“Business is war” may be an overdone metaphor but it’s not without validity. Like the “Bomber Gap” and the “Missile Gap,” worries about falling behind the competition, and the resulting threat of annihilation, always lurk in the minds of business executives, If they don’t, they should, because not all gaps are imaginary (the Bomber Gap and the Missile Gap were shown to not exist between the US and the USSR, but the 1980’s gap between Japanese and American productivity was all too real). The difference between paranoia and justified concern is converting fear into facts. This post is about organizing your attention toward possible gaps in your company’s supply chain analytics.

Surveillance Gaps

The US Army has a saying: “Time spent on reconnaissance is never wasted.” Now and then, our Smart Forecaster blog has a post that helps you get your head on a swivel to see what’s going on around you. An example is our post on digital twins, which is a hot topic throughout the engineering world.  To recap: using demand and supply simulations to probe for weaknesses in your inventory plan is a form of supply chain reconnaissance.  Closing this surveillance gap enables businesses to take corrective action before an actual problem emerges.

Situational Awareness Gaps

A military commander needs to keep track of what is available for use and how well it is being used. The reports available in Smart Operational Analytics keep you current on your inventory counts, your forecasting accuracy, your suppliers’ responsiveness, and trends in these and other operational areas.  You’ll know exactly where you stand on a variety of supply chain KPIs such as service level, fill rates, and inventory turns.  You’ll know whether actual performance is aligned with planned performance and whether the inventory plan (i.e., what to order, when, from whom, and why) is being adhered to or ignored.

Agility Gaps

The business environment can change rapidly. All it takes is a tanker stuck sideways in the Suez Canal, a few anti-ship ballistic missiles in the Red Sea, or a region-wide weather event. These catastrophes may fall as much on your competitors’ heads as on yours, but which of you is agile enough to react first? Exception reporting in Demand Planner and Smart Operational Analytics can detect major changes in the character of demand so you can quickly filter out obsolete demand data before they poison all your calculations for demand forecasts or inventory optimization. Smart Demand Planner can give advance warning of a pending increase or decrease in demand. Smart Inventory Optimization can help you adjust your inventory replenishment tactics to reflect these shifts in demand.

Innovation Gaps

Whether you refer to your competition as “The Other Guys” or “Everybody Else” or something unprintable, the ones you have to worry about are the ones always looking for an edge. When you choose Smart as your partner, we’ll give you that edge with innovative but field proven predictive solutions.  Smart Software has been innovating predictive modeling since birth over 40 years ago.

• Our first products introduced multiple technical innovations: assessment of forecast quality by looking into the future not the past; automatic selection of the best among a set of competing methodologies, exploiting the graphics in the first PCs to allow easy management overrides of statistical forecasts.

• Later we invented and patented a radically different approach to forecasting the intermittent demand that is characteristic of both spare parts and big-ticket durable goods. Our technology was patented, received multiple awards for dramatically improving the management of inventory.  The solution is now a field proven approach used by many leading businesses in service parts, MRO, aftermarket parts, and field service.

• More recently, Smart’s cloud platform for demand forecasting, predictive modeling, inventory optimization, and analytics, takes all relevant data otherwise locked in your ERP or EAM systems, external files, and other disparate data sources, organizes it in the Smart Data Pipeline, structures it into our common data model, and processes it in our AWS cloud.  Smart uses the power of our patented probabilistic demand simulations in Smart Inventory Optimization to stress test and optimize the rules you use to manage each of your inventory items.

It’s my job, along with my cofounder Dr. Nelson Hartunian, our data science team, and academic consultants, to continue to push the envelope of supply chain analytics and bring the benefits back to you by continuously rolling out new versions of our products so you don’t get stuck in an innovation gap – or any of the others.

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.

The Importance of Optimized Service Parts Planning:

Optimized service parts management plays a vital role in mitigating inventory risks and ensuring critical spare parts availability. While subjective planning may work on a small scale, it becomes insufficient when managing large inventories of intermittently demanded spare parts. Traditional forecasting approaches simply fail to accurately account for the extreme demand variability and frequent periods of zero demand that is so common with spare parts.  This results in large misallocations of stock, higher costs, and poor service levels.

