The Right Math for the Right Problem Theory

The right math for the right problem


This narrative underscores the efficacy of Demand Driven Material Requirements Planning (DDMRP), emphasizing “the right math for the right problem.” It critiques the assumption that intricate solutions are inherently more effective, showcasing how DDMRP leverages straightforward mathematical principles to significantly enhance inventory management and service levels.

The discussion then pivots to showcasing real-world success stories and theoretical frameworks that validate DDMRP’s approach. Through examples of inventory reduction and cost efficiency, it advocates for using simplified mathematical models to navigate the complexities of supply chain management, stressing the importance of applying the right mathematical approach to each specific challenge.

The Efficiency of Simplified Mathematics in Demand-Driven Planning

Frequently, I encounter skepticism regarding the perceived simplicity of the mathematical principles underpinning Demand Driven Material Requirements Planning (DDMRP). Despite such criticisms, the practical application of these so-called “simple” equations has enabled my clients to achieve remarkable outcomes, including inventory reductions exceeding 40%, service level enhancements of 8%, and eradicating urgent, unforeseen expenses. This has led me to formulate several hypotheses on the subject.

Choosing Appropriate Mathematical Solutions

Kevin Boake introduces "The Right Math for the Right Problem," inspired by Einstein's principles, emphasizing B2WISE DDMRP software's unique ability to simplify complex supply chain challenges. This approach is designed for professionals seeking efficient, practical problem-solving tools in supply chain management.

There’s a common misstep in applying overly complex mathematical theories to problems that require straightforward solutions. For example, employing Einstein’s theory of relativity for solving basic quadratic equations might appear sophisticated but impractical. Every mathematical formula is designed with a specific context in mind. Demanding Planners decipher complex equations amidst operational turmoil is neither effective nor appropriate. Providing Planners with accessible tools that don’t require an advanced degree in mathematics for interpretation can yield unexpectedly positive outcomes.

The Dilemma of Overlooking Precise Calculations

What draws me to mathematics is the assurance of definitive answers. However, having spent two decades advocating for Advanced Planning and Scheduling (APS) optimization solutions, I find it disheartening to see intricate and costly mathematical solutions overlooked. Demand-driven approaches have reaffirmed the value of operating within acceptable inventory ranges and the benefits of initial approximations over precise inaccuracies. Over time, the actual worth of sophisticated mathematics emerges through continuous improvement processes that enhance Order Signal Integrity and identify Model Outliers.

It’s important to clarify that my support for advanced mathematics is unwavering. At b2wise, we integrate cutting-edge tools such as AWS’ Machine Learning for refined demand forecasting and Right Size Inventory’s Monte Carlo Simulations for optimizing inventory levels, but only after establishing a solid foundation with basic mathematical principles.

In conclusion, the correct mathematical approach, tailored to the specific needs and timing of supply chain planning, can significantly enhance inventory management and operational efficiency. The initial step involves simplifying mathematical models to ensure they are practical and accessible for Planners. Subsequently, more complex mathematical solutions can be employed to refine and optimize supply chain operations further.


Q1: How does simplifying mathematical models benefit supply chain planning? A1: Simplifying models makes them more accessible and practical for planners, ensuring immediate improvements in inventory management and operational efficiency without the need for advanced mathematical knowledge.

Q2: When should more complex mathematical solutions be introduced? A2: Complex solutions should be considered once the foundational models are effectively implemented and there’s a need for further refinement and optimization of supply chain operations.

Q3: Can simplified mathematical models truly enhance operational efficiency? A3: Yes, by focusing on the essentials and eliminating unnecessary complexity, simplified models can provide clear, actionable insights, leading to significant improvements in efficiency.

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