Whenever I work on any supply chain process improvement engagement, I intuitively classify them as either “Routine Problem” or a “Non- Routine Problem”. A routine problem is something which has a straightforward simple solution. Other simple way to look at routine problem is where you can apply an algorithm to solve the problem – If your supply side costs are high – A cost based optimization engine used effectively will help. However “Non-Routine Problems” are more abstract, problems where even supply chain veterans do not have a simple explanation. (Another rather easier way of identifying a non-routine problem is when your boss explains you the problem and then – There is a long silence from both sides).
With the increasing complexity owing to global nature of today’s supply chains, more and more supply chain problems are falling in the latter category. Couple this with increasing number of events which can cause volatility in your demand and supply markets. Solving a “Non-Routine” problem requires a strategy unique to the problem. One of the common strategy is to think of the problem in terms of “Waste”. A supply chain is not efficient may be because potentially there is a lot of waste that has been created due to various imbalances, events, customer demands, supply fluctuations etc. That is where data analytics can play a huge role in not only quickly identifying this waste but also anticipating the future waste.
These days I am working on a “Non- Routine” problem – “Improve the production capacity utilization so as to cover up the fixed plant costs and sell the additional delta production (due to improved utilization) for a discount (so as not to increase inventory costs)- And generate extra revenue in the entire process”. I classify this as a non-routine problem because a single algorithm will not be able to solve this problem. Capacity decisions impact all areas of operations management as well as other functional areas of the organization. Also it is important to understand that the focus is on improving utilization (=Actual Output/Design Capacity) and not on improving efficiency – (=Actual Output/Effective Capacity)
A major step in this regard is to improve processes to increase throughput (Increase actual output, with same design capacity). This can be achieved only by a much focused short term planning on increasing capacity usage, by proper scheduling of jobs and personnel, and appropriate allocation of machinery. Apart from this, a simple dashboard which can give useful insights into current capacity utilization can be very useful. Understanding the level of waste that exists in the system and the target one can achieve, if some waste is removed can go a long way in designing the optimum solution. Thus marrying the traditional lean concepts with today’s analytics.
The key here is to identify the right metrics to be used in drawing any conclusions. It is important that chosen metrics should provide information on all different areas which can impact the given scenario (The good old management mantra of Mutually Exclusive and Collectively Exhaustive, applies here too) E.g. all different types of waste in the process such as, wastes due to quality (rework), time (idle time of machines, efficiency of machines etc.) to give a clear picture. Metrics like First Time Through would provide indications on quality, whereas metrics like Equipment Efficiency provide approaches into capacity expansion. Also a close watch need to be kept on demand forecast accuracy and the process for discounting the additional production.
- First Time Through =(Total units processed – Number of units rejected/reworked)/ Total units processed
- Equipment Efficiency = Machine availability * Performance
- Machine Availability = (Total machine time available – downtime)/total time
- Performance efficiency = Actual run rate/ideal run rate
To conclude, an effective capacity planning process to maximize effective capacity by identification of various parameters will be required. Advanced analytics-driven “control towers” can aid the same by capturing the operational data and monitoring real-time critical events to provide visibility into capacity usage.