sees fit. In the case above, the manufacturing vice president defined his estimates of production rate during start up at three month intervals on his appraisal of product mix and volume in the simulated factory. This worked much better than any modelled relation attempted. The goal of the modeller is to generate an abstraction that adapts comfortably to how the planner considers his resources allocation problem. The process of programming the well-defined variables should involve the planners and modeller in order that both: Evaluate the sensitivity of the environment to change in the selected variables. Discover new methods of combining variables. Resolve inconsistencies or ambiguities between planners and the environment. This latter process often serves as a basis for data collection to define missing relations or to test in the environment the validity of assumed relationships. A clear definition of how the simulation function is essential for the mutual consideration of relationships that govern the operation of the simulation. CONCLUSIONS An overt goal of most simulation projects
for planning is that operationally the simulation model is to serve as an analytical tool for the manager. To serve effectively it must be formulated to produce results which are compatible with the planning procedures. The modeller and manager should continuously appraise what is more economical and effective for the model to accomplish versus the planner. At this point in time it does not seem economically feasible to model completely an environment as effectively as a good human decision maker. However, a simulation model can perform quickly and accurately a long involved sequence of well specified events to produce an answer in predefined terms. How these answer& will be used is important in the development of the model and should be considered at each step of the programme. The goal of the model developer is to develop a model which can amplify the manager's insight to a resource allocation problem. At present the method of amplification seems to be a prompt evaluation of a variety of plans under a range of assumed conditions which the planner defines. The most important reason for designing an adaptable simulation model development is the very survival of the simulation. An adaptable and changing model is essential if the model is to be used over a extended period of time. Assuming the model is to operate as an agent for improving the planning process, its main function may well be as a stimulus to search for a definition of what the manager has not included in the model. This improvement process seems to be one of continuous redefinition of the manager's concept of the pertinent forces in the environment and growth in the modeller's ability to adequately represent these forces. The model must continuously reflect the manager's improved concepts or fall into disuse by the decision makers. A successful simulation project for planning will stimulate the continuous growth of the participants as evidenced by an improving model. • REFERENCES (1) J. L. McKenney, "A Clinical Study of the Use of a Simulation Model", The Journal of Industrial Engineering, January 1967. (2) For example, G. L. Urban, "A New Product and Decision Model", Management Science, April 1968, p. 8490. W. W. Leontief, "Proposal for Better Business Forecasting", Harvard Business Review, Vol. 42, No.6, p. 166. (3) R. D. Buzzel and R. A. Bauer, "Mating Be-
havioral Science and Simulation", Harvard Business Review, Vol. 42, No.5, p. 116. (4) D. W. Crane, "A Simulation Model of Corporation Demand Deposits" in K. J. Cohen, and F. S. Hammer, Analytical Methods in Banking, Irwin, 1966.
59
University,
Cambridge,
A manager in a continuouscausalsearchfactors and
CTIVE PLANNING INVOLVES THE
analysis of the significant influencing the future of his business. This search process is normally conducted by creating comprehensive plans which commit resources of the business for three to five years. These plans are then revised and extended as the environment changes, in accordance with some periodic schedule. The function of the simulation model in the planning process per se is to serve as an experimental device to allow the manager to evaluate alternative plans and in the process consider different concepts of his business. By concept we mean an articulation of the significant factors influencing the profitability of a firm and how they seem to relate to the firm. A simulation model is an explicit representation of how these influences could affect the firm. As such it provides a manipulatable structure with which to represent alternative actions the firm could take and define the economic outcome of such responses. We suggest the model can also serve as a stimulus to create a broader range of testable concepts which improve as to their pertinance in explaining and identifying forces influencing the future of the firm through time, thus, a stimulus to learning. It is for this reason that simulation model development is a unique and powerful approach to planning.1 Prior to the present era, planning structures or models were often rigorous economic models concerned with longterm predictability rather than specific This article is based on a paper presented to the Fall 1968 Joint Computer Conference of the American Federation of Information Processing Societies.
