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The chapter details the philosophy and the methodology of Six Sigma. Literature abounds with versions of Six Sigma and discourses on other quality management tools and models, frequently portraying the models as exclusive of each other. This chapter examines the philosophical underpinnings of Six Sigma and its methodological contrivances. The literature review stretched across specialist journals, blogs and texts. Ten quality specialists and six trainers/tutors within Six Sigma curriculum were engaged in qualitative discussions. Gathered data was treated synthetically within a continual comparative framework. Conspicuous observations were the migration of Six Sigma into the service industry accompanied by data science and data analytics. The use of control charts, check sheets, graphs, flowcharts, scatters, Pareto and Ishikawa processes stood at the centre of high-intensity users of Six Sigma. Six Sigma is used in a somewhat monolithic or dogmatic way by many. Failure to appreciate the symbiotic relationship among elements of Six Sigma, or within other models and between Six Sigma and other quality models, would attest to the need to improve conceptual, managerial and technical understanding of the value of Six Sigma in the pursuit of continual improvement, however measured. Further to this, it is recommendable that organisations deliberately embed quality training in their Learning and Development curriculum focusing on skilling more than on fulfilling external, or top-level imperatives.
The National Institute of Distance Online Learning (NIDOL-SA), South Africa
*Address all correspondence to: dougmatorera@gmail.com
1. Introduction
Six Sigma is the highest performance level where defects should be below 3.4 per million opportunities. The chapter in Section 2 discusses the growth, philosophy and methodology of Six Sigma, while Section 3 highlights the methodological versatility of Six Sigma within its philosophy of continuous improvement. Throughout the pre-Six Sigma stages, the guiding philosophy remains to improve quality, a generic goal, with sets of contextually relevant procedures, processes and methodologies all hinged on data gathered about the market performance of products and services. In Section 4, the duet of Data Science and Data Analytics (DS-to-DA) is discussed, arguing that these disciplines progressively elevate Six Sigma to a data-entrenched and evidence-based strategy. The space of descriptive, diagnostic, predictive, exploratory and prescriptive DS-to-DA is exposed. In Section 5, various quality management tools are exemplified, mostly with illustrations. There is need to creatively assimilate these tools into the corresponding DS-to-DA framework. The identified tools do not treat data to the same depth, with the same breath, and should not be treated as equals in influencing decision-making. In Section 6, the compatibility of Six Sigma with other quality models is presented. An argument that Six Sigma subsumes most of the quality models ranking high in the league of quality models is made. Six Sigma is a tertiary model, and variably the other quality models augment it. In effect, they are presumptions in the edification of Six Sigma. For instance, Six Sigma assumes that quality improvement involves upping Design configuration, Processes alignment and content, Market intelligence and Technology optimisation. These assumptions effectively make up the models of Total Quality Management, Business Process Reengineering, Balanced ScoreCard and Design Thinking. Six Sigma further assumes the continual interface mapping done to identify ‘noise’ and waste, eliminating them, thus saving on time and other resources. In Section 7, mention is made of the increasing importance of Six Sigma in the public and private sectors, accounting for testified quality and performance improvements. Six Sigma is inherently lean, that is its birthmark.
Six Sigma!!! Are there any stages or levels, like 1, 2, 3, 4, 5 before the Sixth? The position of this chapter is YES, however these are little talked about and acknowledged. It is not thinkable that an organisation would lower its defects rate to a point of 3.4 in a million opportunities right from scratch in a single blow. The Sigma levels preceding the sixth would be creatively determined in the organisation by its key quality protagonists. We look at Six Sigma as the point in a Sigma Series where the organisation/performance has reduced waste and noise to 3.4 defects per million opportunities (DPMO) or 0.00034 per cent. Quality of goods and services is the oldest preoccupation for the human race, right from when humans started using their most primitive tools and engage in making choices on throughputs in the creation of services and in the production of goods. Organisations mature in their mentality, abilities, capabilities, methodologies and efficiency in the creation of services and production of goods, and in servicing their markets. The creation and development of growth models in satisfying the market is the quintessence of a Sigma approach to doing quality.
But how did Motorola Company engineer Bill Smith, 1986, come to Six Sigma and not 5 Sigma or 7 Sigma? In the context of Motorola at the time, mathematical/statistical analytics had a special place in the production chain. Remember it was the epoch of ‘if you cannot measure it, you cannot control it’. We presume that Engineer Bill Smith and Motorola did appreciate the pre-Six Sigma stages but just did not care being vociferous about them. Let us see how we would arrive at a point-destination of—Six Sigma, based on the statistical graph representing a moment of symmetrical distribution of the bell curves that are used in statistics to represent standard deviations. A single sigma (One Sigma) would symbolise an over/above and a below single standard deviation from the mean. At the Six Sigma point, the failure mode (defect rate) will be extremely low (should be 3.4 defects in a million chances). In a graphical representation, this point has six bell curves over (+) the process mean and six bell curves below (−) the process mean (Figure 1). See the illustration below for a statistical and graphical representation, and the visualisation of same at Table 1.
Sigma series showing NIDOL-SA’s intended mathematical indicators at each Sigma Level
Sigma Level
1-Sigma
2-Sigma
3-Sigma
4-Sigma
5-Sigma
6-Sigma
Level indicator. (least mark average)
50% (extant capabilities)
70% (proximal milestone)
80% (post proximal target)
90% (medium-term milestone)
95% (penultimate milestone)
99% (ultimate milestone/target)
Interpretation
50 in 100 students averaging below 99%
30 in 100 students averaging below 99%
20 in 100 students averaging below 99%
10 in 100 students averaging below 99%
5 in 100 students averaging below 99%
1 in 100 students averaging below 99%
50 in 100 students averaging above 99%
70 in 100 students averaging above 99%
80 in 100 students averaging above 99%
90 in 100 students averaging above 99%
95 in 100 students averaging above 99%
99 in 100 students averaging 99% and above
Table 1.
Illustration of NIDOL-SA’s adopted six sigma-based targets and milestones (source: author).
