Simulation as experimentation

Resources are generally more constrained in an SME manufacturer than for a large corporate organisation; there isn’t the luxury of a research and development department, or an intelligence unit that deals with reporting, analysis and planning.

This situation becomes much clearer for those who have experience of working within an SME. It is not that the staff cannot think innovatively, or solve problems for themselves. It just isn’t feasible to commit any time to experimentation as the actual cost to daily production is too high.

SMEs are often preoccupied by the orders that need delivering now, and cannot halt production to ‘have a go’ at a potentially interesting idea.

For owner-managers, there is a tension between the requirement to plan for the future, and the pressing need to deliver the next order. Some SME manufacturer’s don’t plan too extensively and are at the mercy of the prevailing market conditions. They rely on an ability to be agile, to ‘duck-and-dive’ in response to external factors.

Those manufacturers that do forecast generally apply it to sales, and then use historical experience to translate this into approximations for stock-holding and subsequently the demands that might be placed upon the factory.

Theory tells us that having access to more data improves the quality of our decisions. Well, this is true up to a point. Too much data, especially raw, granular data, requires too much effort (and know-how) to get its into the condition where it can be useful.

IIoT technologies have data production and sharing as a functional priority, and while this might give the production supervisor some new insights about how the plant operates, the volume of data that is accumulated will soon become too much to comprehend.

As such, the ‘experience’ model of production management cannot scale to accommodate the tidal wave of data that a digital transformation can produce.

Indeed, many of my discussions with SME staff is about how they can make better use of the data that they already have. The discussion starts by the company wanting to see how they can embrace Industry 4.0, and then we end up exploring how they are using the data that is being produced by their existing plant.

For instance, how are the log files from Machine X being used for planning? In the majority of cases, the data is being saved, and that is the end of it.

Some of the operations data is waiting to be tapped into as a valuable source of information, such as the electrical power signatures that all plant produce. Hidden in those operations is a wealth of behavioural, condition, and performance data that only requires current sensors to detect.

But, aside from taking the plunge and actually deploying some IIoT equipment, there is a general reluctance to disrupt the manufacturing schedule as it would appear to create too much of a financial risk.

So, while the owner-manager realises that they need to strategise for the future, they might only restrict their planning activity to an accounting view. Management accounts provide an abstract means of modelling a business, but for a manufacturer this might not be enough if they are attempting to optimise to a finer degree.

Essentially, the accounting method of modelling is a high-level simulation of hoe the business might react to external stimuli. What is needs therefore is a less abstract view of the factory, that a) permits lower-level decisions to be taken about important processes, and b) translates the high-level accounting view into a more realistic set of reactions for the manufacturing system itself.

Simulation is a topic that can be a big turn-off for busy people. It sounds academic, it will probably involve complex mathematics, and it will take too long to learn.

However, while these statements may be true in some circumstances, there is a much lower barrier to simulation than a lot of people realise.

Considerable insight can be quickly gained with a spreadsheet, and this is something that is on everybody’s desktop computer these days. Simulation programming languages such as SimPy or ManPy can even open up the power of simulation to non-programmers, though many people find that a spreadsheet is sufficient for their needs.

Simulation improves the depth of your “what if?” questions, and also answers some of them, which in turn, enhances your understanding of the manufacturing operation as a whole.

With a little practice, simulation can become a tool for evaluating various options, before you make a decision. This can really empower SMEs to ‘experiment’ with the introduction of IIoT, before they spend a penny!

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