Inventory managers have the problem of handling tens or even hundreds of thousands of products, each with unique properties, demanding sophisticated and time-consuming calculations. The proactive management of big inventories becomes unfeasible in the absence of a systematic approach and effective analytic tools.
Without inventory optimization, businesses run the risk of overpaying and underperforming. Manufacturers, distributors, and MRO inventory managers frequently err on the side of caution when setting stocking levels to prevent expensive shortages. Establishing the ideal stock levels for manufacturers, distributors, and MRO should be a science, not an art.
Learn industry best practices on how to optimize inventory to save on costs, meet demand, and streamline your supply chain below.
Why Days of Supply Targets Don’t Work when Computing Safety Stocks
CFOs tell us they need to spend less on inventory without impacting sales. One way to do that is to move away from using targeted day of supply rules to determine reorder points and safety stock buffers. Here is how a target days of supply/coverage approach works:
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.
Managing Inventory amid Regime Change
If you hear the phrase “regime change” on the news, you immediately think of some fraught geopolitical event. Statisticians use the phrase differently, in a way that has high relevance for demand planning and inventory optimization. This blog is about “regime change” in the statistical sense, meaning a major change in the character of the demand for an inventory item.
The Supply Chain Blame Game: Top 3 Excuses for Inventory Shortage and Excess
The supply chain has become the blame game for almost any industrial or retail problem. Shortages on lead time variability, bad forecasts, and problems with bad data are facts of life, yet inventory-carrying organizations are often caught by surprise when any of these difficulties arise. So, again, who is to blame for the supply chain chaos? Keep reading this blog and we will try to show you how to prevent product shortages and overstocking.
Goldilocks Inventory Levels
You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.
An Example of Simulation-Based Multiechelon Inventory Optimization
Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.
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
Keeping inventory investments in check while maintaining high customer service levels is a constant balancing act. Without proper controls, excess inventory grows throughout your supply chain, locking up vital working capital that constrains your company’s growth. Every day, the ERP system makes purchase order suggestions and manufacturing orders based on planning drivers such as safety stock, reorder points, and Min/Max levels. Ensuring that these inputs are understood and continually optimized will generate substantially better returns on your inventory assets. Unfortunately, many organizations rely on rule of thumb logic, institutional knowledge, and “one-size-fits all” forecasting logic that assigns all items within a particular group the same service level target. These approaches yield suboptimal policies that cause inventory costs to balloon and service performance to suffer. Compounding the problem is the sheer volume of data – thousands of items stocked at multiple locations means planners don’t have the bandwidth to proactively review these inventory drivers on a regular basis. This results in outdated reorder points, safety stocks, order quantities, and Min/Max settings that further contribute to the problem.
Solution
Smart Inventory Optimization (SIO™) is available on Smart’s Inventory Planning and Optimization Platform, Smart IP&O. It delivers inventory policy decision support and the means to share, collaborate, and track the impact of your inventory planning policy. This can help realize millions in savings by improving customer service and reducing excess stock. You can forecast metrics such as service level, fill rate, holding costs, ordering costs, and stock out costs. Users can identify overstocks and understocks, adjust stocking policies when demand changes, share proposed policies with other stakeholders, collect feedback, and establish a consensus inventory plan. And unlike traditional inventory planning systems that rely on rule of thumb approaches or require the user to arbitrarily set suboptimal service level targets, Smart Inventory Optimization prescribes the optimal service levels for you. Users can optionally assign service level constraints to ensure the optimization engine respects business rules. SIO provides the required inventory planning parameters for a variety of replenishment policies such as Reorder Point/Order Quantity, Min/Max, Safety Stock Planning, and Order Up to levels.
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With Smart Inventory Optimization you can:
- Identify where you are overstocked and understocked.
- Modify planning parameters based on your business rules, service targets, and inventory budget.
- Leverage the optimization logic in SIO to prescribe planning parameters and service levels for you.
- Compare proposed policies to the benchmark.
- Collaborate and develop a consensus inventory plan.
- Automatically generate revised planning parameters as demand and other inputs change.
Smart Inventory Optimization
Optimal Inventory Levels
Reduce excess stock
Improve service levels
Minimize buyer transactions
Maximize return on assets
Organizational Consensus
Balance service levels
Identify stockout risk
Identify overstocks
No finger-pointing
Operational Connectivity
Align process with strategic objectives
Empower team to “make it so”
Optimize as conditions change
Pass results to ERP
Who is Inventory Optimization for?
Smart Inventory Optimization is for executives and business savvy planners who seek to:
- Yield maximum returns from inventory assets.
- Address the problem of highly variable or intermittent demand.
- Broker the service vs. cost tradeoffs between different departments.
- Develop a repeatable and efficient inventory planning process.
- Empower the team to ensure operational plan is aligned with strategic plan.
What questions can Inventory Optimization answer?
- What is the best service level achievable with the inventory budget?
- What service levels will yield the maximum return?
- If lead times increased, what would it cost to maintain service?
- If I reduce inventory, what will the impact on service be?
- If order quantity increases, what will the impact on service and costs be?
- What is the order quantity that balances holding and ordering costs?
Inventory forecasting for the inventory executive
Smart Inventory Optimization empowers you to:
- Predict service performance and inventory costs.
- Assess business impact of “what-if” inventory policies.
- Align inventory policy with corporate strategy.
- Establish an operational framework that guides the planning team.
- Reduce inventory and improve service.