The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
Improve Forecast Accuracy, Eliminate Excess Inventory, & Maximize Service Levels
In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by Managing Error. This video is the first in our series on effective methods to Improve Forecast Accuracy. We begin by looking at how forecast error causes pain and the consequential cost related to it. Then we will explain the three most common mistakes to avoid that can help us increase revenue and prevent excess inventory. Tom concludes by reviewing the methods to improve Forecast Accuracy, the importance of measuring forecast error, and the technological opportunities to improve it.
Forecast error can be consequential
Consider one item of many
- Product X costs $100 to make and nets $50 profit per unit.
- Sales of Product X will turn out to be 1,000/month over the next 12 months.
- Consider one item of many
What is the cost of forecast error?
- If the forecast is 10% high, end the year with $120,000 of excess inventory.
- 100 extra/month x 12 months x $100/unit
- If the forecast is 10% low, miss out on $60,000 of profit.
- 100 too few/month x 12 months x $50/unit
Three mistakes to avoid
1. Ignoring error.
- Unprofessional, dereliction of duty.
- Wishing will not make it so.
- Treat accuracy assessment as data science, not a blame game.
2. Tolerating more error than necessary.
- Statistical forecasting methods can improve accuracy at scale.
- Improving data inputs can help.
- Collecting and analyzing forecast error metrics can identify weak spots.
3. Wasting time and money going too far trying to eliminate error.
- Some product/market combinations are inherently more difficult to forecast. After a point, let them be (but be alert for new specialized forecasting methods).
- Sometimes steps meant to reduce error can backfire (e.g., adjustment).
You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.
Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.
For most small-to-medium manufacturers and distributors, single-level or single-echelon inventory optimization is at the cutting edge of logistics practice. Multi-echelon inventory optimization (“MEIO”) involves playing the game at an even higher level and is therefore much less common.