Proceedings of the 2010 Industrial Engineering Research Conference A. Johnson and J. Miller, eds. Decision Analysis Tool for Process Improvement Decisions Kevin Grove, Lance Saunders, and Christian Wernz Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg, Virginia 24061 USA Abstract One of the main objectives of Decision Analysis is to ensure that all relevant objectives are included in the decision making process. This paper discusses the process used to develop a mechanism that incorporates all of the qualitative objectives in the decision of which process improvement action to implement in a warehouse. The paper discusses the development of the decision making hierarchy, decision tree, and engineering economic analysis used to determine the preferred action. The model provides value to managers by providing them with a decision making tool that formalizes their personal intuition and leads to more informed decisions. Keywords: Decision Analysis, Engineering Economy, Process Improvement, Industrial Engineering 1. Problem Statement This paper will investigate the decision of what alternative to choose for process improvement in the electric warehouse at a large public utility. The warehouse issues $15 million in materials using 22,000+ transactions in its electronic inventory system on average each year. Top management has focused on process improvements to reduce cost and improve efficiency within the warehouse over the last 3 years. Currently, all transactions such as receipts, issues, and cycle counts have to be manually recorded and then entered later into the electronic inventory system. This data entry approach leads to unnecessary, non-value added steps in the process. Furthermore, workers and management have complained about the inventory accuracy in the electronic inventory system. The company has developed three alternatives for approaching this problem. They are: Process Improvement Team Bar-Coding Technology Bar-Coding Technology and RFID Cost is not the only factor in the decision, thus standard cost analysis is not appropriate for evaluating these alternatives. This paper discusses the process employed to develop a mechanism that incorporates all of the qualitative objectives and alternatives in the company s decision, and is shown in Figure 1. This mechanism incorporates a variety of decision making tools such as a decision hierarchy and data elicitation techniques to obtain data that can be used for quantitative analysis [1-3]. These mechanisms were the basis for the development of a utility that was used to compare the benefits expected for each alternative [4]. Figure 1: Flowchart of Process Used to Develop Decision Making Tool
2. Determining the Objectives In order to determine what objectives needed to be included in the analysis we interviewed the decision maker (Procurement Manager) and other stakeholders. The results of these interviews are summarized below, and were the basis for the decision-making hierarchy shown in Figure 2. Company Objectives 1. IT System Confidence One of the current obstacles to change in the warehouse is the employees confidence in the accuracy of the electronic inventory system. Eliminating this lack of confidence would improve buy-in for other future improvements (e.g., replenishment process), and is the department manager s primary objective for the current process improvement project. 2. Cost Savings Another main objective for implementing new technology to automate inventory is cost savings. The technology will eliminate the need for warehouse personnel to record the transactions manually on paper, as well as update the electronic inventory system manually. 3. Inventory Accuracy The current process of manually updating the electronic inventory system creates a delay from when the transaction actually occurs to when it is recorded in the electronic inventory system. The ability to update the system in real time will improve the accuracy of the electronic inventory system, and also eliminate two opportunities to create errors in recording transactions. The company currently measures inventory accuracy as the percent of items on daily cycle counts in which the manual count matches the on-hand inventory in the electronic inventory system. 4. Cost of Technology Another factor in the decision to implement any technology is the investment cost. This will include the cost of the hardware, software, interface, consultants, internal resources, and training. 5. Cost Data Accuracy Materials being credited to the correct work order and account will provide the company the opportunity for more accurate analysis of project costs. Cost data can flow directly to the bar code reader without having to rely on crews and warehouse personnel to provide and record this information. 6. Supply Chain Cost Savings One ancillary benefit of more accurate inventory data is the ability to perform better analysis of material usage, and thus better forecasting. Also, more timely transactional updates will lead to elimination of buffers that compensate for the current delay. Decision Hierarchy The fundamental objectives of the problem were consolidated into process management, labor flexibility, and project implementation cost. The company agreed that these objectives described the problem in a complete manner, are non-redundant, concise, specific, and understandable by the company [2, 5]. The procurement manager stated that he felt an inordinate amount of time was being spent over-managing the process because there was always a fire to fight. He also felt that while the workforce would not be decreased, that labor time savings would allow storekeepers to concentrate on more value added activities and more flexibility during absenteeism. This led to management cost savings and storekeeper time savings being measures for Improve Process Management and Increase Labor Flexibility respectively. The measure for implementation cost was very straightforward, as a cost could be calculated for each alternative. Figure 2 shows the objectives hierarchy, and the associated measure for each fundamental objective.
