The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Want to Optimize Inventory? Follow These 4 Steps

Want to Optimize Inventory? Follow These 4 Steps

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.

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Managing Demand Variability

Managing Demand Variability

Anybody doing the job knows that managing inventory can be stressful. Common stressors include: Customers with “special” requests, IT departments with other priorities, balky ERP systems running on inaccurate data, raw material shortages, suppliers with long lead times in far-away countries where production often stops for various reasons and more. This note will address one particular and ever-present source of stress: demand variability.

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Assessing How Suppliers Influence Your Inventory Costs

Assessing How Suppliers Influence Your Inventory Costs

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.

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Getting “Halfway There” with Demand Planning

Getting “Halfway There” with Demand Planning

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.

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The Advantages of Probability Forecasting

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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Beware of Simple Rules of Thumb for Managing Inventory

Beware of Simple Rules of Thumb for Managing Inventory

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.

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Worst Practices in Forecasting

Worst Practices in Forecasting

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?

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The Trouble With Turns

The Trouble With Turns

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.

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Managing the Inventory of Promoted Items

Managing the Inventory of Promoted Items

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.

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The Scourge of Skewness

The Scourge of Skewness

Demand planners have to cope with multiple problems to get their job done. One is the Irritation of Intermittency. The “now you see it, now you don’t” character of intermittent demand, with its heavy mix of zero values, forces the use of advanced statistical methods, such as Smart Software’s patented Markov Bootstrap algorithm. But even within the dark realm of intermittent demand, there are degrees of difficulty: planners must further cope with the potentially costly Scourge of Skewness.

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A Check on Forecast Automation with the Attention Index

A Check on Forecast Automation with the Attention Index

A new metric we call the “Attention Index” will help forecasters identify situations where “data behaving badly” can distort automatic statistical forecasts (see adjacent poem). It quickly identifies those items most likely to require forecast overrides—providing a more efficient way to put business experience and other human intelligence to work maximizing the accuracy of forecasts. How does it work?

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Too Much or Too Little Inventory?

Too Much or Too Little Inventory?

Do you know which items have too much or too little inventory? What if you knew? How would you go about cutting overstocks while still ensuring a competitive service level? Would you be able to reduce stockouts without incurring a prohibitively expensive inventory increase? How would these changes impact service levels, costs and turns—for individual items, groups of items and overall?

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Handling Extreme Supply Chain Variability at Rev-A-Shelf

Handling Extreme Supply Chain Variability at Rev-A-Shelf

Does your extended supply chain suffer from extreme seasonal variability? Does this situation challenge your ability to meet service level commitments to your customers? I have grappled with this at Rev-A-Shelf, addressing unusual conditions created by Chinese New Year and other global events, and would like to share the experience and a few things I learned along the way.

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Demand Forecasting in a “Build to Order” Company

Demand Forecasting in a “Build to Order” Company

We often come into contact with potential customers who claim that they cannot use a forecasting system since they are a “build-to-order” manufacturing operation. I find this a puzzling perspective, because whatever these organizations build requires lower level raw materials or intermediate goods. If those lower level inputs are not available when an order for the finished good is received, the order cannot be built. Consequently, the order could be canceled and the associated revenue lost.

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