Pharma managers should learn to watch their operations differently, see opportunities for improvement on their shop floor, and learn from top-performing peers and from leaders in other industries how to capture these opportunities.
The pharmaceutical industry is lagging other industries in operational efficiency, as indicated by OEE ranges of 10-60 percent, up to six-month lead times, and other measures. Few pharma managers understand shop-floor operations and their potential for improvement, and few learn from their industry peers — even those whose performance is far superior. But even the best-performing pharmaceutical plant is miles away from the efficiency achieved at the average Toyota plant. Only those pharmaceutical companies and plants with enough courage and imagination to learn and apply lessons from other industry leaders can hope to break from the pack.
DIAGNOSING BY "GENCHI GENBUTSU"
When Taichi Ohno, Toyota’s head of production engineering after the second world war, visited Ford’s plants in Michigan, he may have been impressed by what he saw in the industry leader’s facilities. But beyond understanding the manufacturing system, he found many areas for improvement on the production line, such as a leveled pace of production and a smaller work-in-progress inventory. His visit, and the knowledge he took back to Japan, show how looking at systems or processes with fresh eyes can reveal new insights at the most basic level. This is exactly what pharmaceutical companies need to do now to uncover opportunities for improvement in operations.
This type of visit offers several lessons. Years before Toyota codified its core manufacturing principles, Mr. Ohno had already found new approaches to looking at production processes. The principle was “Genchi Genbutsu” — go and see for yourself. Managers watch processes, in person, to understand the fundamentals of what adds value and what does not. This approach would later become one of Toyota’s most famous slogans, and it is fundamental to production process diagnostics.
No matter how good their information, pharma managers rarely find inefficiencies in operations behind a desk. Yet all too often, managers accept the data in front of them without challenge. Anybody who wants to know what really goes on in manufacturing, including people’s difficulties and daily worries, must go to the shop floor and look, listen, question and understand.
We find that many pharma managers get wrapped up in other areas and fail to understand how much value can be added. They spend too little time watching operations or meeting and talking with employees. People on the shop floor often tell us that managers seem uncomfortable during visits. Not sure how they should behave, what they should be looking for or asking, many managers are actually relieved when they can return to the safety of their offices.
WHAT SHOULD THEY BE LEARNING?
Pharmaceutical managers need to “learn to see” waste and variability in familiar processes, rather than just the barriers to change. Managers must ask “what would it take” rather than simply report “why we can’t do it.” Looking for waste and variability, especially in your own operations, requires courage. It also requires observation. In more than 50 plant walks and diagnostics we have conducted, we have seen pharma executives gain tremendous insights and benefits.
When we brought pharma managers to the shop floor, we sometimes found that less than half the equipment was running, and that multiple weeks of work in progress (WIP) was stored in many different places. On other occasions, we found different operators working on the same equipment with no clear work descriptions, and saw for ourselves the heavy burdens of batch documentation.
Results from these diagnostics speak for themselves: Many plants can improve their productivity and throughput times by 30 percent, and some by 50 percent.
LEADING PHARMA PLANTS' BEST PRACTICES
Some pharma plants perform up to 20 times better than their lowest-performing peers, according to POBOS benchmarks for over 200 facilities. What distinguishes the top performers?
What we notice first when visiting top performing plants is the activity on the shop floor. Paradoxically, it is relatively low! Corridors are lonely, lines are populated sparingly and people aren’t running around. Multi-machine handling helps make this possible. But implementing this seemingly simple principle requires three enablers that distinguish top performers:
• Machine effectiveness at top-performing plants is significantly higher, with OEEs as high as 60 percent. Lines have fewer minor stops and breakdowns; product changeovers are executed efficiently; lines are run at a speed that meets the targeted output and quality. Underlying high machine effectiveness is a problem-solving approach where production operators, maintenance technicians and engineers collaborate in the pursuit of continuous improvement. Line performance is monitored systematically and continuously. Where a gap appears between planned and actual performance, the team responds immediately by looking for the root cause and implementing countermeasures.
• Efficient workplace design also plays a key role. Good plant layout enables multi-tasking. This includes a minimum of physical barriers on the shop floor to allow operators to flow between areas (e.g., automatic doors, same clean room classification throughout production). Line operators at top plants have all they need at arm’s length, and work stations are close to each other to reduce walking and searching time.
• Elimination of non-value-adding activities. Obviously, high machine effectiveness and efficient workplace design help reduce non-value added activities like repairing, waiting and walking. But top performers push it further. For example, by adopting the “critical to quality” principle, they reduce documentation by limiting the number of inputs to the items that really matter for product quality. Beyond removing non-value adding tasks, they also try to simplify activities. Fewer and simpler tasks mean fewer mistakes. The number and frequency of in-process controls are reduced to what is appropriate based on the process need and knowledge, rather than habit or tradition.