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
You Need to Team up with the Algorithms

You Need to Team up with the Algorithms

This article is about the real power that comes from the collaboration between you and our software that happens at your fingertips. We often write about the software itself and what goes on “under the hood”. This time, the subject is how you should best team up with the software.

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Rethinking forecast accuracy: A shift from accuracy to error metrics

Rethinking forecast accuracy: A shift from accuracy to error metrics

Measuring the accuracy of forecasts is an undeniably important part of the demand planning process. This forecasting scorecard could be built based on one of two contrasting viewpoints for computing metrics. The error viewpoint asks, “how far was the forecast from the actual?” The accuracy viewpoint asks, “how close was the forecast to the actual?” Both are valid, but error metrics provide more information.

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Every Forecasting Model is Good for What it is Designed for

Every Forecasting Model is Good for What it is Designed for

With so much hype around new Machine Learning (ML) and probabilistic forecasting methods, the traditional “extrapolative” or “time series” statistical forecasting methods seem to be getting the cold shoulder. However, it is worth remembering that these traditional techniques (such as single and double exponential smoothing, linear and simple moving averaging, and Winters models for seasonal items) often work quite well for higher volume data. Every method is good for what it was designed to do. Just apply each appropriately, as in don’t bring a knife to a gunfight and don’t use a jackhammer when a simple hand hammer will do.

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How Are We Doing? KPI’s and KPP’s

How Are We Doing? KPI’s and KPP’s

Dealing with the day-to-day of inventory management can keep you busy. But you know you have to get your head up now and then to see where you’re heading. For that, your inventory software should show you metrics – and not just one, but a full set of metrics or KPI’s – Key Performance Indicators.

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Recent Posts

  • Why inventory planning shouldn’t rely exclusively on simple rules of thumbWhy Inventory Planning Shouldn’t Rely Exclusively on Simple Rules of Thumb
    For too many companies, a critical piece of data fact-finding ― the measurement of demand uncertainty ― is handled by simple but inaccurate rules of thumb. For example, demand planners will often compute safety stock by a user-defined multiple of the forecast or historical average. Or they may configure their ERP to order more when on hand inventory gets to 2 x the average demand over the lead time for important items and 1.5 x for less important ones. This is a huge mistake with costly consequences. […]
  • Direct to the Brain of the Boss- Inventory AnalysisDirect to the Brain of the Boss – Inventory Analytics and Reporting
    In this blog, the spotlight is cast on the software that creates reports for management, the silent hero that translates the beauty of furious calculations into actionable reports. Watch as the calculations, intricately guided by planners utilizing our software, seamlessly converge into Smart Operational Analytics (SOA) reports, dividing five key areas: inventory analysis, inventory performance, inventory trending, supplier performance, and demand anomalies. […]
  • You need to team up with the algorithms for Inventory ManagementYou Need to Team up with the Algorithms
    This article is about the real power that comes from the collaboration between you and our software that happens at your fingertips. We often write about the software itself and what goes on “under the hood”. This time, the subject is how you should best team up with the software. […]
  • Rethinking forecast accuracy, A shift from accuracy to error metricsRethinking forecast accuracy: A shift from accuracy to error metrics
    Measuring the accuracy of forecasts is an undeniably important part of the demand planning process. This forecasting scorecard could be built based on one of two contrasting viewpoints for computing metrics. The error viewpoint asks, “how far was the forecast from the actual?” The accuracy viewpoint asks, “how close was the forecast to the actual?” Both are valid, but error metrics provide more information. […]
  • Using Key Performance Predictions to Plan Stocking Policies
    I can't imagine being an inventory planner in spare parts, distribution, or manufacturing and having to create safety stock levels, reorder points, and order suggestions without using key performance predictions of service levels, fill rates, and inventory costs. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Top Differences between Inventory Planning for Finished Goods and for MRO and Spare PartsTop Differences Between Inventory Planning for Finished Goods and for MRO and Spare Parts
      In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage. […]
    • Centering Act Spare Parts Timing Pricing and ReliabilityCentering Act: Spare Parts Timing, Pricing, and Reliability
      In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]
    • 5 Steps to Improve the Financial Impact of Spare Parts Planning5 Steps to Improve the Financial Impact of Spare Parts Planning
      In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage. […]
    • Bottom Line strategies for Spare Parts Planning SoftwareBottom Line Strategies for Spare Parts Planning
      Managing spare parts presents numerous challenges, such as unexpected breakdowns, changing schedules, and inconsistent demand patterns. Traditional forecasting methods and manual approaches are ineffective in dealing with these complexities. To overcome these challenges, this blog outlines key strategies that prioritize service levels, utilize probabilistic methods to calculate reorder points, regularly adjust stocking policies, and implement a dedicated planning process to avoid excessive inventory. Explore these strategies to optimize spare parts inventory and improve operational efficiency. […]

    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