56
opportunities. In addition, the model was typically characterized in f.ymbolic terms foreign to the experience of the manager and difficult to relate directly to decision problems. He typically dealt with the model and the model builder as most other staff activities within a business by gauging their usefulness in resolving planning issues. If the staff man was persuasive, the manager over time, would rely upon the modeller and his model to help him commit future resources. It would seem, we are approaching a stage of conceptual expertise wherein computer programmes can be flexible enough to adapt to a manager's concepts of his business, and of equal importance a growing number of modellers have the capacity to operationally represent managerial concepts. An operational model can serve as a dynamic set of hypotheses with which to develop a consistent codified set of concepts about a business. The model allows prompt evaluation with data or judgment and then easy modification of the hypotheses for further testing. The richness of the vocabulary available to the modeller and the broad spectrum of alternative methods of representing data, allow a full range of tools to be utilized in distilling the essence of the manager's concepts. Further, they can be programmed to generate output statements pertinent to decision problems. In addition, programmes allow detailed documentation including flow~charts so that they can be understandable to the manager. We propose this opportunity to explicitly define and test business concepts can be a powerful process to improve planning if the manager becomes involved in developing the model. In brief, we see a
great advantage in bringing the power of the scientific method to the manager directly, by allowing him to assume an active role in the design and evolution of simulation models. An appropriate modelling project can combine the insight of the manager with the analytic capacity of the scientist. GUIDELINES FOR MODEL DEVELOPMENT The following guidelines outline one approach to model development which has been successful in obtaining the active involvement of the manager in a project. The guidelines are as follows: 1. Simulation model development should be conducted as a project to aid the planning process. 2. An important characteristic of the project is the evolution of goals, uses and specification of the model, as it relates to the planning process. 3. The manager's intuition is typically the operational reference of the pertinent environment; therefore, one role of the model is to improve the consistency of the manager's intuition and to make him aware of new information requirements. 4. A prime function of the model is to amplify the intuition of the manager generating a spectrum of analyses for a range of codifiable conditions. 5. The entire project should be considered in part a learning experience for both modellers to communicate and define adaptable concepts and for the manager to consider how explicit statements and concise definition can improve his understanding of the world.
LONG RANGE PLANNING
ulation Mod el proved Plan ning The sole criterion of the success of a simulation model for planning is the utilization of the model to allocate resources. An unused model, no matter how elegant, is a failure. However, it may be that the most significant use a manager makes of a simulation model is during its development, is to refine his own concept of his business. The discussion below deals with how these guidelines serve to encourage the manager to contribute to the development of a model and what the modeller should have in mind to facilitate this contribution. USING THE GUIDELINES TO DEVELOP A PLANNING MODEL How these guidelines might aid the development of a simulation model is presented in the following synopsis of a simulation project in an industrial firm. The firm, a consumer goods producer, with sales in excess of $200 million was planning to enter the European market. This was its first venture overseas and the executives felt a need for improved decision-making procedures to cope with the unknown seemingly more complex situation. The staff aids to the executive committee had suggested that a simulation model might assist them in allocating capital overseas to assure an orderly and profitable entry into the new market. A series of seminars conducted by the staff with outside consultants was initiated to explore methods of planning in general and the potential of simulation models in particular. A topic of one of the seminars was the evolutionary nature of simulation models with the expectant change in understanding of their environment by the individuals using the model. This session MARCH, 1970
produced a lively interest in developing a model tailored to the needs of the executive committee. The eventual result was a tentative five-year programme for the improvement of the firms strategic planning. A three-year capital budget of approximately $70,000 per year was allocated to a project for the development of a simulation model. Progress review periods were to be held every six months by the executive vice president of sales who was responsible for long-range planning. All vice presidents of the firm and members of the executive committee were to be the active managers of the project and they agreed to spend up to four hours per week to develop improved planning procedures. The initial proposal developed in the seminar cailed for a tentative model to be operational in one calendar year at which time a redefinition of project goals and model specifications would take place. The initial planning problem the model was to aid was the allocation of capital resources in order that the company could most effectively enter and become established in the European market. The initial model, as specified by the executive committee, called for a representation of the necessary resources measured in required dollars for specific future calendar months. The model would simulate the production of goods in European countries to meet simulated demand for the company's products identified by price and type of product. It was the responsibility of the managers to identify the sequence in which products would be introduced, probable competitive actions, estimates of total market growth and available capital. The model was