The key to optimized service parts management lies in understanding the trade-off between service and cost. Inventory Optimization and Demand Planning Software powered by probabilistic forecasting and Machine learning Algorithms can help companies better understand the cost vs. benefit of each inventory decision and wield inventory as a competitive asset. By generating accurate demand forecasts and optimal stocking policies such as Min/Max, Safety Stock Levels, and Reorder Points in seconds, companies can know how much is too much and when to add more. By wielding inventory as a competitive asset, companies can drive up service levels and drive down costs.

Improve the Financial Outcome of Spare Parts Planning

1. Accurate forecasting is crucial to optimize inventory planning and meet customer demand effectively. State-of-the-art demand planning software accurately predicts inventory requirements, even for intermittent demand patterns. By automating forecasting, companies can save time, money, and resources while improving accuracy.
2. Meeting customer demand is a critical aspect of service parts management. Companies can enhance customer satisfaction, loyalty, and increase their chances of winning future contracts for the asset-intensive equipment they sell by ensuring the availability of spare parts when needed. Through effective demand planning and inventory optimization, organizations can reduce lead times, minimize stockouts, and maintain service levels, thereby improving the financial impact of all decisions.
3. Financial gains can be achieved through optimized service parts planning, including the reduction of inventory and product costs. Excess storage and obsolete inventory can be significant cost burdens for organizations. By implementing best-of-breed inventory optimization software, companies can identify cost-effective solutions, driving up service levels and reducing costs. This leads to improved inventory turnover, reduced carrying costs, and increased profitability.
4. Procurement planning is another essential aspect of service parts management. Organizations can optimize inventory levels, reduce lead times, and avoid stockouts by aligning procurement and the associated order quantities with accurate demand forecasts. For example, accurate forecasts can be shared with suppliers so that blanket purchase commitments can be made. This provides the supplier revenue certainty and, in exchange, can hold more inventory, thereby reducing lead times.
5. Intermittent demand planning is a particular challenge in spare parts management. Conventional rule-of-thumb approaches fall short in handling demand variability effectively. This is because traditional approaches assume demand is normally distributed when in reality, it is anything but normal. Spare parts demand random bursts of large demand intersperse many period of zero demand.  Smart Software’s solution incorporates advanced statistical models and machine learning algorithms to analyze historical demand patterns, enabling accurate planning for intermittent demand. Companies can significantly reduce stockout costs and improve efficiency by addressing this challenge.

Evidence from Smart Software’s Customers:

Investing in Smart Software’s Inventory Optimization and Demand Planning Software enables companies to unlock cost savings, elevate customer service levels, and enhance operational efficiency. Through accurate demand forecasting, optimized inventory management, and streamlined procurement processes, organizations can achieve financial savings, meet customer demands effectively, and improve overall business performance.

• Metro-North Railroad (MNR) experienced an 8% reduction in parts inventory, reaching a record high customer service level of 98.7%, and reduced inventory growth for new equipment from a projected 10% to only 6%. Smart Software played a crucial role in identifying multi-year service part needs, reducing administrative lead times, formulating stock reduction plans for retiring fleets, and identifying inactive inventory for disposal. MNR saved costs, maximized disposal benefits, improved service levels, and gained accurate insights for informed decision-making, ultimately improving their bottom line and customer satisfaction.
• Seneca Companies, an industry leader in automotive petroleum services, adopted Smart Software to model customer demand, control inventory performance, and drive replenishment. Field service technicians embraced its use, and total inventory investment decreased by more than 25%, from $11 million to $8 million, while maintaining first-time fix rates of 90%+.
• A leading Electric Utility implemented Smart IP&O in just 3 months and then used the software to optimize its reorder points and order quantities for over 250,000 spare parts. During the first phase of the implementation, the platform helped the Utility reduce inventory by $9,000,000 while maintaining service levels. The implementation was part of the company’s strategic supply chain optimization initiative.

