Operational Excellence is high on the agenda at many companies. Production process optimization presents new challenges, but it also offers new opportunities.
Global competition is still in its infancy, and we have no idea how difficult the market will eventually become. At the last VDE Congress, EU Commissioner Günter Verheugen zeroed in on the key issue. He pointed out that the challenge facing us is to maintain the position which we had in the year 2000. It is futile to try to compete with low labor costs if we want to protect our standard of living. We will have to rely on innovative, intelligent technologies to create competitive production processes which will help us retain our manufacturing base in Europe. This was obviously a direct challenge to engineers to take control of their own fate. If you look at the actual manufacturing process, labor costs in the process industry normally account for only a relatively small proportion of production costs (in the region of 10%). Operational excellence is the term which is used to describe efforts to reduce labor costs and improve the performance of the production process.
The aim is to “eliminate loss and waste in production”, or perhaps “bring production up to the optimum level” is the better wording. Process management makes an important contribution to production efficiency by significantly enhancing process performance (equipment utilization, yield, product quality, availability, safety and delivery performance) and reducing cost (energy consumption, raw material consumption, inventory levels, human resources and capital resources).
Bayer Technology Services (BTS) uses process management tools to improve the entire operational process, from production right through to logistics.
BTS can also optimize subprocesses on existing production systems. OpX optimization offers significant potential, and project experience has shown that state-of-the-art process management techniques alone can produce the following results:
• 8% reduction in energy consumption
• 10% higher throughput
• 15% reduction in inventory
• 70% cost reduction for lab analysis
• 80% reduction in time to product release
• 30% fewer off-spec goods
• 20% reduction in time to production system start-up
• 5% higher system availability
• 90% fewer production planning alerts
• 10% fewer batches per production order
Only a moderate investment is needed to achieve savings that can be as high as 10% of production costs. The expected payback period is 2–12 months.
Based on three main elements
Back in 2004, ARC stated that operational excellence is based on three main elements: performance intelligence, performance management and performance enablement . To achieve operational excellence, BTS prefers the six-sigma-based DMAIC approach (define & measure, analyze & improve, control) on OpX projects. DMAIC was described in detail by Friedrich back in 2005 . According to Friedrich, the current state of production is assessed during an initial optimization potential analysis (define & measure).
The focus during this phase is on the major cost pools and identification of the key performance indicators (KPI) which affect production efficiency. The next step is to define the content and scope of the optimization project. During the second phase (analyze & improve), the team looks into the cause and effect relationships and develops the action plan, project plan and budget for the final implementation phase. In the third phase (control), it is essential for the success of the entire project that the solutions which have been identified are adapted to the existing systems and workflows, the workers are brought on board and trained early on and monitoring and control functions are implemented. There is no other way to ensure long-term success.
Process control technology
Process optimization is still based on process control technology, but the role of online analysis continues to increase. These two elements define the useable information set which can be acquired from the process. Process control systems can use actuators to intervene in the process. Without this capability, even the most intelligent OpX IT systems are completely useless in a production environment. The latest advances in technology present a whole series of challenges to the engineering team. The days when their main task was to control pressure, temperature and flow should now be long gone. The goal today is not to get the process up and running, but rather to focus on overall performance.
The task of process control engineers goes beyond selection of the right instrumentation. They also have to evaluate a whole range of technical parameters and then work together with colleagues from advanced process control to determine the best sensor placement based on dynamic modeling. No manufacturing team is interested in column temperatures. What they really want is to optimize column efficiency and energy consumption. It is equally important to recognize when temperature measurement is no longer sufficient to manage the process, as is shown by the aromatics predistillation example in Fig. 2. Methods like concentration measurement must be used in this application, and placement is critical. As is clearly evident from Fig. 3, process management will not lead to operational excellence without process models which accurately reflect production and logistics flows. These models turn data into information and help exploit the maximum potential of the process . Data acquisition, which involves continuous evaluation and interpretation of the data, plays a key role. A statistical toolkit and a comparison with rigorous process models are a big help. Operational excellent only makes sense if you have a team of well-qualified and experienced engineers who are able to use this approach effectively.