In this blog, we are steering the conversation towards the transformative potential of technology in inventory management. The discussion centers around the limitations of simple thinking in managing inventory control processes and the necessity of adopting systematic software solutions. Dr. Tom Willemain highlights the contrast between Smart Software and the basic, albeit comfortable, approaches commonly employed by many businesses. These elementary methods, often favored for their ease of use and zero cost, are scrutinized for their inadequacies in addressing the dynamic challenges of inventory management.

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​The importance of this subject lies in the critical role inventory management plays in a business’s operational efficiency and its direct impact on customer satisfaction and profitability. Dr. Tom Willemain points out the common pitfalls of relying on oversimplified rules of thumb, such as the whimsical nursery rhyme used by one company to determine reorder points, or the gut feel method, which depends on unquantifiable intuition rather than data. These approaches, while appealing in their simplicity, fail to adapt to market fluctuations, supplier reliability, or changes in demand, thus posing significant risks to the business. The video also critiques the practice of setting reorder points based on multiples of average demand, highlighting its disregard for demand volatility, a fundamental consideration in inventory theory.

Concluding, the presenter advocates for a more sophisticated, data-driven approach to inventory management. By leveraging advanced software solutions like those offered by Smart Software, businesses can accurately model complex demand patterns and stress-test inventory rules against numerous future scenarios. This scientific method allows for the setting of reorder points that account for real-world variability, thereby minimizing the risk of stockouts and the associated costs. The video emphasizes that while simple heuristics may be tempting for their ease of use, they are inadequate for today’s dynamic market conditions. The presenter encourages viewers to embrace technological solutions that offer professional-grade accuracy and adaptability, ensuring sustainable business success.

In this blog, we will discuss today’s fast-paced and unpredictable market and the constant challenges businesses face in managing their inventory and service levels efficiently. The main subject of this discussion, rooted in the concept of “Probabilistic Inventory Optimization,” focuses on how modern technology can be leveraged to achieve optimal service and inventory targets amidst uncertainty. This approach not only addresses traditional inventory management issues but also offers a strategic edge in navigating the complexities of demand fluctuations and supply chain disruptions.

Understanding and implementing inventory optimization technology is important for several reasons. First, it directly impacts a company’s ability to meet customer demands promptly, thereby affecting customer satisfaction and loyalty. Second, effective inventory management controls operational costs, reducing unnecessary stock holding and minimizing the risk of stockouts or overstocking. In an era where market conditions change rapidly, having a robust system to manage these aspects can be the difference between thriving and merely surviving.

At the heart of inventory management lies a paradox: the need to be prepared for fluctuating demand without succumbing to the pitfalls of overstocking, which can lead to increased holding costs, obsolescence, and wasted resources. Conversely, understocking can result in stockouts, lost sales, and diminished customer satisfaction, ultimately impacting a company’s reputation and bottom line. The unpredictable nature of market demands, compounded by potential supply chain disruptions and changing consumer behavior, adds complexity to this balancing act.

Technology plays a pivotal role here. Modern inventory optimization software integrates probabilistic models, sophisticated forecasting algorithms, and simulation capabilities. These systems help companies respond swiftly to changing market conditions. Furthermore, adopting such technology fosters a culture of data-driven decision-making, ensuring businesses are not merely reacting to uncertainties but proactively strategizing to mitigate their impacts.

Here are brief discussions of the relevant algorithmic technologies.

Probabilistic Inventory Optimization: Traditional inventory management approaches rely on deterministic models that assume a static, predictable world. These models falter in the face of variability and uncertainty. Enter probabilistic inventory optimization, a paradigm that embraces the randomness inherent in supply chain processes. This approach employs statistical models to represent the uncertainties in demand and supply, enabling businesses to account for a full range of possible outcomes.

Advanced Forecasting:  A cornerstone of effective inventory optimization is the ability to anticipate future demand accurately. Advanced forecasting techniques, such as [we don’t sell this outside of SmartForecasts or maybe not even there anymore, so don’t mention it], time series analysis, and machine learning, extract exploitable patterns from historical data.

Safety Stock Calculation: A Shield Against Uncertainty:

Forecasts that include estimates of their own uncertainty enable safety stock calculations. Safety stock acts as a buffer against the unpredictability of demand and supply lead times. Determining the optimal level of safety stock is a critical challenge that probabilistic models address adeptly. With the right safety stock levels, businesses can maintain high service levels, ensuring product availability without the burden of excessive inventory.