This is the point of the least defect rate—the aspiration of all organisations. An organisation can depart from the notoriety of the 68-95-99 doctrinaire and remodel the interpretation of the graph to suit their organisation’s strategic goals. For instance, NIDOL remodelled the 68-95-99 interpretation to its 50-60-70-80-95-99 milestones. We appreciate that ‘Six Sigma’ arose in a very mathematical, statistical environment where measures were quantified and graphed. After this aeon, innovators began exploring the migration of Six Sigma into efforts to improve quality in the services sector and other product-service interfaces where numbers alone were not excellent tools to (fore)tell performance. Let us go through the script below.
We entered a market that already had 82 wealthy providers. Our success would be only if we beat all or a handful on customer satisfaction. We started onto Design Thinking, then realised we needed something more riveting, resolute, and ruthless. We subsumed Design Thinking into Six Sigma. We picked up average mark as our teaching-learning performance metric. We said our Six Sigma point is when the NIDOL Grade 12 student average mark is 99+%, an equivalent of 1 student in a hundred averaging below 99+%. This is a ruthlessly stretchy and tough target in education, be it at class, school, or institutional level. We pegged a 5 Sigma at 95%, then 4 Sigma at 90%, and 3 Sigma at 80% and 2 Sigma at 70% and lastly 1 Sigma at 50%. The extant student average was at 51% then, meaning effectively we were operating at 1-Sigma.
We welcome the fact that there is no logic or rule that precludes the creative and innovative application of the graph in similar settings. This is the academic-pragmatic value from Table 1 as adapted in the NIDOL-SA (an Education Institute) example. In a numerical sense, the target of Six Sigma as a performance goal remains as its mathematical and statistical definition—a point of performance where excellence is better than 0.00034% defects (3.4 DPMO). The formulae below are generally used as measures of the Six Sigma test:
Equation 1. Six sigma equation to determine the defects per million opportunities.
Table 1 highlights vertical succession of milestones to the point of the organisation’s vision. Assuming each proceeding Sigma level becomes a target, the organisation can work out transition strategies to enable the escalation from its extant to that proximal/proceeding Sigma level. For instance, a Strategy Plan to take the organisation from 1-Sigma to 2-Sigma, then another Strategy Plan for 2-Sigma to 3-Sigma, etc., until a Strategy Plan for taking the organisation from 5-Sigma to 6-Sigma. This is a stage in an organisation’s Strategy Plan that turns ‘Six Sigma’ into everything from a metric to a Strategy. We are beginning to discuss ‘Six Sigma’ in that perspective.
The implication was that each Department, subject, teacher, and student would appreciate in what Sigma level they were operating or living in. This knowledge was crucial in having everybody engage themselves in critical assessment and evaluation of shortcomings and corrective measures for a higher Sigma level. Three-layer Performance Improvement Plans were designed: Subject-Operational Strategy Plan, Department-Management Strategy Plan, Campus-Leadership Strategy Plan.
The Six Sigma philosophy of continuous improvement through total transformation of everything that matters for the desired superior target buttresses in a methodology of setting milestones, as in 1-, 2-, 3-, 4-, 5- and 6-Sigma. each transition strategy plan operationalised each of the roadmaps: Technology for Six Sigma (TFSS), Six Sigma Process Design (SSPD), Marketing for Six Sigma (MFSS) and Design for Six Sigma (DFSS), contextually. See Figure 2, and note that each can operate monolithically, and with others dyadically, triadically, and in foursome. Each roadmap can be self-sufficient for some challenges just needing its respective Voices. Variably a roadmap needs to work with sister roadmaps in dyads, triads, or quartet permutations at variable levels and intensity of integration. The variance is time (historically) and contextually determined. The level and amount of exigency in the transition between milestones varies within the whole Six Sigma journey and from organisation to organisation. This versatility allows for Six Sigma to be used in diverse situations.
Figure 2.
Quartet of six sigma roadmaps (source adaptation from: Ref. [2]).
The overlapping is a strategic capability that is irrevocably fundamental in everchanging, high-tech, high-speed markets. The fact that the structure, purpose and operationalisation of the Six Sigma roadmaps is situational means there is need to co-adapt the data mining tools and the team mindsets. This co-adaptation calls for academic, technical and cultural agility. This creativity and versatility are inherent in Six Sigma. With experience we would notice that the roadmaps SSPD, MFSS, DFSS and TFSS will be consistently present despite their weight and influence varying in time and space. This quartet is crucial in giving the Strategy Team process, design, technological and market intelligence, and agility.
At the ground level, Six Sigma is referred to as a tool, and as it ascends the scale it is variably referred to as a metric, a method, a methodology, a model, an initiative, or a Strategy [3]. This is understandable because Six Sigma is talked about, often loosely, by non-Sigma-ists, by laymen, by theorists, and by Six Sigma Belt holders as all of the aforementioned. This is far from implying that Six Sigma is a lousy putative concept. These actors and contributors have varying degrees of its understanding. In essence, Six Sigma is pretty nifty. Looking at its reliance on data, we can argue that Six Sigma’s foundation is data operationalised within the philosophy of ‘measure, report and act’. Figure 3 show the miscellany of tools that a Sigma effort could use to mine data that could ultimately be analysed for high-level, high-impact decision-making. The tools will compute the collected data to differing levels. It is possible to command the level of computation that relate to the depth of data treatment that suits and aligns with your desired outcomes.
Figure 3.
Some data-gathering tools used in six sigma (author: synthesis from literature).
These include: Dub InterViewer (allows for face-to-face and internet research from a centralised platform), Focus On Fire (excellent with capturing of different forms of data), Fulcrum (easy with design and data collection), GoSpotCheck (for real-time data and information where instant decisions are required), QuickTapSurvey (for customised surveys and data-gathering, whatever the situation be), Repsly Mobile CRM (an all-in-one field management tool that offers opportunities up to high-level executions), Zonka Feedback (provides customisable customer satisfaction surveys and feedback forms). The decision of the type, quality and quantity of data to be gathered is as critical as are the tools to use in gathering the data, the depth of analyses, and the level and scope of reconciliation of the analytics.
4. Data Science and Data Analytics in Six Sigma contexts
To have a fuller picture of the data at hand, it matters to run analyses that are integrative and logical. We need to have a profound understanding of the situation at hand, and it is always prudent to start off from thick descriptions of that situation. Figure 4 shows the benefit that each analysis and its analytics would bestow to a philosophy and methodology of continuous improvement like Six Sigma.
Figure 4.
