Feynman’s perspective illuminates our journey:  “In its efforts to learn as much as possible about nature, modern physics has found that certain things can never be “known” with certainty. Much of our knowledge must always remain uncertain. The most we can know is in terms of probabilities.” ― Richard Feynman, The Feynman Lectures on Physics.

When we try to understand the complex world of logistics, randomness plays a pivotal role. This introduces an interesting paradox: In a reality where precision and certainty are prized, could the unpredictable nature of supply and demand actually serve as a strategic ally?

The quest for accurate forecasts is not just an academic exercise; it’s a critical component of operational success across numerous industries. For demand planners who must anticipate product demand, the ramifications of getting it right—or wrong—are critical. Hence, recognizing and harnessing the power of randomness isn’t merely a theoretical exercise; it’s a necessity for resilience and adaptability in an ever-changing environment.

Embracing Uncertainty: Dynamic, Stochastic, and Monte Carlo Methods

Dynamic Modeling: The quest for absolute precision in forecasts ignores the intrinsic unpredictability of the world. Traditional forecasting methods, with their rigid frameworks, fall short in accommodating the dynamism of real-world phenomena. By embracing uncertainty, we can pivot towards more agile and dynamic models that incorporate randomness as a fundamental component. Unlike their rigid predecessors, these models are designed to evolve in response to new data, ensuring resilience and adaptability. This paradigm shift from a deterministic to a probabilistic approach enables organizations to navigate uncertainty with greater confidence, making informed decisions even in volatile environments.

Stochastic modeling guides forecasters through the fog of unpredictability with the principles of probability. Far from attempting to eliminate randomness, stochastic models embrace it. These models eschew the notion of a singular, predetermined future, presenting instead an array of possible outcomes, each with its estimated probability. This approach offers a more nuanced and realistic representation of the future, acknowledging the inherent variability of systems and processes. By mapping out a spectrum of potential futures, stochastic modeling equips decision-makers with a comprehensive understanding of uncertainty, enabling strategic planning that is both informed and flexible.

Named after the historical hub of chance and fortune, Monte Carlo simulations harness the power of randomness to explore the vast landscape of possible outcomes. This technique involves the generation of thousands, if not millions, of scenarios through random sampling, each scenario painting a different picture of the future based on the inherent uncertainties of the real world. Decision-makers, armed with insights from Monte Carlo simulations, can gauge the range of possible impacts of their decisions, making it an invaluable tool for risk assessment and strategic planning in uncertain environments.

Real-World Successes: Harnessing Randomness

The strategy of integrating randomness into forecasting has proven invaluable across diverse sectors. For instance, major investment firms and banks constantly rely on stochastic models to cope with the volatile behavior of the stock market. A notable example is how hedge funds employ these models to predict price movements and manage risk, leading to more strategic investment choices.

Similarly, in supply chain management, many companies rely on Monte Carlo simulations to tackle the unpredictability of demand, especially during peak seasons like the holidays. By simulating various scenarios, they can prepare for a range of outcomes, ensuring that they have adequate stock levels without overcommitting resources. This approach minimizes the risk of both stockouts and excess inventory.

These real-world successes highlight the value of integrating randomness into forecasting endeavors. Far from being the adversary it’s often perceived to be, randomness emerges as an indispensable ally in the intricate ballet of forecasting. By adopting methods that honor the inherent uncertainty of the future—bolstered by advanced tools like Smart IP&O—organizations can navigate the unpredictable with confidence and agility. Thus, in the grand scheme of forecasting, it may be wise to embrace the notion that while we cannot control the roll of the dice, we can certainly strategize around it.

This video blog holds essential insights for those working with the complexities of inventory management. The session focuses on striking the right balance within the inventory tradeoff curve, inviting viewers to understand the deep-seated importance of this equilibrium. If you’ve ever had to manage stock, you’ll know it feels like a bit of a tug-of-war. On one side, you’re pulling towards less inventory, which is great for saving money but can leave your customers high and dry. On the other, you’re considering more inventory, which keeps your customers happy but can be a pain for your budget. To make a smart choice in this ongoing tug-of-war, you need to understand where your current inventory decisions place you on this tradeoff curve. Are you at a point where you can handle the pressure, or do you need to shuffle along to a more comfortable spot?

If you can’t answer this question, it means that you still rely on outdated methods, risking the potential for surplus inventory or unmet customer needs. Watch the video so you can see exactly where you are on this curve and understand better about whether you want to stay put or move to a more optimal position.

And if you decide to move, we’ve got the tools to guide you. Smart IP&O’s advanced “what-if” analysis enables businesses to precisely evaluate the impact of different inventory strategies, such as adjustments to safety stock levels or changes in reorder points, on their balance between holding costs and service levels. By simulating demand scenarios and inventory policies, Smart IP&O provides a clear visualization of potential financial outcomes and service level implications, allowing for data-driven strategic decisions. This powerful tool ensures businesses can achieve an optimal balance, minimizing excess inventory and related costs while maintaining high service levels to meet customer demand efficiently.  

