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Segmentation could be called as the process of dividing the population into distinct subsets or segments that behave in the same way or have similar features. As each segment is homogeneous, they are likely to respond similarly, within. For effective segmentation, segments need to be measurable (The very purpose is to measure effects within a segment and between segments), identifiable (This is mandatory if all data is to be correctly segmented), accessible (The efforts at segmenting should not become more than the benefits obtained by solving the problem), actionable (The segments arrived at must be practically feasible to work on) and large enough to be effective (Each segment should have a critical enough mass). The segments arrived at need to be based on a logic that can be related to the problem being investigated or business goals being pursued. These questions may help drive the analysis of the segmented data. · Is there one defect category that occurs more frequently than others? · What factors contribute the most to the variation in Project Y? · Do results differ across factors? Segmentation, sub-segmentation, cross-segmentation and matrix-segmentation divide the data population into homogeneous data segments. Multiple data segmentation can be used effectively to isolate problem transactions that give us a handle to work on solving the problem. The criteria for this segmentation can be the natural transaction categories in the process or specially created criteria. In the case of the former i.e. segmentation along transaction categories, the population is split into various segments and extent to which the transactions in each segment have been impacted by the problem is measured. This will help in identifying those segments which are most impacted adversely by the problem. Thus, the problem segment or segments have been identified. Then, by identifying the characteristics and features of these problem segment or segments that are significantly different from those of the other segments which are not impacted by the problem, it is possible to identify those characteristic or feature that are most impacted by the problem. These could be the immediate cause of the problem. This will then need to be root-cause analysed and appropriate controls implemented. This way, it is possible to avoid shooting in the dark when trying to find the root cause of the problem. Segmentation along transaction categories has helped to narrow down the areas to be root cause analysed, thus saving time, effort and money in the problem solving exercise. In the case of the latter, i.e. segmentation along specially created criteria, it is possible to formulate criteria along suspected or potential root causes. By segmenting the data population along potential root causes, the segment or segments impacted most by the problem can be identified and along with the segments, the root causes themselves can be identified. Here, by almost directly identifying the root cause, even more savings of time, effort and money can be achieved. Here, data segmentation is actually being used to verify root causes. Going further on root causes, Segmentation analysis also assists us in planning and implementing different corrective actions for different segments that contribute effectively to improvement. A repetition of the segmentation post improvements and measurement of the problem impact will reveal the effectiveness of the corrective actions implemented. Thus Segmentation Analysis supports preparation for, conduct of and verifying effectiveness of root cause analysis.

Need to strike an equilibrium between these costs There is definitely a need to strike a balance between Prevention & Appraisal Costs on one side and Internal Failure & External Failure Costs on the other. Any investments in an organization need to be justified, preferable in a tangible manner. The best way to justify investments on Prevention & Appraisal Costs would be to assess the Internal and External Failure Costs avoided by the investment. Approach to reach the best scenario Data collection, consolidation, Analysis and Actionable insights from the analysis would be the way to go on optimising Cost of Quality. For all existing Prevention and Appraisal activities, there need to be costs assessed both one time and also recurring. Prevention and Appraisal actions could be training of people, process embedded checks, testing at various points of the process of components, sub-assemblies and assemblies etc. These costs will need to be assessed for a line and also per product. These costs will need to be tracked and reported monthly. Concepts of Depreciation of Facilities and Net Present Value etc. will need to be used. The Finance Team would be able to help in this regard. In addition to the above, the results from the Prevention and Appraisal actions will need to be used to assess their effectiveness in eliminating or preventing poor Quality products from being produced and from reaching the market. The costs of repair and rework and retesting will need to be tracked and periodically reported. The feedback from customers including complaints will also need to be used in assessing the effectiveness of the preventive and appraisal actions. Using the two, estimates will need to be made of the quantity of poor quality products that reach the market. The historical data regarding customer rejections will obviously need to be used here. From this, the cost of recall including penalties, fines, repair, rework, rechecking and re-dispatch will need to be computed. Additionally, the cost of business lost due to poor Quality will need to be assessed from past data. For the future, the cost of business that can be lost needs to be predicted using