Six Sigma data analyses and analytics cycle. (author: synthesis from literature).
4.1 Descriptive analytics and the question of what happened
Descriptive analytics is a statistical interpretation used to analyse data of historical, and current outcomes, phenomena, and events thus making a platform from which to track patterns and trends, and understand their what, where and when. Despite being fundamentally a data mining and aggregation technique, descriptive analysis and analytics are superficial, lacking inferences and predictive prowess. Two things are important in realising the full potential of descriptive analytics: husband descriptive analysis with other analytics and ensure sanity of the data and the methodology by which the data is obtained and analysed.
4.2 Diagnostic analytics and the question of why did this happen
The discipline of diagnostic analytics uses descriptive, explanatory and confirmatory data analyses to determine the reasons behind specific events. Here, protagonists identify root causes (Ishikawa), determining correlations (Scatter) and uncovering relationships between different variables. This highlights the “bundling effect” in data analytics.
4.3 Predictive analytics and the question of what would happen
Predictive analytics, basing on descriptive and diagnostic analytics and statistical algorithms, strive to forecast future outlooks and evaluate the probability of various scenarios. In this regard, predictive analyses stand abreast exploratory analytics.
4.4 Exploratory analytics and question of what suits us best
Exploratory analytics culminates in a set of alternatives, each with its pros and cons. It is the epitome of the SMARTWI, morphs specific scenarios, assigns measures to the scenarios’ features, examines the feasibility of elements, components, and the whole, and designs budgets-schedules relating to the elements, parts and ‘wholes’. Exploratory analytics further compare scenarios’ Goal Relevance Score and Return on Investment. They further compare the Internal Market and Vision Impact Factor of the scenarios. Predictive analytics should be left to what they ought to be—to predict futures. This separate treatment approach will not contaminate and mislead the exploratory analytics. Exploratory analytics would culminate in a prioritised list of strategies or a very specific recommendation for a strategy to adopt. Now comes the infrastructure of policies, procedures and responsibilities, accountabilities and every other accoutrement of leadership-management to make the adopted strategy work. Making adopted strategies work is the field of prescriptive analytics.
4.5 Prescriptive analytics and the question of what should be done for our vision’s sake
Prescriptive analytics uses optimisation and simulation techniques to determine the best course of action for desired outcomes. It should show the sets of ‘coercions’, ‘carrots’, catalysts, enablers and everything that would ensure that all is happening in the direction and interest of the organisation’s vision.
5. Linking quality control/assurance tools with Six Sigma
We have shown some of the tools used in managing quality and linked them with data sciences and data analytics in the context of Six Sigma. These links vary in strengths but are all the same, valuable for continuous improvement. They are usable in non-Sigma contexts with other quality management models as well. We discuss some of them as we continue below.
5.1 The control chart
Philosophy: The quality of products and services is a function of the efficiency with which variance from a desirable norm is controlled.
Methodology: Compute data to establish a desirable line of best quality fit, develop strategies that reduce variance and weed away the negative constraints, enhancing positive constraints.
Figure 5 illustrates a Control Chart and of importance is the magnitude of the whole field of variance and the range of such variation. The control chart consists of a graph that shows how a process changes over time. The control chart hosts three line graphs: The upper line shows the upper control limit, the lower line shows the lower control limit and the middle line represents average points at each (X:Y) coordinate. See Figure 5 of an illustration of a Control Chart with Upper Control Limit (UCL), Lower Control Limit (LCL) and Control Line. The lines are determined from historical data, which should be representative of the most desirable conditions. These are worthy of replicating and ensuring their sustainability. A process variation will be described as consistent if it resembles the historical patterns (desirable variations). A process variation is inconsistent if it was skewed either towards the upper line or the lower line or if it staggers once towards the upper line then towards the lower line.
Philosophy: Checking out discrepancies at the source as frequently is the hallmark of Total Quality Management (TQM).
Methodology: Ubiquitously and continuously measure, report and act on all quality constraints from the coalface to the highest escalation.
Check sheets are usually in the form of simple forms allowing users to record data about a process or a factor. These are easy to use and understand tools, quite good at making clear pictures of the state of a factor or organisation. The most used check sheets are the Defect Check Sheet, Tally Check Sheet and Defect-Location Check Sheet. The collected data can be tabulated and or ranked in ascending or descending order. The frequency of a specific event or factor can be shown. Among the advantages with the use of check sheets are their cost effectiveness and efficiency in displaying data. Check sheets are generally a consistent way of displaying data for effective quality assurance.
5.3 Histogram/frequency distribution diagram
Philosophy: The cost of quality-ing is a function of your assiduity in treating neutralisers, enhancers and destroyers of quality.
Methodology: Do the Fishbone and the SWOT analyses, reinforce quality-value enhancers, subdue quality-value neutralisers and destroyers.
A histogram consists of bar charts showing the distribution or pattern of observations that are grouped into convenient class intervals. There are several software applications that can be used in the construction of histograms. You can construct a histogram following the steps: (a) choose an item of interest/subject for the measurement or study, (b) choose the most relating software application for treating your observations, (c) enter your data into the software application, (d) command the application to arrange your entries in ascending/descending order, (e) study the data and reason out a class interval which you will apply to the whole data, (f) operationalise the class interval, and your data will be arranged into groups, (g) note the amount of observations/frequency in each group, (h) decide on appropriate scales for the Y- and the X-axes on your proposed graph and (i) command the software application to construct the histogram, and interpret your histogram (Figure 6).
Figure 6.
Illustration of a histogram (author: NIDOL).
Ensure the Histogram is fully identified, for example Distribution of Canteen Complaints by level of impact to NIDOL Jan-2023-to-Aug. 2023 at NIDOL-SA, Pretoria Campus. 10 Sept. 2024: Dr. Mymmy Matorera BSA - VOCALIST.
5.4 Flowcharts
Philosophy: The totality of quality of a product/service depends on the totality of quality of each single step in the Product Planning Matrix and the production.
Methodology: Ensure that the concept, the path and the activities of value creation, and of interface mapping are optimised for quality and looped with corporate quality-ing strategy.
Flowcharts show the path taken in a process of producing a good or of creating a service as in Figure 7. The flowchart will show points of critical value-adding activities.
Figure 7.
Illustration of a flowchart (author).
