The Smart Forecaster
Pursuing best practices in demand planning, forecasting and inventory optimization
If there is ‘no try, only do or not do’, then the history of inventory management has seen a lot of not do. So what special powers are needed to turn an ordinary inventory professional into an Inventory Optimizer?”
Service level and fill rate are two important metrics for measuring how effectively customer demand is satisfied. These terms are often confused and understanding the differences can help improve your inventory planning process. This video blog (Vlog) helps illustrate the difference with a simple example using Excel
Many companies adopt a “customer first, better to have the inventory and not need it” approach to inventory planning. While well intentioned, this approach often ignores the role that diminishing returns and opportunity costs play in inventory management impacting the organizations ability to quickly respond to demand.
This guest blog details the differences between Min-Max and Reorder Point- Order Quantity replenishment logic and why it is important. It is authored by Phillip Slater, Founder of SparePartsKNowHow.com the leading educational resource for spare parts management. Mr. Slater is a global leader and consultant on materials management and specifically, engineering spare parts inventory management and optimization.
When setting a target service level, make sure to take into account factors like current service levels, replenishment lead times, cost constraints, the pain inflicted by shortages on you and your customers, and your competitive position.
When the pressure is on to cut inventory and improve performance, you might want to move fast much like a hitter who wants to hit a home run. And in some cases, swinging for the fences might be the recommended approach. More often than not, a progressive approach to inventory optimization is more effective
“Trillions of records of millions of people…Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues.”
Software for inventory optimization is most often used to crank out the analytical results you need to run your day-to-day business, such as Reorder Points (also known as Mins) and Order Quantities. This specialized software helps you find the sweet spot that balances inventory costs against item availability during routine operations.
Demand planning takes time and effort. It’s worth the effort to the extent that it actually helps you make what you need when you need it. But the job can be done well or poorly. We see many manufacturers who stop at the first level when they could easily go to the second level. And with a little more effort, they could go all the way to the third level, utilizing probabilistic modeling to convert demand planning results into an inventory optimization process.
Managing inventory requires executives to balance competing goals: high product availability versus low investment in inventory. Executives strike this balance by stating availability targets and budget constraints. Then supply chain professionals translate these “commander’s intentions” into detailed specifications about reorder points and order quantities.
Most demand forecasts are partial or incomplete: They provide only one single number: the most likely value of future demand. This is called a point forecast. Usually, the point forecast estimates the average value of future demand. Much more useful is a forecast of full probability distribution of demand at any future time. This is more commonly referred to as probability forecasting and is much more useful.
In this blog, we review 10 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.
Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?
In our travels around the industrial scene, we notice that many companies pay more attention to inventory Turns than they should. We would like to deflect some of this attention to more consequential performance metrics.
In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness—a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution.