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Optimizing operational plans to minimize manufacturing overhead costs

strengthen profits by optimizing costs with analytics resized 600

In a departure for this blog from the technical focus on analytics – gaining insights from (data) history - here we focus on how businesses can efficiently make better decisions when there are multiple ways of achieving the same goal. In any situation, the more variables, or options, one has, the more difficult it can be to decide what to do.

What is a "model" and why is it central to predictive analytics

how models fit into predictive analytics resized 600

You will see the word ‘model’ used throughout this website and the world of analytics. To get back to fundamentals, here is an attempt to define what a model is. A model is representation of reality. Think of a model car or airplane. If made well, they are highly recognizable by what they are supposed to mimic. As the last sentence implies, models may be of varying degrees of fidelity to the original. In fact, a model is inherently imperfect, incomplete, and an approximation, otherwise, it is no longer a model and it “IS” the thing it is representing. The level of detail a model captures will depend on what purpose it is supposed to serve, and whether the resources required to create it are worth the benefit provided by the model.

Complexity science needs tools, complexity management needs rigor

DNA complexity

I was surprised to learn that there is a lot of debate over the defintion of the term "gene" among geneticists! In spite of this seemingly fundamental hurdle, no one can argue that the study and applications of genetics has greatly benefited society. In fact every day brings some news about a new gene therapy or the discovery of a genetically related illness. The same cannot be said unfortunately about either complexity science or complexity management.

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