Optimizing Service Parts Planning for Competitive Advantage

Optimized service parts management is crucial for companies seeking to improve efficiency, reduce costs, and ensure the availability of necessary spare parts. Organizations can unlock significant value in this field by investing in Smart Software’s Inventory Optimization and Demand Planning Software. Companies can achieve better financial performance and gain a competitive edge in their respective markets through improved data analysis, automation, and inventory planning.

Smart Software is designed for the modern marketplace, which is volatile and always changing. It can handle SKU proliferation, longer supply chains, less predictable lead times, and more intermittent and less forecastable demand patterns. It can also integrate with virtually every ERP solution on the market, by field-proven seamless connections or using a simple import/export process supported by Smart Software’s data model and data processing engine. By using Smart Software, companies can leverage inventory as a competitive asset, enhance customer satisfaction, drive up service levels, push down costs, and save substantial money.

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.

The key reality is that many items, especially spare and service parts, have unpredictable, intermittent demand. (Supplier lead times can also be erratic, especially when parts are sourced from a backlogged OEM.)  We have observed that while manufacturers and distributors typically experience intermittent demand on just 20% or more of their items the percentage grows to 80%+ for MRO based businesses.  This means historical data often show periods of zero demand interspersed with random periods of non-zero demand. Sometimes, these non-zero demands are as low as 1 or 2 units, while at other times, they unexpectedly spike to quantities several times larger than their average.

This isn’t like the kind of data usually faced by your peer “demand planners” in retail, consumer products, and food and beverage. Those folks usually deal with larger quantities having proportionately less randomness. And they can surf on prediction-enhancing features like trends and stable seasonal patterns. Instead, spare parts usage is much more random, throwing a monkey wrench into the planning process, even in the minority of cases in which there are detectable seasonal variations.

In the realm of intermittent demand, the best forecast available will significantly deviate from the actual demand. Unlike consumer products with medium to high volume and frequency, a service part’s forecast can miss the mark by hundreds of percentage points. A forecast of one or two units, on average, will always miss when the actual demand is zero. Even with advanced business intelligence or machine learning algorithms, the error in forecasting the non-zero demands will still be substantial.

Perhaps because of the difficulty of statistical forecasting in the inventory domain, inventory planning in practice often relies on intuition and planner knowledge. Unfortunately, this approach doesn’t scale across tens of thousands of parts. Intuition just cannot cope with the full range of demand and lead time possibilities, let alone accurately estimate the  probability of each possible scenario. Even if your company has one or two exceptional intuitive forecasters, personnel retirements and product line reorganizations mean that intuitive forecasting can’t be relied on going forward.

The solution lies in shifting focus from traditional forecasts to predicting probabilities for each potential demand and lead time scenario. This shift transforms the conversation from an unrealistic “one number plan” to a range of numbers with associated probabilities. By predicting probabilities for each demand and lead time possibility, you can better align stock levels with the risk tolerance for each group of parts.

Software that generates demand and lead time scenarios, repeating this process tens of thousands of times, can accurately simulate how current stocking policies will perform against these policies. If the performance in the simulation falls short and you are predicted to stock out more often than you are comfortable with or you are left with excess inventory, conducting what-if scenarios allows adjustments to policies. You can then predict how these revised policies will fare against random demands and lead times. You can conduct this process iteratively and refine it with each new what-if scenario or lean on system prescribed policies that optimally strike a balance between risk and costs.

So, if you are planning service and spare parts inventories, stop worrying about predicting demand the way traditional retail and CPG demand planners do it. Focus instead on how your stocking policies will withstand the randomness of the future, adjusting them based on your risk tolerance. To do this, you’ll need the right set of decision support software, and this is how Smart Software can help.

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.

Consider a public transit agency.  In most major cities, the annual operating budgets will exceed $3 billion.  Capital expenses for trains, subway cars, and infrastructure may reach hundreds of millions annually. Consequently, a spare parts inventory valued at $150 million might not grab the attention of the CFO or general manager, as it represents a small percentage of the balance sheet.  Moreover, in MRO-based industries, many parts need to support equipment fleets for a decade or more, making additional stock a necessary asset. In some sectors like utilities, holding extra stock can even be incentivized to ensure that equipment is kept in a state of good repair.