Scenario Planning: Preparing for Multiple Futures:

The future is inherently uncertain, and a single forecast can never capture all possible scenarios. Advanced methods that create a range of realistic demand scenarios are the essential form of probabilistic inventory optimization. These techniques allow businesses to explore the implications of multiple futures, from best-case to worst-case situations. By planning against these scenarios, companies can enhance their resilience in the face of market volatility.

Navigating the Future with Confidence

The uncertain landscape of today’s business environment necessitates a shift from traditional inventory management practices to more sophisticated, probabilistic approaches. By embracing the principles of probabilistic inventory optimization, companies can strike a durable balance between service excellence and cost efficiency. Integrating advanced forecasting techniques, strategic safety stock calculations, and scenario planning, supported by Smart Inventory Planning and Optimization (Smart IP&O), equips businesses to transform uncertainty from a challenge into an opportunity. Companies that embrace this approach report significant improvements in service levels, reductions in inventory costs, and enhanced supply chain agility.

For example, less critical Items forecasted to achieve 99%+ service levels represent opportunities to reduce inventory. By targeting lower service levels on less critical items, inventory will be “the right size” over time to the new equilibrium, decreasing holding costs and the value of inventory on hand. A major public transit system reduced inventory by over $4,000,000 while improving service levels.

Optimizing Inventory Levels also means savings realized on one subset of items can be reallocated to carry a broader portfolio of “in stock” items, allowing revenues to be captured that would otherwise be lost sales. A leading distributor was able to stock a broader portfolio of parts with savings used from inventory reductions and increased part availability by 18%.

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 this video blog, we explore the integral role that inventory models play in shaping the decision-making processes of professionals across various industries. These models, whether they are tangible computer simulations or intangible mental constructs, serve as critical tools in managing the complexities of modern business environments. The discussion begins with an overview of how these models are utilized to predict outcomes and streamline operations, emphasizing their relevance in a constantly evolving market landscape.

​The discussion further explores how various models distinctly influence strategic decision-making processes. For instance, the mental models professionals develop through experience often guide initial responses to operational challenges. These models are subjective, built from personal insights and past encounters with similar situations, allowing quick, intuitive decision-making. On the other hand, computer-based models provide a more objective framework. They use historical data and algorithmic calculations to forecast future scenarios, offering a quantitative basis for decisions that need to consider multiple variables and potential outcomes. This section highlights specific examples, such as the impact of adjusting order quantities on inventory costs and ordering frequency or the effects of fluctuating lead times on service levels and customer satisfaction.

In conclusion, while mental models provide a framework based on experience and intuition, computer models offer a more detailed and numbers-driven perspective. Combining both types of models allows for a more robust decision-making process, balancing theoretical knowledge with practical experience. This approach enhances the understanding of inventory dynamics and equips professionals with the tools to adapt to changes effectively, ensuring sustainability and competitiveness in their respective fields.

For some of our customers, weather has a significant influence on demand. Extreme short-term weather events like fires, droughts, hot spells, and so forth can have a significant near-term influence on demand.

There are two ways to factor weather into a demand forecast: indirectly and directly. The indirect route is easier using the scenario-based approach of Smart Demand Planner. The direct approach requires a tailored special project requiring additional data and hand-crafted modeling.

Indirect Accounting for Weather

The standard model built into Smart Demand Planner (SDP) accommodates weather effects in four ways:

1. If the world is steadily getting warmer/colder/drier/wetter in ways that impact your sales, SDP detects these trends automatically and incorporates them into the demand scenarios it generates.
2. If your business has a regular rhythm in which certain days of the week or certain months of the year have consistently higher or lower-than-average demand, SDP also automatically detects this seasonality and incorporates it into its demand scenarios.
3. Often it is the cussed randomness of weather that interferes with forecast accuracy. We often refer to this effect as “noise”. Noise is a catch-all term that incorporates all kinds of random trouble. Besides weather, a geopolitical flareup, the surprise failure of a regional bank, or a ship getting stuck in the Suez Canal can and have added surprises to product demand. SDP assesses the volatility of demand and reproduces it in its demand scenarios.
4. Management overrides. Most of the time, customers let SDP churn away to automatically generate tens of thousands of demand scenarios. But if users feel the need to touch up specific forecasts using their insider knowledge, SDP can make that happen through management overrides.

Direct Accounting for Weather

Sometimes a user will be able to articulate subject matter expertise linking factors outside their company (such as interest rates or raw materials costs or technology trends) to their own aggregate sales. In these situations, Smart Software can arrange for one-off special projects that provide alternative (“causal”) models to supplement our standard statistical forecasting models. Contact your Smart Software representative to discuss a possible causal modeling project.

Meanwhile, don’t forget your umbrella.