Making Improvement Work in Pharmaceutical Manufacturing Some Case Studies. Ronald D. Snee

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1 Making Improvement Work in Pharmaceutical Manufacturing Some Case Studies Ronald D. Snee ISPE Midwest Extended Education and Vendor Day Overland Park, KS 2 May 2007 King of Prussia PA New York NY Washington DC San Diego CA Tunnell Consulting, Inc. 900 East Eighth Avenue, Suite 106 King of Prussia, PA phone fax All rights reserved. No part of this document may be redistributed or reproduced in any form or by any means, without the prior written permission from Tunnell Consulting, Inc.

2 Agenda Pharmaceutical Environment How Improvement Initiatives Can Fail Manufacturing Process Complexity Improvement Case Studies and Learnings Process Variation, Understanding and Compliance Conclusion 2

3 Pharmaceutical Environment Complex organizations undergoing massive change Manufacturers are looking to reduce variation and improve efficiency Manufacturing processes are complex Regulatory compliance is essential There are many opportunities for improvement Improvement Can Be a Very Profitable Business 3

4 Process Improvement in Pharma Process improvement methods have been used in Pharma for some time = Manufacturing wide deployment = Problem solving method Successes and Failures have been reported What have we learned? = Organizational Deployments = Project Execution 4

5 Three Areas in Which Pharmaceutical Improvement Initiatives Can Fail Deployment of Improvement Initiative = Strategy, goals, management systems,.. Management of Improvement Project Portfolios = Resources, scope, financial value,.. Problem Solving Execution Methods and Tools = Improvement approaches, process complexity, process variation, process understanding,... 5

6 Improvement Deployment Success Factors Committed and Involved Leadership Top Talent Supporting Infrastructure: = Management Team, Champions, Improvement Masters and Improvement Team Leaders = Improvement Management Systems Holistic Improvement Methodology 6

7 Improvement Deployment Failure Modes Management Systems: Little leadership from top management including deployment plans strategy, goals, etc. Poor or infrequent management reviews Top talent not used Poor support from Finance, IT, HR, Maintenance, QC Lab Focus is on training not improvement Poor communication of initiative and progress Lack of appropriate recognition and reward 7

8 Improvement Deployment Failure Modes (Cont d) Project Selection and Management: Projects not tied to business goals and financial results Poorly defined project scope, metrics and goals Wrong people assigned to projects Project leaders and teams don t have sufficient time to work on projects Many projects lasting more than 6 months Little technical support from Improvement Master Large project teams more than 4-6 persons per team Infrequent team meetings 8

9 Problem Solving Execution Methods and Tools Improvement Approaches Process Complexity Process Variation Process Understanding 9

10 Pharmaceutical Manufacturing Processes are Complex Have many steps and variables inputs and outputs = Blending, Compression, Coating, Packaging, Combination of mechanical, physical and chemical forces at play Variation can be high due to variation of the ingredients = Many variables require tight control under FDA regulation Measurement systems are often not adequate Ingredients typically interact with processing variables Multiple process performance requirements = Product Quality, Process Capacity, Process Speed Improvement Approach Must be Able to Handle Any Type of Improvement Need 10

11 Tableting Process and Its Variables Process Variables Blending Time and Temperature Compression Speed and Force Coating Air Temperature and Moisture Water Addition API Excipients Blend Compress Coat Package Dissolution Content Uniformity Yield Waste Environmental Variables Ambient Temperature and Humidity Blending and Compression Rooms Raw Material Lot Operators Equipment 11

12 Complex Processes Require Different Types of Improvement Streamline Flow of Materials and Information = Reduce Complexity, Downtime, Cycle Time, Waste Product Quality Product Delivery = Consistency is critical to success Process and Product Cost Reduction Process Variation Reducing Waste Process Control Process Operating Window Sweet Spot Process and Product Robustness Project Type Defines the Appropriate Approach and Tools 12

