A fundamental aspect of supply chain management is accurate demand forecasting. Some product items have an intermittent demand pattern that makes them all but impossible to forecast with traditional, smoothing-based forecasting methods. We address the problem of forecasting intermittent demand (or irregular demand), i.e. random demand with a large proportion of zero values. This pattern is characteristic of demand for companies that manage large inventories of service and spare parts in industries such as aviation, aerospace, automotive, high tech, and electronics, as well as in MRO (Maintenance, Repair and Overhaul).
Accurate forecasting of demand is important in inventory control, but the intermittent nature of demand makes forecasting especially difficult for service parts planning. Similar problems arise when an organization manufactures slow-moving items and requires sales forecasts for planning purposes. Because forecasts of intermittent and lumpy demand have been so unreliable, most companies forecast inventory requirements relying primarily on subjective business knowledge, forecast only a fraction of their higher volume inventory, use simple “rule of thumb” estimates, or traditional statistical forecasting that incorrectly assumes a particular type demand distribution for inventory control.
Learn industry best practices on how to improve intermittent demand forecasting and create supply chain efficiencies in the articles below.
A Primer on Probabilistic Forecasting
If you keep up with the news about supply chain analytics, you are more frequently encountering the phrase “probabilistic forecasting.” Probabilistic forecasts have the ability to simulate future values that aren’t anchored to the past. If this phrase is puzzling, read on.
Goldilocks Inventory Levels
You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.
Increasing Revenue by Increasing Spare Part Availability
Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers. But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.
Maximize Machine Uptime with Probabilistic Modeling
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.
Engineering to Order at Kratos Space – Making Parts Availability a Strategic Advantage
The Kratos Space group within National Security technology innovator Kratos Defense & Security Solutions, Inc., produces COTS s software and component products for space communications – Making Parts Availability a Strategic Advantage
The Advantages of Probability Forecasting
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.
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Inventory Optimization for Manufacturers, Distributors, and MRO
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The Problem
Some product items have an intermittent demand pattern that makes them all but impossible to forecast with traditional, smoothing-based forecasting methods. Items with intermittent demand – also known as lumpy, volatile, variable or unpredictable demand – have many zero or low volume values interspersed with random spikes of demand that are often many times larger than the average. This problem is especially prevalent in companies that manage large inventories of service and spare parts in industries such as aviation, aerospace, automotive, high tech, and electronics, as well as in MRO (Maintenance, Repair and Overhaul).
Intermittent demand
In these businesses, as much as 80% of the parts and product items may have intermittent or lumpy demand. Intermittent demand makes it difficult to accurately estimate the safety stock and service level inventory requirements needed for successful supply chain planning. Because forecasts of intermittent and lumpy demand have been so unreliable, most companies forecast inventory requirements relying primarily on subjective business knowledge, forecast only a fraction of their higher volume inventory, use simple “rule of thumb” estimates, or traditional statistical forecasting that incorrectly assumes a particular type demand distribution for inventory control. The result is that billions of dollars are wasted every year because of either excess inventory costs or poor customer service due to stock-outs.
Intermittent demand – also known as lumpy, volatile, variable or unpredictable demand.
The Smart Solution
SmartForecasts and Smart Inventory Optimization use a unique empirical probabilistic forecasting approach that results in accurate forecasts of inventory requirements where demand is intermittent. The solution works particularly well whenever demand does not conform to a simple normal distribution. Our patented, APICS award-winning “bootstrapping” technology rapidly generates tens of thousands of possible scenarios of future demand sequences and cumulative demand values over an item’s lead time. These scenarios are statistically similar to the item’s observed data, and they capture the relevant details of intermittent demand without relying on the assumptions commonly made about the nature of demand distributions by traditional forecasting methods. The result is a highly accurate forecast of the entire distribution of cumulative demand over an item’s full lead time. With the information these demand distributions provide, you can easily plan your company’s safety stock and service level inventory requirements for thousands of intermittently demanded items with nearly 100% accuracy.
The Benefits
Companies using our powerful intermittent demand forecasting and planning solution typically reduce standing inventory by 20% in the first year, increase parts availability 10-20%, and reduce the need for and associated costs of emergency transshipment to close gaps in their supply chain. Repair and service parts inventories are truly optimized, leading to more efficient operations, improvements in customer service, and significantly less cash tied up in inventory.
White Paper: Smart Software Gen2
In this white paper, we introduce “Gen2”, our next generation of probabilistic modeling technology that powers the Smart IP&O Platform. We recount the evolution of Smart Software’s forecasting methods and we detail how Gen2 substantially expands the capabilities that have made Gen1 so useful to so many companies. Finally, we will also give a high-level view of the probability math behind Gen2 . Fill in this form and we'll email you the paper.