The points are important in that they indicate interval periods, time spend at each station, who worked on the product/service at the station and what value is added to the product at the station. Important managerial lookouts include ensuring that those assigned at each station have the best knowledge, skills and competences to audit each input, to value-maximise each input and to control what should be output further.
5.5 Scatter diagrams
Philosophy: Treat quality in the context of relationships that enhance, neutralise or destroy it (Figure 8).
Methodology: Understand and manipulate factors and factor-relationships in ways that enhance quality performance.
Scatter Diagrams look at three aspects of a relationship among factors influencing the quality of a product or service. Firstly, the strength of the relationship among the factors that influence quality of the product/service. See illustration Figure 9. Secondly, the direction or nature of the relationship in which the factors could be sharing a positive relationship or a negative relationship or the relationship could be positive over some range and then negative over another range. There is yet a variance in which there may be no relationship at all. Quality strategists must take courage and intellect in taking decisions about coupling, or decoupling, configuring, or reconfiguring, dismember or re-member value-factors. A value-factor being anything that can destroy (negative value-factor) or enhance (positive value-factor) the ultimate quality of a product or service, it should be treated as a critical success factor.
Philosophy: Understand the vital few that generate the greatest satisfaction in your most valuable market(s) much as you should understand the vital few that create the greatest dissatisfaction in your wider market.
Methodology: Use the various tools to isolate the features in the Product Planning Matrix and Product Performance Matrix, track impact of each feature continually and keep updating and readapting, readapting and readapting to delight the market.
A Pareto Diagram is a Statistical Process Control (SPC) tool that arranges items in the order with which they contribute to a factor/feature of a product/service. When used in the SPC and quality improvements, the Pareto Diagram will facilitate the prioritisation of projects for improvements, the prioritisation of the setting up of corrective action teams such as ad hoc teams, skunkworks or quality circles. Pareto Diagrams are used for the identification of products and services receiving the highest number/frequency of complaints, of the most frequent complain types, and the identification of the most frequent causes of joy or dissatisfaction on a product or service. Mr. Vilfredo Pareto argue that 80% of the failure modes are caused by just 20% of difficulties. Inversely, 20% of activities (vital few) created 80% of the appreciated value (useful many) in a product or service. This distribution is widely referred to as the 80:20 Principle.
5.6.1 Procedure in design and development of a Pareto diagram
Create a table of values using item and each item’s frequency or quantity contribution. You can sum into a single group all items with very marginal frequencies or amounts of contributions and name this group ‘Other’.
5.7 Cause-and-Effect diagram (Ishikawa diagram)
Philosophy: Quality is improved by working away the negative constraints, strengthening the positive constrains, and keeping collaborative teamwork and vigilance.
Methodology: Keep vigilance on indicators of failures, trace and track them to their root causes and weed away the conditions causing the root cause.
When the Ishikawa Diagrams is done well it shows the relationship between a cause(r) and the symptom/effect/result. The cause-effect diagram helps in the generation of systems-based ideas, insights and frameworks about causes of failures and successes. The cause-effect diagram helps in giving a structured understanding of the dynamics and dynamic relationships among factors/influencers of either negative or positive constraints relating to quality of products and services. In most cases, Ishikawa Diagramming needs group of members who have excellent knowledge, skills, and competences around the quality issue under study, the market of users, consumers, and interpersonal skills. There are non-technical competences of critical importance in Ishikawa Diagramming that includes Design Thinking, Growth Mindsets, Civil Vulnerability and Cultural Versatility. In essence, movement through each fish bone involves brainstorming, hearing, listening, accommodating and appreciating being accommodated. These are all important social, team and discussive skills without which an Ishikawa Diagram would make little sense to quality assurance and quality control. Constructing a useful Ishikawa Diagram requires the spirit of collaboration, cooperation, communication, commitment and civil vulnerability and accommodations.
5.7.1 Designing an Ishikawa diagram
Create a table of values using item and each item’s frequency or quantity contribution. You can sum into a single group all items with very marginal frequencies or amounts of contributions and name this group ‘Other’.
The cause-and-effect diagram as a quality tool hooks on the Six Sigma philosophy of discerning indicators of deficiencies from authentic causes and timely, accurately and profoundly responding to the root causes of quality failure modes. In designing the Ishikawa Diagram, you would need to assemble the following: (a) complaints database, (b) compliments database, (c) suggestions database, (d) customer competitive survey database, (e) Research & Development database, (f) Engineering/specialist presence and (g) VOCALIST’s presence.
Appoint a rapporteur (the best would be team-rapporteurship made up of Tech-specialist, PR specialist, VOCALIST). It is important that the three have frequent pre-brainstorming interactions and previews of the prospectives during the brainstorming sessions. An invitation, preferably with a preamble or agenda, to the brainstorming session must be shared in time. In running the brainstorming session, it matters to identify the root causes (RC) and for each RC identify sub-causes. This approach results in an Ishikawa Diagram with each RC leading to sub-causes, leading to Result/Problem. The RCs should be ranked in terms of their relative impact on the Result/Problem. Done well, the team would be able to output threads of relations in the generation, development and impact of causes/failures.
A special team (skunkworks) can be assigned to ONLY clean the Ishikawa Diagram. After the cleaning, participants should reconvene to validate the cleaned Ishikawa Diagram. The team can now design and develop a response mechanism to the ranked RCs. The remedying strategy is shown here after the BIG CYCLE. Figure 10 shows an illustration of a Fish bone diagram. It is important to give title to the cleaned ID, for instance:
Figure 10.
Illustration of an Ishikawa process (author: NIDOL).
Cause-Effect analysis for the 3% drop in market share (20 learners), NIDOL-SA, East London Campus, 12 Feb. 2024: Dr. Mymmy Matorera BSA – and VOCALISTs. (Dr Daka, Dr. Drew, Prof. Kumbie, Sir Dr. Yaron, Maam Dr. Sally, Dr. Marthinus)
I team rapporteur-ed an Ishikawa at NIDOL, we quickly noticed the Brainstorms were heavy on symptoms with isolated pieces of real problematic issues. We restructured our mental model of the Ishikawa Process to: a) focus and draw conversations to the four-quality cardinals, b) define the causes in the framework of SSPD, MFSS, DFSS, and TFSS with each strand culminating in the Root Cause. Then we clarified the Real Cause. Beyond the Root Cause, we proposed the Solution and configured the corrective steps. It is always best to frame the chain of response(s) to the Root Cause immediately then validate shortly thereafter.
5.7.2 From analyses and analytics to Ideal Competitor
The deployment and use of the quality management tools, techniques and the subsequent data analysis and analytics should culminate in a comprehensive understanding of the quality landscape of the organisation. This atlas of positive and negative constraints should now become the basis for a roadmap of the vision of the organisation. You should realise that at this point, if the drive for quality was protagonised by growth-minded facilitators and discussions and participation were effectively prioritised; the organisation must now be at a point of high-level vision sharing. At this point, it matters going back to the Balanced ScoreCard (BSC), revise it and adapt it to become an evidence-based BSC. We now have evidence from running through the Six Sigma.
After we played all the analyses and analytics, we wrote up a new vision befitting our context and the competitive pressure in the market. A skunkworks ran through the Situation Analysis Document with an eye to clean it and augment it. The skunkworks then drafted a model of an Ideal Competitor. This is an imagery of a competitor who uniquely combined every aspect thought to enhance quality performance: experienced workforce, unlimited resources, excellent leadership, clear alignments, clear connectivities, etc.
6. Compatibility of Six Sigma with other quality models
Literature abounds with quality tools, techniques, models and strategies for quality improvement. Six Sigma features among the top leagues. These models show lots of convergences and some differences. They also differ in clarity and sophistication. Six Sigma relates with such quality models as: Business Process Reengineering, Quality Function Deployment, Total Quality Management, Business Score Card, Design Thinking, ISO Series, etc. Each has elements of the others, and we discuss these relations below.
6.1 Business process reengineering
The quintessence of Business Process Reengineering is running a thick description of the status quo that is presently impacting quality in an operation, sector, brand or business unit. Positive and negative constraints of quality are identified, and their presence explained including blow-by-blow and intrusive inspections of the dynamics of the factors. Processes are re-evaluated using data analysed through the various tools and techniques: histograms, control charts, etc. Members can now work process-by-process, examining them for value-addition, fit, and congruence with concurrent processes, upstream processes, and downstream processes. Another level of process analyses would be evaluating organisation-to-organisation, business-to-business, business-to-department, department-to-department, department-to-team, team-to-team, team-to-individual(role), individual(role)-to-individual(role) and all other diagonals and permutations. Examining the processes in triads and quartets is welcome. Your intuitions and the Ishikawa Process can give hints as to the scope of such process analyses. Clearly describing these, explaining their undesirable aspects, diagnosing how things went wrong, exploring variants, and prescribing new relationships and modes of operations would complete the process reengineering.
6.2 Six Sigma and QFD
QFD and Six Sigma share several commonalities. Both use each other variably as a tool or methodology. This accounts for them using similar techniques and sometimes with variations. Among the common techniques are as follows:
Design for Six Sigma (DFSS) (Voice of Business plus Voice of Customer),
Marketing for Six Sigma (MFSS) (Voice of Market plus Voice of Business),
Technology for Six Sigma (TFSS) (Voice of Employee plus Voice of Customer),
Six Sigma Process Design (SSPD) (Voice of Employee plus Voice of Market),
DMAIC (Define, Measure, Analyse, Improve, Control) and
6.2.1 Design for Six Sigma (DFSS) (Voice of Business plus Voice of Customer)
In DFSS, the idea is to design goods and services that satisfy, delight, or “woow” the customer. The key voices that supply the required data for DFSS are Voice of Business (VoB) and Voice of Customer (VoC). Products and services are designed for Business goals and must be competitive on the market. A Product Planning Matrix needs show the features and the hierarchy of demand from the Business, customer and competitive survey point of view. The Kano Diagram would assist greatly in DFSS—the classification of features is visualised in a Kano Diagram.
6.2.2 Marketing for Six Sigma (MFSS) (Voice of Market plus Voice of Business)
In MFSS, the idea is to collect Voice of Market (including competitor, markets where you do not operate, near-markets of similar goods and services and buyers) and Voice of Business (including local and international legislation relating to the good/service and their neighbours). The methodology in MFSS is to reduce defects, reduce overmarketing, reduce disproportionate investment and expenditures on markets, goods, and services for a low Return on Investment. This does not rule out aggressive entry and determination to create a market in new areas or with new customers. MFSS is not short-termist and too cherishes delayed profits, as long as there is an infrastructure that assures innovation, the highest quality, efficiency and cost control.
6.2.3 Technology for Six Sigma (TFSS) (Voice of Employee plus Voice of Customer)
TFSS blends the VoE and the VoC. The assumption is that the employee who finally works to produce the goods and services will ultimately influence the content and form of the goods and services. While the Kano Process and the Product Planning Matrix (PPM) play their roles in quality of goods and services, the knowledge, skills and competences of the employee are of paramount importance. You should be appreciating the importance of passing employees through the Six Sigma Belts training, in-servicing, involving them in conferences and other organisational cycles.
6.2.4 Six Sigma Process Design (SSPD) (Voice of Employee plus Voice of Market)
Six Sigma Process Design roadmap husbands Voice of Employee with Voice of Market to propose remediations to the current failure modes (defects). Processing VoE and VoM should help greatly in understanding the undesired aspects of extant production, delivery, marketability, etc., of the goods and services. Thus, processes are modified, dismembered, removed and redesigned, to ensure greater market and customer satisfaction. Interface Mapping should help greatly in identifying process noise, overloads and valuelessness, and with these indicators, greater alignment, value-loads and right-sizing should be achievable. Quality Function Deployment, Management by Objectives, Total Quality Management (TQM), Balanced ScoreCard (BSC) and Business Process Reengineering (BPR) thrive on putting responsibility for quality on everyone at the basic level and this is the hallmark of Six Sigma. Further to this commonality, all assume that quality should be deployed to the nodes where the quality creating activity will happen. Thus, these quality points should be optimised to their fullest.
6.3 DMAIC, DMADV and Six Sigma
The (secondary) techniques Design Measure Analyse Improve Control (DMAIC) and Design, Measure, Analyse, Develop, Validate (DMADV) are used within Design for Six Sigma (DFSS), Market for Six Sigma (MFSS), Technology for Six Sigma (TFSS) and Six Sigma Process Design (SSPD) to perform the groundwork that can be started and finished with such tools and techniques as the extended Pareto Process (Diagram) or the extended Ishikawa Process. Normally, the Pareto and the Ishikawa Define, Measure and Analyse the issue/problem at hand. Let the discussion below help us fully appreciate the intimacy between DMAIC and DMADV—too similar, they sound like two names for the same thing. The first three features DMA (define, measure and analyse) are common. Let us now explore the remaining -IC (improve - control) in DMAIC and -DV (develop - validate) in DMADV.
Define: The numerous tools will help in computing the data and escalate to a position of clearly defining the root cause, symptoms and constraints (positive, negative, null) [4, 5, 6], etc.
Measure: Six Sigma is data-based, measures of inputs, throughput value-addition ratios, output—outcome market impact improvement ratios are measured, reported and acted on with an eye to continuously improve towards zero variance among: a) Hypothesised quality and Planned quality; b) Planned quality and Marketed quality; c) Marketed quality and Observed quality; d) Observed quality and Perceived quality. The identification of the few and many in Pareto Process, of the factors in the Kano Process, are critical measures in Six Sigma.
Analyse: Six Sigma recognises that there is massive data that can be gathered about a product/service right from conception to post-use in the market. Such Voice of Customer (VoC) data would help optimise both Business and Quality performances if passed through descriptive, exploratory, explanatory, diagnostic, prescriptive, predictive analysis and analytics.
Improve Vs. Develop: The platform of improvement is normally the (re)design: putting add-ons; trimming; re-membering features; eliminating features and repurposing. Essentially, we (re)design (rethinking and restructuring) to improve performance of goods and services.
Control Vs. Validate: Validation implies comparing X to a desirable Y, and when the X does not measure up, it is discarded, (re)worked, redesigned and made to comply with the criteria. There are sets of pre-manufacture criteria, including training of the worker in the production chain, their licencing and certification, all meant to control and validate the process of producing to desirable quality.
6.4 Total Quality Management
Total Quality Management relates to Six Sigma in that both thrive on the philosophy and methodology of empowering processes and personnel so that they perform to the highest quality levels in everything they undertake. The relationship goes deeper in that TQM can be used to prime an organisation for adoption of Six Sigma. During this pre-Six Sigma stage, systems and personnel could be schooled on the Six Sigma and have the organisation (re)structured, (re)cultured and readied for Six Sigma.
6.5 The Balanced ScoreCard
With the Balanced Score Card (BSC), Six Sigma can have the organisation theoretically broken down into its departments [7]. The balanced assumes that each department is held as important to quality as all others. The structure breakdown will improve the visibility of the work breakdown structure in the organisation or sector where the Balanced ScoreCard is being applied. The work breakdown should have resulted in a list of Key Performance Indicators (KPIs). The work breakdown structure in turn improves visibility of the activity breakdown structure. These key activities can be subjected to interface mapping. When done well, the interface mapping activity can show which activities are being duplicated, are time wasters, are valueless gap-fillers, can be thinned, can be combined, should be reassigned (given off to other personnel or taken over from another personnel), etc. Now, each key activity is treated as a critical success factor (CSF).
6.5.1 Key Performance Indicators (KPI) and Critical Success Factors (CSF)
In continuation from above, a critical success factor is a factor that if not performing well will greatly affect quality of the goods and services. Now, the Key Performance Indicators (KPIs) and the Critical Success Factors (CSFs) are assigned target scores that reflect their perceived importance to quality of goods and services. Elements of the Balanced ScoreCard (BSC) are carried out at different stages of Six Sigma. In BSC, as with Six Sigma, the organisation is broken down into its Key Performance Units and get them assigned scored targets and the related workers focus on scoring 100% on each measured/scored targets.
Critical factors in assuring the success of continuous quality improvement through Six Sigma include a set of knowledge, skills and competences on the part of the contributors. There are structural issues emphasising the importance in aligning the Strategy-Culture-Structure of the organisation. Other factors relate to the alignment of the Vision-Mission-Values, and others to the Goals-Objectives-Activities and the Objectives-FPAs-KPIs-CSFs (Figure 11).
Figure 11.
Illustration of the derivative relationship vision -KPIs -CSFs -KSCs (adapted from Ref. [2]).
The double arrow shows the flow of collaboration, cooperation, communication, commitment and psychological contracting among the individual, team, department, organisation and company ownership. The curving arrow shows the tail from which input arises up to level of connection and back with the arrowhead showing the lower-level adoption of agreed quality parameters. For instance, the individual raises own targets to the team and the team reconciles and (re)aligns these targets with team objectives and agree on the reconciled and (re)aligned targets. And the team reconciles and (re)aligns Key Performance Indicators with the department and the two levels agree that the team will work on the reconciled and (re)aligned KPIs. Individuals help in forming the team level quality antecedents (objectives, Key Performance Indicators (KPIs), Critical Success Factors (CSFs), Knowledge-Skill-Competences (KSCs)) which when adopted determine the targets at the individual or role level. The teams will help in shaping the department-medium level quality antecedents (goals, KPIs, CSFs, KSCs). The department (in total or represented by a skunkworks/task-team/quality cycle) will help shape the organisation high-level antecedents (vision, KPIs, CSFs, KSCs). The board of directors may require top management to suggest a skunkworks to model an Ideal Competitor.
The KSC refer to the critical set of knowledge, skills and competences. The set of knowledge, skills and competences that relate to success of a Six Sigma effort would depend on the nature of the project. It is important that organisations adopt recruitment, selection, placement and development strategies that ensure that the workforce is conceptually, managerially and behaviourally fit for a company operating in a competitive environment. Technical and non-technical competences matter greatly in teamworking and innovative work practices.
Where necessary running Learning and Development Programmes that school workers through the Six Sigma Belt levels is encouraged. However, care should be taken in selecting content and the teaching-learning activities. It is important to ensure that the Learning and Development content is congruent to your organisational needs. Course plagiarism or uncritical use of external providers should be considered critically. Moving workforce through Six Sigma level training would benefit the organisation greatly. Briefly, the Six Sigma Belts’ levels involve:
White Belt, an entry point into the Six Sigma journey and involves the appreciation of mathematical-statistical tools in data collection.