13 Pharmaceutical Manufacturing Process Improvement Requires a Variety of Skills Cross-Functional Team - Skills Required Formulation Science Engineering Principles Statistical Modeling Data Management Many times Improvement teams have limited formulation science and data management skills 13

14 Example 1 Meeting Market Demand Improving Process Flow and Yield Need to Increase Process Capacity to Meet Market Demand Batch Record Release Speed Up Manufacturing Review Use Lean DMAIC Yield Improvement Use DMAIC Inventory Reduced $5.2MM Batch Release Cycle Time Reduced 35-55% Yield Increased 25% Cost Savings $200k/yr Process Output Meets Market Demand 14

15 Example 1 - Process Yield Improvement Daclizumab Fermentation Yield Lot 1 Total Grams Here we see a step change in yield What is the cause? Raw Material Lot Variation Better Raw Material Specs Required Grams Subgroup A:Batch Lot Lot 3 UCL=1083 Mean=799.9 LCL=516.5 Used DMAIC to Identify Key Drivers of Process Yield Raw Material Lot Variation Had the Largest Effect 15

16 Example 1 - Batch Records Release Systems Analysis Batch records documentation system a major source of deviations Process Analysis Batch record release process was a critical bottleneck Project Goal = Reduce the cycle time of the batch records release process by 50% to increase order response time and reduce inventory Results = Management Review Cycle time reduced: 55% for Product A Inventory reduction $3.6MM 36% for Product B Inventory reduction $1.6MM = Reduced capital costs, floor space & handling ($200k/yr) 16

17 Example 1 Batch Record Review Time Reduced 55% I Chart of ReviewTime(days) by Stage 50 Before After Individual Value Improvements resulted in a 55% reduction in Cycle Time freeing up $3.6MM in Inventory UCL=20.79 _ X= Review Time Variation Also Reduced Lot LCL=

18 Example 2 Effect of Interactions Between Ingredients and Process Variables Process Validation = New formulation in development for 8 years validation not complete = Using Design for Six Sigma methods product successfully launched in 1 year = DOE identified key interactions between raw materials and process variables = New measurement method identified need to tighten raw material specs Lean Six Sigma Increases Process Understanding Enabling Timely Process Optimization and Control 18

19 Example 2 Improved Dissolution Performance Resolved a dissolution variation problem caused by a complex relationship between process variables and raw material properties Raw Material Property 1 Contour Profiler Matrix Plot Green areas show the combinations of Raw Materials and Process Variables that will produce the desired dissolution performance Five Key variables identified using the tools of Design for Six Sigma DOE, Regression and Optimization Raw Material Property 2 Process Parameter 1 Process Parameter 2 Process Parameter 3 Process Parameter 4 19

20 Example 3 Improving Process Flow with Simulation Distribution Center Design layout and resources Production planning and scheduling = Product mix and batch size Quality Control Lab = Sample flow and = Resources and facilities equipment requirements Quality Systems Document Flow = Forecast Resource Requirements 20

21 Example 3 Quality Systems Document Flow Background Pharmaceutical manufacturer needs to be able to: = Forecast resource requirements for the Quality Systems Organization: Resources needed today and in subsequent years = Resources needed to handle additional workloads: e.g., new product introduction = Test out potential process improvements prior to making process changes and disrupting operations 21

22 Example 3-Quality Systems Document Flow Improvement Approach Discrete-events simulation model constructed by Quality Systems subject matter expert and process modeler Process map constructed and baseline data collected on volumes of work: = Cross-functional team data collection approach used Work standards for cycle time constructed Model validated Process improvement scenarios evaluated Employee trained to use the model for ongoing analyses 22

23 Example 3 Quality Systems Document Flow - Results Organization has a tool to predict future resource needs and annual budgeting: = New product introduction was forecast to require 8 additional resources to handle load (functions needing resources were specified) Organization better understands their work processes both within and between functions Standard performance metrics, drivers of work and task cycle time determined Model used to test a variety of process change options 23

