SNHU Management Forecasting Software Simulation Summary Paper

Now we get started. You are in charge of a multimillion-dollar company!

Log in to Capsim. Be sure to review the Round 0 reports. The link can be found on your Dashboard.

complete rounds 1-3 click file save decision as you move through

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Pay particular attention to the Industry Report:

Also pay careful attention to the Foundation FastTrack (the yearly industry “newspaper”):

Remember to save your work as you go and to “Update Official Decisions” at the end of the module when you are done.

Module Overview

In this module, you will begin with an overview of how decisions based on the ability to forecast affect the business results. For many companies, forecasting is one of the most critical aspects of planning. Forecasts serve as an input to numerous decisions, which can only be as good as the forecast upon which they are based. These decisions include production planning, workforce scheduling, inventory management, purchasing, capital budgeting, and various resource allocations. Forecasts are used by the proformas to calculate financial projections (see Part 6 of your Team Member Guide). In the simulation tool, if you enter a forecast that is unrealistically high, the proformas will take that forecast and project unrealistic revenue. Accurate forecasting is a key element for company success. Manufacturing too many units could result in higher inventory carrying costs. Manufacturing too few units could result in stockouts and lost sales opportunities, which can cost even more.

Given the important role of the forecasting function, it is important for managers to understand current methodologies used. However, past forecasting surveys have looked at forecasting practices in all industry segments combined. The management of manufacturing organizations is in many ways different from that of service organizations. Most service organizations frequently face demand levels of high variability within a very short planning horizon. Further, they are unable to inventory their products as manufacturing firms do, and they rely on labor to meet peak demand periods. By contrast, manufacturing planning horizons tend to be longer, and forecasting is often linked to an inventory control system, such as material requirements planning (MRP). Because of these differences, combining information on forecasting practices in manufacturing and service firms can lead only to broad generalizations and is not helpful in understanding practices in a specific industry segment (Sanders, 1997).

Section 10.1 in your Team Member Guide provides you with a basic forecasting method, which is illustrated by the following example:

Last year’s sales can be a good starting point for this year’s forecasts. For example, if the segment growth rate for the upcoming year is 9.2%, you can say, “All things being equal, we can expect to sell 9.2% more units this year than we did last year.”

Assume next year’s growth rate for Traditional is 9.2%, and your Traditional product sold 1,100,000 units last year without stocking out (running out of inventory):1,100,000 × 0.092 = 101,200

Adding 101,200 to last year’s sales of 1,100,000 units gives you a starting forecast for the upcoming year of 1,201,200 units.

In forecasting, it is imperative to consider whether the top products in the segment can meet customer demand. Forecasting is the ability to examine the top product’s capacities. A question to ask yourself is, can the competition manufacture sufficient units? If not, this may be an opportunity to exploit. In this type of industry you need to also keep in mind the intermittent demands of your products as well as inventory control. Ensure you also factor these elements into your forecasting.

Many researchers have tried to discuss the relationships between technological performance and other influential factors, such as strategic management. In your forecasting, keep in mind that technological performance within your segment does indeed influence your decision making at the strategic and policy levels. A good example of this is provided in Technovation: Forecasting Innovation Performance Via Neural Networks—A Case of Taiwanese Manufacturing Industry (Wang & Chien, 2004)(Wang & Chien, 2004).

References

Sanders, N. R. (1997). The status of forecasting in manufacturing firms. Production and Inventory Management Journal, 38(2), 32–35.

Wang, T., & Chien, S. (2004). Forecasting innovation performance via neural networks—a case of Taiwanese manufacturing industry.

Technovation, 26(5-6), 635–643.