Belmont, MA, June 2023 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced the award of US Patent 11,656,887, “SYSTEM AND METHOD TO SIMULATE DEMAND AND OPTIMIZE CONTROL PARAMETERS FOR A TECHNOLOGY PLATFORM.”

The patent directs “technical solutions for analyzing historical demand data of resources in a technology platform to facilitate management of an automated process in the platform.” One important application is optimization of parts inventories.

Aspects of the invention include: an advanced bootstrap process that converts a single observed time series of item demand into an unlimited number of realistic demand scenarios; a performance prediction process that executes Monte Carlo simulations of a proposed inventory control policy to assess its performance; and a performance improvement process that uses the performance prediction process to automatically explore the space of alternative system designs to identify optimal control parameter values, selecting ones that minimize operating cost while guaranteeing a target level of item availability.

The new analytic technology described in the patent will form the basis for the upcoming release of the next generation (“Gen2”) of Smart Demand Planner™ and Smart IP&O™. Current customers and resellers can preview Gen2 by contacting their Smart Software sales representative.

Research underlying the patent was self-funded by Smart, supplemented by competitive Small Business Innovation Research grants from the US National Science Foundation.

About Smart Software, Inc.
Founded in 1981, Smart Software, Inc. is a leader in providing businesses with enterprise-wide demand forecasting, planning, and inventory optimization solutions.  Smart Software’s demand forecasting and inventory optimization solutions have helped thousands of users worldwide, including customers such as Disney, Arizona Public Service, Ameren, and The American Red Cross.  Smart’s Inventory Planning & Optimization Platform, Smart IP&O gives demand planners the tools to handle sales seasonality, promotions, new and aging products, multi-dimensional hierarchies, and intermittently demanded service parts and capital goods items.  It also provides inventory managers with accurate estimates of the optimal inventory and safety stock required to meet future orders and achieve desired service levels.  Smart Software is headquartered in Belmont, Massachusetts, and our website is www.smartcorp.com.

Many companies looking to improve their forecasting process don’t know where to start. It can be confusing to contend with learning new statistical methods, making sure data is properly structured and updated, agreeing on who “owns” the forecast, defining what ownership means, and measuring accuracy. Having seen this over forty-plus years of practice, we wrote this blog to outline the core focus and to encourage you to keep it simple early on.

1. Objectivity. First, understand and communicate that the Demand Planning and Forecasting process is an exercise in objectivity. The focus is on getting inputs from various sources (stakeholders, customers, functional managers, databases, suppliers, etc.) and deciding whether those inputs add value. For example, if you override a statistical forecast and add 20% to the projection, you should not just assume that you automatically got it right. Instead, be objective and check whether that override increased or decreased forecast accuracy. If you find that your overrides made things worse, you’ve gained something: This informs the process and you know to better scrutinize override decisions in the future.

2.  Teamwork. Recognize that forecasting and demand planning are team sports. Agree on who will captain the team. The captain is responsible for creating the baseline statistical forecasts and supervising the demand planning process. But results depend on everyone on the team making positive contributions, providing data, suggesting alternative methodologies, questioning assumptions, and executing recommended actions. The final results are owned by the company and every single stakeholder.

3. Measurement. Don’t fixate on industry forecast accuracy benchmarks. Every SKU has its own level of “forecastability”, and you may be managing any number of difficult items. Instead, create your own benchmarks based on a sequence of increasingly advanced forecasting methods. Advanced statistical forecasts may seem dauntingly complex at first, so start simple with a basic method, such as forecasting the historical average demand. Then measure how close that simple forecast comes to the actual observed demand. Work up from there to techniques that deal with complications like trend and seasonality. Measure progress using accuracy metrics calculated by your software, such as the mean absolute percentage error (MAPE). This will allow your company to get a little bit better each forecast cycle.

4. Tempo. Then focus efforts on making forecasting a standalone process that isn’t combined with the complex process of inventory optimization. Inventory management is built on a foundation of sound demand forecasting, but it is focused on other topics: what to purchase, when to purchase, minimum order quantities, safety stocks, inventory levels, supplier lead times, etc. Let inventory management go to later. First build up “forecasting muscle” by creating, reviewing, and evolving the forecasting process to have a regular cadence. When your process is sufficiently matured, catch up with the increasing speed of business by increasing the tempo of your forecasting process to at least a monthly cadence.

