Utilities have unique supply chain optimization requirements, primarily ensuring high uptime by keeping all critical machines running continuously. Achieving this involves maintaining a high availability of spare parts to guarantee a consistent, reliable, and safe supply. Additionally, as regulated entities, utilities must also carefully manage and control costs.

Managing supply chains efficiently

To maintain a reliable electricity supply at 99.99%⁺ service levels, for example, utilities must be able to respond quickly to changes in demand in the near term and accurately anticipate future demand. To do so, they must have a well-organized supply chain that allows them to purchase the necessary equipment, materials, and services from the right suppliers at the right time, in the right quantities, and at the right price.

Doing so has become increasingly more challenging in the last 3 years.

• Requirements for safety, reliability, and service delivery are more stringent.
• Supply chain disruptions, unpredictable supplier lead times, intermittent spikes in parts usage have always been problematic, but now they are more the rule than the exception.
• Deregulation in the early 2000’s removed spare parts from the list of directly reimbursed items, forcing utilities to pay for spares directly from revenues[1]
• The constant need for capital combined with aggressively climbing interest rates mean costs are scrutinized more than ever.

As a result, Supply Chain Optimization (SCO) has become an increasingly mission-critical business practice for utilities.  To contend with these challenges, utilities can no longer simply manage their supply chain — they must optimize it.  And to do that, investments in new processes and systems will be required.

[1] Scala et al. “Risk and Spare Parts Inventory in Electric Utilities”. Proceedings of the Industrial Engineering Research Conference.

Inventory Planning and Optimization   

Targeted investments in inventory optimization technology offer a path forward for every utility.  Inventory Optimization solutions should be prioritized because they:

1. Can be implemented in a fraction of the time required for initiatives in other areas, such as warehouse management, supply chain design,  and procurement consolidations. It is not uncommon to start generating benefit after 90 days and to have a full software deployment in less than 180 days.
2. Can generate massive ROI, yielding 20x returns and seven figure financial benefits annually. By better forecasting parts usage, utilities will reduce costs by purchasing only the necessary inventory while controlling the risk of stockouts that lead to downtime and poor service levels.
3. Provide foundational support for other initiatives. A strong supply chain rests on the foundation of solid usage forecasts and inventory purchasing plans.

Using predictive analytics and advanced algorithms, inventory optimization helps utilities maximize service levels and reduce operational costs by optimizing inventory levels for spare parts. For example, an electric utility might use statistical forecasting to predict future parts usage, conduct inventory audits to identify excess inventory, and leverage analytical results to identify where inventory optimization efforts should focus first. By doing this, the utility can ensure that machines are running at optimal levels and reduce the risk of costly delays due to a lack of spares.

By using analytics and data, you can identify which spare parts and equipment are most likely to be needed and order only the necessary items. This helps to ensure that equipment has high up-time. It rewards regular monitoring and adjusting of inventory levels so that when operating conditions change, you can detect the change and adjust accordingly. This implies that planning cycles must operate at a tempo high enough to keep up with changing conditions. Leveraging probabilistic forecasting to recalibrate spares stocking policies for each planning cycle ensures that stocking policies (such as min/max levels) are always up-to-date and reflect the latest parts usage and supplier lead times.

Service Levels and the Tradeoff Curve

The Service Level Tradeoff Curve relates inventory investment to item availability as measured by service level. Service level is the probability that no shortages occur between when you order more stock and when it arrives on the shelf. Surprisingly few companies have data on this important metric across their whole fleet of spare parts.

The Service Level Tradeoff Curve exposes the link between the costs associated with different levels of service and the inventory requirements needed to achieve them.  Knowing which components are important to maintaining high service levels is key to the optimization process and is determined by several factors, including inventory item standardization, criticality, historical usage, and known future repair orders. By understanding this relationship, utilities can better allocate resources, as when using the curves to identify areas where costs can be reduced without hurting system reliability.

With inventory optimization software, setting stocking policies is pure guesswork: It is possible to know how any given increase or decrease will impact service levels other than rough cut estimates.  How the changes will play out in terms of inventory investment, operating costs, and shortage costs, is something no one really knows.  Most utilities rely on rule of thumb methods and arbitrarily adjust stocking policies in a reactive manner after something has gone wrong such as a large stockout or inventory write off.  When adjustments are made this way, there is no fact-based analysis detailing how this change is expected to impact the metrics that matter:  service levels and inventory values.

Inventory Optimization software can compute the detailed, quantitative tradeoff curves required to make informed inventory policy choices or even recommend the target service level that results in the lowest overall operating cost (the sum of holding, ordering, and stock-out costs).  Using this analysis, large increases in stock levels may be mathematically justified when the predicted reduction in shortage costs exceeds the increase in inventory investment and associated holding costs.  By setting appropriate service levels and recalibrating policies across all active parts once every planning cycle (at least once monthly), utilities can minimize the risk of outages while controlling expenditures.