expected to derive a profit figure for the defined conditions and allow conditions to be easily changed to observe the impact of alternatives. The modellers began the project by attempting to define what factors the managers considered in their planning decisions and which of these seemed best to include on the model. To accomplish this definition a series of meetings with each manager was held to identify the critical elements which influence his operation. Further, how these influences might be formulated in the simulation model. For example, the marketing vice president thought total market, market share, price level of products, and pattern of growth were the basic factors in describing a market. Research was then started on how one could create a statement that would describe the impact of these factors in the European market. A series of definition papers were developed for each country on how these factors would be combined to describe the total European market for the next ten years. In addition, the papers identified what data would be required and what reports would be generated from the model. A series of memoranda on the production, distribution and financial systems were generated to serve as a basis for seminars with all modellers and managers to discuss what should be included in the simulation model to generate more appropriate plans. After each seminar small groups would often discuss a specific aspect such as how a new product should be represented in the production process or how the time lags in the distribution system could be measured and made dynamic. The seminars were followed by additional two- to three-hour individual manager 57
conferences with two or three members of the modelling group to insure the manager's ideas were accurately represented in the model and to explain how the model was functioning with other manager's definitions. A continual effort was made by the modellers to define all terms as clearly as possible for inclusion in a glossary that all participants received. The glossary established a common understanding on words all too often not well-defined such as: assumptions, sensitivity, programming, and planning horizon. The glossary also identified all time lags assumed in the model such as two weeks delay between order receipt and delivery. The glossary aided in educating the manager in a bit of modelling jargon and preventing the modellers from using terms without defining them. It was invaluable in documentation of the model. Concurrent with the manager conferences, data were collected to define the specific form of the relations. Thus, total market data was obtained for each country in Europe and various schemes tested to resolve how to represent growth utilizing historical data. This required the modellers to work with the staff assistants of the managers in an analysis of present measures of what the managers felt to be important. The available accounting data often did not prove sufficient and, therefore, new information had to be created and stated in accessible format. For example, data were collected and distilled to develop a productive capacity model which related the total cost and elapsed time of producing a given quantity of product to the mix of products and the level of production. The elapsed time required to acquire additional productive capacity or change product mix was defined in accordance with how the manufacturing vice president thought capacity responded. It was not until after six months of discussion during which time data was being collected to formulate the manager's concept into a model that the programming of the model for computer manipulation was started. Simultaneous with the programming effort, a second series of meetings were held with the managers on how they might utilize the simulation in their on-going planning procedures. It was felt important to maintain the managers' interest in model development as it was conjectured that during the programming process several revisions in the managers' model would be necessary. The individual meetings soon became formalized into bi-monthly planning meetings to disucss the state of the model and how it might be used to evaluate alternative resource 58
allocations in the European market. One question of burning interest was the capital requirements for establishing a plant in Belgium or Germany including cash flow characteristics. This initiated a project on rules for funds flow and how to represent legal delays. These discussion& aided the modellers in defining appropriate time units, ranges of accuracy, specific output requirements and potential changes in the input variables. They served to keep the managers informed on the state of the model and its limitations. As the model entered the final debugging stage the meetings focussed more onto methods of testing the model for validity and formulating plans for evaluation by the model. It was decided to run the model one country at a time as each one seemed unique. In these later meetings the managers began to develop expertise in explicity defining a feasible range of circumstances which could be tested on the model. A test set of data was developed representing the past two years and a set of parameters for generating the next eight years for Italy. This, in turn, caused the modellers to improve the