24 Example 4 Improving Controlled Release Solid Dose Form Manufacturing Product Quality Equipment Performance Operating Systems 24

25 Example 4 Controlled Release Solid Dose Form Batch defect rate too high = Missing release spec 6-8% of the time Approach: = Analyze production data on 140 lots representing good and bad production (222 characteristics of each lot) = Focus interviews of personnel at various levels = Review documents = Observe the process Key process and raw material most important sources of variation 25

26 Example 4 - Table of Effects Summary Primary factors correlating with variation in dissolution include: = Uniformity and completeness of a specific sub-unit operation = Raw Material = Downstream unit operation Factor Contribution to Variation Sub-Unit Operation ~40% Raw Material 1 ~45% Downstream Unit Operation ~5% Measurement & Other ~10% 26

27 Example 4 Root Cause Mixing operation improvements = Methods and rate of ingredient addition = Location of mixing impeller = Tighter ranges for mixing speeds and times = Consistency of blender set-up These improvements = Were identified by the data analysis and process observations = Resulted in shifting the process average and reducing process variation around the average 27

28 Example 4 - Confirmation Trial Trial Dissolution(%) Lot # 28

29 Example 4 - Recent History 15.0 Dissolution (%) Mean = 8.4% SD = 1.3% Improved Sub-Unit Operation UCL= Mean = 4.3% SD = 0.7% Avg= LCL= Lot # 4.1% decrease $750K/yr Savings 29

30 Example 4 Coater Reliability Improvement Process interruptions resulting in product losses = Fishbone analysis helped identify/prioritize causes and subsequent Engineering Initiatives to resolve Project focus expanded and data collected on process delays = Interruptions - delays that occurred during spray cycle Pareto analysis and process mapping identified several systems that were the root causes of delays. Symptoms were: = Lack of spare parts = Batch record forms not available = Repairs made by cannibalizing other equipment render the cannibalized equipment inoperable = Lack of standard procedures 30

31 Example 4 Coater Reliability Improvement Institutionalized delay tracking/resolution process = Documented root cause investigations & action items = Institutionalized process delays dashboard & weekly reviews Improved spare parts management process = Streamlined approval process = Improved inventory procedures SOPs upgraded/developed for key operations = P/M schedules modified based on delay tracking data Many Enabling Systems are Needed to Ensure that Your Equipment is Reliable 31

32 Example 4 Coater Reliability Improvement Results Interruptions reduced from 18 to 6 (2H06 vs. 1H06) = Interruptions due to chronic equipment problems reduced from 14 to 3 = 3 interruptions due to software problems not equipment issues = Product losses reduced by $1.7MM/yr = Delays/batch reduced by 55% (from.33 to.15) Reliability improvement success stimulates additional improvement work = Apply improvement process to other parts of the operation = Improve equipment utilization Improve planning and scheduling Reduce idle time Reduce batch cycle, cleaning and set-up times 32

33 Be Careful in Applying Lean Principles Example: Spray Coating Process Spray nozzles adjusted separately resulting in time consuming and difficult setups Bracket installed to adjust nozzles reduced setups from 12 to 1 Efficiency increased but product failed to meet specs Designed experiment used to identify ways to adjust the process to produce product within specs Message: = Be sure you know the impact of any process change = Collect data to ensure the desired effect is produced 33

34 Importance of Understanding Variation Variation determines quality, cost and delivery and must be addressed by any improvement methodology Measures of Performance Quality Cost Delivery Customer Satisfaction Key Drivers Target Value, Variation Target, Variation, Flow, Costs Fixed and Variable Flow, Variation Quality, Cost and Delivery 34

35 Process Variation Decreases Process Flow Process variation = Produces defects, scrap, rework and the hidden factory = Root causes include: Differences between operators and process teams Raw material lot differences Poor process understanding and control Results in reduced process flow due to = Increased inventory, material movement, overproduction and wasted motion Analysis of Process Variation Must be Part of Process Flow Improvement Studies 35

