Planning for flexible manufacturing systems

For decades there has been talk of the ability to design flexible manufacturing systems, that can accommodate variations in demand and remain efficient.

The variation in demand might be volume; the goods may be seasonal and have one or more peaks per annum. Alternatively, the product configuration or type might change in response to customer demand, in which case the manufacturing system would have to change what it produces to meet that demand.

A lot of manufacturing plant is not easy to move around, which might help the logistics of moving materials between different machines for different products. If we could arrange the machines in a particular configuration, we could mimic the benefits of a flow line, where the plant is organised to minimise material handling operations between each stage of production.

And so we have the ‘job shop’, where plant is loosely organised so that there is space for the loading and unloading of raw materials and components, yet the machines are close enough to try and minimise the distance by which material is transported in between workstations.

Some factories have products that make up a larger proportion of their orders, and the job shop is optimised around these items; others group their plant by general operation: cutting, drilling, fabrication, assembly, finishing, etc. What remains are pure job shops where the machines are used as and when for each individual order, and there is little opportunity to organise  for additional efficiency.

The emergence of new production technologies such as additive manufacturing presents new opportunities for how we might plan production.

3D printers can manufacture a wide range of different products, from the same workstation. Such variety in production capability has not been witnessed before as an automated system. Prior to automation, a craft worker could manufacture many different products from their workstation. Attempts to produce multi-capability machining centres have extended the range of what can be produced, but this is nowhere near the potential variety of additive manufacture.

So what does this mean for production planning?

For the traditional flow line, the objective of planning was to optimise the balance between work-in-progress (WIP) holdings and the uncertainty of disruption caused by machine breakdowns or interruptions to material supply.

For a job shop, the planning is more complex and is an attempt to manage the utilisation of each of the machines, whilst also minimising the lead-time to the customer.

The key concept here is complexity. Flow lines may be lengthy and consist of many workstations, but the direction of material flow is known and throughput is governed by the slowest  operation. That might be a machine, or a faster machine that has broken down temporarily, that is causing the ‘bottleneck’. It is straightforward to a) identify bottlenecks in flow lines, and b) ‘chase’ them by resolving any issues.

This is a much more complex for a job shop, where the permutations of the order mix, when combined with other uncertainties, compound to become a manufacturing system that is difficult to optimise.

In a lot of cases, job shops are loosely managed. At best they might be managed by simple rules of thumb, such as ‘keep Machine B running at all costs’ or ‘keep batch sizes between 10 and 25’. Such rules bring a degree of stability to a system, though there are always circumstances that upset the order, and these tend to be more problematic in job shop environments.

Simulating a manufacturing system therefore sounds like a rational thing to do. If we can simulate the factory operations, we could:

• understand what the plant utilisation, and capacity constraints of the system might be;
• pre-empt where the bottlenecks might lie;
• explore the impact on production of different plant configurations;
• develop more optimal production schedules prior to commencing manufacture.

For flow lines this is relatively straightforward. But there is often a reluctance to create simulation models for job shops as they are believed to be too complicated. How can all of the possible combinations be captured to create a meaningful representation of the system to be optimised?

Simulation is a relatively inaccessible subject for most manufacturers. It does require specialist knowledge, but what most people do not realise is that knowledge is easy to acquire and to put to use.

Manufacturers with flow lines have generally made use of simulation to good effect. But job shops are not universally difficult to simulate, particularly if the number of different workstations is limited.

Even in more complex scenarios, it can be useful to model and simulate part of the overall system, particularly if you are attempting to see the effect of a specific intervention to try.

One of the difficulties of simulating manufacturing systems can be the lack of detailed process information. Cheap IIoT devices can sort that situation quickly. Once the data is captured, a simple simulation model can start to help you understand just how flexible your system needs to be, and what the impact of that flexibility on your plant resources might be.

Additive manufacturing workstations combine flexibility of configuration, with an almost flow-like relationship to other operations. Many items can be almost completely manufactured  by one process step. This simplifies the approach to modelling, meaning that simulation is going to be an important part of manufacturing in the future.

To conclude:

• planning is an important activity for manufacturing, especially in a volatile environment;
• simulation can help us understand the complexities of a manufacturing system without the cost and hassle of experimenting in real-life;
• flow lines are often straightforward to simulate, and historically we have not put as much faith into the modelling of job shops;
• we don’t have to simulate an entire manufacturing system to derive real benefit;
• inexpensive IIoT equipment can capture process data that is valuable for simulation;
• advanced production technologies such as additive manufacturing simplify the complexities of simulations and production scheduling;
• flexible manufacturing requires flexible simulation and planning.

Some time ago I was speaking to a representative from a global Original Equipment Manufacturer (OEM), who said that 75% of their manufacturing was performed by Small and Medium-sized Enterprises (SME). As the OEMs strive to deliver more options for customers (mass customisation is one example), they in turn create a demand for their SME suppliers to be more flexible.

Such flexibility can only be delivered by a digitally-enabled supply chain, and most likely means that manufacturing simulation is here to stay.