model's ability to accurately represent a set of conditions. The results of this iterative process was an awareness of the importance of experimental design and new insight to the evolutionary aspect of the simulation project. Most individuals were convinced it was a rewarding experience. The strong commitment was fortunate as early simulation runs proved to generate quantities of useless output. The first simulations were intended to represent the ten years of sales experience in the Italian market. The simulations on the average produced bizarre sales and production demands after the first or second simulated year. The one bright side was that the cash flows resulting from the sales were consistent with past experience. The managers were not dismayed and suggested procedures which the modeller could incorporate which would aid in the understanding of simulation results. Typical error prevention procedures called for the managers to estimate for the next two, five, and eight years feasible product price ranges, and estimates of production capacity, given the present base of the company. These estimates served as minimum and maximum limits on capacity and sales. The model operated within these bounds to evaluate the proposed price structure, time of product introduction and other aspects of their plan. They then considered the output of the simulation in terms of these limits. If the output indicated the simulation results hit an upper limit and remained there, the planners discounted the answer, because of model deficiencies
but would judge that the plan might be a better one than a plan which drove the model to the lower limits. These procedures have afforded a basis for jointly testing plans and their assumptions while evaluating the sensitivity of the simulation model to a variety of inputs in order to investigate the model's validity. THE SIMULATION PROJECT TODAY There has been an obvious growth in the attitude of the modellers and managers as to what should be in the model and what should be excluded. A few of the original factors included as determinants of demand have been tested and found unimportant. But of more interest is the number of new factors that seem to be of a more basic and casual nature than the original factors. Originally, population had been considered as a basic determinant of demand; now age distribution, wealth distribution, geographical distribution, and other factors of the economy in a given country are being considered as determinants of market potential. Continual evaluation of factors in the model including the definition of assumptions and defense or explanation of these assumptions is now accepted by modellers and managers alike. Finally, measures specified at the start have been superseded by new ones. Specific dollar requirements and time specifications originally desired as outputs have been replaced by requirements of rate of market penetration or equity growth and likely range of profits. In general, most measures of performance are more sophisticated than when the project began. The managers seem to be evaluating alternative plans with the model to support their intuition. They suggest that the model has improved their judgment by testing some variables which heretofore were thought very important and found wanting as indicators of future influential environmental forces. The model development in part has forced the managers to define their time assumptions explicitly and to codify cost assumptions to accommodate manipulation. This has resulted in an expansion of the accounting system to allow an evaluation of future plans rather than only a reporting of the accumulated costs of past activities. For example, costs are recorded by product in accordance with length of time since introduction. This change has improved the firm's planning procedures and given a better data for developing an improved model. At present the model can almost be considered a pn?fessional goal for the management of the company as they are LONG RANGE PLANNING
committed to its future development. They do not rely upon it for specific decisions, but seem to feel it a useful tool for improving their planning procedures. Perhaps at some future date they will rely upon it as a partner in decision making as well as process improvement. DISCUSSION OF SIMULATION MODEL PROJECTS A conclusion of this experience and other reported simulation developments would suggest that constituting the project as a research and development venture on the managing process provides a useful orientation 3 • This orientation is important as it not only develops a useful model but it can engender an open attitude. This learning attitude is helpful when exploring how one can formulate heretofore nonexplicit relations. In addition, a development project by nature should commit a management to a sizeable budget over an extended period of time. The results of this expenditure are uncertain and, therefore, the project should regularly be appraised as to its effectiveness. This appraisal process is especially important in regard to simulations intended to aid the planning process. The definition of criteria to evaluate improvement of the planning process is a difficult art and requires experimentation and attention. However, focussing on this aspect of the model's impact provides an appropriate perspective for considering the effectiveness of the model. The appraisal should allow adequate elapsed time for the development of a series of plans concurrent with the implementation of the model. During this time the defense for continuing financial support for the model probably rests on the degree to which it stimulates the management to consider their planning process. After the model is being utilized as an active aid, support should be judged on documental evidence produced by the managers involved. The model should not be judged solely on appropriateness of results, number of plans evaluated, or mechanics of operation. These can be modified by utilizing different resources to develop the model. It should be judged on how effective it is in improving the planning process. The prime reason for a preordained ~xtended life of a planning model project is that the only constant characteristic of a simulation model is change. The product of this evolutionary process is assisted if the changing nature of the model is understood by all associated with the model from the very start of the project. Models with a tradition of change will encourage the managers to attempt to define hazy ideas and to experiment with MARCH, 1970
