Statistical Forecasting Blog

Optimize Inventory planning parameters,improve service levels and increase revenue

Forecasting is a fully developed business process that most organizations still struggle with today. Almost everyone’s top priority is probably to be able to consistently and accurately forecast Sales, Demand, Costs, Inventory, etc.  The inability to obtain a good forecast frequently has a significant business impact. Inaccurate forecasting leads to overstocking or running out, resulting in high costs and excess, impacting the bottom line and the success of the company.

A good forecast should give you enough confidence to make sound business decisions. For a more efficient forecast, consider these best practices:

  • What are the most common forecasting methods, and why do they produce inaccurate results.
  • How to achieve better ROI and optimal processes through scale, granularity, and agility
  • How to improve forecasting accuracy
  • How to use simple machine learning and artificial intelligence tools to get accurate and scalable forecasts
How much time should it take to compute statistical forecasts?

How much time should it take to compute statistical forecasts?

How long should it take for a demand forecast to be computed using statistical methods? This question is often asked by customers and prospects. The answer truly depends. Forecast results for a single item can be computed in the blink of an eye, in as little as a few hundredths of a second, but sometimes they may require as much as five seconds. To understand the differences, it’s important to understand that there is more involved than grinding through the forecast arithmetic itself. Here are six factors that influence the speed of your forecast engine.

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5 Tips for Creating Smart Forecasts

5 Tips for Creating Smart Forecasts

Many companies use the term “smart forecasting” or “smart forecasts” without firm justification. Let’s distinguish between the Smart brand and being smart about the way you do your forecasting work. Demand forecasting is a critical part of the demand planning and S&OP process. Here are 5 tips that will help you execute your forecasts intelligently.

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Supply Chain Math:  Don’t Bring a Knife to a Gunfight

Supply Chain Math: Don’t Bring a Knife to a Gunfight

Math and the supply chain go hand and hand. As supply chains grow, increasing complexity will drive companies to look for ways to manage large-scale decision-making. Math is a fact of life for anyone in inventory management and demand forecasting who is hoping to remain competitive in the modern world. Read our article to learn more.

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Scenario-based Forecasting vs. Equations

Scenario-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.

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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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Recent Posts

  • Worker on a automotive spare parts warehouse using inventory planning softwareService-Level-Driven Planning for Service Parts Businesses
    Service-Level-Driven Service Parts Planning is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support. […]
  • Implementing Demand Planning and Inventory Optimization Software with the Right DataImplementing Demand Planning and Inventory Optimization Software with the Right Data
    Data verification and validation are essential to the success of the implementation of software that performs statistical analysis of data, like Smart IP&O. This article describes the issue and serves as a practical guide to doing the job right, especially for the user of the new application. […]
  • Do your statistical forecasts suffer from the wiggle effectDo 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: […]
  • Digital Transformations for Utilities that will Boost MRO Performance7 Digital Transformations for Utilities that will Boost MRO Performance
    Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
  • The top factors that impact influence the speed of your forecast engineHow much time should it take to compute statistical forecasts?
    How long should it take for a demand forecast to be computed using statistical methods? This question is often asked by customers and prospects. The answer truly depends. Forecast results for a single item can be computed in the blink of an eye, in as little as a few hundredths of a second, but sometimes they may require as much as five seconds. To understand the differences, it’s important to understand that there is more involved than grinding through the forecast arithmetic itself. Here are six factors that influence the speed of your forecast engine. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Worker on a automotive spare parts warehouse using inventory planning softwareService-Level-Driven Planning for Service Parts Businesses
      Service-Level-Driven Service Parts Planning is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support. […]
    • Digital Transformations for Utilities that will Boost MRO Performance7 Digital Transformations for Utilities that will Boost MRO Performance
      Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
    • 6 Do’s and Don’ts for Spare Parts Planning6 Do’s and Don’ts for Spare Parts Planning
      Managing spare parts inventories can feel impossible. You don’t know what will break and when. Feedback from mechanical departments and maintenance teams is often inaccurate. Planned maintenance schedules are often shifted around, making them anything but “planned.” Usage (i.e., demand) patterns are most often extremely intermittent, i.e., demand jumps randomly between zero and something else, often a surprisingly big number. […]
    • 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. […]

    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