Facilty Design & Management

Mining the Hidden Factory

Overall equipment effectiveness (OEE) can be useful in developing a pharmaceutical manufacturing excellence program, and provide a welcome bottom-line boost.

By Robert C. Hansen, P.E.

Like Lean Manufacturing and Six Sigma, Overall Equipment Effectiveness (OEE) is a key tool for achieving operational excellence. Although the pharmaceutical industry is five to 12 years behind discrete manufacturing in applying this concept, more pharmaceutical companies, including AstraZeneca and Merck, are including OEE as part of their operational excellence toolkits and discovering how it can help quantify efficiency and improve overall operations.

This article will present a brief overview of OEE and show how the concepts behind it can help drive manufacturing excellence at your operations. It will also highlight the critical link between OEE data and business metrics, notably Income from Operations (IFO). This connection is essential in communicating to management, and in helping you develop the most effective strategy for achieving operational excellence at your facility.

OEE’s roots can be found in Total Productive Maintenance, a term coined by Seiichi Nakajima [1] that quantifies all losses or deviations from perfect manufacturing. TPM is completely in line with Lean Manufacturing and the Toyota production system.

Simply defined, OEE is: The amount of ideal production time relative to the actual scheduled time used, or the amount of good product produced relative to the amount of total product that could have been made with 100% first-pass yield at the ideal rate for the actual scheduled time used.

OEE = Availability × Speed Rate × Quality Rate, where:

Availability = actual run time ÷ actual scheduled time used

Speed Rate = ideal speed rate ÷ actual speed rate (maximum of 1.0)

Quality Rate = amount of good product ÷ total amount of product made.

Also important to OEE is Total Effective Equipment Performance (TEEP), or the amount of ideal production time relative to the actual calendar time used, or the amount of good product produced relative to the amount of total product that could have been made with 100% first-pass yield at the ideal rate for the actual calendar period used. This metric is used to determine overall asset usage before investing capital for capacity expansion. A simple “back of the envelope” calculation can be done, and usually the TEEP number is significantly lower than the current OEE value, particularly for pharmaceutical companies that work a relatively low number of shifts and weekends when compared with other industries.

Figure 1 may be helpful in allowing you to visualize OEE and TEEP. The gold portion of the chart represents the amount of “perfect production” time, or “Theoretical Factory” time, arrived at by dividing the total amount of good product produced by the ideal speed rate of units per time increment.

Losses generally take one of the following forms:

  • Planned —
  • Unlike TEEP, OEE excludes all planned downtime. Planned downtime would include major shutdowns, periods during which there are no orders, planned holidays, weekends, shifts not worked, planned lunches and breaks and meetings, experiment time, and downtime for completing orders early.

  • Operational —
  • These losses occur when the equipment is not running, such as during changeovers, pit stop maintenance, process stops for cleaning and unplanned equipment problems, quality problems, lack of manpower, lack of information or lack of supplies. Operational losses can be planned or unplanned, and data collection is critical in eliminating unplanned losses and mitigating planned downtime. Automated data collection can be particularly useful, especially data organized based on product run.

  • Speed —
  • These types of losses occur when operations do not run at the ideal speed, or the highest accredited speed or equipment design speed limits. (This factor must be 1.0 or less. If it is greater than 1.0, it means that a new ideal speed has been established.) Minor equipment stops and jams accumulate as speed losses. So do instances where runtime speed is reduced.

    If the specific equipment system being reviewed is not the bottleneck of the product flow, then the ideal speed rate could be defined as the desired rate to feed the bottleneck. OEE is then measured against desired speed with the understanding that the maximum speed factor is 1.0. All events that require higher than desired speed to meet order rate should be recorded for analysis and improvement opportunity.

  • Quality —
  • This type of loss results when any produced units are not transferred on the first pass. Rework has zero OEE value until it is corrected and transferred.

OEE is important for operations, such as pharmaceutical manufacturing and packaging, that run fewer than 24 hours per day, or are not sold out. TEEP is the important metric for 24/7 or “sold out” operations.

Recently, demonstrated world-class OEE levels were determined for the pharmaceutical industry (Pharmaceutical Manufacturing, January 2006, p. 11). Levels were determined for different business conditions, depending on the number of products produced by the equipment system. The following was information combined from Granulation, Compression, Film Coating, C/E, and hybrid operations:

Number of Stock Keeping Units (SKUs) % OEE
Low SKU* (1-2 SKUs) 83%
Medium SKU (3-5 SKUs) 80%
High SKU (6-12 SKUs) 73%
Very High (≈20 SKUs) 63%

Changeover reduction

Since the drug industry is highly regulated and extremely safety-conscious, SKU changeovers are critical events. For even the most efficient pharmaceutical companies, the best demonstrated changeover time is still close to 2 hours. Compare that to the times achieved by other industries, some of which have reduced die changes from over 12 hours to less than 10 minutes. Changeover time reduction is likely to remain a top goal for pharmaceutical companies.

