What place is there in the factory for any talk of science?

In fact, it all sounds a bit technical, too theoretical, and probably of no use in practice. Theory doesn’t take account of machine breakdowns, interruptions to material supplies, or machinists not turning up for their shift.

Science is often taken for meaning *mathematics*, which of course can be considered technical, convoluted, theoretical, abstract, essential or elegant, depending upon your viewpoint.

A mathematician might argue that the detail of manufacturing operations are too much of a distraction. Therefore, you need to use abstraction to build a simplified, holistic model. That model might then provide insight as to how the individual operations inter-relate to produce the results that are being achieved.

A production manager might counter this by maintaining that “the devil is in the detail”. The minutiae that is being removed from the mathematical model is of prime importance. In fact, the only way to understand the whole factory model is through experience and the subsequent intuition that it develops.

When pressed, most factory staff accept that some degree of mathematics is useful, from the basic accounting and measurement of operations, through to monitoring, reporting and even forecasting.

And so the “complex stuff” might be relegated into the realms of simulation and modelling. Or the latest software package for that matter (including the desire to become a Data Scientist by using R or Python to *solve everything*).

But science is much more fundamental than the production of mathematical equations, the purchase of new software, or the acquisition of new programming language skills. It is about posing questions, and then using some method to try and disprove those questions, until an improvement is observed.

Isn’t that what a production manager does when they attempt to implement Kanban, or lean methods? In fact, the lean movement (and Statistical Process Control before it) relies on the awareness and application of some fundamental mathematics to help managers take rational decisions.

It is through the understanding and intuition that is acquired by applying scientific principles to manufacturing that progress can be made, and that previous beliefs can be identified as incorrect.

It is interesting that a casual conversation with a production supervisor or manager will elicit that they have problems with meeting deadlines when the work-in-progress levels increase.

So why do the same businesses persist with ERP software systems, which specifically assume that lead-times are constant, irrespective of the amount of inventory in the system?

I’ve witnessed organisations who have thrown out Kanban, as it creates conflicts with the works orders that are generated by the ERP system. Unfortunately for them, that’s the point; there is something inherently wrong with the software that they are choosing to persist with.

These are the sorts of situations where manufacturing science can help. The mindset of science will create staff who are open to change, ready to question tradition, and be able to acquire whatever essential mathematical skills are required to do the job.

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