Perhaps the most critical aspects of the response to equipment breakdown are those relating to achieving a first-time fix as rapidly as possible. Having the proper spares available can be the difference between completing a single trip and increasing the mean time to repair, bearing the costs associated with several visits, and causing customer relationships to degrade.

Using modern software, you can benchmark past performance and leverage probabilistic forecasting methods to simulate future performance. By stress-testing your current inventory stocking policies against all plausible scenarios of future parts usage, you will know ahead of time how current and proposed stocking policies are likely to perform. Check out our blog post on how to measure the accuracy of your service level forecast to help you assess the accuracy of inventory recommendations that software providers will purport to provide benefit.

Leveraging Advanced Analytics and AI

When introducing automation, each utility company has its own goals to pursue, but you should begin with assessing present operations to identify areas that may be made more effective. Some companies may prioritize financial issues, but others may prioritize regulatory demands such as clean energy spending or industry-wide changes such as smart grids. Each company’s difficulties are unique, but modern software can point the way to a more effective inventory management system that minimizes excess inventory and places the correct components in the right places at the right times.

Overall, Supply Chain Optimization initiatives are essential for utilities looking to maximize their efficiency and reduce their costs. Technology allows us to make the integration process seamless, and you don’t need to replace your current ERP or EAM system by doing it.  You just need to make better use of the data you already have.

For example, one large utility launched a strategic Supply Chain Optimization (SCO) initiative and added best-in-class capabilities through the selection and integration of commercial off-the-shelf applications.  Chief among these was the Smart Inventory Planning and Optimization system (Smart IP&O), comprising Parts Forecasting / Demand Planning and Inventory Optimization functionality. Within just 90 days the software system was up and running, soon reducing inventory by $9,000,000 while maintaining spares availability at a high level. You can read the case study here Electric Utility Goes with Smart IP&O.

Utilities can ensure that they are able to manage their spare parts supplies in an efficient and cost-effective manner better preparing them for the future.  Over time, this balance between supply and demand translates to a significant edge. Understanding the Service Level Tradeoff Curve helps to understand the costs associated with different levels of service and the inventory requirements needed to achieve them. This leads to reduced operational costs, optimized inventory, and assurance that you can meet your customers’ needs.

Just as the renowned astronomer Copernicus transformed our understanding of astronomy by placing the sun at the center of our universe, today, we invite you to re-center your approach to inventory management. And while not quite as enlightening, this advice will help your company avoid being caught in the gravitational pull of inventory woes—constantly orbiting between stockouts, surplus gravity, and the unexpected cosmic expenses of expediting?

In this article, we’ll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We’ll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we’ll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we’ll explore ways to enhance your service-level-driven inventory plan consistently.

In service-oriented businesses, the consequences of stockouts are often very significant.  Achieving high service levels depends on having the right parts at the right time. However, having the right parts isn’t the only factor. Your Supply Chain Team must develop a consensus inventory plan for every part, then continuously update it to reflect real-time changes in demand, supply, and financial priorities.

Managing inventory with Service-level-driven planning combines the ability to plan thousands of items with high-level strategic modeling. This requires addressing core issues facing inventory executives:

• Lack of control over supply and associated lead times.
• Unpredictable intermittent demand.
• Conflicting priorities between maintenance/mechanical teams and Materials Management.
• Reactive “wait and see” approach to planning.
• Misallocated inventory, causing stockouts and excess.
• Lack of trust in systems and processes.

The key to optimal service parts management is to grasp the balance between providing excellent service and controlling costs. To do this, we must compare the costs of stockout with the cost of carrying additional spare parts inventory. The costs of a stockout will be higher for critical or emergency spares, when there is a service level agreement with external customers, for parts used in multiple assets, for parts with longer supplier lead times, and for parts with a single supplier. The cost of inventory may be assessed by considering the unit costs, interest rates, warehouse space that will be consumed, and potential for obsolescence (parts used on a soon-to-be-retired fleet have a higher obsolescence risk, for example).

To arbitrate how much stock should be put on the shelf for each part, it is critical to establish consensus on the desired key metrics that expose the tradeoffs the business must make to achieve the desired KPIs. These KPIs will include Service Levels that tell you how often you meet usage needs without falling short on stock, Fill Rates that tell you what percentage of demand is filled, and Ordering costs detail the expenses incurred when you place and receive replenishment orders. You also have Holding costs, which encompass expenses like obsolescence, taxes, and warehousing, and Shortage costs that pertain to expenses incurred when stockouts happen.