Pharmaceutical operations are unique, in that both discrete and batch processes are used to manufacture products in their final packaged form. Discrete manufacturing is usually automated, and the ideal speed is the design speed of the equipment (i.e., a discrete process such as a bottle filling operation is designed to fill a specified number of units per minute and the process usually occurs as the bottle is moving through a defined distance along a curved track).

Drug companies embarking on an OEE program typically start with their discrete processes, probably because they are better defined and fairly straightforward for measuring and analyzing event times and speeds. However, OEE can also be applied to batch operations, where it can allow significant improvement to be made.

With batch processes, it is critical to define the portions of the transformation process that will add the most value. Standard operating procedures (SOPs) must be developed for every portion of the cycle.

It is also essential to quantify the ideal rate of operation for each portion of the process, which is usually expressed as the best time that could be achieved by operating equipment that functioned “like new.” The figure that results is used as a baseline against which to measure losses due to fouled heat exchangers, pump degradation or clogged pipes. Specific “best practice” heat ramps and process curves should be defined, as well as proper mixing of ingredients, dwell times and transfer processes.

Once these steps have been taken, the first, and usually the biggest, improvement that results is that cycles will be executed exactly the same way every time, on every shift, using established best practices and quality ingredients.

The next step is to reduce the time required for non-value-adding operations such as cleaning, decontamination and start preparations including inspections and information downloads, to drive them to “pit stop” conditions. Changeover practices should be sped up as much as possible, and all actions examined to see if they can be done outside of the “critical path.” Examples might include using alternating parts and cleaning off-line. Even for value-adding portions of the critical path, parallel processing can be done — for example, preheating or cooling equipment or ingredients while a production line is running.

Using statistical process control (SPC) instead of testing or implementing process analytical technologies (PAT) would be ways to eliminate losses in time and material due to testing. These practices could also minimize materials left over after transfer and mitigate losses from pumping, tanks or production lines. A good calibration system will be needed, in order to confirm that all measurements are accurate and that they are providing good information.

Defining the quality factor

Some organizations suffer from variable batch process yields and argue that the “quality factor,” or good quantity versus total quantity produced, would be impossible to define.

In such cases, carefully designed experiments will make the process more scientific, and demonstrate the sensitivity of all parts of the process and materials. At the very least, careful records should be used to define the “best ever” yield, to set the standard for demonstrated capability of current operations.

Another way to visualize the OEE chart (Figure 1, below) is to look at the chart vertically, with the gold portion at the bottom. When viewed this way, the height of the chart is the “capacity ceiling,” and the gold portion represents current capability.

Next, draw a line for world-class TEEP, which would be approximately two percent lower than the world-class level for OEE, or at least 81%, or about 295 days, for the pharmaceutical industry.

The difference between that number and the number of Theoretical Factory days that your organization would need in order to produce at an ideal rate represents the effectiveness loss, or the profits that your organization loses every year.

Mining the Hidden Factory: Figure 1
Perfect production time, here shown in gold, equals the total amount of good product produced divided by the ideal speed rate of units per time increment. Planned, operational, speed and quality losses all detract from optimal OEE or TEEP.

OEE and TEEP information and the loss database can be used to move performance toward manufacturing excellence. Your current performance can be measured and benchmarked against world-class levels to reveal the “loss gap” relative to your competitors.

Pareto charts can be developed and analyzed to determine which areas to focus on—areas that will provide the “most bang for the buck,” or the greatest improvement in performance relative to the effort they require.

The best-ever performance data stored in your plant historian or other historical database can be used to raise the performance bar for each production shift. Engineering departments should use OEE performance expectations to set design specifications and acceptance testing of new equipment.

A number of approaches can be used to improve performance [2] in any individual work center. Steps that can be taken include:

  • Speeding up changeover practices;


  • Establishing equipment reliability practices;


  • Developing equipment maintenance strategies;


  • Taking a strategic approach to annual shutdowns;


  • Collecting and saving data, then using the information in root cause failure analysis;


  • Designing reliability into equipment systems.

OEE can then be used, strategically, to measure the value stream effectiveness of a multistep production process. In this case, the system’s OEE is determined by measuring the OEE of the constraint minus all downstream quality and waste losses. In order to determine this number, it is essential that production performance data be collected and analyzed for every work center, based on individual product run.

One way to do this is to assume that inventory levels are fixed and then examine the demonstrated throughput capability of each process step. Process maps, based on product line, should be used to examine design vs. actual throughput capacity for planned schedule time. This approach can be especially useful in situations where the same equipment systems are “shared,” and used to manufacture several different products.