Yellow Belt, an escalation that involves appreciation of statistics as it links to quality and the operationalisation of quality control and assurance frameworks.
Green Belt involves development of competences in operationalising quality methods and initiatives.
Black Belt involves high-level understanding of constraints in quality and quality infrastructure optimisation.
6.6 Design Thinking and Six Sigma
Design Thinking resembles Six Sigma in the egalitarian way decisions are arrived at and in that Learning and Development of the workforce is given high priority. According to Dam [8] the five stages of Design Thinking: Empathise, Define, Ideate, Prototype and Test are embedded in Six Sigma. For instance, Empathise is embraced in the Voices (Voice of Market—VoM; Voice of Business—VoB; Voice of Employee—VoE; Voice of Customer—VoC), which expressed the perspectives of all who have a stake in the product and services’ quality. Define is embodied in the need to define by specific terms and metrics the data about the inputs, processes, outcomes and propositions. Ideate is encompassed in the need to generate action and frameworks from data and participation of all and the use of every data analysis and analytic tool. Prototyping arises from use of the various models such as TFSS, DFSS, MFSS, TFSS, DMAIC and DMADV. Testing is inherently fabricated into every step and act of Six Sigma. Six Sigma protagonists test their own thinking, skills, competences, perspectives, perceptions as well as the models they use and the prototype, product and service on a continuous basis.
6.7 Lean Six Sigma and Six Sigma
Organisations attempting to helicopter Six Sigma on themselves, without sufficient self-study and critical evaluation may soon discover that they have lots of noise, garbage and wastes to deal with well before they start on performance improvement per se. At Section 2, we highlighted the strategic need to schedule your succession of milestones to your desired Six Sigma level. In the next section, we highlight the importance of the SMARTWI practice in the design and operationalisation of milestone throughputs. You should also notice the SS (Six Sigma) in each of the roadmaps—SSPD, MFSS, DFSS and TFSS. The essence of the SSs is the reduction of defects to 3.4 DPMO. We argue this ‘reduction’ in specify, measure and achieve (SMA) of SMARTWI which implies that we stick to what is specified in the milestone, what matters for quality as specified in Voice of Customer Analyses (VoC-ALYSIS). The broader implication in roadmapping, running the VoC-ALYSIS and the SMARTWI is that the redundancy, noise, obsolence and oversizes are identified and eliminated. This is the quintessence of the Reduction Vector in Figure 12. Interface Mapping, which should be done heavily during the initiation of ‘Six Sigma’, has its focus on leaning models, practices and deliveries in ways that tend hypothesised quality to delivered quality. My point is that leaning is inherent in Six Sigma and it would sound a tautology to claim anything like Lean Six Sigma. Lest too, we leave an impression of a Six Sigma that is without SSPD, MFSS, DFSS, TFSS, interface mapping and SMARTWI. It is a given in quality management that we would cover the costs of enhancing quality by just removing the redundancies, noise and duplications. Time and geography may erode the lustre of certain features in enhancing quality and these must be removed—the philosophy of negation of negation. The conjugate philosophical category of ‘the transformation of quantity into quality’ sits at the heart of Six Sigma and share the idea of ‘leaning’. Effectively, Six Sigma operates on the four vectors shown in Figure 12. A vector is a conception of magnitude and direction and applied in Six Sigma this could be about how much to add or remove:
positive/addition vector: This vector represents the addition (positive) of new features to the product and services based on VoE, VoM, VoB and VoC.
negative/reduce vector: This involves the removal/reduction of product/service features based on VoE, VoM, VoB and VoC. This vector epitomises the lean in Six Sigma.
standardisation vector by remodelling, conforming and adding/reducing features or their aspects in response to VoB and VoM.
innovation vector, that is intuition or Research & Development remodelling and inventing technologies (TFSS), designs (DFSS), processes (SSPD) that thrill the market (MFSS).
Figure 12.
The quartet of quality vectors in six sigma (author: synthesis of literature).
7. Six Sigma: within, with and outside other quality models
We have noted the array of tools and techniques used in Six Sigma efforts within Data Science and within Data Analytics and in the post-data stages of decisions executions. Taking the position of Ficalora and Cohen, Six Sigma is usable as a metric, a method, a methodology, a model, an initiative and a wider strategy.
As a metric: At this lowest level of use, Six Sigma is viewed as a system or standard of measurement particularly of data relating to inputs, throughputs, outcomes, and the reactions and responses of the market to features on goods and services.
As a method: At this level of use, Six Sigma is viewed as a particular systematic or established procedure or process for accomplishing or approaching something.
As a model: At the altitudes of a model Six Sigma is viewed as a simplified description, often mathematical – statistical, of a system or process to assist calculations, predictions, and guide activities of producing goods and creating services of high quality.
As a methodology: At the level of a methodology Six Sigma operates as a system of methods used in a discipline seeking continual improvement in the quality of goods and services. Effectively a methodology would constitute of several methods, each designed to achieve a set of objectives in the Six Sigma framework. While the use of multiple methods is generally not uncommon in science, technology and business, much commoner are the confusions and inadequacies in fully exploiting the operational dynamics and the synergistic potentialities gainable with a more focussed and calculated approach (Figure 13).
Figure 13.
Multivariate use of Six Sigma (author: synthesis from Ref. [3]).
As a strategy: As a strategy Six Sigma operates as a business initiative or plan of actions designed to achieve a long-term strategic goal. Normally, this is overall continual improvement of quality, growth, sustainability and profitability. Six Sigma is used to understand the current situation of the organisation, the desired future state of the organisation, (re)configure a strategy to the desirable future, evaluate performances and create the Imagery of Ideal Competitor.
Further, Ficalora and Cohen [3] and Cote [9] observed that data is a powerful tool that organisations can exploit variably, and with thorough-going use, data invaluably aids decision-making, impact strategies and improve organisation performance. The search for superior quality using Six Sigma ascends to the levels where it is used in very sophisticated ways. This is when the data impacts decisions and processes deeply related to ‘Sigma Sigma’.
Columbus [10] observed that 56% respondents to its Global State of Enterprise Analytics affirmed the positive impact of data analytics in agility and decision efficacy, and 64% affirmed improved efficiency and productivity, and 51% affirmed enhanced financial performance. A further 46% of respondents were positive that data analysis and analytics enhanced their ability to acquire and retain customers, and 46% thinking that data analytics helped them identify new products and services. A further 43% felt data analytics and analysis helped them achieve competitive advantage.
