What is the difference between Demand planning and Inventory optimization ?

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

What is the difference between Demand planning and Inventory optimization ? 

The Smart Demand Planning app (SDP) provides demand forecasts. The SDP forecasting engine is also the core of the Smart Inventory Optimization app (SIO), which stress-tests various inventory policies using a number of demand scenarios to find optimal inventory policy settings.

 

 

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What is the wiggle effect? It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern. It’s important to make sure your forecasts don’t wiggle unless there is a real pattern. Here is a transcript from a recent customer where this issue was discussed:

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      Forecasting Techniques for a more profitable business

      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, defines and compares the most useful Forecasting Techniques: Exponential Smoothing, Single Exponential Smoothing, Holt’s Method and Winter’s Method.  These videos explain the basic thinking under each technique as well as the math behind them, how they are used in practice and the tradeoff of each method.

      Forecasting Techniques for a more profitable business
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          TOP 3 COMMON INVENTORY POLICIES

          The Smart Forecaster

           Pursuing best practices in demand planning,

          forecasting and inventory optimization

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              Share, develop, and manage consensus demand plans

              The Smart Forecaster

               Pursuing best practices in demand planning,

              forecasting and inventory optimization

              Share, develop, and manage consensus demand plans to ensure inventory policy matches business strategy.

              Smart Inventory Optimization (SIO) creates planning scenarios. SIO starts with a “Live” scenario that shows where you are now. Various team members can create their own scenarios, perhaps dividing the work by product line or sales territory. One decision maker can then merge these scenarios into a consensus plan that becomes the “Goal” scenario, which drives the ERP system’s replenishment planning.

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              Do your statistical forecasts suffer from the wiggle effect?

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              What is the wiggle effect? It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern. It’s important to make sure your forecasts don’t wiggle unless there is a real pattern. Here is a transcript from a recent customer where this issue was discussed:

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                  FORECAST DRIVEN INVENTORY MANAGEMENT

                  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 forecast-based inventory management policy, also known as MRP logic. This is the fourth in our series on major approaches to managing inventory.  We begin by looking at some very simple and then more robust models of inventory dynamics that help us determine how much to order or manufacture and when. We then consider how to calculate lead time and account for lead time variability. Tom concludes by describing the importance of safety stock, it’s role in properly buffering against demand and supply uncertainty, and how best to calculate it. 

                   

                  Leave a Comment

                  RECENT POSTS

                  Do your statistical forecasts suffer from the wiggle effect?

                  Do your statistical forecasts suffer from the wiggle effect?

                  What is the wiggle effect? It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern. It’s important to make sure your forecasts don’t wiggle unless there is a real pattern. Here is a transcript from a recent customer where this issue was discussed:

                  How to Handle Statistical Forecasts of Zero

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                      When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think. […]
                    • Portrait of factory worker woman with blue hardhat holds tablet and stand in spare parts workplace area. Concept of confident of working with spare parts planning software.Spare Parts Planning Isn’t as Hard as You Think
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