Demand Planning Best Practices

Smart Software

Supply chain management involves planning and implementation. Demand planning, based on a statistical projection, evaluates inventory, marketing, and demand-influencing factors and defines where to distribute products to fulfill the anticipated demand. Usually kicks off the planning side of SCM.   

To increase the precision of the demand forecasts utilized in the supply chain, companies invest a lot of time and money in forecasting activities. The objectives of a precise demand plan and a lean supply chain process may be hampered by a bad process design.

Learn industry best practices on how to improve demand planning and create supply chain efficiencies.

Beyond the forecast – Collaboration and Consensus Planning

Beyond the forecast – Collaboration and Consensus Planning

The whole point of demand forecasting is to establish the best possible view of future demand. This requires that we draw upon the best data and inputs we can get, leverage statistics to capture underlying patterns, put our heads together to apply overrides based on business knowledge, and agree on a consensus demand plan that serves as cornerstone to the company’s overall demand plan.

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Demand Planning with Blanket Orders

Demand Planning with Blanket Orders

Our customers are great teachers who have always helped us bridge the gap between textbook theory and practical application. Our latest bit of schooling concerns how to effectively incorporate “blanket orders” into the demand planning process.

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Recent Posts

  • Businessman and businesswoman reading and analysing spreadsheetThe top 3 reasons why your spreadsheet won’t work for optimizing reorder points on spare parts
    We often encounter Excel-based reorder point planning methods. In this post, we’ve detailed an approach that a customer used prior to proceeding with Smart. We describe how their spreadsheet worked, the statistical approaches it relied on, the steps planners went through each planning cycle, and their stated motivations for using (and really liking) this internally developed spreadsheet. […]
  • Style business group in classic business suits with binoculars and telescopes reproduce different forecasting methodsHow to interpret and manipulate forecast results with different forecast methods
    This blog explains how each forecasting model works using time plots of historical and forecast data. It outlines how to go about choosing which model to use. The examples below show the same history, in red, forecasted with each method, in dark green, compared to the Smart-chosen winning method, in light green. […]
  • Factory worker engineer working in factory using tablet computer to check maintenance boiler water pipe in factory.Why Spare Parts Tradeoff Curves are Mission-Critical for Parts Planning
    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. […]
  • What to do when a statistical forecast doesn’t make senseWhat to do when a statistical forecast doesn’t make sense
    Sometimes a statistical forecast just doesn’t make sense. Every forecaster has been there. They may double-check that the data was input correctly or review the model settings but are still left scratching their head over why the forecast looks very unlike the demand history. When the occasional forecast doesn’t make sense, it can erode confidence in the entire statistical forecasting process. […]
  • 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
    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. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Businessman and businesswoman reading and analysing spreadsheetThe top 3 reasons why your spreadsheet won’t work for optimizing reorder points on spare parts
      We often encounter Excel-based reorder point planning methods. In this post, we’ve detailed an approach that a customer used prior to proceeding with Smart. We describe how their spreadsheet worked, the statistical approaches it relied on, the steps planners went through each planning cycle, and their stated motivations for using (and really liking) this internally developed spreadsheet. […]
    • Factory worker engineer working in factory using tablet computer to check maintenance boiler water pipe in factory.Why Spare Parts Tradeoff Curves are Mission-Critical for Parts Planning
      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
      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. […]
    • 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. […]

    Problem

    An accurate forecast is a critical supply chain driver, but many organizations have a limited view of what comes next. Forecasts developed by sales teams or customers are often inaccurate and biased toward sales goals or budgets. Forecasts are often provided only at aggregate levels leaving unspecified which items will be at which locations. Planning teams are left to interpret sales figures and convert them into actionable forecasts of the item mix.  Incorporating sales feedback, determining which decades old forecast model to use, managing the consensus forecast process, and tracking forecast accuracy are manual processes.  They are often managed in complex spreadsheets that are difficult to use, share and scale, and don’t account important features of demand such as seasonality and trend.

    Solution

    Smart Demand Planner™ is a consensus demand planning and statistical forecasting solution available on Smart’s Inventory Planning and Optimization Platform, Smart IP&O. Smart Demand Planner, powered by the SmartForecasts® Engine, aligns strategic business forecasting at any level of your product hierarchy with granular forecasts of the item mix to improve forecast accuracy. It provides a statistically sound, objective foundation for your sales and operations planning process (S&OP).  Smart Demand Planner’s collaborative workbench enables forecast overrides to be applied, imported, reviewed, and approved by authorized users driving the consensus plan. Forecast accuracy is measured, helping ensure that the best possible forecast is delivered to the business at both the aggregate and item mix levels. The result is more efficient sales planning, budgeting, production scheduling, ordering, and inventory planning.

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      Accurate Demand Forecasts

      Capture trend and seasonality
      Predict range of future demand
      Flag exceptions
      Cleanse historical data

      Operational Consensus

      Collaborate with key stakeholders
      Review at any level of hierarchy
      Apply overrides and achieve consensus

      Consistent Repeatable Process

      Common system
      No spreadsheets
      Embed and reuse forecast rules
      Monitor accuracy and fine-tune

      Who is Smart Demand Planner for?
      • Demand Planners.
      • Forecast Analysts.
      • Material & Inventory Planners.
      • Operational Research Professionals.
      • Sales Analysts.
      • Statistcally Minded Executives.
      What questions can Smart Demand Planner answer?
      • What is my short and long term demand most likely to be?
      • Which areas of the business and products are trending?
      • What is the forecast at different levels of my hierarchy (customer, item, family)?
      • What is the likely range of future demand?
      • Which forecasts need to be reviewed (exception reporting)?
      • What is our forecast error (accuracy) for each item, group, overall?
      • Are forecast overrides adding value to the process?
      What can Smart Demand Planner do?
      • Accurately forecast demand for thousands of items in any unit of measure, powered by the SmartForecasts® engine.
      • Capture trends, seasonal, and cyclical patterns at any level.
      • Outlier detection and correction to enhance the quality of historical data.
      • Identifies “causal” factors such as price and economic data and models their impact on demand.
      • Patented Intermittent Demand Planning via APICS award winning “Bootstrapping” technology.
      • Create forecasts at any level of the hierarchy – customer, item, product groups, regions.
      • Share forecasts with internal and external stakeholders such as sales and suppliers.
      • Apply, document, and track forecast overrides.
      • Quantify the impact that promotions will have on future demand.
      • Apply user defined forecasting methods and rules.

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