3. Modeling the Decision Figure 2: Decision Objectives Hierarchy The procurement manager is the single decision maker who must decide which process improvement approach to propose for the current budget cycle. The decision maker has identified three actions for improvement which could be implemented. As mentioned above, these actions are forming a process improvement team, implementing a barcode system to track inventory, and implementing a barcode system combined with an RFID system. The decision maker also stated that no process improvements has to be implemented this budget cycle if there are no attractive actions, so there is also a do nothing option available. The notation of these actions will be as follows: a i = action of the decision maker, i = 1,,2,3,4 a 1 = No process improvement initiative a 2 = Form process improvement team a 3 = Implement barcode system a 4 = Implement barcode + RFID system Each of these actions has an associated cost. The organization has completed preliminary research into these costs and has determined with estimates. Initial analysis will assume these costs are both known and fixed (future research will include a component of uncertainty into these estimates). The notation for alternative costs C(a i ) are as follows: C(a 1 ) = 0 C(a 2 ) = $75,000 C(a 3 ) = $200,000 C(a 4 ) = $275,000 The next challenge of the problem is identifying the outcomes that process improvement could have on the accuracy of the system. Currently, the inventory accuracy on a month to month basis has a natural fluctuation of between 80% and 85% of items that are correct on the daily cycle count. The decision maker does not believe consistently perfect accuracy is achievable in the near future, but thinks any given month could achieve 100% accuracy and the best possible outcome for the available actions is a month to month accuracy that varies between 95% and 100%. Based on this information, the outcomes of process improvement could fall anywhere within the 80% to 100% range with a natural fluctuation. To simplify the problem the outcome space x j was partitioned into four outcomes [3]: x 1 = 80%-85% month to month inventory accuracy x 2 = 85%-90% month to month inventory accuracy x 3 = 90%-95% month to month inventory accuracy x 4 = 95%-100% month to month inventory accuracy
These outcomes cover the current conditions of the system, the best outcome that is possible, and two outcomes that represent levels of improvement above the current system but below the best possible outcome. While the actual outcome of a process improvement may not fall strictly into one of the outcomes modeled here, this approach was chosen because it allowed the decision maker to clearly assess each outcome in terms of likelihood and benefits to the organization. With the actions and outcomes clearly defined, the next step was to elicit the probabilities that could be associated with particular combinations of each. This was done through an interview with the decision maker. The elicitation structure was arranged so that the experts (manager, supervisor, coordinators, and analyst) could make judgments that were justifiable and resolvable [3]. The notation associated with these probabilities and the results from the interview are as follows: P(x j a i ) = Probability of outcome x j given alternative a i, Table 1: Probability of an outcome given an action Action Outcome a 1 a 2 a 3 a 4 x 1 1 0.25 0.05 0.05 x 2 0 0.5 0.2 0.1 x 3 0 0.2 0.5 0.45 x 4 0 0.05 0.25 0.4 The structure chosen for this problem was that of a decision problem under uncertainty, for which a decision tree is the most common tool used for analysis [6]. Figure 2 summarizes the alternatives available to the company, and the associated probability of each outcome given an alternative is chosen as a decision tree. The next step in this problem is to determine the benefits associated with each outcome. Figure 3: Decision Tree of Problem with Likelihood of each outcome
4. Calculating Benefits of Each Outcome The final part of the model is to quantify the value each outcome brings to the organization. L(x 4 ) was calculated by estimating the labor savings that would be realized by the best possible outcome. This would be defined as all material requests being received before materials are required (no non-emergency walk-up requests), elimination of manual data entry into the electronic inventory system, and elimination of the weekly physical count of poles and transformers. L(x 3 ) was calculated by assuming that all of L(x 4 ) benefits except inventory of poles and transformers could be achieved, and L(x 2 ) by assuming that only elimination of walk-up requests would be realized. M(x 4 ) was calculated by eliciting the current time spent on inventory related activities, and then the number of potential hours that could be saved by the best possible outcome. Eliciting the intermediate savings for management was difficult for the stakeholders, and thus the assumption was made that management savings scale at the same rate of change as labor savings. Using the hourly pay associated with each job, this information could then be used to quantify the yearly management benefits M(x j ), the yearly labor benefits L(x j ), and the yearly total benefits B(x j ) where: B(x j ) = M(x j ) + L(x j ) Table 2: Jobs and costs associated with