Six Sigma is a process-focused quality-improvement initiative. The ‘processes’ in manufacturing/operational and transactional environments are somewhat distinct and thus demand partially different toolsets during the implementation of Six Sigma as well as in a BB training program. From Table 5.1, it can be seen that the major difference between the manufacturing/operational and transactional roadmap is in the Analyze and Improve phases, with slight differences in other phases. OR/MS techniques such as forecasting, queuing, simulation and modeling are essential tools in the Analyze phase since system-level analysis is usually needed in a transactional environment. In the Improve phase, major tools used in manufacturing/operational environment are DOE techniques; in contrast, queuing and mathematical program- ming techniques are usually needed for transactional environments. From Table 5.1, OR/MS techniques appear to be much more applicable in a transactional environ- ment. However, it should be noted that Six Sigma BBs working in manufacturing sectors are also expected to tackle transactional issues. This underscores the impor- tance and necessity of integrating OR/MS techniques with Six Sigma. The current evolution of Six Sigma is not simply a transition from the original manufacturing sectors to service sectors but a vehicle for making deep cultural change, inculcating system thinking and problem-solving, leading to quantifiable benefits.
5.3.1 Basic OR /MS techniques 5.3.1.1 Decision analysis
Various decision-analysis techniques are useful tools for making ‘good’ decisions in- volved in Six Sigma deployment as well as other business operations. Effectively made decisions have a profound impact on overall business performance. Multiobjective decision-analysis techniques can be used in, for example, Six Sigma projects, material, vendor, product and process selection. Multiobjective decision-analysis techniques are also useful tools to assist organizations’ strategic and tactical decision-making.
Meanwhile, sensitivity analysis is usually done in conjunction with decision-analysis to assess the sensitivity of the decisions made with uncertain factors. While decision- analysis techniques could be applied in the whole process of Six Sigma deployment as each phase may entail some decision-making, its major role is in the Define phase of the integrated Six Sigma roadmap discussed in Section 5.3.2.
5.3.1.2 Mathematical programming
Mathematical programming techniques include various mathematically rigorous op- timization tools such as linear programming, integer programming, mixed integer
A New Roadmap for Six Sigma Black Belt Training 53 Table 5.1 An expanded list of Six Sigma tools.
Manufacturing/operational
environment Transactional Environment
Define Project selection Project selection
Probabilistic risk thinking and strategic planning
Probabilistic risk thinking and strategic planning
Decision analysis Decision analysis
Process mapping Process mapping
Project management tools Project management tools Measure QFD and Kano analysis QFD and Kano analysis
Gap analysis Sampling (data quantity and data
quality)
Sampling (data quantity and data quality)
Measurement system analysis Measurement system analysis SPC Part I (concepts, implications of
instability)
Run charts (or time series graphs) Capability analysis Capability analysis
Monte Carlo simulation and statistical distributions
Analyze Basic graphical improvement tools Basic graphical improvement tools
FMEA FMEA
Hypothesis testing Hypothesis testing
Confidence intervals
ANOVA ANOVA
Correlation and regression analysis Correlation and regression analysis Reliability models and measures
Cost analysis Forecasting
Basic queuing systems Simulation and modeling Improve DOE (factorial, fractional factorial,
blocking, nested and RSM)
DOE (factorial, fractional factorial and blocking)
Robust design
Optimization and control of queues Mathematical programming
techniques Heuristics
Sensitivity analysis Sensitivity analysis
Control Mistake proofing Mistake proofing
Validation testing Validation testing
Control plans Control plans
SPC Part II: Control charts Basic control charts
programming, nonlinear programming, network programming, dynamic program- ming, goal programming, multiobjective mathematical programming, and stochastic programming. Problems selected for Six Sigma projects are not limited to the field of engineering but cover quality issues in transactional, commercial and financial ar- eas as well, with an explicit and strong customer focus.8Mathematical programming
54 Fortifying Six Sigma with OR/MS Tools
Table 5.2 A summary of OR/MS techniques integrated into Six Sigma phases.
OR/MS tools
Define Mathematical programming techniques for resource allocation and project selection
Decision analysis
Project management tools
Analysis Forecasting
Basic queuing systems Simulation and modeling
Improve Optimization and control of queues Mathematical programming techniques Heuristics
techniques, sometimes in conjunction with sensitivity analysis, can be exploited to solve such problems. These techniques have been predominantly used in production planning and operations management. They can be deployed in Six Sigma projects for project selection and planning during the Define phase of the Six Sigma deployment for selecting an optimal number of projects or to achieve profit maximization or cost minimization goals in general. Problems such as Six Sigma resources allocation, Six Sigma facilities layout and location, and production and service planning can also be solved using mathematical programming techniques. These applications may take a wide variety of forms depending on the particular problem situation and the various objectives involved. For example, given some limited capital budget, the decision of how to select a subset of proposed Six Sigma projects to invest in can be readily mod- eled as a single or multiobjective knapsack problem. Solution techniques for problems of this type are discussed by Martello and Toth,9 and by Zhang and Ong,10 among others.