An MRO business or Aftermarket Parts Planning team might desire a 99% service level across all parts – i.e., the minimum stockout risk that they are willing to accept is 1%. But what if the amount of inventory needed to support that service level is too expensive? To make an informed decision on whether there is going to be a return on that additional inventory investment, you’ll need to know the stockout costs and compare that to the inventory costs. To get stockout costs, multiply two key elements: the cost per stockout and the projected number of stockouts. To get inventory value, multiply the units required by the unit cost of each part. Then determine the annual holding costs (typically 25-35% of the unit cost). Choose the option that yields a total lower cost. In other words, if the benefit associated with adding more stock (reduced shortage costs) outweighs the cost (higher inventory holding costs), then go for it. A thorough understanding of these metrics and the associated tradeoffs serves as the compass for decision-making.

Modern software aids in this process by allowing you to simulate a multitude of future scenarios. By doing so, you can assess how well your current inventory stocking strategies are likely to perform in the face of different demand and supply patterns. If anything falls short or goes awry, it’s time to recalibrate your approach, factoring in current data on usage history, supplier lead times, and costs to prevent both stockouts and overstock situations.

Enhance your service-level-driven inventory plan consistently.

In conclusion, it’s crucial to assess your service-level-driven plan continuously. By systematically constructing and refining performance scenarios, you can define key metrics and goals, benchmark expected performance, and automate the calculation of stocking policies for all items. This iterative process involves monitoring, revising, and repeating each planning cycle.

The depth of your analysis within these stocking policies relies on the data at your disposal and the configuration capabilities of your planning system. To achieve optimal outcomes, it’s imperative to maintain ongoing data analysis. This implies that a manual approach to data examination is typically insufficient for the needs of most organizations.

For information on how Smart Software can help you meet your service supply chain goals with service-driven planning and more, visit the following blogs.

–   “Explaining What  Service-Level Means in Your Inventory Optimization Software”  Stocking recommendations can be puzzling, especially when they clash with real-world needs.  In this post, we’ll break down what that 99% service level means and why it’s crucial for managing inventory effectively and keeping customers satisfied in today’s competitive landscape.

–  “Service-Level-Driven Planning for Service Parts Businesses” Service-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.

–   “How to Choose a Target Service Level.” This is a strategic decision about inventory risk management, considering current service levels and fill rates, replenishment lead times, and trade-offs between capital, stocking and opportunity costs.  Learn approaches that can help.

–   “The Right Forecast Accuracy Metric for Inventory Planning.”  Just because you set a service level target doesn’t mean you’ll actually achieve it. If you are interested in optimizing stock levels, focus on the accuracy of the service level projection. Learn how.

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.

Initially, during the 1980s, the usual practice of using annual data for forecasting and the introduction of monthly data was considered innovative. This period marked the beginning of a trend toward increasing the resolution of data analysis, enabling businesses to capture and react to faster shifts in market dynamics. As we progressed into the 2000s, the norm of monthly data analysis was well-established, but the ‘cool kids’—innovators at the edge of business analytics—began experimenting with weekly data. This shift was driven by the need to synchronize business operations with increasingly volatile market conditions and consumer behaviors that demanded more rapid responses than monthly cycles could provide. Today, in the 2020s, while monthly data analysis remains common, the frontier has shifted again, this time towards daily data analysis, with some pioneers even venturing into hourly analytics.

The real power of daily data analysis lies in its ability to provide a detailed view of business operations, capturing daily fluctuations that might be overlooked by monthly or weekly data.  However, the complexities of daily data necessitate advanced analytical approaches to extract meaningful insights. At this level, understanding demand requires grappling with concepts like intermittency, seasonality, trend, and volatility. Intermittency, or the occurrence of zero-demand days, becomes more pronounced at a daily granularity and demands specialized forecasting techniques like Croston’s method for accurate predictions. Seasonality at a daily level can reveal multiple patterns—such as increased sales on weekends or holidays—that monthly data would mask. Trends can be observed as short-term increases or decreases in demand, demanding agile adjustment strategies. Finally, volatility at the daily level is accentuated, showing more significant swings in demand than seen in monthly or weekly analyses, which can affect inventory management strategies and the need for buffer stock. This level of complexity underscores the need for sophisticated analytical tools and expertise in daily data analysis.

In conclusion, the evolution from less frequent to daily time series forecasting marks a substantial shift in how businesses approach data analysis. This transition not only reflects the accelerating pace of business but also highlights the requirement for tools that can handle increased data granularity. Smart Software’s dedication to refining its analytical capabilities to manage daily data highlights the industry’s broader move towards more dynamic, responsive, and data-driven decision-making. This shift is not merely about keeping pace with time but about leveraging detailed insights to forge competitive advantages in an ever-changing business environment.

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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.