Looking for Trouble in Your Inventory Data

In this video blog, the spotlight is on a critical aspect of inventory management: the analysis and interpretation of inventory data. The focus is specifically on a dataset from a public transit agency detailing spare parts for buses. With over 13,700 parts recorded, the data presents a prime opportunity to delve into the intricacies of inventory operations and identify areas for improvement.

Understanding and addressing anomalies within inventory data is important for several reasons. It not only ensures the efficient operation of inventory systems but also minimizes costs and enhances service quality. This video blog explores four fundamental rules of inventory management and demonstrates, through real-world data, how deviations from these rules can signal underlying issues. By examining aspects such as item cost, lead times, on-hand and on-order units, and the parameters guiding replenishment policies, the video provides a comprehensive overview of the potential challenges and inefficiencies lurking within inventory data. 

We highlight the importance of regular inventory data analysis and how such an analysis can serve as a powerful tool for inventory managers, allowing them to detect and rectify problems before they escalate. Relying on antiquated approaches can lead to inaccuracies, resulting in either excess inventory or unfulfilled customer expectations, which in turn could cause considerable financial repercussions and inefficiencies in operations.

Through a detailed examination of the public transit agency’s dataset, the video blog conveys a clear message: proactive inventory data review is essential for maintaining optimal inventory operations, ensuring that parts are available when needed, and avoiding unnecessary expenditures.

Leveraging advanced predictive analytics tools like Smart Inventory Planning and Optimization will help you control your inventory data. Smart IP&O will show you decisive demand and inventory insights into evolving spare parts demand patterns at every moment, empowering your organization with the information needed for strategic decision-making.

 

 

Can Randomness be an Ally in the Forecasting Battle?

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.

 

 

 

Finding Your Spot on the Inventory Tradeoff Curve

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.  

 

 

The Three Types of Supply Chain Analytics

​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.

 

 

Warning Signs that You Have a Supply Chain Analytics Gap

“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.