Constraint management

Constraint management concepts [3] can be used to identify those steps or procedures that are limiting performance. OEE or TEEP analysis can then be applied to those constraints, to leverage the “hidden factory,” or unexploited capability of the process. Three levers are available to manufacturers:

  • Operating expense;


  • Throughput;


  • Inventories.

Of these, throughput is the most important, followed by operating expense and inventory levels, which are equally important. “Right-sizing” the number of steps required for any process and minimizing inventories will help companies advance their OEE efforts, and help them achieve and sustain new goals, and eventually break away from their competitors.

Note that OEE is not the only metric that organizations can use to make business decisions. OEE is not about inventory levels, but it encourages the combination of high availability, equipment reliability and quality which reduces variability, allows inventory reduction and lowers unit costs.

Sometimes organizations run processes at lower speeds, based on fears that higher speed will lead to waste or equipment problems and downtime. Although there may be a connection between high speed and waste, in most cases, waste and equipment problems can be traced directly to variability in operations or materials used. Statistical methods such as design of experiments can be used to determine the connection.

OEE and the bottom line

As you develop an OEE strategy, it is important to link OEE performance to your company’s profits. The best indicator of manufacturing contribution is Income from Operations (IFO), which is defined as sales minus the costs of goods sold (COGS) before interest and taxes.

Financial OEE must focus on line of business (or major SKU), which is another reason why OEE data should be organized based on product run. The financial link is defined as:

Financial OEE = current IFO at current OEE ÷
 projected IFO at world-class OEE

Because the information will be organized by product, specific financial data can be applied for:

  • materials
  • labor (by work center including overtime)
  • quality losses (by work center)
  • fixed costs of equipment systems (portion of plant budget by product)
  • actual production schedule time used
  • number of good units made.

All of this information should be known, and available, as should the current unit cost. The unit cost of distribution should also be determined and combined with material usage.

Taking a product-specific approach to this analysis allows any management team to estimate the profit margin. One way to compute the margin is to allocate the current IFO to the current products. Dividing the allocated IFO by the product COGS and multiplying by 100 computes the percent margin for that product.

Understanding the benefit of higher OEE can be a compelling reason to bring change to an organization. Higher OEE means that more units can be produced using existing resources, and only material cost, energy usage and distribution costs are increased; all other sales income goes to the bottom line.

The Total in TPM includes marketing and sales such that improvement plans involve everyone. As strategic improvement plans are developed and implemented, marketing should also strategically reduce prices to refill the factory (i.e., a nickel difference at the gas pump makes everyone go to the less expensive product).

Figure 2 (below) provides an example of Financial OEE including a 5% reduction in unit price for the improved OEE condition. Lowering unit price should refill the current production schedule and may further increase sales.

Mining the Hidden Factory: Figure 2
Figure 2 represents a hypothetical business line in which operating results are improved dramatically by increased OEE. The top line indicates costs and sales associated with 61.5% OEE, while the bottom line shows the results of operating at 83% OEE. Here, higher OEE results in a $12 million boost in income from operations.



The various parameters, including an original margin of 30% and a system OEE of 61.5%, are assumptions. Insert your own numbers for a major product and see the impact for your situation.

As shown in Figure 2, for current conditions, the average sale price is equal to the COGS plus 30% profit margin divided by the total number of units, or an average sale price of $0.3822 per unit.

This hypothetical business line is losing $12 million/year ($56.1 - $44.1 million) and currently has stagnated market share. With an aggressive OEE strategy, the company could realize a 27.2% increase in IFO and a 35% increase in market share without any capital expenditure. This is a win-win-win for the customer, the line of business and the company.

The advantage of an aggressive OEE strategy is that the business line can start the effort immediately, using equipment and processes that already exist, and reap early returns. Breakaway results [4] will go to those who can climb quickly toward world-class OEE. After all, as Laurence Haughton writes, “The Big do not eat the Small. The Fast eat the Slow.”


References

  1. Nakajima, Seiichi. Introduction to TPM: Total Productive Maintenance. Productivity Press, 1988.


  2. Hansen, R. Overall Equipment Effectiveness: A Powerful Production/Maintenance Tool for Increased Profits. Industrial Press, 2001.


  3. Goldratt, E. Theory of Constraints. Great Barrington, Mass. North River Press, 1990.


  4. Fred, Charles. Breakaway: Deliver Value to your Customers — Fast! Jossey-Bass, 2002.


About the Author

Robert Hansen, P. E., CMRP, is owner of R. C. Hansen Consulting, LLC. He can be contacted at rch4oee@aol.com.

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