​BACKGROUND

Most of Smart Software’s blogs, webinars and white papers describe the use of our software in “normal operations.” This one is about “irregular operations.”  Smart Software is in the process of adapting our products to help you cope with your own irregular ops. This is a preview.

I first heard the term “irregular operations” when serving a sabbatical tour at the U.S. Federal Aviation Administration in Washington, DC. The FAA abbreviates the term to “IROPS” and uses it to describe situations in which weather, mechanical problems or other issues disrupt the normal flow of aircraft.

Smart Inventory Optimization® (“SIO”) currently works to provide what are known as “steady state” policies for managing inventory items. That means, for instance, that SIO automatically calculates values for reorder points (ROP’s) and order quantities (OQ’s) that are meant to last for the foreseeable future. It computes these values based on simulations of daily operations that extend years into the future. If and when the unforeseeable happens, our regime change detection method reacts by removing obsolete data and allowing recalculation of the ROP’s and OQ’s.

We often note the increasing speed of business, which shortens the duration of the “foreseeable future.” Some of our customers are now adopting shorter planning horizons, such as moving from quarterly to monthly plans. One side effect of this change is that IROPS have become more consequential. If a plan is based on three simulated years of daily demand, one odd event, like a large surprise order, doesn’t matter much in the grand scheme of things. But if the planning horizon is very short, one big surprise demand can have a major effect on key performance indicators (KPI’s) computed over a shorter interval – there is no time for “averaging out”. The planner may be forced to place an emergency replenishment order to deal with the disruption. When should the order be placed to do the most good? How big should it be?

SCENARIO: NORMAL OPS

To make this concrete, consider the following scenario. Tom’s Spares, Inc. provides critical service parts to its customers, including SKU723, a replacement circuit board sold under the trade name WIDGET. Demand for WIDGET is intermittent, with less than one unit demanded per day. Tom’s Spares orders WIDGETs from Acme Products, who take either 7 or 10 days to fulfill replenishment orders.

Tom’s Spares operates with a short inventory planning horizon of 28 days. The company operates in a competitive environment with impatient customers who only grudgingly accept backorders. Tom’s policy is to set ROP’s and OQ’s to keep inventory lean while maintaining a fill rate of at least 90%. Management monitors KPI’s on a monthly basis. In the case of WIDGETS, these KPI targets are currently met using an ROP=3 and an OQ=4, resulting in an average on hand of about 4 units and average fill rate of 96%.  Tom’s Spares has a pretty good thing going for WIDGETS.

Figure 1 shows two months of WIDGET information. The top left panel shows daily unit demand. The top right shows daily units on hand. The bottom left panel shows the timing and size of replenishment orders back to Acme Products. The bottom right shows units backordered due to stockouts. In this simulation, daily demand was either 0 or 1, with one demand of 2 units. On hand units began the month at 7 and never dropped below 1, though in the next month there was a stockout resulting in a single unit on backorder. Over the two months, there were 4 replenishment orders of 4 units each sent to Acme, all of which arrived during the two-month simulation period.

GOOD TROUBLE DISRUPTS NORMAL OPS

Now we add some “good trouble” to the scenario: An unusually large order arises part way through the planning period. It’s “good” because more demand implies more revenue. But it’s “trouble” because the normal ops inventory control parameters (ROP=3, OQ=4) were not chosen to cope with this situation. The spike in demand might be so big, and so disadvantageously timed, as to overwhelm the inventory system, creating stockouts and their attendant backorders. The KPI report to management for such a month would not be pretty.

Figure 2 shows a scenario in which a demand spike of 10 units hits in the third day of the planning period. In this case, the spike puts the inventory under water for the rest of the month and creates a cascade of backorders extending into the next month. Averaging over 1,000 simulations, month 1 KPI’s show an average on hand of 0.6 units and a miserable 44% fill rate.

FIGHTING BACK WITH IRREGULAR OPERATIONS

Tom’s Spares can respond to an irregular situation with an irregular move by creating an emergency replenishment order. To do it right, they have to think about (a) when to place the order (b) how big the order should be and (c) whether to expedite the order.

The timing question seems obvious: react as soon as the order hits. However, if the customer were to provide early warning, Tom’s Spares could order early and be in better position to limit the disruption from the spike. However, if communication between Tom’s and the customer making the big order is spotty, then the customer might give Tom’s a heads-up later or not at all.

The size of the special order seems obvious too: pre-order the required number of units. But that works best if Tom’s Spares knows when the demand spike will hit. If not, it might be a good idea to order extra to limit the duration of any backorders. In general, the less early warning provided, the larger the order Tom’s should make. This relationship could be explored with simulation, of course.

The arrival of the replenishment order could be left to the usual operation of Acme Products. In the simulations above, Acme was equally likely to respond in 7 or 14 days. For a 28-day planning horizon, taking a risk on getting a 14-day response might be asking for trouble, so it may be especially worthwhile for Tom’s to pay Acme for expedited shipping. Maybe overnight, but possibly something cheaper but still relatively fast.

We explored a few more scenarios using simulation. Table 1 shows the results. Scenarios 1-4 assume a surprise additional demand of 10 units arrives on Day 3, triggering an immediate order for  additional replenishment. The size and lead time of the replenishment order varies.

Scenario 0 shows that doing nothing in response to the surprise demand leads to an abysmal 41% fill rate for that month; not shown is that this result sets of the next month for continued poor performance. Regular operations won’t do well. The planner must do something to respond to the anomalous demand.

Doing something in response involves making a one-time emergency replenishment order. The planner must choose the size and timing of that order. Scenarios 1 and 3 depict “half sized” replenishments. Scenarios 1 and 2 depict overnight replenishments, while scenarios 3 and 4 depict guaranteed one week response.

The results make clear that immediate response is more important than the size of the replenishment order for restoring the Fill Rate. Overnight replenishment produces fill rates in the 70% range, while one-week replenishment lead time drops the fill rate into the mid-50% to mid-60% range.

TAKEAWAYS

Inventory management software is expanding from its traditional focus on normal ops to an additional focus on irregular ops (IROPS). This evolution has been made possible by the development of new statistical methods for generating demand scenarios at a daily level.

We considered one IROPS situation: surprise arrival of an anomalously large demand. Daily simulations provided guidance about the timing and size of an emergency replenishment order. Results from such an analysis provide inventory planners with critical backup by estimating the results of alternative interventions that their experience suggests to them.

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.