Process Excellence in the Manufacturing Value Chain

Learn how J&J is using Six Sigma, Lean and other techniques to achieve process excellence in its manufacturing operations.

The pharmaceutical industry is facing serious challenges today to maintain revenues, margins and compliance in a more competitive global environment. Companies can no longer afford to tolerate batch rejections, back orders and nonconformance issues, and many are taking decisive steps to prevent these problems from occurring in the first place.

Johnson & Johnson's response to these challenges was to establish a corporate "Process Excellence" program several years ago. The program was designed to provide a systematic method for measuring, analyzing and improving all the company's business processes, continuously. Its goal was to identify critical areas where improvement would create breakthrough results in market penetration and organizational speed, and reduce the cost of doing business. The program was designed to allow the company to make continuous improvements in cost and defect reductions and productivity, and to leverage improved compliance as a competitive advantage.

As a growing number of pharmaceutical companies are realizing, the toolkits and methodologies of Six Sigma and Lean can add significant value. Each is useful, even when applied in isolation. However, they become even more powerful when integrated. So, instead of applying these techniques individually, J&J is using dashboard metrics, Six Sigma, Lean and Design Excellence, to address manufacturing improvement.

Six Sigma addresses the need to reduce variability, Lean attempts to reduce waste and improve the flow of value to the customer, while Design Excellence aims to apply both these concepts, proactively, to design and development processes. This article will review some of the key concepts and methods involved, discuss how they have been integrated and applied at J&J's Pharmaceutical Sourcing Group Americas (PSGA), and some of the results achieved so far.

Achieving success with this integrated approach requires the following:
  • A strong clear "Case for Action" for making the given process change, including a business case based on data
  • Use of prioritization tools to ensure adequate selection of projects and scope of work
  • Use of tools to ensure control and institutionalization of improvements
  • A jointly developed and shared vision aligning executive leadership and associates in manufacturing

 

First, let's review some of the basic principles and tools involved in this integrated approach to process excellence.

Lean Manufacturing aims to eliminate waste and establish a total quality ethic, with quality at the source. It includes partnerships with customers, and improving process reliability on the manufacturing floor. It aims to simplify, integrate or automate processes whenever possible, and integrates continuous improvements.

Ancillary related concepts include:

Rhythm, the idea that there is a "natural" or preferred sequence of manufacturing operations that will minimize changeovers and setups.

Poka yoke, principles of error reduction or "mistake-proofing." The goal, in this case is to:

 

  • Make it more difficult to create errors
  • Ensure that it is possible to reverse them
  • Make it obvious when errors are occurring
  • Detect deviations from procedure or fixed value (for example, number of parts)

 

These concepts must then be implemented in process design, resulting in less complex processes and procedures that can tolerate error without resulting in product defects.

Tools used include:

Value Stream Mapping. This set of exercises is critical for any continuous improvement plan. VSM examines how any particular task is handled now, and how it might be handled more efficiently in the future, and provides a structure for implementing improvements.

The exercise allows teams to distinguish between activities that add value to operations or are absolutely necessary to deliver customer requirements and those than don't add value, which can be eliminated. It also allows teams to differentiate both of these categories from "sustaining activities," which aren't necessary to delivery customer requirements, but may be necessary to sustain the business, or can't be eliminated due to constraints. Sustaining activities are typically incorporated into the new process, but may be targeted for gradual elimination during continuous improvement. Several factors, including the time required for each activity, are collected and analyzed in value stream.

Value stream has been employed for several operations and processes within our facilities. Typically this involves mapping the whole process, including details of every stage and measurements of time required to execute. These exercises have led the teams to eliminate non-value added activities, streamline others. This has been achieved by implementing optimized procedures for changeovers and investingin new equipment, in order to further improve efficiency.

Five Ss - The five Ss stand for: Sort, Set in Order, Shine, Schedule and Score. 5S is a method that focuses on organizing the workplace, and the way that materials and equipment are set up in the workplace. It also standardizes work procedures, reducing waste and opportunities for errors.

Six Sigma - which probably needs no definition, measures the degree of variability as number of deviations per unit number of processes, operations or products. It is an organizational approach to performance excellence which aims, systematically, to eliminate variation by Defining, Measuring, Analyzing, Implementing and Controlling processes, abbreviated as DMAIC. Within Six Sigma projects, a variety of tools are employed. These include:

Failure Mode and Effects Analysis (FMEA) - a systematic group of activities that is intended to recognize and evaluate the potential failure of a product, piece of equipment, or process, and the effects that failure could have. Solutions that would eliminate or reduce the chances of that failure occurring are identified, and the entire processe is documented. FMEA identifies existing and potential failures and their causes and effects, and prioritizes failure modes based on a "Risk Priority Number' (RPN):

RPN = Occurrence x Severity x Detection.

