Case for a unified efficiency metric
In the current business scenario, manufacturing businesses are under pressure to outperform the prevailing economic trend. There is a need for a broader organizational perspective/metric that needs to be taken into account. Without this, it would result in the transfer of, if not addition of overall ‘waste’ to the organization.
In the current business scenario, manufacturing businesses are under pressure to outperform the prevailing economic trend. This pressure manifests itself in the need to hold onto competitive advantage. This results in pricing pressures being applied from competition as well as customers. This need would also be prevalent for holding onto existing market share or preventing the overall market size from shrinking as well.
The challenges thrown strike at the following areas:
1. Reduction in cost
2. Reduction in lead times
3. Increase in product proliferation
The demand today is low lead time, low cost, mass customization. This is driving manufacturing firms towards tailoring product offerings knowing fully well that the offering may have an extremely short life cycle – during which they have to recover the development cost as well as corporate objectives of new product introduction. Thus a manufacturing firm is being further driven towards the aforementioned three points and the need to manage the same in the most efficient manner. The need for tighter control on these only seeks to enhance the need for higher real time visibility of data to drive more informed and responsive decision making. This also means an extremely hard look at the metrics to be considered for such dashboards. The measures as I see remaining as important could be grouped into:
- Availability Metrics eg. Resources and material, On schedule for activity fractals
- Productivity Metrics eg. Resources applied, Interactions made/Interventions required,
- Quality Metrics eg. Impacted overall cost of produce against budgets, Throughputs, Effective reduction in cycle time
The change(??) lies in the manner in which these metrics are analyzed. The viewing of these in isolation for a manufacturing process is what skews judgment on performance and could lead to erroneous decision making. These now need to be very tightly coupled with key performance indicators from other functional departments as well. The metrics that these tie into from an Order Capture and Sales perspective are as follows:
- Availability Metrics:
- Availability of personnel to process the order
- Time for making the available information to process the order to next stage
- Quality Metrics:
- Order capture scheduled ship date entry against published lead times
- Forecasting efficiency metrics – The approach needs to provide a higher emphasis on SKU based, margin driven system as opposed to an overall dollar, quantity basis.
- Pricing discipline metrics – Margins earned and actual cost of sales need to be evaluated here in conjunction with the operational costs for building and distributing the product.
- Expedite and Push Outs for planned orders
- Capture of expedite fee when scheduled ship date is under published lead time
- Productivity Metrics:
- Time to process an order
- Number of personnel needing to touch an order in its processing cycle
- Complete information availability cycle time for orders
- Number of changes to an order
- Number of interventions required/requests for information once an order is entered
Similar metrics could be looked at for the Picking and Shipping process as well. The overall metric needs to take the effect of the aforementioned into account when developing the same for the business.
The development of such a metric requires a cross functional approach in evaluating performance. The days of functional/divisional efficiencies being the factor to drive overall efficiency are now passé. The sum of efficiencies gained might actually be driving the overall efficiency of a process down. The metric to be applied needs to be much more inclusive.
In one of my assignments, the Engineering department in a bid to reduce the number of transactions performed, got consent for a process by which a lot of stocked subassemblies used on the floor were made phantom in the system thereby removing the part numbers in the system. The result of this was:
- Engineering had fewer ECOs to release
- Operations had a nightmare on their hands with regards to material availability, as though the item showed in stock it had actually been used in the subassembly which now was not being tracked in the system
Another example in this regard was the definition of On Time Delivery as a performance metric for Operations and Shipping personnel. Order Entry was expected to record the customer request date as the scheduled ship date. The consequence of this was the order entry team putting in dates as the customer requested perfectly – with little or no check on what the lead times for the products actually were. Operations team was reduced to creating a separate commitment metric which tempered the dates down as per the actual committed lead time for the orders. This was a result of elimination of the process of conversations with customers with the order entry team. The order entry team was now supposed to take orders on faxes thereby saving the interaction time. The absence of the interaction window, no association with overall metric were again shown to be the cause of what choice of metric in isolation could lead to.
What emerges is departmental kaizens are good but unless there is a broader organizational perspective/metric that is taken into account, it would result in the transfer of, if not addition of overall ‘waste’ to the organization.
Simplistic as this may sound, the key lies in being able to put together what each department believes is the contributing factors and their weight age to the overall metric. Formalizing, tracking and actionizing the same diligently then become the next set of challenges in the evolution path of the firm’s quest to its objectives.
