The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Managing inventory requires executives to balance competing goals: high product availability versus low investment in inventory. Executives strike this balance by stating availability targets and budget constraints. Then supply chain professionals translate these “commander’s intentions” into detailed specifications about reorder points and order quantities.

A High-Stakes Race Between Supply and Demand

 

Let’s focus on reorder points (also known as mins). They work as follows. As on-hand inventory decreases in response to demand, it eventually drops down to or below a trigger value, the reorder point or min. At that point, it’s like a gun goes off to start a race between supply and demand. A replenishment order is sent to restock the item, but there is a replenishment lead time, so the restocking is not instantaneous. While your system waits for resupply, demand continues to whittle away at the stock on hand. It is bad news if demand wins the race, because then you won’t be in position to provide what somebody is demanding. Then they either get it from a competitor or get back-ordered and unhappy: either way, stocking out is a bad outcome for you and your customer.

The risk of stocking is controlled by your staff’s choice of reorder points. If they are set too high, stock-outs are rare but inventory is bloated. Set them too low and stock-outs abound. So how should reorder points be set?

Avoiding Foolish Follow-Through

 

Several factors govern stock-out risk. Each item in your inventory has its own demand history and lead time. Together with your chosen availability targets, these factors determine the best choice of reorder point. But the relationships are statistical and require good analysis to work out. Inventory Optimization Software can compute the proper reorder point for each of tens of thousands of items. But instead of relying on proper analysis, many companies fall back on simple rules of thumb or just “doing what we always do”.

In place of using the right math, companies often rely on rules of thumb that serve them poorly. Here are some examples in order of most common to least common.

1) Multiples of Average Demand

 

Setting reorder points at some (arbitrary) multiple of average demand starts to rely on actual facts. But it ignores the key demand attribute that drives stock-out risk: demand variability. Two items with the same average demand but very different levels of variability will require very different reorder points to insure the same low risk of stock-out. (See Figure 1)

2) Gut feel

 

Some companies have self-styled supply chain gurus. Even if they actually are Jedi masters, it’s impossible to keep up with tens of thousands of items whose reorder points should be reviewed frequently.  And if the logic that drives decision making is buried in a hard to use spreadsheet that only they know how to use, the company risks not being able to execute the inventory plan without that one individual –a risky proposition.

3) Average Demand + some multiple of Demand Variability

 

This approach is taught in many “Inventory 101” courses. But it implicitly assumes some facts about demand that are very often not true: that demand has a Normal (“bell-shaped”) distribution and that demand in one period does not relate to demand in the previous time period(s).  Assumptions of independence and reliance on normal distribution models just don’t cut it.

4) Nursery rhymes

 

Not at all the norm, hence being last on the list, but we heard of one company that used one simple rule for all items: “If it’s down to four, order more”. It’s crazy to believe that one rule applies to all items at all times. But at least it rhymes.

Your people can do better than to rely on any of these approaches. Do you know whether your company is using any one of them?

Getting It Right

 

The right way to set reorder points uses the tools of probability theory. The details depend on whether you are selling finished goods or spare parts. Spare parts are usually more difficult to manage because they have quirky demand patterns: high intermittency (lots of zero demands), high skewness (lots of small demands but with some whoppers too), and auto-correlation (“feast or famine” behavior). Modern Reorder Point Software takes these quirks into account to set reorder points that insure the desired level of item availability. Importantly, they also let your people see explicit trade-off curves, so they can strike the balance you want — at the item by location level – between stock-out risk and inventory investment.

Inventory is a major item on the balance sheet and needs high-level attention. At many manufacturers, service parts can represent up to half of revenue. Modern software lets the C-Suite move beyond, incomplete math and other inadequate approaches to managing inventory.

 

 

Figure 1:  Two equally important items with the same average demand get assigned the same stocking policy that determines the Min (reorder point) as 2 x average lead time demand.  Despite the “same” stocking policy service performance varies significantly with the stable Item A experiencing overstocks and the volatile Item B experiencing stock outs.

Leave a Comment

Related Posts

Daily Demand Scenarios

Daily Demand Scenarios

In this Videoblog, we will explain how time series forecasting has emerged as a pivotal tool, particularly at the daily level, which Smart Software has been pioneering since its inception over forty years ago. The evolution of business practices from annual to more refined temporal increments like monthly and now daily data analysis illustrates a significant shift in operational strategies.

Learning from Inventory Models

Learning from Inventory Models

In this video blog, the spotlight is on a critical aspect of inventory management: the analysis and interpretation of inventory data. The focus is specifically on a dataset from a public transit agency detailing spare parts for buses.