FMEA not only facilitates troubleshooting efforts, but makes it easier to develop strategies for corrective actions.

Project Charters. To allow teams to implement these process excellence tools more effectively, "Project Charters" are developed for each process undergoing improvement.

These charters state the problem, goals, and "business case" for the work, explaining clearly to senior management why working on this problem will have a positive impact on the bottom line. The Charter also defines scope, projection, cost and benefits. Once work has progressed, milestones are recorded in the Charter as well.

Example 1: Improving the Equipment-Operator Interface

At two PSGA manufacturing sites, we analyzed processes to see where competitiveness could be improved, by reducing the loss of sales and business focus resulting from lack of process reliability.

We established two cross-functional teams, one at each site, made up of staff from manufacturing, technical operations, and quality assurance departments. The teams were set up to assess the main root causes of any potential quality issues at the facility and identify changes and solutions that would be most effective in removing those root causes. The teams would also develop data to validate the focus of current improvement projects, identify robust control strategies to institutionalize improvements, and identify new key projects of longer duration.

Using the DMAIC approach, the teams' first task was to develop measures based on "Voice of the Customer" data, interviews with customers, as well as Critical to Quality metrics and "SIPOC" maps, summarizing Supplier Inputs to the Process and Outputs to the Customer.

The teams then developed a compliance and cost ranking system that allowed for prioritization and reduced the scope to one specific focus area per site.

Then, the teams analyzed priority areas to identify problems, separating value added from non-value added activities. Multivariate analysis, stratification and other techniques were then used to identify causes. Tools included Pareto charts, cause and effects diagrams, and a solutions-prioritization matrix.

As a result of the analysis, the teams found that, at one of the facilities, significant improvements could be achieved by improving the operator-equipment interface, equipment setup and maintenance procedures, and by reducing complexity in the work area, and improving operator expertise.

Solutions included revising procedures for equipment set up and maintenance.

Operator awareness was increased through training, while visual guides were also developed to heighten this awareness. In addition, to reduce complexity in the area, critical equipment was analyzed using FMEA, while the entire operation was subjected to a full Five-S analysis.

As a result of the work, measured quality improved dramatically, in one of the areas for as much as 75% in a period of 9 months.

Example 2: Improving Process Characterization.

Process Excellence approaches were also used to improve a solid tablet product. We were the only market supplier for this product, which had been developed in the 1960s and there was limited development history available.

The objectives of the project were to optimize In-process testing and its correlation to final release testing results. In addition, we wanted to reduce any potential variability and improve process capability.

There were a number of steps involved in the process:
  • Dry blending
  • Starch paste manufacturing
  • Granulation
  • Drying
  • Milling
  • Blending
  • Compression
Again, we first set definitions using Voice of the Customer and CTQ data, as well as SIPOC maps.

Statistical Analysis and Design of Experiment

Baseline data from 20 batches was developed for paste preparation, granulation and compression, since little historical data were available. The team then analyzed the data through process analysis, multivariate analysis, stratification and other techniques Hypothesis testing using regression, chi-square, t-Test and other techniques were used to verify root causes. Design of Experiments and response surface optimization were then used to quantify relationships between variables.

The data collected during the project's measurement phase and analyzed in cause and effect diagrams and priority matrices provided factors for "Design of Experiment" studies. Regression analysis and DOE data analysis provided data that allowed the team to consider improvements. These improvements were then piloted on a small scale, and plans are now underway to implement them on a large scale.

References

Breyfogle, III, F., "Implementing Six Sigma: Smarter Solutions Using Statistical Methods," Second Ed., John Wiley and Sons, Hoboken, N.J.

Pande, P., Neuman, R., and Cavanagh, R., "The Six Sigma Way Team Fieldbook: An Implementation Guide for Process Improvement Teams," McGraw-Hill, New York, N.Y., 2002.

Womack, J. and Jones, D., "Lean Thinking- Banish Waste and Create Wealth in Your Corporation," Simon and Schuster, New York, N.Y., 1996.

Rotcher, M., and Shook, J., "Learning to See. Value Stream Mapping to Add Value," The Lean Enterprise Institute, Brookline, Mass.

Hirano, H., "Putting 5S to Work," the PHP Institute of America, 1998.

About the author
Noemi Santiago is Director of Technical Operations, Solids, at Johnson & Johnson's Pharmaceutical Sourcing Group of the Americas (PSGA). Noemi has held several positions within J&J, including Technical Operations Process Leader and Operations Excellence Leader at the Ortho Pharmaceutical site. In the area of process excellence, she has driven the design and implementation of a process-centered organization, and has been an active sponsor of several successful Six Sigma projects resulting in significant improvements in compliance and cost reduction within pharmaceutical manufacturing areas. Noemi has a Ph.D. in Microbiology and Immunology from the University of Puerto Rico.


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