Lean Initiatives Come Up Short For Pharma Manufacturers

Survey shows Lean Manufacturing result not as impressive for pharmaceutical industry as it is for automotive.

According to a recent survey of over 1,500 pharmaceutical manufacturers, Lean Manufacturing hasn’t had the same high success rate in the pharmaceutical industry as it has in the automotive and high-tech industries. The survey was conducted by Invistics, a developer of manufacturing performance optimization solutions for complex, asset-intensive industries.

The survey showed that while more than half of the respondents said their companies have implemented Lean, Six Sigma or Operational Excellence, less than half of those Lean initiatives have produced satisfactory results.

“Many companies in industries that traditionally haven’t applied Lean and Six Sigma are now trying to benefit from these techniques and they are learning that it is not as straightforward as they expected it to be,” said Invistics President and CEO Scott Geller.

“The good news is that new approaches helping to ensure sustained success are emerging, along with Lean manufacturing software solutions that apply Lean principles to the reality of shared equipment and product proliferation found in complex batch processes and packaging options.”

The report, “Processing Lean: Modifying Traditional Techniques for Complex Environments,” identifies the seven areas of value streams, organization, performance measures, bottlenecks and capacity planning, inventory optimization, pull scheduling and lot sizing as areas where traditional Lean techniques need to be modified to fit a complex environment.

In a traditional Lean environment, value streams have dedicated cells for similar products. For a complex environment, the report suggests the use of flow paths in which products are grouped into families that visit similar pieces of equipment, thus eliminating the need for dedicated equipment.

Workers in a complex environment need to be able to work on numerous products according to demand, and they need to be organized around the flow paths to ensure effective product flow.

When it comes to performance, manual or card-based systems are impractical, as they would require too many resources for implementation. By monitoring each flow path for cycle time, throughput and delivery performance, workers can gain an understanding of how the entire process is operating.

Pull scheduling in a traditional Lean environment involves using Kanban cards to indicate part number and quantity and to move additional products into place when a product is sold or consumed. One of the main drawbacks for this type of pull scheduling for a complex environment is that it requires inventory for all products be on the floor at all times. For thousands of parts and variable demand, this is not the best option. Instead, alternative techniques such as “Generic Kanban,” CONWIP, Drum Buffer Rope or POLCA should be considered, since they have cards that represent flow paths, can buffer bottlenecks from starvation and reduce inventory while ensuring throughput remains stable.

Using a “rule of thumb” approach to setting optimal capacity level may work for traditional Lean environments, but variability in equipment processing times, downtimes, setup times and product demand needs to be factored in for complex environments.

Variability also affects inventory optimization and must be a factor in calculating optimal levels for complex environments. Increases in variability in demand, product mix, setup times, process times and machine reliability cause the timeframe during which a set inventory level is valid to shrink.

Finally, in terms of lot sizing, traditional environments set a target capacity utilization and find the smallest interval of time that utilization of equipment will equal their target. Using demand over that time period, the lot size for each part is calculated. Variability in product demand means that this approach does not work well when applied in a complex environment. Instead, variability must again be factored in to ensure optimal lot sizes.

More in Operations