The Methods of Forecasting

The Methods of Forecasting

Demand planning and statistical forecasting software play a pivotal role in effective business management by incorporating features that significantly enhance forecasting accuracy. One key aspect involves the utilization of smoothing-based or extrapolative models, enabling businesses to quickly make predictions based solely on historical data. This foundation rooted in past performance is crucial for understanding trends and patterns, especially in variables like sales or product demand. Forecasting software goes beyond mere data analysis by allowing the blending of professional judgment with statistical forecasts, recognizing that forecasting is not a one-size-fits-all process. This flexibility enables businesses to incorporate human insights and industry knowledge into the forecasting model, ensuring a more nuanced and accurate prediction.

Recent Posts

  • Overcoming Uncertainty with Service and Inventory Optimization TechnologyOvercoming Uncertainty with Service and Inventory Optimization Technology
    In this blog, we will discuss today's fast-paced and unpredictable market and the constant challenges businesses face in managing their inventory and service levels efficiently. The main subject of this discussion, rooted in the concept of "Probabilistic Inventory Optimization," focuses on how modern technology can be leveraged to achieve optimal service and inventory targets amidst uncertainty. This approach not only addresses traditional inventory management issues but also offers a strategic edge in navigating the complexities of demand fluctuations and supply chain disruptions. […]
  • Daily Demand Scenarios Smart 2Daily Demand Scenarios
    In this Videoblog, we will explain how time series forecasting has emerged as a pivotal tool, particularly at the daily level, which Smart Software has been pioneering since its inception over forty years ago. The evolution of business practices from annual to more refined temporal increments like monthly and now daily data analysis illustrates a significant shift in operational strategies. […]
  • The Cost of Doing nothing with your inventory Planning SystemsThe Cost of Spreadsheet Planning
    Companies that depend on spreadsheets for demand planning, forecasting, and inventory management are often constrained by the spreadsheet’s inherent limitations. This post examines the drawbacks of traditional inventory management approaches caused by spreadsheets and their associated costs, contrasting these with the significant benefits gained from embracing state-of-the-art planning technologies. […]
  • Learning from Inventory Models Software AILearning from Inventory Models
    In this video blog, the spotlight is on a critical aspect of inventory management: the analysis and interpretation of inventory data. The focus is specifically on a dataset from a public transit agency detailing spare parts for buses. […]
  • The methods of forecasting SoftwareThe Methods of Forecasting
    Demand planning and statistical forecasting software play a pivotal role in effective business management by incorporating features that significantly enhance forecasting accuracy. One key aspect involves the utilization of smoothing-based or extrapolative models, enabling businesses to quickly make predictions based solely on historical data. This foundation rooted in past performance is crucial for understanding trends and patterns, especially in variables like sales or product demand. Forecasting software goes beyond mere data analysis by allowing the blending of professional judgment with statistical forecasts, recognizing that forecasting is not a one-size-fits-all process. This flexibility enables businesses to incorporate human insights and industry knowledge into the forecasting model, ensuring a more nuanced and accurate prediction. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Why MRO Businesses Need Add-on Service Parts Planning & Inventory SoftwareWhy MRO Businesses Need Add-on Service Parts Planning & Inventory Software
      MRO organizations exist in a wide range of industries, including public transit, electrical utilities, wastewater, hydro power, aviation, and mining. To get their work done, MRO professionals use Enterprise Asset Management (EAM) and Enterprise Resource Planning (ERP) systems. These systems are designed to do a lot of jobs. Given their features, cost, and extensive implementation requirements, there is an assumption that EAM and ERP systems can do it all. In this post, we summarize the need for add-on software that addresses specialized analytics for inventory optimization, forecasting, and service parts planning. […]
    • Spare-parts-demand-forecasting-a-different-perspective-for-planning-service-partsThe Forecast Matters, but Maybe Not the Way You Think
      True or false: The forecast doesn't matter to spare parts inventory management. At first glance, this statement seems obviously false. After all, forecasts are crucial for planning stock levels, right? It depends on what you mean by a “forecast”. If you mean an old-school single-number forecast (“demand for item CX218b will be 3 units next week and 6 units the week after”), then no. If you broaden the meaning of forecast to include a probability distribution taking account of uncertainties in both demand and supply, then yes. […]
    • Whyt MRO Businesses Should Care about Excess InventoryWhy MRO Businesses Should Care About Excess Inventory
      Do MRO companies genuinely prioritize reducing excess spare parts inventory? From an organizational standpoint, our experience suggests not necessarily. Boardroom discussions typically revolve around expanding fleets, acquiring new customers, meeting service level agreements (SLAs), modernizing infrastructure, and maximizing uptime. In industries where assets supported by spare parts cost hundreds of millions or generate significant revenue (e.g., mining or oil & gas), the value of the inventory just doesn’t raise any eyebrows, and organizations tend to overlook massive amounts of excessive inventory. […]
    • Top Differences between Inventory Planning for Finished Goods and for MRO and Spare PartsTop Differences Between Inventory Planning for Finished Goods and for MRO and Spare Parts
      In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage. […]