Factories in the process industry have no shortage of metrics and targets. Sometimes its hard to see the wood from the trees in terms of the customer needs. Enablers such as safety, quality and the environment are now basic prerequisites for market entry and yet add to the metric overload.
In this post we identify the 7 shop-floor process factory performance metrics that we think, matter most to the customer whether that be the end user or the senior supply chain manager.
1 – Variability
The broader supply chain sees a factory as a black box that produces product. Variability and cycle time are really the only two performance metrics that supply chain managers get concerned about. All other metrics fall into the category of costs.
So, as long as the product is being supplied within agreed pricing terms, the other metrics are nice to know but not a concern for the broader supply chain (providing of course that those other metrics are not the root cause of increased variability or cycle time).
Over 95% of variability from a factory is caused by factory performance issues rather than raw material supply issues. This is because the buffers between raw material suppliers and the factory serve to minimise the flow-on impacts of raw material supply shortages.
Our experience is that a good production line and crew can achieve schedule targets within +/- 10%, providing of course that the targets are based on reality.
When a production crew falls short of a scheduled target they invariably run longer to make up the shortfall. This relies on some confidence that they can make up time when they changeover to the next product.
This behaviour, along with some statistical benefits, mean that its better to measure variability over at least 10 production runs. Individual runs that have a variability of +/-10% usually smooth out to +/-5% over a 10 run group. +/-5% variability against an agreed schedule is usually a good target for the production folks.
2 – Cycle Time
Cycle time is the other metric that the broader supply chain considers. Cycle time (sometimes referred to as replenishment time) has the potential to improve supply chain responsiveness and minimise outbound stocks at this time of unprecedented uncertainty. This metric has the potential to do such good for the broader supply chain and yet few businesses, let alone factory personnel, actually engage in its improvement.
A discussion about cycle time reduction in the process industry can be quite complex. This is because many different products, with different running characteristics, are run off the same line. So optimisation of cycle time, with due consideration to total business costs, needs input from many stakeholders and can involve significant analysis work.
The good news is that the Product Wheels concept, discussed in 7 essentials of a process factory scheduling cycle, can be used to help identify optimum cycle times across a product portfolio on a process line.
3 – Capacity and Utilisation
Factories in the process industry should not be more than 85% loaded within an S&OP planning horizon. 15% is generally the minimum capacity buffer required to absorb demand variation, inbound raw material variation and factory performance variation. It should be said that overtime shifts, deployment of discretionary labour (e.g. casuals) and crew allocation adjustments (e.g. stopping a line to run another) are all valid forms of capacity buffer but these are levers that should be managed carefully to minimise costs.
The capacity of a line is determined by how a business has chosen to crew it. So for example, a line can be 85% loaded but run only on an 8 hour day shift, 5 days per week. In this case the utilisation of the line is only 24%. Utilisation is a measure of asset performance so any discussion about capacity should also be had in the context of utilisation to ensure robust business plans are made.
Capacity and utilisation metrics and targets could be used on the factory floor and yet most of the time these are hidden. This is because senior managers often fear complacency if too many production personnel were allowed to understand the existence of capacity and time buffers. Better to let everyone think that all lines are at maximum capacity (without discussing utilisation) and focus on 4, 5, 6 and 7 below. The jury is still out on this one.
4 – Rate
The achievable instantaneous rate for each product at the scheduled machine should be defined and monitored. Often production personnel are unsure of the scheduled machine let alone the scheduled instantaneous production rate for each product.
5 – Efficiency
Efficiencies that matter are those measured from the time a line is asked to perform at a certain consistent, instantaneous rate until when it is stopped in order to undergo a changeover or some sort or a planned adjustment or intervention. This “Running Efficiency” target will change between products and different line configurations. These Running Efficiencies should be measured and targeted.
6 – Changeover Time
In the process industry it is unusual to see cumulative changeover times exceed 15% of total crewed time for a line.
Also, in the process industry, changeover times are a function of line design. The changeover time is dependent on how the line was configured before the changeover and what the configuration needs to be after the changeover.
Our experience is that there are usually only 2-3 from-to line configuration changes that make up over 50% of the total changeover time on a line. These should be know, monitored and improved.
7 – Planned Time
Planned time is simply any activity, other than running or changeovers, which occurs while a line is crewed. This can include start-up preparation, clean-in-place operations, maintenance activities (not including breakdown/reactive maintenance), etc.
Planned time in the process industry is almost entirely a function of line design. It usually constitutes around 5% of total crewed time and can be influenced by better scheduling, continuous improvement activities and operator training. In any case, total planned time is worth having as a KPI.