Excellence in Forecasting Blog

Optimize Inventory planning parameters,improve service levels and increase revenue

A Primer on Probabilistic Forecasting

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.

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Improve Forecast Accuracy by Managing Error

Improve Forecast Accuracy by Managing Error

In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by Managing Error. This video is the first in our series on effective methods to Improve Forecast Accuracy.  We begin by looking at how forecast error causes pain and the consequential cost related to it. Then we will explain the three most common mistakes to avoid that can help us increase revenue and prevent excess inventory.

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Four Useful Ways to Measure Forecast Error

Four Useful Ways to Measure Forecast Error

In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by measuring Forecast Error. We begin by overviewing the various types of Error Metrics: Scale-dependent error, Percentage error, Relative error, and Scale-free error Metrics. While some error is inevitable, there are ways to reduce it, and forecast metrics are necessary aids for monitoring and improving forecast accuracy. Then we will explain the special problem of intermittent demand and divide-by-zero problems. Tom concludes by explaining how to assess forecasts of multiple items and how it often makes sense to use weighted averages, weighting items differently by volume or revenue.

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Recent Posts

  • Mature bearded mechanic in uniform examining the machine and repairing it in factoryPlanning for Consumable vs. Repairable Parts
    When deciding on the right stocking parameters for spare and replacement parts, it is important to distinguish between consumable and repairable parts. These differences are often overlooked by inventory planning software and can result in incorrect estimates of what to stock. Different approaches are required when planning for consumables vs. repairables. […]
  • Four Common Mistakes when Planning Replenishment TargetsFour Common Mistakes when Planning Replenishment Targets
    How often do you recalibrate your stocking policies? Why? Learn how to avoid key mistakes when planning replenishment targets by automating the process, recalibrating parts, using targeting forecasting methods, and reviewing exceptions. […]
  • Smart Software is pleased to introduce our series of webinars, offered exclusively for Epicor Users.Extend Epicor Kinetic’s Forecasting & Min/Max Planning with Smart IP&O
    Epicor Kinetic can manage replenishment by suggesting what to order and when via reorder point-based inventory policies. The problem is that the ERP system requires that the user either manually specify these reorder points, or use a rudimentary “rule of thumb” approach based on daily averages. In this article, we will review the inventory ordering functionality in Epicor Kinetic, explain its limitations, and summarize how to reduce inventory, and minimize stockouts by providing the robust predictive functionality that is missing in Epicor. […]
  • Scenario based Forecasting vs EquationsScenario-based Forecasting vs. Equations
    Traditionally, software has served as a delivery vehicle for equations. This is fine, as far as it goes. But we at Smart Software think you would do better by trading in your equations for scenarios. Learn why Scenario-based planning helps planners better manage risk and create better outcomes. […]
  • Extend Microsoft 365 BC and NAV with Smart IP&OExtend Microsoft 365 BC and NAV with Smart IP&O
    Microsoft 365 BC and NAV can manage replenishment by suggesting what to order and when via reorder point-based inventory policies. The problem is that the ERP system requires that the user manually specify these reorder points and/or forecasts. In this article, we will review the inventory ordering functionality in Microsoft BC & NAV, explain its limitations, and summarize how to reduce inventory, and minimize stockouts by providing the robust predictive functionality that is missing in Dynamics 365. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Blanket Orders Smart Software Demand and Inventory Planning HDBlanket Orders
      Our customers are great teachers who have always helped us bridge the gap between textbook theory and practical application. A prime example happened over twenty years ago, when we were introduced to the phenomenon of intermittent demand, which is common among spare parts but rare among the finished goods managed by our original customers working in sales and marketing. This revelation soon led to our preeminent position as vendors of software for managing inventories of spare parts. Our latest bit of schooling concerns “blanket orders.” […]
    • Hand placing pieces to build an arrowProbabilistic Forecasting for Intermittent Demand
      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. […]
    • 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 […]
    • wooden-figures-of-people-and-a-magnet-team-management-warehouse inventoryManaging 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. […]

      Problem

      Generating accurate statistical forecasts isn’t an easy task. Planners need to keep historical data continually up to date, build and manage a database of forecasting models, know which forecast methods to use, keep track of forecast overrides, and report on forecast accuracy. These steps are typically managed in a cumbersome spreadsheet that is often error-prone, slow, and difficult to share with the rest of the business. Forecasts tend to rely on one-sized fits all methods that require seasonality and trend to be added manually resulting in inaccurate predictions of what comes next

      Solution

      SmartForecasts™ Cloud is a statistical forecasting solution available on Smart’s Inventory Planning and Optimization Platform, Smart IP&O. It provides a statistically sound, objective foundation for your sales and operations planning process (S&OP). SmartForecasts automatically selects the most accurate forecasting method, allows users to fine-tune statistical and user-defined models, enables forecast and historical overrides, and automatically measures forecast error and bias. Forecast overrides are tracked and historical data is automatically updated eliminating the manual effort accompanying spreadsheet-based solutions. Coupled with Smart’s ERP connectors, results can be pushed to your ERP system of choice automatically. The result is more efficient sales planning, budgeting, production scheduling, ordering, and inventory planning process.

        Get the Product Sheet

        SmartForecasts ® Cloud

        Logo for Statistical modeling and optimization

        Accurate Demand Forecasts

        Gears logo ERP Integrations

        Best Forecasting Methods

        A11 Excel Problem Planning

        Imports Historical Data

        What can you do with SmartForecasts?
        • Run a forecasting tournament that selects the right forecasting method for each item.
        • Hand-craft forecasts using several time-series forecasting methods and non-statistical methods.
        • Automatically predict trends, seasonality, and cyclical patterns.
        • Imports demand data from files
        • Leverage ERP connectors to automatically import demand data and return forecast results
        Who is SmartForecasts for?

        • Demand Planners.
        • Forecast Analysts.
        • Material & Inventory Planners.
        • Operational Research Professionals.
        • Sales Analysts.
        • Statistcally Minded Executives.

        A Reliable and Secure Platform