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.
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.
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.
How to Choose a Target Service Level
When setting a target service level, make sure to take into account factors like current service levels, replenishment lead times, cost constraints, the pain inflicted by shortages on you and your customers, and your competitive position.
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.
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.
Recent Posts
Fifteen questions that reveal how forecasts are computed in your companyIn 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. […]
The top 3 reasons why your spreadsheet won’t work for optimizing reorder points on spare partsWe 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. […]
How to interpret and manipulate forecast results with different forecast methodsThis 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. […]
Why Spare Parts Tradeoff Curves are Mission-Critical for Parts PlanningWhen 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 senseSometimes 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
- The top 3 reasons why your spreadsheet won’t work for optimizing reorder points on spare partsWe 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. […]
- Why Spare Parts Tradeoff Curves are Mission-Critical for Parts PlanningWhen 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. […]
Spare Parts Planning Isn’t as Hard as You ThinkWhen 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. […]
Service-Level-Driven Planning for Service Parts BusinessesService-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.