The quality of products and services that a provider provisions to the market reflects their extant capabilities. Because the market of goods, and of buyers and suppliers is everchanging it matters to co-adapt to these changes. The implication is that we need build strategic capabilities on a generalistic level as on a specific level. Developing your workforce through the Six Sigma Belt training cannot be overemphasised. Facilitation skills are important in Six Sigma. Six Sigma projects are effectively orderly synchronised activities that must have Specific, Measurable, Attainable, Realistic, and Time-framed (SMART) tasks, activities, and objectives. SMARTWI is an extended version of the common SMART guideline with the addition of two social rewards: Worthwhile and Interesting. Unless we find an activity to be worthwhile, we are least interested in its quality. It is needful to be SMARTWI with the strategic capabilities that are being created so as to continuously improve quality.
Specific: Be it within the job description, ad hoc assignment or any other task, most people feel a sense of intrinsic accountability. We want to be able to feel clearly and specifically, where we are and what the endpoint of a day’s work or an assignment will be. We get joy knowing clearly what the problem is, as much as what specifically needs to be done to solve it. We are delighted when we explain specifically how we went about solving the problem. Six Sigma tools help in trying to specifically identify structural, functional, interface and behavioural constraints (positive and negative) in the pursuit of superior quality. Describe the issue by specifics, diagnose them, explain the scenarios and explore the option to their specifics.
Measurable: In Six Sigma, measurements are a great instrument for decision-making. We want to understand the ‘size’ of the problem, the number of factors involved, the measure of the problem’s sophistication, the amount of resources needed to solve the problem, the ‘size’ of damage over time and priority level of the problem. Effectively what are the measurables of the problem, the throughput, the benefit and the costs. The subsequent action is informed by the measurables. In Six Sigma, there are many tools and techniques that will help in doing the measures.
Achievable: Achievability of decisions is important. In economies of limited resources, our managerial and leadership capabilities must not too be limited and stunted. There are numerous tools for running Predictive Analytics. With their use, we should be able to ensure achievability of tasks and objectives. Achievability may also be examined from research. Have similar organisations run same or similar objectives/projects with success? Achievability can be a perception on the different discrete aspects, blow-by-blow, but achievability on the full scope of the project can be a different story—that is the discipline of realism, Realistic. Where the tools show negative constraints then inputs and throughputs can be modified to allow the achievability of higher quality tasks and objectives.
Realistic: Thesaurus give achievable as a synonym of realistic. In Six Sigma, notwithstanding their resemblances, the two are not synonyms. Realistic refers to the questions: (a) is this achievable in our context?, with our resources in the extant and in the short-term future? with the other corporate capabilities? In realism, there is no achievability without availability of resources. Predictive, exploratory, financial and economic analytics can all help in building an answer to whether an objective will be realistic. Realistic further differs from achievability in that it checks whether there will be sufficient return on investing in an objective or project—realistic to our goals and objectives as a team/organisation. It does not matter that with the analytics a project/objective would score negative. A negative realistic score/indication should start the organisation on a new trial—what if? Ask yourselves what if we increase/decrease factor A, will it become realistic? Try to get the project/objective realistic from a modification that does not cause or cause the least change in project goals/scope of objective and is least expensive, has shortest time span, is least disruptive to the assembled plan(s), and will keep the morale, interest of the workforce and stakeholders least affected.
Time frame: Time is a resource, and we all need to maximise its benefits to the organisation. It must be guarded jealously. Some of the ways of serving on time is to keep lean throughputs, right-sized workforce, value-making interfaces, and overall organisation structural, cultural and market agility. It is therefore important that each actor and each sector focus on exploring ways to shorten their projects’ critical paths, so that we all spend the shortest possible time on a task, of course, not at the expense of superior quality.
Worthwhile: Worthwhile objectives/targets which those working to achieve them feel they are worth their while to put their time and effort on. The worthiness people put on a target/objective the more they work on it with a purpose to greatly achieve. The worthwhileness is both emotional and substantive, the effort must create a feeling of greatness as much as a feeling of accomplishment. How do organisations create a sense of task/objective/project worth? Tell it, sell it, market it, consult-involvement-build and share for commitment. A widely shared vision is created, which is worth to all and sundry done under it.
Interesting: Six Sigma is data intensive, the tools are amazing and the visualisations enlight, the discussions being educative. The journey of continuous improvement through Six Sigma should evoke lots of interest in the participants. Most corporate activities are teamwork activities. Teamwork brings about team learning which has lots of discoveries. Discoveries ignite interest. Good facilitation enhances the interest in pursuing project objectives. The more interest we have in a task the more effort and quality time we invest in the task.
Having been midwifed at Motorola with a heavy mathematical-statistical bias traditional Six Sigma has been used to depict the highest performance level in a Sigma series that should culminate in a defect rate even lower than 0.00034% or 3.4 DPMO. Six Sigma has been migrated into the services industry with success and has fast become a model, methodology and strategy for excellence in both goods and service industries. Being a data-based model, Six Sigma stands on the shoulders of a plethora of data-gathering and processing tools and techniques. Illustrations are shown. Being an evidence-based methodology, Six Sigma is buttressed by a family of intertwining data analytics. The ultimate success of Six Sigma methodology remains a function of the expertise with which the data analytics are handled. This underscores how needful it is to school and develop workers on a continual basis, to understand the field of operation of the quality control and assurance tools including their zones of overlap, and how to examine their value and compatibility with the goals for which they are being used. Relevance, compatibility and ‘multiplier or enhancement effect’ are critical in quality management. Six Sigma needs this trio (Relevancy, Compatibility, Mutual Enhancement) all the more, and all the time. Lack of understanding and the strategic capability of working them has left many alleging fatuity of many quality models. Six Sigma accommodates many facets of many quality models as much as it has fed many of these with its tools. The quintessence of Six Sigma remains the sourcing of data, its analysis and absorption in decision-making that focus on continual improvements of designs, processes, market-orientation and assimilation of technology in the organisation.
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Written By
Douglas Matorera
Submitted: 06 May 2024Reviewed: 12 June 2024Published: 11 December 2024
Open access peer-reviewed chapter