36 Customers Experience Variation We have tended to use all our energy and Six Sigma science to move the mean The problem is, as has been said, the mean never happens The customer only feels the variance that we have not yet removed. Jack Welch GE Annual Report 1998 Customers prefer the delivery times of Supplier B Delivery Supplier Time (days) Mean Range A 17, 2, 5, 12, B 8, 10, 10, 9,

37 Understanding Process Variation Leads to Process Understanding You can t improve a process you don t understand = Sustainable improvement depends on process understanding You understand a process when you can reliably predict its performance over time. Developing models of the form Y=f(X) is an effective way to develop process understanding = Understanding process variation is required to predict and sustain process performance 37

38 Process Understanding A process is generally considered to be well understood when (1) All critical sources of variability are identified and explained (2) Variability is managed by the process, and (3) product quality attributes can be accurately and reliably predicted over the design space established for the materials used, process parameters, manufacturing, environmental and other conditions FDA 2004 You Can t Control or Improve a Process You Don t Understand 38

39 Process Understanding and Compliance The root causes of many compliance issues relate to processes that are neither well understood nor controlled. 39

40 Leveraging the Power of Process Understanding Compliance Excessive # Investigations Release Delays Redundant Risk Management Systems Siege Mentality Organizational Silos Right First Time Robust, Standardized and Controlled Processes Predict Process Performance Process Understanding 40

41 Process Understanding Link Between Lean Six Sigma and Compliance Characterize Process Variation Lean Six Sigma Process Understanding Enhanced Compliance Reduced Risk Reduced Process Variation Enhanced Prediction of Process Performance 41

42 Characteristics of Process Understanding Critical variables (Xs) that drive the process are known Critical uncontrolled (Noise) variables that affect the process output (Ys) are known and = Process has been designed to be insensitive to these uncontrolled variations (robustness) Measurement systems are in place and the amount of measurement variation is known Process capability is known Effective Process control procedures and Control Plans are in place Lean Six Sigma Provides the Methods and Tools for Developing Process Understanding 42

43 Conclusion Improvement is an imperative = Quality, Cost and Delivery must improve There are many types of opportunities for improvement One size does not fit all = Match the improvement approach to the project = Use an approach that works in all aspects of the business solving all types of improvement needs Process variation affects both process flow and product quality Compliance flows from process understanding Improvement Can Be a Very Profitable Business Enhanced Process Performance, Compliance and Financial Impact 43

44 References Hoerl, R. W. and R. D. Snee (2002) Statistical Thinking Improving Business Performance, Duxbury Press, Pacific Grove, CA. McGurk, T. L. and R. D. Snee (2005) A Systematic Approach to deviation reduction through Six Sigma, Pharmaceutical Technology Sourcing and Management, Vol. 1, Issue 7, Snee, R. D., K. H. Kelleher and S. Reynard (1998)"Improving Team Effectiveness". Quality Progress, May 1998, Snee, R. D. (2006) Lean Six Sigma and Outsourcing Don t Outsource a Process that You Don t Understand, Contract Pharma, October, Snee, R. D. (2007) 10 Steps to Process Improvement: Pharmaceutical Manufacturing, November-December 2006, 30-31; January 2007, Snee, R. D. and R. W. Hoerl (2003) Leading Six Sigma A Step-by-Step Guide Based on Experience with GE and Other Six Sigma Companies, Financial Times Prentice Hall, New York, NY. Snee, R. D. and R. W. Hoerl (2005) Six Sigma Beyond the Factory Floor Deployment Strategies for Financial Services, Health Care, and the Rest of the Real Economy, Financial Times Prentice Hall, NY, NY. 44

45 For Further Information, Please Contact: Ronald D. Snee, PhD Tunnell Consulting 900 East Eighth Avenue, Suite 106 King of Prussia, PA (610) or visit our website at: 45

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