Remarks

Revising a company’s forecasting process can be a major step. Sometimes it happens when there is executive turnover, sometimes when there is a new ERP system, sometimes when there is new forecasting software. Whatever the precipitating event, this change is an opportunity to rethink and refine whatever process you had before. But trying to eat the whole elephant in one go is a mistake. In this blog, we’ve outlined some discrete steps you can take to make for a successful evolution to a better forecasting process.

Reveal how forecasts are used with these 4 questions.

Often companies will insist that they “don’t use forecasts” to plan inventory.  They often use reorder point methods and are struggling to improve on-time delivery, inventory turns, and other KPIs. While they don’t think of what they are doing as explicitly forecasting, they certainly use estimates of future demand to develop reorder points such as min/max.

Regardless of what it is called, everyone tries to estimate future demand in some way and uses this estimate to set stocking policies and drive orders. To improve inventory planning and make sure you aren’t over/under ordering and creating large stockouts and inventory bloat, it is important to understand exactly how your organization uses forecasts. Once this is understood, you can assess whether the quality of the forecasts can be improved.

Try getting answers to the following questions. It will reveal how forecasts are being used in your business – even if you don’t think you use forecasts.

1.  Is your forecast a period-by-period estimate over time that is used to predict what on-hand inventory will be in the future and triggers order suggestions in your ERP system?

2. Or is your forecast used to derive a reorder point but not explicitly used as a per-period driver to trigger orders? Here, I may predict we’ll sell 10 per week based on the history, but we are not loading 10, 10, 10, 10, etc., into the ERP. Instead, I derive a reorder point or Min that covers the two-period lead time + some amount of buffer to help protect against stock out. In this case, I’ll order more when on hand gets to 25.

3. Is your forecast used as a guide for the planner to help subjectively determine when they should order more?  Here, I predict 10 per week, and I assess the on-hand inventory periodically, review the expected lead time, and I decide, given the 40 units I have on hand today, that I have “enough.” So, I do nothing now but will check back again in a week.

4. Is it used to set up blanket orders with suppliers? Here, I predict 10 per week and agree to a blanket purchase order with the supplier of 520 per year. The orders are then placed in advance to arrive in quantities of 10 once per week until the blanket order is consumed.

Once you get the answers, you can then ask how the estimates of demand are created.  Is it an average? Is it deriving demand over lead time from a sales forecast?  Is there a statistical forecast generated somewhere?  What methods are considered? It will also be important to assess how safety stocks are used to protect against demand and supply variability.  More on all of this in a future article.

Did you know that it was Benjamin Franklin who invented the lightning rod to protect buildings from lightning strikes? Now, it’s not every day that we must worry about lightning strikes, but in today’s unpredictable business climate, we do have to worry about supply chain disruptions, long lead times, rising interest rates, and volatile demand. With all these challenges, it’s never been more vital for organizations to accurately forecast parts usage, stocking levels, and to optimize replenishment policies such as reorder points, safety stocks, and order quantities.  In this blog, we’ll explore how companies can leverage innovative solutions like inventory optimization and parts forecasting software that utilize machine learning algorithms, probabilistic forecasting, and analytics to stay ahead of the curve and protect their supply chains from unexpected shocks.

Spare Parts Planning Solutions
Spare parts optimization is a key aspect of supply chain management for many industries. It involves managing the inventory of spare parts to ensure they are available when needed without having excess inventory that can tie up capital and space. Optimizing spare parts inventory is a complex process that requires a deep understanding of usage patterns, supplier lead times, and the criticality of each part for the business.

In this blog, our primary emphasis will be on the crucial aspect of inventory optimization and demand forecasting. However, other approaches highlighted below for spare parts optimization, such as predictive maintenance and 3D printing, Master Data Management, and collaborative planning should be investigated and deployed as appropriate.

1. Predictive Maintenance: Using predictive analytics to anticipate when a part is likely to fail and proactively replace it, rather than waiting for it to break down. This approach can help companies reduce downtime and maintenance costs, as well as improve overall equipment effectiveness.
2. 3D printing: Advancements in 3D printing technology are enabling companies to produce spare parts on demand, reducing the need for excess inventory. This not only saves space and reduces costs but also ensures that parts are available when needed.
3. Master Data Management: Data Management platforms ensure that part data is properly identified, cataloged, cleansed, and organized. All too often, MRO organizations hold the same part number under different SKUs. These duplicate parts serve the same purpose but require different SKU numbers to ensure regulatory compliance or security.  For example, a part used to support a government contract may be required be sourced from a US manufacturer to stay in compliance with “Buy America” regulations.  It’s critical that these part numbers be identified and consolidated into one SKU, when possible, to keep inventory investments in check.
4. Collaborative Planning: Collaborating with suppliers and customers to share data, forecasts, and plan demand can help companies reduce lead times, improve accuracy, and reduce inventory levels. Forecasting plays an essential role in collaboration as sharing insights on purchases, demand, and buying behavior ensures suppliers have the information they need to ensure stock availability for customers.