Besides the Define phase, applications of mathematical programming techniques are interspersed in all subsequent phases. In particular, as the objective of mathemat- ical programming techniques is optimization, various techniques can naturally be weaved into the Improve phase to solve various optimization problems. For example, a general framework for dual response problem can be cast using multiobjective math- ematical programming.11,12 Nonlinear optimization techniques can be applied, for example, to optimize mechanical design tolerance13and product design capability,14 as well as to estimate various statistical parameters. In the Control phase, nonlinear optimization techniques have been applied to optimize the design of control charts, including economic design, economic-statistical design and robust design, design of sampling schemes and control plans. Examples of these applications can be found in many papers15−29. Some of these techniques are included in the proposed Six Sigma roadmap discussed in Section 5.3.2.
In addition, heuristics, the most popular ones of which include the classical meta- heuristics of simulated annealing, genetic algorithms and tabu search, are a class of effective solution techniques for solving various mathematical programming and combinatorial optimization problems, among others. It is thus proposed that a brief introduction to heuristics should also be included in the training of Six Sigma BBs and
A New Roadmap for Six Sigma Black Belt Training 55 the deployment of Six Sigma, particularly in the Improve phase. Detailed treatment, however, can be deferred to an MBB program.
5.3.1.3 Queuing
Queuing theory is concerned with understanding the queuing phenomenon and how to operate queuing systems in the most effective way. Providing too much service capacity to operate a system incurs excessive costs; however, insufficient service ca- pacity can lead to annoyingly long waiting times, dissatisfied customers and loss of business. Within the context of business-improvement, queuing techniques have frequently been applied to solve problems pertaining to the effective planning and op- eration of service and production systems. Specific application areas include service quality, maintenance management, and scheduling. Queuing techniques have been widely applied in such areas as manufacturing, service industries (e.g. commercial, social, healthcare services), telecommunications, and transportation. Queuing tech- niques can play a useful role in Six Sigma deployment, particularly in analyzing and improving a system providing services.
5.3.1.4 Simulation and modeling
Simulation is an exceptionally versatile technique and can be used (with varying degrees of difficulty) to investigate virtually any kind of stochastic system.2For in- stance, simulation can help to improve the design and development of products as well as manufacturing and service processes for a wide variety of systems (e.g. queu- ing, inventory, manufacturing, and distribution). Simulation has been successfully deployed in DFSS to replace costly preliminary prototype testing and tolerancing.
Also, simulation provides an attractive alternative to more formal statistical analysis in, for example, assessing how large a sample is required to achieve a specified level of precision in a market survey or in a product life test.21 Bayle et al.22 reported the approach of integrating simulation modeling, DOE and engineering and physical ex- pertise to successfully design and improve a braking subsystem that would have not been accomplished by any individual tool or method alone.
For system operations analysis, simulation is an indispensable companion to queu- ing models as it is much less restrictive in terms of modeling assumptions.23Queuing and simulation techniques also play important roles in inventory control24and supply chain management in organizations.
5.3.1.5 Forecasting
Every company needs to do at least some forecasting in order to strategize and plan;
the future success of any business depends heavily on the ability of its management to forecast well.2However, the availability of ‘good’ data is crucial for the use of forecast- ing methods; otherwise, it would turn into ‘garbage in, garbage out’. The accuracy of forecasts and the efficiency of subsequent production and service planning are related to the stability and consistency of the processes which are, in turn, influenced by suc- cessful applications of standard Six Sigma tools. Six Sigma tools and methods identify and eliminate process defects and diminish process variation. Six Sigma also requires
56 Fortifying Six Sigma with OR/MS Tools
that data be collected in an accurate and scientific manner. The combination of defect elimination, variation diminishing, and more accurate scientific data collection allows forecasting to be conducted more easily and effectively, which will, in turn, help to improve the effectiveness of production and service planning, operations scheduling and management. On the other hand, if the processes are erratic, then forecasting and subsequent production and service planning and operations scheduling will be much less effective or useful. Important applications of forecasting techniques within the context of operations management include demand forecasting, yield forecast- ing, and inventory forecasting which is essentially the conjunction of the first two.
In addition, forecast results are important inputs to other OR/MS techniques such as mathematical programming, queuing, simulation and modeling.
5.3.2 A roadmap that integrates OR/MS techniques
In the development of the new curriculum, we also consider the deliverables for each of the DMAIC phases. Table 5.3 presents a matrix relating the deliverables and an integrated toolset following the DMAIC roadmap.
