It all starts with the forecast. Forecasting is all about turning unknowns into knowns (or reasonable approximations). The more unknowns you can make known, the easier it is to make inventory management decisions. But more importantly, as you increase your accuracy at estimating future demand, you are able to increase the performance of your inventory. In other words, you are able to increase your service levels while at the same time reducing your inventory investment and risk of obsolescence.
I chose to dedicate three entire chapters to forecasting because I feel it is that important. Knowing how to calculate a moving average or apply exponential smoothing to a series of numbers is not forecasting. You also need to know how to calculate and apply seasonality indexes, trend adjustments, event adjustments, and various overrides. In addition, you need to understand demand, and how your forecast interacts with it. Plus, you need to understand when a forecast requires human intervention. In Inventory Management Explained you will learn:
- Forecasting terminology.
- History-based forecasting methods including Exponential Smoothing, Moving Average, Weighted Moving Average, Last-period Demand, and Last-Relative-Period Demand.
- How to select your Forecast Interval.
- How to calculate and apply Seasonality Indexes.
- How to "normalize" demand.
- How to break down the various elements that make up trend.
- How to incorporate a Trend Adjustment into your forecast that accounts for trend lag.
- How to use Excel's Regression Analysis tool, and why you would or would not want to incorporate regression into your forecast.
- How to incorporate Event Indexes and other overrides into a forecast.
- How to effectively measure forecast error, and what to do with this measurement.
- What Adaptive Smoothing is.
- What "black-box" forecasting is.
- Why human input is an important part of any forecasting system.
This is all done through spreadsheet examples combined with plain English explanations.