DECISION MODELING WITH MICROSOFT EXCEL Chapter 11 Copyright 2001 Prentice HallIMPLEMENTATION

Just as knowledge of Excel is insufficient without modeling concepts, your knowledge of spreadsheet modeling alone is insufficient for truly affecting decision making in organizations. INTRODUCTION Creating a model itself, although an important first step, is far from sufficient in the process of systematically improving decision making in the real world of business enterprise. Inadequate modeling is just one of the reasons why decision-makers do not make good decisions.

The purpose of this chapter is to help you understand why improving the quality of modeling alone will not necessarily lead to improved real-world decisions. This chapter will cover critical oversights that users new to the concepts of modeling make in attempting to move forward to apply those ideas in actual decision-making situations. The upside and downside potential risks of applying modeling concepts will be discussed so that you will come away with a balanced perspective of the pros and cons of applying modeling in business practical situations.

WHAT, AFTER ALL, IS A MODEL? It is difficult to define a model. One definition might be: Consider the following evolution of a model: A model is an abstraction of a business situation suitable for spreadsheet analysis to support decision making and provide managerial insights. To many managers, a model is exquisitely crafted and professionally polished in appearance, highly intuitive, self-documenting, easy to use, completely validated and generalizable enough to be applied in a variety of settings by many people.

A Prototype Model CompleteDebugged Runable by Its Author Validated with Test Data Believed to Deliver Value An Institutionalized Model Sustained by the Organization Integrated into Organization's Decision Processes Coordinated in Function with Other Models and Systems Useable by Other Managers Maintainable and Extensible by Others Need Data Supplied and Maintained by Others Effort: 10X-100X Effort: 1X A Modeling Application Usable by a Client Manager Well Documented Hardened to Reject Unusual Data Inputs Extensible by Author or Client Manager Validated with Real-World Data Known to Deliver Value Effort: 10X An Institutionalized Modeling Application Effort: 100X – 1000X

This framework is a variation of one originally proposed by C. West Churchman, et. al. Modeler,ProjectManager,DecisionMaker,Client Curse of Player Separation ClientModeler Project Manager DecisionMaker The Separation of Players Curse

The Curse of Scope Creep Narrow Modeling Project Single Model Single Objective Focused Activity Few Players Few Stakeholders Low Effort Low Cost Low Development Risk Informal Coordination & Project Management Low Project Visibility Scale Diseconomies in Information Systems for Model Scale Diseconomies in Model & Database Maintenance Deterioration in Model Use as Early Adopters Move on Low Potential Organization-wide Impact Curse of Scope Creep Wide Modeling Project Multiple (Replicated) Models Multiple Objectives Diffused Activity Many Players Many Stakeholders High Effort High Cost High Development Risk Formal Coordination & Project Management High Project Visibility Scale economies in Information Systems for Model Scale Economies in model & Database Maintenance Support for Model Use Independent of Early Adopters High Potential Organizational- wide Impact

Other Frequent Sources of Implementation Failure However, inadequate attention to political issues that arise from the use of a model is far more prevalent as a source of failure in modeling. Easily addressed issues in modeling failure are model logic, model inadequacy, etc. When a model fails, it is all too common to blame the model when in fact, it was due to inadequacies of the whole process of developing and implementing the model.

Another problem is the potential loss of continuity either during the development of a model itself or later during implementation caused by departure of key players, or the loss of organizational memory of a successful model. A source of difficulty in modeling is the attempt to develop a modeling application before assessing issues of the data availability necessary to support that application. An important consideration early in the model development phase is the matching of available data to a possibly less-adequate model as a way of avoiding implementation problems later.

An infrastructure must be created that guarantees that the data and systems will be maintained in a way that serves the users of the model. A more subtle and insidious shortcoming of modeling concerns the identification of shortcomings at one level of an organization as being caused by failures or inadequacies at a higher, often more abstract, level of the organization. In this case, the best thing to do is to tune the model to work well given other organizational inadequacies that might be addressed more effectively at a later time.