We have seen concerns about excess stock arise when warehouse space is limited. I recall, early in my career, witnessing a public transit agency’s rail yard filled with rusted axles valued at over $100,000 each.  I was told the axles were forced to be exposed to the elements due to insufficient warehouse space. The opportunity cost associated with the space consumed by extra stock becomes a consideration when warehouse capacity is exhausted. The primary consideration that trumps all other decisions is how the stock ensures high service levels for internal and external customers.  Inventory planners worry far more about blowback from stockouts than they do from overbuying.  When a missing part leads to an SLA breach or downed production line, resulting in millions in penalties and unrecoverable production output, it is understandable.

Asset-intensive companies are missing one giant point. That is, the extra stock doesn’t insulate against stockouts; it contributes to them. The more excess you have, the lower your overall service level because the cash needed to purchase parts is finite, and cash spent on excess stock means there isn’t cash available for the parts that need it.  Even publicly funded MRO businesses, like utilities and transit agencies, acknowledge the need to optimize spending, now more than ever.  As one materials manager shared, “We can no longer fix problems with bags of cash from Washington.”  So, they must do more with less, ensuring optimal allocation across the tens of thousands of parts they manage.

This is where state-of-the-art inventory optimization software comes in, predicting the required inventory for targeted service levels, identifying when stock levels yield negative returns, and recommending reallocations for improved overall service levels.  Smart Software has helped asset intensive MRO based businesses optimize reorder levels across each part for decades. Give us a call to learn more. 

Those of you who track hot topics will be familiar with the term “digital twin.” Those who have been too busy with work may want to read on and catch up.

What is a digital twin?

While there are several definitions of digital twin, here’s one that works well:

A digital twin is a dynamic virtual copy of a physical asset, process, system, or environment that looks like and behaves identically to its real-world counterpart. A digital twin ingests data and replicates processes so you can predict possible performance outcomes and issues that the real-world product might undergo. [Source: Unity.com]. For additional background, you might go to Mckinsey.com.

What is the difference between a digital twin (hereafter DT) and a model? Primarily, a DT gets connected to real-time data to maintain the model as an up-to-the-minute representation of the system you are working with.

Our current products might be called “slow-motion DT’s” because they are usually used with non-real-time data (though not stale data, since it is updated overnight) and applied to problems like planning the next quarter’s raw material buys or setting inventory parameters for a month or longer.

Are people using digital twins in my industry?

My impression is that the penetration of DT’s may be highest in the aerospace and nuclear industries. Most of our customers are elsewhere: in manufacturing, distribution, and public utilities such as transportation and power. Soon we’ll be offering new products that come closer to the strict definition of a DT that is connected intimately to the system it represents.

DT Preview

Most users of Smart Inventory Optimization™ (SIO) run the application periodically, typically monthly. SIO analyzes current demand for inventory items and recent supplier lead times, converting these into demand and supply scenarios, respectively. Then users either interactively (for individual items) or automatically (at scale) set inventory control parameters that will provide the long-term average performance they want, balancing the competing goals of minimizing inventory while guaranteeing a sufficient level of item availability.

Smart Supply Planner (SSP) operates in a more immediate way to react to contingencies. Any day could bring an anomalous order that spikes up demand, such as when a new customer places a surprising initial stocking order. Or a key supplier could experience a problem at its factory and be forced to delay shipment of your planned replenishment orders. In the long run, these contingencies average out and justify the recommendations coming out of SIO. However, SSP will give you a way to react in the short run to seize opportunities or dodge bullets.

At its core, SSP operates like SIO in that it is scenario driven. The differences are that it uses short planning horizons and uses real-time initial conditions as the basis for its simulations of inventory system performance. Then it will provide real-time recommendations for interventions that offset the disruption caused by the contingencies. These would include cancelling or expediting replenishment orders.

Summary

Digital twins let you try out plans “in silico” before you implement them in the factory or warehouse. At their core are mathematical models of your operation but connected to real-time data. They provide a “digital sandbox” in which you can try out ideas and get immediate predictions of how well they will work. Much more than a spreadsheet, DT’s will soon be the key tool in your inventory planning toolbox.