IIoT Kanban – not so easy

Any factory floor supervisor knows that the more raw material/components/work-in-progress that is pumped into a manufacturing system, the longer the lead-time. Put another way, the order is delivered late, and subsequent orders cannot be planned with any certainty as you don’t know when they will be ready. This is not good for business.

MRP attempted to deal with planning by inferring a fixed lead-time for each stage of production. This didn’t work either, though it doesn’t stop manufacturers persisting with MRP based information systems as they cannot find a better solution.

Kanban, from Japan, directly deals with the issue of work-in-progress levels, by controlling them directly. Each Kanban card represents a unit of work to be produced. When you run out of cards, you cannot release more material into the system, even though you could increase the utilisation of a machine, or reduce your wastage percentages.

This discipline can feel counter-intuitive at first, especially when a machine is sat idle and you just know that you could get ahead with one extra batch.

The effects of too much WIP is elegantly demonstrated by a sausage machine. If you  put too much sausage meat into the grinder, the sausages are uneven and mis-shapen. If you carry on adding meat, the grinder just blocks up.

When you get the flow of material into the grinder balanced with the application of the sausage skin, everything works nicely.

The consequences of letting WIP go rampant are clear when there are tangible, physical products. But what about processes that do not result in a product?

What can we do with information ‘products’?

Kanban is also developing a following with project managers who would like to follow similar principles; manage the workload at any given time to avoid blockages and breakdowns.

More experience is required in that it can be more challenging to estimate the amount of work for a given task, as opposed to knowing the machining cycle time of a particular component.

But, the use of IIoT to capture data does help build a corpus of information upon which to predict future durations for tasks, irrespective of whether there is any physical product involved or not.

A lot of the promise of digital manufacturing is the ability to delegate coordination, optimisation and decision-making to the machines. This means that the machines will have to be aware of their surroundings so that they can make judgements that do not violate the WIP protocols of a system.

This means that digital manufacturing is more than IIoT equipment. It needs architectures and conceptual thinking to ensure that the necessary behaviours are in place to replicate and eventually replace human-centric interventions.

We may need an agent-oriented view of our systems as these attempt to map behaviours into functionality, and a fundamental aspect of such behaviours is that they are communicated between agents socially.

Kanban’s apparent simplicity actually disguises a set of complex behaviours that humans take for granted.

The machines have a way to go yet.