The type of training BBs should receive is a function of the environment in which they work,25and training curricula should be designed accordingly. It is also important in the presentation of the tools to provide roadmaps and step-by-step procedures for each tool and each overall method.25 The characteristics of Six Sigma that make it effective are the integration of the tools with the DMAIC improvement process and the linking and sequencing of these tools.25 While most curricula proposed in the literature manifest their integration,26,27 the linking and sequencing of the tools is less apparent.25In this chapter, leveraging on previous programs and our consulting experience, a sequence of deliverables and the associated tools needed in a typical BB project is conceived, bearing in mind the tasks that need to be accomplished in DMAIC phases and the applicability of traditional Six Sigma techniques together with those techniques outlined in Section 3.1.
Table 5.3 presents a matrix that summarizes the DMAIC framework for both manu- facturing/operational and transactional environments. The vertical dimension of the matrix lists the deliverables in each DMAIC phase and the horizontal dimension lists the tools/techniques that could be used to serve the purposes in the vertical dimen- sion. The flow of deliverables is self-explanatory as they represent tasks/milestones in a typical DMAIC process. The toolset across the horizontal dimension has been for- tified with OR/MS techniques to meet the higher expectation of Six Sigma programs in delivering value to an enterprise. It should be noted that while it is conceivable that a specific OR/MS technique could be applied in multiple phases, we have made each basic OR/MS technique appear with intentional precision within the DMAIC process corresponding to its major areas of application for the purpose of conciseness. Through experience, literature and case study reviews, these are the areas where a majority of Six Sigma projects would benefit from the proposed OR/MS tools. Nonetheless, the placement of various techniques is by no means rigid, due to the broad scope of cover- age in Six Sigma projects. The matrix can be used as a roadmap for BBs to implement their projects and as a training curriculum for a new breed of Six Sigma BBs. While more elaborate techniques can also be included, it is felt that the current toolset is the most essential and can be covered in a typical 4-week training program for BBs.
Table5.3ARoadmapthatIntegratesOR/MSTechniques TOOLS AND TECHNIQUES Definition; leadership and implementation issues
Project selection Probabilistic risk thinking and strategic planning
Decision analysis Project management tools
Quality function deployment and Kano analysis
Gap analysis Process mapping
Data sampling techniques (data quality and quantity)
Measurement systems analysis SPC Part I (concepts, implications of instability) Run charts
Monte Carlo simulations and statistical distributions
Capability analysis Exploratory data analysis
Failure modes and effects analysis
Cause--effect analysis Statistical hypothesis testing Confidence intervals
Analysis of variance (ANOVA) Correlation and simple linear regression Reliability models and measures Cost analysis Forecasting Basic queuing systems
Simulations and statistical distributions Design of experiments
Response surface methodology Advanced experimentation: robust design concepts and techniques
Optimization and control of queues Mathematical programming techniques Heuristics
Sensitivity analysis Mistake proofing Validation testing Control plans
SPC II: control charts Basic control charts
Formulating strategies with emphasis on risk Translating strategies into action plans Selecting and scoping of projects Planning of projects Listening to the voice of customers Identifying processes Identifying potential KPIVs and KPOVs Translating customers' needs into business requirements Understanding and learning from data Understanding and dealing with uncertainties Understanding instability and variations Knowing current capabilities Analyzing data Assessing risks Estimating cost components Performing scenario/what if analysis Analyzing job sequences and cycle time Evaluating options and work-flow designs Selecting KPIVs and KPOVs Establishing transfer function Improving performance measures Reducing variability in processes Implementing robust processes Verifying achievement Sustaining Improvements
CONTROL IMPROVE
ANALYZE MEASURE
DEFINE
Understanding Six Sigma
DELIVERABLES
IMPLEMENTATION PHASES TRAINING PHASESDEFINEMEASUREANALYZEIMPROVECONTROL
57
58 Fortifying Six Sigma with OR/MS Tools
Six Sigma is well known to be a highly applied and result-oriented quality engineer- ing framework and curriculum as compared to other programs such as the Certified Quality Engineer programs run by the American Society of Quality.26The basis of its strength does not lie in each individual tool but in the effective integration of the vari- ous tools, with a strong emphasis on statistical thinking in the reduction of variability in products and processes. However, as discussed in the preceding sections, tools and techniques within the existing Six Sigma framework are inadequate to deal with many problems in product and service delivery processes. In a bid to close the gap, a stronger Six Sigma toolset containing OR/MS techniques capable of dealing with many of such problems has been proposed. The linking and sequencing of the proposed tools are driven towards a practical integration within the Six Sigma DMAIC framework.