Inventory control is the element of your inventory management system that involves what’s currently in stock. Inventory control requires being aware of the state and placement of all the items to maximize the merchandising process. With the correct technology in place, it is possible to monitor items more effectively, organize things better, generate accurate data and reports, optimize inventory spaces, and reduce overstocking and stockouts —all leading to increased revenue.
For a more efficient inventory setup, consider these best practices:
- Develop a well-organized floor layout. The warehouse must be ordered logically.
- Use clear signs and labels. Relevant labeling expedites delivery. Label racks, containers, and items clearly.
- Apply cycle counting. Cycle counting involves numbering tiny pieces across cycles.
- Using inventory optimization software helps you identify the least-cost service level based on your targeted stockout risk with minimal inventory carrying cost.
- Use warehouse management systems. Inventory control systems and Warehouse management systems streamline item tracking and decrease human error.
Four Common Mistakes when Planning Replenishment Targets
How often do you recalibrate your stocking policies? Why? Learn how to avoid key mistakes when planning replenishment targets by automating the process, recalibrating parts, using targeting forecasting methods, and reviewing exceptions.
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.
Fact and Fantasy in Multiechelon Inventory Optimization
For most small-to-medium manufacturers and distributors, single-level or single-echelon inventory optimization is at the cutting edge of logistics practice. Multi-echelon inventory optimization (“MEIO”) involves playing the game at an even higher level and is therefore much less common.
Assessing How Suppliers Influence Your Inventory Costs
Software for inventory optimization is most often used to crank out the analytical results you need to run your day-to-day business, such as Reorder Points (also known as Mins) and Order Quantities. This specialized software helps you find the sweet spot that balances inventory costs against item availability during routine operations.
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. […]
Challenges: Addressing the Root Causes of Inventory Pain
Intermittent Demand
Highly variable & intermittent demands make consistently accurate projections all but impossible. Countless hours are spent trying to anticipate what will come next rather than calibrating the organization’s risk tolerance and harnessing that information to determine required levels of supply. Intermittent demand – also known as lumpy, volatile, variable or unpredictable demand – have many zero or low volume values interspersed with random spikes of demand that are often many times larger than the average. Intermittent demand makes it difficult to accurately forecast demand and inventory requirements because there aren’t any inherent patterns. And any patterns that may exist are overwhelmed by the random spikes in demand. Many companies make the mistake of “chasing the forecast” insisting that sales or technicians provide better estimates of demand or turn to unreliable forecasting techniques in a quest to predict the next spike. Many resort to forecasting inventory requirements such as Min/Max levels and Reorder Points relying primarily on subjective business knowledge and simple “rule of thumb” estimates. The result is that millions of dollars are wasted every year because of either excess inventory costs or poor customer service due to stock-outs
Ad Hoc Process
The failure to establish common metrics makes it difficult to adjudicate conflicting priorities. For example, Finance may prefer to conserve cash, while Sales and Maintenance insist that they never stock out. The result is often a test of wills with forecast and inventory planners caught in the middle. This often results in decision making based on a pain avoidance response. For example, order quantities will often go up immediately following a stockout to ensure the outage never recurs. This tends to be a one-way ratchet until inventory carrying costs become an obvious drain of much needed cash. When inventory is out of balance, finger pointing often results. Operations is often stuck in the middle between sales and finance. Without a clear direction from the executive team on service goals, inventory budgets, and an insistence that sales and finance come to the table knowing that tradeoffs will have to be made, the planning team becomes disempowered and the cycle continues. An objective, quantifiable performance measure such as service level changes the discussion, putting a dollar valued on a negotiable level of service.
Rule of Thumb
Safety stock levels, reorder points, lead times, and order quantity directly influence the service vs. cost relationship. Every day, the ERP system makes purchase order suggestions and manufacturing orders based on these drivers. Ensuring that these inputs are understood and optimized will generate better returns on inventory assets. Organizations that are able to do so will see improvements in service and reductions in inventory costs. Unfortunately, the specific inventory policy being utilized is often unclear to many management teams. In absence of a clearly defined and communicated policy, planners are forced to develop their own unique approaches. These self-developed approaches are most often a combination of simple rules of thumb and institutional knowledge. Inventory executives are simply ill equipped to shape inventory according to the changing needs and priorities of the business. Inventory costs balloon and service performance suffers when unable to answer questions such as: “What is my current reorder point and reorder quantity policy, what level of service and inventory cost will this policy yield in the future, and how will performance and costs be influenced by specific changes to the policy.” Rule of thumb approaches don’t answer these questions. In fact, they ignore the critical role of of demand and supply uncertainty. This results in excess inventory for predictable items and more frequent stock outs on less predictable items.
SKU Proliferation
Whether ordering a commonly demanded, inexpensive item of an expensive intermittently demanded item, today’s customers expect high customer service levels. This means 100% of what is ordered must be shipped from stock or within a few days. Quoting a delivery time of more than few days may result in a cancelled order and/or violation of service level agreement costing thousands and jeopardizing customer relationships. To remain competitive, companies often must maintain a very large catalog of items all with potentially different demand patterns and volumes. Thousands of parts potentially stocked at dozens of locations means planners don’t have the bandwidth to proactively review inventory drivers. This results in outdated reorder points, order quantities, min/max levels, and safety stocks that trigger replenishment at the wrong time for the wrong amount ensuring poor allocation of inventory investments and low planner productivity
Smart Inventory Optimization
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.