Inventory Optimization
Abraham Lincoln was once quoted as saying, “Give me six hours to chop down a tree, and I will spend the first four sharpening the axe”? Lincoln knew that preparation and optimization were key to success, just like organizations need to have the right tools, such as inventory optimization software, to optimize their supply chain and stay ahead in the market. With inventory optimization software, organizations can improve their forecasting accuracy, lower inventory costs, improve service levels, and reduce lead times. Lincoln knew that sharpening the axe was necessary to accomplish the job effectively without overexerting.  Inventory Optimization ensures that inventory dollars are allocated effectively across thousands of parts helping ensure service levels while minimizing excess stock.

Spare parts play a decisive role in maintaining operational efficiency, and the lack of critical parts can lead to downtime and reduced productivity. The sporadic nature of spare parts demand makes it difficult to predict when a specific part will be required, resulting in the risk of overstocking or understocking, both of which can incur costs for the organization.  Additionally, managing lead times for spare parts poses its own set of challenges. Some parts may have lengthy delivery times, necessitating the maintenance of adequate inventory levels to avoid shortages. However, carrying excess inventory can be costly, tying up capital and storage space.

Given the myriad of challenges facing materials management departments and spare parts planners, planning demand, stocking levels, and replenishment of spare parts without an effective inventory optimization solution is akin to attempting to chop down a tree with a very blunt axe! The sharper the axe, the better your organization will be able to contend with these challenges.

Smart Software’s Axe is the Sharpest
Smart Inventory Optimization and Demand Planning Software uses a unique empirical probabilistic forecasting approach that results in accurate forecasts of inventory requirements, even where demand is intermittent. Since nearly 90% of spare and service parts are intermittent, an accurate solution to handle this type of demand is required.   Smart’s solution was patented in 2001 and additional innovations were recently patented in May of 2023 (announcements coming soon!).  The solution was awarded as a finalist in the APICS Technological Innovation Category for its role in helping transform the resource management industry.

The Role of Intermittent Demand
Intermittent demand does not conform to a simple normal or bell-shaped distribution that makes it impossible to forecast accurately with traditional, smoothing-based forecasting methods.  Parts and items with 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. This problem is especially prevalent in companies that manage large inventories of service and spare parts in industries such as aviation, aerospace, power and water supply and utilities, automotive, heavy asset management, high tech, as well as in MRO (Maintenance, Repair, and Overhaul).

Scenario Analysis
Smart’s patented and award-winning technology rapidly generates tens of thousands of possible scenarios of future demand sequences and cumulative demand values over an item’s lead time. These scenarios are statistically similar to the item’s observed data, and they capture the relevant details of intermittent demand without relying on the assumptions commonly made about the nature of demand distributions by traditional forecasting methods. The result is a highly accurate forecast of the entire distribution of cumulative demand over an item’s lead time. The bottom line is that with the information these demand distributions provide, companies can easily plan safety stock and service level inventory requirements for thousands of intermittently demanded items with nearly 100% accuracy.

Benefits
Implementing innovative solutions from Smart Software such as SmartForecasts for statistical forecasting, Demand Planner for consensus parts planning, and Inventory Optimization for developing accurate replenishment drivers such as min/max and safety stock levels will provide forward-thinking executives and planners with better control over their organization’s operations.  It will result in the following benefits:

1. Improved Forecasting Accuracy: Accurate demand forecasting is fundamental for any organization that deals with spare parts inventory management. Inventory optimization software uses sophisticated algorithms to analyze historical usage patterns, identify trends and forecast future demand with a high degree of accuracy. With this level of precision in forecasting, organizations can avoid the risk of overstocking or understocking their spare parts inventory.
2. Lower Inventory Costs: One major challenge that supply chain leaders face when dealing with spare parts inventory management is the cost associated with maintaining an optimal stock of spares at all times. By optimizing inventory levels using modern technology systems like artificial intelligence (AI), machine learning (ML), and predictive analytics, organizations can reduce carrying costs while ensuring they have adequate stocks available when needed.
3. Improved Service Levels: When it comes to repair and maintenance services, time is money! Downtime due to the unavailability of critical spare parts can result in lost productivity and revenue for businesses across industries such as manufacturing plants, power generation facilities, or data centers managing IT infrastructure equipment. Optimizing your spare parts inventory ensures that you always have the right amount on hand, reducing downtime caused by waiting for deliveries from suppliers.
4. Reduced Lead Times: Another benefit that accrues from accurate demand forecasting through modern warehouse technologies is reduced lead time in delivery which leads to better customer satisfaction since customers will receive their orders faster than before thus improving brand loyalty. Therefore, the adoption of new strategies driven by AI/ML tools creates value within supply chain operations leading to increased efficiency gains not only limited reductionism cost but also streamlining processes related to production scheduling, logistics transportation planning among others

