Simple is Good, Except When It Isn’t

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.

​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.

 

 

Learning from Inventory Models

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.

 

 

What is the difference between Demand planning and Inventory optimization ?

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

What is the difference between Demand planning and Inventory optimization ? 

The Smart Demand Planning app (SDP) provides demand forecasts. The SDP forecasting engine is also the core of the Smart Inventory Optimization app (SIO), which stress-tests various inventory policies using a number of demand scenarios to find optimal inventory policy settings.

 

 

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      Share, develop, and manage consensus demand plans

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      Share, develop, and manage consensus demand plans to ensure inventory policy matches business strategy.

      Smart Inventory Optimization (SIO) creates planning scenarios. SIO starts with a “Live” scenario that shows where you are now. Various team members can create their own scenarios, perhaps dividing the work by product line or sales territory. One decision maker can then merge these scenarios into a consensus plan that becomes the “Goal” scenario, which drives the ERP system’s replenishment planning.

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          In this Video Dr. Thomas Willemain, co–Founder and SVP Research, talks about forecast-based inventory management policy, also known as MRP logic. This is the fourth in our series on major approaches to managing inventory.  We begin by looking at some very simple and then more robust models of inventory dynamics that help us determine how much to order or manufacture and when. We then consider how to calculate lead time and account for lead time variability. Tom concludes by describing the importance of safety stock, it’s role in properly buffering against demand and supply uncertainty, and how best to calculate it. 

           

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