How to Measure Performance and drive improvement for Bulk API Manufactures using Standard Work

A costly problem for many pharmaceutical and biotechnology companies is that vital data and analytics around bulk API production performance isn’t being transferred effectively upwards from operator to senior management. In addition, the data being captured isn’t aligned to objectives such as the achieving of production schedule targets.

 

Downtime losses that add 30% to 40% to batch manufacturing cycle times are not uncommon and the result is the failure to achieve production schedules, decreased site profitability and missed sales.

How do we change a problem into an opportunity?

One proven solution is to employ ‘Standard Work’.

Plant performance can be measured on a daily or shift basis by defining the best possible batch cycle time as a ‘Golden Batch’. This is a batch where no stoppages or unplanned downtime exists and all elements – Man, Machine, Materials, Methods – are operating at 100%.

Most importantly you now create a visual representation of where the batch is, using units of time, and where the batch should be at any given time.

Average downtime per annum, per mid-sized site is 10,000 hours and the average cost per hour of downtime is $2.5k.

Definitions

  • The actual output for a shift would be the number of Standard Work hours completed.
  • Downtime = When Batch throughput is <100%, downtime is incurred
Batch performance formula

%

Plant performance

How do we go from theory to practice?

At an operational level whenever downtime occurs the Operator logs the time lost electronically and categorises the root cause of the downtime event under Man, Machine, materials and method. As a result:

Downtime events can now be trended, analysed, escalated and informed decisions taken.

You now have a "Batch Performance" metric for the entire process.

So what are the benefits?

Historical downtime data and information that is captured daily can be reported on over time, for example, downtime Pareto analysis will identify the ‘bad actors’ when it comes to reliability.

Your downtime analysis is not just focussed on equipment performance but all inputs to the process – Man, Machine, Materials, Method.

Each of your supervisors, operators and supporting departments now have clarity, accountability and ownership of their processes so that there is a greater chance for the plant, as a whole, achieving the all-important weekly schedule.

You now have a clear target point and expectation for the end of each shift because every shift now has a start-and-end point, irrespective of the batch cycle time.

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