time-saving management benefits Position # of Employees Hours/Week Saved Cost per Hour Cost Savings per Year Analyst 1 5 $40 $10,400 Coordinator 2 14 $45 $65,520 Supervisor 1 5 $55 $14,300 Manager 1 3 $75 $11,700 Table 3: Yearly process management, labor, and total benefits of outcome x j Outcome Percentage of Time Saved M(x j ) L(x j ) B(x j ) x 1 0% $0 $0 $0 x 2 26.5% $27,009 $8,048 $35,057 x 3 64.1% $65,331 $19,467 $84,798 x 4 100% $101,920 $30,387 $132,307 5. Calculating the Best Alternative With all the components of the model addressed, an analysis was conducted in order to assess each alternative. First, the expected yearly benefit of each alternative was calculated using the following equation: [( )] = ( )( ) Next the model required a timeframe in order to compare these expected yearly benefits of alternatives to the present day expected costs of alternatives. Budget cycles within the organization are 5 years, and correspond to the 5-year strategic plans of the organization. The decision maker would like any investment in process improvement to pay for itself within the current budget cycle. The reason for this is that if they are beneficial, it would provide leverage for process improvement proposals in future budget cycles. It was decided that this was a reasonable timeframe for modeling the decision, and also a conservative analysis timeframe considering the benefits of the actions could be realized beyond 5 years. The decision maker also noted that the process improvement options could take up to a year to implement, train employees, and sustain the process. Therefore the model assumes no benefits during the first year (a conservative assumption). Finally, the decision maker agreed that discounting any future benefits was
appropriate for this situation, and a rate of 8% was chosen based on input from the accounting department. Using this information, the expected yearly benefits in Table 3 were assigned to years 2-5 of the analysis and discounted at a rate of 8% to today s dollars in order to calculate the discounted expected present value of alternatives, V(a i ) [7]. Costs of the project were then subtracted from the discounted benefits in order to determine an expected net present value for each alternative. The following equation was used for this calculation: ( ) = ( ) ( ) The results of these calculations are summarized in Table 4. Table 4: Discounted expected net values of action a i a 1 a 2 a 3 a 4 Expected Yearly Benefit E[B(a i )] $0 $41,103 $82,487 $94,587 Discounted Expected Present Value V(a i ) $0 $126,051 $252,961 $290,069 Expected Present Cost C(a i ) $0 $75,000 $200,000 $275,000 Discounted Expected Net Value NV(a i ) $0 $51,051 $52,961 $15,069 Based on the calculations for expected net present value, all three process improvement actions have benefits that outweigh the costs and therefore the DM would consider actions a 2 -a 4 for the current budget cycle. Action a 3, which is an implementation of a bar-code system, has the highest net value and would be the preferred course of action based only on the factors considered in the model. While the recommended action is the one that results in the highest expected value, other factors may affect the decision maker s preference. These may include management or employee buy-in due to existing preferences towards a particular action, other projects limiting resources to spend on an action, or external factors which may add uncertainty to the costs of each action. Future work on this project will attempt to quantitatively assess any uncertainties that exist in the costs of actions. 6. Conclusion This analysis demonstrates how elicitation techniques and structured decision analysis can be used to provide decision support in organizations. Many organizations do not have the time or resources to perform cost-benefit studies on every important decision and rely on the expertise and judgment of managers. Our model provides value to managers by providing them with a decision making tool that formalizes their personal intuition and leads to more informed decisions. Developing and applying these models is inexpensive, and the framework for creating them can be applied to many decisions. Additionally, by keeping the decision maker involved in the process and the models themselves straightforward the decision maker can clearly evaluate the information the model provides and use it in to make decisions based on quantitative data and not qualitative intuition. References 1. Keeney, R. and H. Raiffa, Decisions with multiple objectives: Preferences and value tradeoffs. 1993: Cambridge Univ Pr. 2. Keeney, R., Structuring objectives for problems of public interest. Operations Research, 1988. 36(3): p. 396-405. 3. Hora, S.C., ed. Eliciting Probabilities from Experts. Advances in Decision Analysis. 2007, Cambridge University Press: New York. 22. 4. Bell, D., Risk, return, and utility. Management Science, 1995. 41(1): p. 23-30. 5. Keeney, R., ed. Developing Objectives and Attributes. Advances in Decision Analysis. 2007, Cambridge University Press: New York. 22. 6. von Winterfeldt, D.E., W., ed. Defining a Decision Analytic Structure. Advances in Decision Analysis. 2007, Cambridge University Press: New York. 22. 7. Sullivan, W.G.W., E.M.; Koelling, C.P., Engineering Economy. 2009, Upper Saddle River: Peason Education, Inc.