formulating relationships, as conjectures can be changed if desired. It will also induce the modellers to design their procedures to accommodate changing definitions and specifications. A tactic for developing useful simulation models which are adaptable is to start by working with the responsible managers to model the aspects of the business they feel important. A manager with significant budgetary responsibility is assumed to have had an adequate involvement with his environment to have developed an understanding of what elements are critical to the success of his operation. His measures of these elements often range from precise dollar figures to vague intuitive impressions, but all are important and real to him. It seems reasonable to accept the planner's notion of his business as fact and to attempt to substantiate his concept by programming a model to imitate the concept. Normally, it is impossible to explicitly define all the factors a planner considers. In addition, individual managers will not be consistent with each other or emphasize the same aspects of the environment. To cope with these concomitant ambiguities, the model should be programmed to codify as many factors as possible with freedom for the manager to modify the impact and range of each variable. Where variables cannot be defined, provision should be made for direct planner influence as he sees fit. In the case above, the manufacturing vice president defined his estimates of production rate during start up at three month intervals on his appraisal of product mix and volume in the simulated factory. This worked much better than any modelled relation attempted. The goal of the modeller is to generate an abstraction that adapts comfortably to how the planner considers his resources allocation problem. The process of programming the well-defined variables should involve the planners and modeller in order that both: Evaluate the sensitivity of the environment to change in the selected variables. Discover new methods of combining variables. Resolve inconsistencies or ambiguities between planners and the environment. This latter process often serves as a basis for data collection to define missing relations or to test in the environment the validity of assumed relationships. A clear definition of how the simulation function is essential for the mutual consideration of relationships that govern the operation of the simulation. CONCLUSIONS An overt goal of most simulation projects
for planning is that operationally the simulation model is to serve as an analytical tool for the manager. To serve effectively it must be formulated to produce results which are compatible with the planning procedures. The modeller and manager should continuously appraise what is more economical and effective for the model to accomplish versus the planner. At this point in time it does not seem economically feasible to model completely an environment as effectively as a good human decision maker. However, a simulation model can perform quickly and accurately a long involved sequence of well specified events to produce an answer in predefined terms. How these answer& will be used is important in the development of the model and should be considered at each step of the programme. The goal of the model developer is to develop a model which can amplify the manager's insight to a resource allocation problem. At present the method of amplification seems to be a prompt evaluation of a variety of plans under a range of assumed conditions which the planner defines. The most important reason for designing an adaptable simulation model development is the very survival of the simulation. An adaptable and changing model is essential if the model is to be used over a extended period of time. Assuming the model is to operate as an agent for improving the planning process, its main function may well be as a stimulus to search for a definition of what the manager has not included in the model. This improvement process seems to be one of continuous redefinition of the manager's concept of the pertinent forces in the environment and growth in the modeller's ability to adequately represent these forces. The model must continuously reflect the manager's improved concepts or fall into disuse by the decision makers. A successful simulation project for planning will stimulate the continuous growth of the participants as evidenced by an improving model. • REFERENCES (1) J. L. McKenney, "A Clinical Study of the Use of a Simulation Model", The Journal of Industrial Engineering, January 1967. (2) For example, G. L. Urban, "A New Product and Decision Model", Management Science, April 1968, p. 8490. W. W. Leontief, "Proposal for Better Business Forecasting", Harvard Business Review, Vol. 42, No.6, p. 166. (3) R. D. Buzzel and R. A. Bauer, "Mating Be-
havioral Science and Simulation", Harvard Business Review, Vol. 42, No.5, p. 116. (4) D. W. Crane, "A Simulation Model of Corporation Demand Deposits" in K. J. Cohen, and F. S. Hammer, Analytical Methods in Banking, Irwin, 1966.
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