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).
If you both make and sell things, you own two inventory problems. Companies that sell things must focus relentlessly on having enough product inventory to meet customer demand. Manufacturers and asset intensive industries such as power generation, public transportation, mining, and refining, have an additional inventory concern: having enough spare parts to keep their machines running.
This technical brief reviews the basics of two probabilistic models of machine breakdown. It also relates machine uptime to the adequacy of spare parts inventory.
Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.
Inventory optimization has become an even higher priority in recent months for many of our customers. Some are finding their products in vastly greater demand; more have the opposite problem. In either case, events like the Covid19 pandemic are forcing a reexamination of standard operating conditions, such as choices of reorder points and order quantities.