Benchmark Study Reveals Practices of Top Performing Manufacturers
Executive SummaryPharmaceutical organizations, large and small, face unprecedented pressure to balance innovation with operational excellence. In the past it was much easier to achieve margins acceptable to Wall Street, given the high margins and relatively low cost of goods of the times. Consequently, operational performance was never an issue, and never received attention … until now, that is. Today's pharmaceutical manufacturers face expiring patents, stiff competition and price pressure from generics and FDA initiatives for process innovation, not to mention that manufacturing costs now exceed R&D operations. These and other factors compel pharmaceutical companies to focus on operational excellence - and it's now gaining strong attention from the executive suite.
The focus on operational excellence has:
Forced companies to better utilize plant assets, people and materials;
Caused plants to increase flexibility - enabling more products to be produced; and
Created interest in looking to other industries to expand best practice knowledge.
From January to June 2007, Informance studied 50 pharmaceutical packaging lines worldwide. Researchers used the Informance Enterprise Manufacturing Intelligence Suite (including patented analytics), and IMPACT Advisory Services to collect data, derive insight and discover correlations to operational success of tactical and strategic actions.
Key Findings Best-in-class pharmaceutical manufacturers exhibit 87% more availability than laggard performers
Best-in-class performers reduce loss due to changeover at a factor 4 times greater than laggards
Equipment failure is a significant contributor to lost capacity; however, best-in-class pharmaceutical manufacturers attribute 7% of lost capacity to equipment failure, versus laggards that experience a staggering 26% of capacity lost due to equipment failure.
Definition of Best-in-ClassTo determine a manufacturer's competitive position, we use overall equipment effectiveness (OEE) as the top indicator of performance. We rank each manufacturer by OEE and view all other key performance indicators (KPIs) in context of this order. The average of the top 20% of each KPI denotes best-in-class performance for that KPI, the average of the middle 50th percentile represents average performance, and the average of the bottom 30th percentile represents laggard performance.
Why do best-in-class CPG companies outpace laggards in their own industry at an astonishingly higher rate than best-in-class pharmaceutical companies over their own laggard peers? When comparing performance to consumer goods companies, we discover that pharmaceutical packaging operations lag far behind in not just OEE, but also other key metrics like availability and asset utilization - both of which have a significant impact on OEE.
OEE: The 1% Rule
Big Six Losses
Now that we have assessed and analyzed the "big six" as a percentage of overall capacity, it's helpful to dive deeper to understand how each of the loss buckets impacts performance. To prioritize these losses further, we evaluated the individual losses as a percentage of the "big six" and compared to loss buckets for CPG. Changeover and shutdown are tied as the largest loss area for pharmaceutical manufacturers, and is where researchers discovered the largest disparity between pharmaceutical and CPG companies. This validates the emphasis on changeover as a primary target of lost capacity for pharmaceutical manufacturers.
These findings indicate that changeover is possibly the leading cause of poor performance by pharmaceutical manufacturers - and their greatest opportunity for improvement. Since there is little difference between average and laggard pharmaceutical performers when it comes to maintaining loss due to changeover, yet we see outstanding performance from best-in-class, it is clear that world-class organizations have successfully reduced lost capacity from changeover, and maintain low or reduced cycle and setup times. In our experience, world-class firms do this by documenting settings, cross-training team members and utilizing quick changeover techniques.
It is interesting to note that best-in-class performers in the pharmaceutical industry actually outperform their best-in-class counterparts in the CPG industry, 4.44 versus 4.74. As with previous gap analysis, we see a pronounced divergence in average pharmaceutical companies, and a much larger divergence, almost double, for laggard performers.
RecommendationsAverage and Laggard Performers
Minor stops, overall big six loss and equipment failures are areas proven by high performance organizations - a good place to start.
Changeover, a key improvement opportunity, has been greatly reduced by best-in-class companies by cross-training team members, utilizing 5s and quick changeover techniques.
There is always room for improvement - with an average OEE of 39%, this holds very true.
A "better-than-CPG" metric such as equipment failures suggests that TPM, or other proactive strategies are in place. Should organizations invest more in these programs? There is always a point of diminishing return, but at this time, the research suggests additional room for improvement.
What's Next in Achieving Operational Excellence?Many operational improvement initiatives have a "bottom's-up" look and feel; they start at the plant and even line-level, and move through the enterprise. One flaw with this approach is that it's difficult, and often impossible, to propagate best practices between plants and throughout the organization. Since the average global enterprise has 22 facilities, forward-thinking companies view multi-site performance analysis as a key value generator for the enterprise.
With the ability to leverage composite metrics for many KPIs across a number of plants, organizations can better determine which investments, and which strategic and tactical changes will provide the greatest impact across the entire enterprise. For example a company can now ask, "Is there a small set of improvements that, if applied globally, will reduce downtime and improve performance across the enterprise?" Many times, the answer is "yes." But, without the right information and ability to effectively evaluate and quantify the financial impact of improvements, it is extremely difficult to find and justify the right combination of strategic and tactical changes for the greatest impact on performance.
About Informance Benchmark Studies
Informance benchmark studies demonstrate how practices of best-in-class companies impact manufacturing performance. Researchers use 5-7 months of real-time manufacturing performance data aggregated using the Informance Enterprise Manufacturing Intelligence platform. The highly granular and rich real-time nature of Informance EMI adds a new dimension for external and internal benchmarking initiatives. By correlating attributes of best-in-class performers across a variety of metrics, executives have the ability to gain insight and direction. Organizations can use Informance benchmark studies as a starting point to understand how they stack up against their peers and develop an action plan for operational improvement. Each year, Informance publishes a number of benchmark studies across manufacturing industries that include consumer packaged goods, pharmaceutical, food and beverage, chemical, and industrial products. Informance has forged new ground in cross-industry benchmarking, so that manufacturing executives and professionals can understand and apply best practices from peers in other industries.
To learn more about Informance benchmark studies, or to schedule a briefing or strategic assessment call (877) 464-6262 or email firstname.lastname@example.org.
About the Author
Vice President, Solutions Consulting, Informance International
Sudy brings to Informance nearly 20 years of business process leadership, and was most recently senior analyst and vice president of global supply management research programs for Aberdeen Group. Prior to Aberdeen, he was a member of the management team of MINDFLOW Technologies, a leading strategic sourcing solutions provider. Sudy has also served in a variety of management roles spanning marketing, sales, product management, program management, and consulting at i2, Hewlett-Packard, Scientific Computing Associates and ParaSoft Corporation. First launching his career as a software developer, Sudy earned his B.S. in Computer Science from the State University of New York at Buffalo.