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.

For example, at a recent Maximo Utilities Working Group event, several prospects stated that “Our EAM will do that” when asked about requirements for forecasting usage, netting out supply plans, and optimizing inventory policies. They were surprised to learn it did not and wanted to know more.

In this post, we summarize the need for add-on software that addresses specialized analytics for inventory optimization, forecasting, and service parts planning.   

EAM Systems

EAM systems can’t ingest forecasts of future usage – these systems simply aren’t designed to conduct supply planning and many don’t even have a place to hold forecasts. So, when an MRO business needs to net out known requirements for planned production or capital projects, an add-on application like Smart IP&O is needed.

Inventory Optimization software with features that support planning known future demand will take project-based data not maintained in the EAM system (including project start dates, duration, and when each part is expected to be needed) and compute a period-by-period forecast over any planning horizon. That “planned” forecast can be projected alongside statistical forecasts of “unplanned” demand arising from normal wear and tear. At that point, parts planning software can net out the supply and identify gaps between supply and demand. This ensures that these gaps won’t go unnoticed and result in shortages that would otherwise delay the completion of the projects. It also minimizes excess stock that would otherwise be ordered too soon and needlessly consumes cash and warehouse space. Again, MRO businesses sometimes mistakenly assume that these capabilities are addressed by their EAM package.

ERP Systems

ERP systems, on the other hand, typically do include an MRP module that is designed to ingest a forecast and net out material requirements. Processing will consider current on hand inventory, open sales orders, scheduled jobs, incoming purchase orders, any bill of materials, and items in transit while transferring between sites. It will compare those current state values to the replenishment policy fields plus any monthly or weekly forecasts to determine when to suggest replenishment (a date) and how much to replenish (a quantity).

So, why not use the ERP system alone to net out the supply plan to prevent shortages and excess? First, while ERP systems have a placeholder for a forecast and some systems can net out supply using their MRP modules, they don’t make it easy to reconcile planned demand requirements associated with capital projects. Most of the time, the data on when planned projects will occur is maintained outside of the ERP, especially the project’s bill of materials detailing what parts will be needed to support the project. Second, many ERP systems don’t offer anything effective when it comes to predictive capabilities, relying instead on simple math that just won’t work for service parts due to the high prevalence of intermittent demand. Finally, ERP systems don’t have flexible user-friendly interfaces that support interacting with the forecasts and supply plan.

Reorder Point Logic

Both ERP and EAM have placeholders for reorder point replenishment methods such as Min/Max levels. You can use inventory optimization software to populate these fields with the risk-adjusted reorder point policies. Then within the ERP or EAM systems, orders are triggered whenever actual (not forecasted) demand drives on-hand stock below the Min. This type of policy doesn’t use a traditional forecast that projects demand week-over-week or month-over-month and is often referred to as “demand driven replenishment” (since orders only occur when actual demand drives stock below a user defined threshold).

But just because it isn’t using a period-over-period forecast doesn’t mean it isn’t being predictive. Reorder point policies should be based on a prediction of demand over a replenishment lead time plus a buffer to protect against demand and supply variability. MRO businesses need to know the stockout risk they are incurring with any given stocking policy. After all, inventory management is risk management – especially in MRO businesses when the cost of stockout is so high. Yet, ERP and EAM do not offer any capabilities to risk-adjust stocking policies. They force users to manually generate these policies externally or to use basic rule of thumb math that doesn’t detail the risks associated with the choice of policy.

Summary

Supply chain planning functionality such as inventory optimization isn’t the core focus of EAM  and ERP. You should leverage add-on planning platforms, like Smart IP&O, that support statistical forecasting, planned project management, and inventory optimization. Smart IP&O will develop forecasts and stocking policies that can be input to an EAM or ERP system to drive daily ordering.

​In this video blog, we explore the critical roles of Descriptive, Predictive, and Prescriptive Analytics in inventory management, highlighting their essential contributions to driving supply chain optimization through strategic foresight and insightful data analysis.

​These analytics foster a dynamic, responsive, and efficient inventory management ecosystem by enabling inventory managers to monitor current operations, anticipate future developments, and formulate optimal responses. We’ll walk you through how Descriptive Analytics keeps you informed about current operations, Predictive Analytics helps you anticipate future demands, and Prescriptive Analytics guides your strategic decisions for maximum efficiency and cost-effectiveness.

By the end of the video, you’ll have a solid understanding of how to leverage these analytics to enhance your inventory management strategies. These are not just tools but a new way of thinking about and approaching inventory optimization with the support of modern software.

“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.