Business Policy Blog

Pursuing Best practices in demand planning, forecasting and inventory optimization

The 3 Types of Supply Chain Analytics

The 3 Types of Supply Chain Analytics

The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.

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Protect your Demand Planning Process from Regime Change

Protect your Demand Planning Process from Regime Change

No, not that kind of regime change: Nothing here about cruise missiles and stealth bombers. And no, we’re not talking about the other kind of regime change that hits closer to home: Shuffling the C-Suite at your company. In this blog, we discuss the relevance of regime change on time series data used for demand planning and forecasting.

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Undershoot is Sabotaging your Service Level!

Undershoot is Sabotaging your Service Level!

Undershoot means that the lead time begins not at the reorder point but below it. Undershoot happens every time the demand that breached the reorder point took the stock down below (not down to) the reorder point. Undershoot picks your pocket before you even begin to roll the dice. It deludes the inventory professional into thinking his or her reorder points are sufficient to achieve their targets, whereas actual performance will not make the grade.

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Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 10 Questions

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 10 Questions

In this blog, we review 10 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

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The Trouble With Turns

The Trouble With Turns

In our travels around the industrial scene, we notice that many companies pay more attention to inventory Turns than they should. We would like to deflect some of this attention to more consequential performance metrics.

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Recent Posts

  • Fifteen questions that reveal how forecasts are computed in your companyFifteen questions that reveal how forecasts are computed in your company
    In a recent LinkedIn post, I detailed four questions that, when answered, will reveal how forecasts are being used in your business. In this article, we’ve listed questions you can ask that will reveal how forecasts are created. […]
  • 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. […]

    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

    What is my inventory position today, on any item?  Where are we stocking out and how often? What are my delivery times?  Why did we ship late?  Do we have too much inventory in one location, not enough in another?  What are my real supplier lead times?   These are obvious, daily questions, and the answers can reveal underlying root causes that when resolved will improve supply chain performance.  But these answers are elusive, often because data is locked up in your ERP and only accessible via limited reporting views or spreadsheets.  Creating these reports manually using Excel requires data imports, reformatting, and distribution to key stakeholders, wasting countless hours of valuable planning time. This means that getting updated information, when you need it, is not always possible. Not having access to these answers means that problems reveal themselves only after it is too late, and opportunities for improving the inventory planning process are overlooked, further contributing to poor performance.

    Solution

    Smart Operational Analytics (SOA™) is a native web reporting solution available on Smart’s Inventory Planning and Optimization Platform, Smart IP&O.  It provides a fast, easily understood, current perspective on the state of your inventory, its performance against critical metrics, actual supplier lead times, opportunities to rebalance stocks across facilities, and helps you uncover root causes of operational inefficiencies.  SOA automatically refreshes as often as you’d like providing all stakeholders immediate, up-to-date reporting on your operations and performance.  You’ll have constant visibility of inventory levels, orders, shipments, and supplier performance to ensure you’ll always be in tune with the state of your operations and resolve issues before they become problems. Enhance visibility. Improve responsiveness. Increase your bottom line.

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      Smart Operational Analytics

      Inventory Analytics

      Quantify inventory value
      Inventory segmentation
      Inventory classification
      Trend metrics over time

      Operational Performance

      Measure service level performance
      Measure fill rate performance
      Calculate turns, holding & ordering costs
      Trend metrics over time

      Supplier Insights

      Measure supplier performance
      Compare supplier lead times
      Rank suppliers across available metrics
      Trend metrics over time

      Who is Operational Analytics for?

      Smart Operational Analytics is for executives, planners, and operations professionals who seek to:

      • Measure inventory costs and performance in real time.
      • Assess and compare Supplier performance.
      • Identify root causes of stockouts, excess inventory, and late deliveries.
      • share KPI’s such as service levels, turns, costs, and more across the organization.
      What questions can Operational Analytics answer?
      • What does my inventory look like? By value, count, classification?
      • Is my inventory trending up, down, or the same?
      • How much of my inventory is overstocked, understocked, or acceptable?
      • Can inventory be transferred from overstocked locations to under stocked locations?
      • Can existing supplier orders be cancelled or deferred?
      • What are my current turns, service levels, and fill rates and how do they trend over time?
      • How many out of stock events occurred this week, this month, this quarter?
      • How are my suppliers performing, how do they compare?
      • What is my supplier lead time and how has it changed over time?
      Inventory and supplier reporting for your enterprise

      Smart Operational Analytics empowers you to:

      • Benchmark service performance and inventory costs.
      • Benchmark supplier performance.
      • Assess and Classify Inventory by class, stage, and more.
      • Share metrics with the organization.

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