Conclusion
Through the utilization of inventory optimization and demand planning software, organizations can overcome various challenges such as supply chain disruptions, rising interest rates, and volatile demand. This enables them to reduce costs associated with excess storage space and obsolete inventory items. By leveraging sophisticated algorithms, inventory optimization software enhances forecasting accuracy, ensuring organizations can avoid overstocking or under-stocking their spare parts inventory. Additionally, it helps lower inventory costs by optimizing levels and leveraging technologies like artificial intelligence (AI), machine learning (ML), and predictive analytics. Improved service levels are achieved as organizations have the right quantity of spare parts readily available, reducing downtime caused by waiting for deliveries. Furthermore, accurate demand forecasting leads to reduced lead times, enhancing customer satisfaction and fostering brand loyalty. Adopting such strategies driven by AI/ML tools not only reduces costs but also streamlines processes, including production scheduling and logistics transportation planning, ultimately increasing efficiency gains within the supply chain.

I’ll bet your maintenance and repair teams would be ok with incurring higher stock out risks one some spare parts if they knew that the inventory reduction savings would be used to spread out the inventory investment more effectively to other parts and boost overall service levels.

I’ll double down that your Finance team, despite always being challenged with lowering costs, would support a healthy inventory increase if they could clearly see that the revenue benefits from increased uptime, fewer expedites, and service level improvements clearly outweighed the additional inventory costs and risk.

A spare parts tradeoff curve will enable service parts planning teams to properly communicate the risks and costs of each inventory decision.  It is mission critical for parts planning and the only way to adjust stocking parameters proactively and accurately for each part.  Without it, planners, for all intents and purposes, are “planning” with blinders on because they won’t be able to communicate the true tradeoffs associated with stocking decisions.

For example, if a proposed increase to the min/max levels of an important commodity group of service parts is recommended, how do you know whether the increase is too high or too low or just right?  How can you fine-tune the change for thousands of spares?  You won’t and you can’t.  Your inventory decision making will rely on reactive, gut feel, and broad-brush decisions causing service levels to suffer and inventory costs to balloon.

So, what exactly is a spare parts tradeoff curve anyway?

It’s a fact-based, numerically driven prediction that details how changes in stocking levels will influence inventory value, holding costs, and service levels.  For each unit change in inventory level there is a cost and a benefit.  The spare parts tradeoff curve identifies these costs and benefits across different stocking levels. It lets planners discover the stock level that best balances the costs and benefits for each individual item.

Here are two simplified examples. In Figure 1, the spare parts tradeoff curve shows how the service level (probability of not stocking out) changes depending on the reorder level.  The higher the reorder level, the lower the stockout risk.  It is critical to know how much service you are gaining given the inventory investment.  Here you may be able to justify that an inventory increase from a reorder point of 35 to 45 is well worth the investment of 10 additional units of stock because service levels jumps from just under 70% to 90%, cutting your stockout risk for the spare part from 30% to 10%!

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Figure 1: Cost versus Service Level

Figure 2: Service Level versus Size of Inventory

In this example (Figure 2), the tradeoff curve exposes a common problem with spare parts inventory.  Often stock levels are so high that they generate negative returns.  After a certain stocking quantity, each additional unit of stock does not buy more benefit in the form of a higher service level.  Inventory decreases can be justified when it is clear the stock level is well past the point of diminishing returns. An accurate tradeoff curve will expose the point where it is no longer advantageous to add stock.

By leveraging #probabilisticforecasting to drive parts planning, you can communicate these tradeoffs accurately, do so at scale across hundreds of thousands of parts, avoid bad inventory decisions, and balance service levels and costs.  At Smart Software, we specialize in helping spare parts planners, Directors of Materials Management, and financial executives managing MRO, spare parts, and aftermarket parts to understand and exploit these relationships.