The Smart Forecaster
Pursuing best practices in demand planning, forecasting and inventory optimization
The New Forecasting Technology derives from Probabilistic Forecasting, a statistical method that accurately forecasts both average product demand per period and customer service level inventory requirements.
In this video tutorial Dr. Thomas Willemain, co–Founder and SVP Research at Smart Software, presents Automatic Forecasting for Time Series Demand Projections, a specialized algorithmic tournament to determine an appropriate time series model and estimate the parameters to compute the best forecasts methods.
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.
In this Video Tutorials Dr. Thomas Willemain, co–Founder and SVP Research, presents Regression Analysis, a specialized statistical modeling technique to improve the accuracy of the forecasts. These videos explain with examples how to use Regression Analysis and the various scenarios where this technique makes a good choice.
Those who produce forecasts owe it to those who consume forecasts, and to themselves, to be aware of the uncertainty in their forecasts. This note is about how to estimate forecast uncertainty and use the estimates in your demand planning process. We focus on forecasts made in support of demand planning as well as forecasts inherent in optimizing inventory policies involving reorder points, safety stocks, and min/max levels.
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.
In this Video Dr. Thomas Willemain, co–Founder and SVP Research, defines and compares the three most used inventory control policies. These policies are divided into two groups, periodic review and continuous review. There is also a fourth policy called MRP logic or forecast based inventory planning which is the subject of a separate video blog that you can see here. These videos explain each policy, how they are used in practice and the pros and cons of each approach.
Safety stock is a critical component in any system of inventory management. Indeed, some inventory software treats safety stock as the key decision variable in the quest to balance inventory cost against item availability. Unfortunately, that approach is not the best way to strike the balance.
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Ensure inventory policy matches business strategy. 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.
This short note is about one way your business can develop a plan to adjust to one of the likely fallouts from the virus: sudden increases in the time it takes to get inventory replenishment from suppliers. Supply chains around the world are being disrupted. If this happens to you, how can you react in a systematic way?
Forecast-based inventory management policy, also known as MRP logic, 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.
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.
The Min/Max inventory policy is one of three available in SIO. When the inventory level drops to or below the Min, a replenishment order is generated. The reorder quantity is the number of units needed to raise the stock up to the Max.
To make the right decision, you’ll need to know how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies.The process of ordering replenishment stock is sufficiently expensive and cumbersome that you also want to minimize the number of purchase orders you must generate.