The Value of Simulation in Modeling Supply Chains

3 BUSINESS SCENARIOS WHERE SIMULATION IS THE BETTER TOOL

3.1 Too Complicated to Optimize

Despite the protests from the optimization crowd, there are some problems that can be too complicated to optimize. Even if we assume that variance does not exist, there are some problems that are very difficult for an optimization.

One type of systems that comes to mind rule based systems. To give an example of a rule-based system, let us consider a product allocation scheme that a company has for allocating constrained product among its customers. It is common to give certain customers (or a certain group of customers) a high priority for product. In this case, they may receive all of the product that they need, or simply a higher percentage of the product that they need as compared to those customers with a lower priority. This type of problem is difficult in an optimization unless it is highly correlated with the objective function. If it is not correlated with the objective function, then the optimization must be run iteratively, or a "clean-up" activity must occur in order to enforce the allocation rule. Either one of these is time consuming, and certainly non-optimal. Simulation is an excellent tool to evaluate the effectiveness of a given rule. Certainly, almost any rule can be modeled and the modeler can determine the system performance based on any changes in the rules.

3.2 Variance, Variance, Variance

This is the primary reason for using simulation over optimization. If variance is a key driver in your supply chain, an optimization will not capture the supply chain dynamics. Whether it is the number 1 reason on my list, demand forecast variance, or supplier reliability, or quality of incoming material, or any of a number of other variance problems, then optimization simply cannot handle it. Simulation is the tool.

To help drive this point home, let me use the demand forecast variance example. Let's say that today is December, 1998 and we are forecasting for April, 1999. That demand that we are forecasting is still 4 months away, but we must order long-lead time materials and perhaps start some production of our own for that demand. If we are in the window for ordering new capital to help produce the demand, we order more capital. The actual demand will not be known until April, 1999. How close is our estimate? It could be off by 10%, 25% or more. Regardless of how bad it may be, it is our best estimate and we have to run the business on it. Let's say that the forecast for April is 1000 units.

Now it's January, 1999. We're three months away from the real demand for April, and we revise our forecast. We've noticed some weakening in the market and we move the forecast downward to 800 units. Now we start canceling previous material orders, or we realize that we have excess raw material inventory. We halt production, or build "unneeded" inventory. The capital that we ordered in December will now be idle in April. The cost of the demand forecast variance continues to add up.

Obviously, this a very simple example of something that happens on a large scale in most major corporations. It is something that re-engineering will never solve, because forecasting is inherently wrong. The best of forecasting techniques will never pinpoint consumer demand. Re-optimizing the plan every forecast cycle will not keep the supply chain from reacting the way described above. This type of variance is the very reason that simulation is a key technology in evaluating supply chains.

3.3 Senior Management Could Care Less About Optimization

If you have heard your senior management use the word "optimize" in the last year, odds are that they are not using it in a technical sense. In a business sense, to optimize is to make something as good as you can, in spite of the variance. In business, an optimal supply chain delivers product even if the demand forecast is dead wrong. An optimal supply chain operates at an acceptable cost regardless of machine breakdowns, labor shortages, and material shortages. To senior management, an optimal supply chain is not optimal at all. An optimal supply chain is robust.

Earlier, we discussed the management need to manage downside risk. Given the outputs from four scenarios in Figure 1, which option would the senior management pick? Even though Scenario 3 is optimal from an average profit standpoint, its downside risks are much greater than Scenario 4. With Scenario 4's ability to control the variance of the supply chain, Scenario 4 would be chosen by management. Again, it is not the optimal decision, it is the most robust decision. Only through simulation would you be able to identify the most robust decision.

4 CONCLUSION

Although optimization has been the analytical tool of choice for supply chain analysis, there are business scenarios where variance plays such a large part that an optimization will not paint a realistic of the business. In these cases, simulation should be used. Using simulation will allow the user to understand the total cost of variance on the business, including labor variance, material obsolescence, material shortages, capital shortages, and most importantly, the demand forecast variance. These problems are common for any business that serves a dynamic market.

REFERENCES

Ingalls, Ricki G., 1994. Manufacturing Enterprise Model User's Manual. SEMATECH.

AUTHOR BIOGRAPHY

RICKI G. INGALLS is a Manufacturing Strategy Manager in the Manufacturing Strategy Group at Compaq Computer Corporation. He has been involved in the application and development of operational modeling tools and techniques in the electronics industry for over 14 years. In his current position, he is responsible for the strategic planning of Compaq's Global Manufacturing Operations, which includes extensive logistical and financial supply chain modeling. He has a B.S. in Mathematics from East Texas Baptist College, a M.S. in Industrial Engineering from Texas A&M University and is currently in a Ph.D. candidate in Management Science at the University of Texas at Austin. Prior to re-joining Compaq, he was on the technical staff of the Operational Modeling Group at SEMATECH, Manager of Operations Analysis at Compaq Computer Corporation, a consultant with the Electronics Automation Application Center of General Electric Co. and an Industrial Engineer with Motorola, Inc.