realistic estimates. There need not be a doomsday prophet approach while doing this as this will artificially inflate the external failure costs by assuming that every faulty product reaching the customer would result in cancellation of all orders. Cost of potential business that could be lost will need to be assessed considering the customer’s brand equity, the likelihood of the customer increasing business in case of issue-free delivery and so on. Any estimated loss of business will need to be weighed down by the probability of the event happening, which will make the assessment more realistic. Now the return on investment can be calculated by: External and Internal Failure Costs Avoided ------------------------------------------------------- Prevention and Appraisal Costs incurred This needs to be periodically computed for different product or service lines, different customers, different products etc. These should be continually reviewed to see if the return is going too low. If for reasons like internal expertise developed, improved technology used or agreements with customers signed, the occurrence of defective products is reduced or probability of such a product reaching the customer is reduced or penalties payable to customers are reduced, then the investments in Prevention and Appraisal actions need to be reviewed and if required, optimised. Best Scenario The best scenario would be one in which all Costs of Quality are dispassionately reviewed in terms of tangible benefits and not for any sentimental or passion related reasons. If Failure costs reduce, it needs to be seen as a success of Prevention and Appraisal action investments. If the existing Prevention and Appraisal costs are consistently yielding results in avoiding Internal and Failure Costs, then a calculated decision needs to be made on conducting a pilot with partly optimized investments e.g. reduce the sampling for testing. If the experiment is successful, the organization should optimize the investments in Prevention and Appraisal. The overall motto should not be “Quality at any cost”, but “Quality at a cost”.

An old proverb goes, “One man’s candy is another man’s poison”. This is true in the case of Hypothesis testing also. The following situations could be examples for a Type 1 error in one situation being a Type 2 error in another situation, which includes Conditions, Environment, Organization, Point of Time etc. Improved Technology In a factory, a process is presently being run using Technology A. The organization is upgrading this process to Technology B. By this, all products that were produced through Technology A would be produced through Technology B. This progress is being tracked by a KPI which is the proportion of product volumes which is produced through Technology B. When this progressing to completion, there is another wonder technology, Technology C that is doing the rounds. The organizational Management decides to bite the Technology C bullet. This progress of this upgrade is also tracked using the same KPI, “proportion of product volumes which is produced through Technology B”. While earlier, the objective was to maximise the proportion of product volumes which is produced through Technology B, now the objective is to minimize the same KPI. In this case, a Type 1 error in A to B upgrade would be a Type 2 error in B to C upgrade. As an example of the above, the manufacture of plates for chains could be considered. The traditional method would be of blanking sheets first and piercing the blanks next. This is Technology A. The first improvement is to improve the blank layout, which uses sheets better and there is less of material wastage leading to cost reduction. Improved blank layout is Technology B. The next improvement is to have a progressive tool, which pierces the sheet and then blanks plates in a pierced condition. This is Technology C. Salvage and repair section of a factory In a typical “Stockholm Syndrome” case, the extent to which the Salvage and repair section in a manufacturing unit is utilized is also a measure of the overall quality of production in the factory. If the Salvage team is kept busy, it would mean that the factory produces too many out of specification products. If there were any improvements being implemented in the Salvage section, then if a hypothesis test were to be conducted, the Type 1 error on a Salvage section parameter would be close to being a Type 2 error for the overall organization. Supplier – Customer contradictions There could be some processes or components or services outsourced as an exception only if the customer organisation is facing a problem. This will mean that more the outsourcing of that particular service, process or component, more the problems the organization is facing. While for the vendor, volume produced is a positive KPI, for the customer organization it is negative. In this situation also, a Type 1 error for the vendor would be a Type 2 error for the customer. Public Service Vs Private Enterprise As part of a service spirit driven, health-driven and value driven campaign, a state or local government implements various measures to supply clean, drinking water to all its residents. This it does by maintaining its natural water resources well, implementing rain water harvesting, strictly controlling effluent disposal into water bodies, purifying water supplied through traditional and modern methods and various other administrative and legal measures. This results in all residents getting good quality drinking water. This also results in a decrease in the cases of patients suffering from water-borne diseases. But the success of the same campaign has also resulted in a reduction in the sales of bottled water and also various types of water purification equipment. If the government’s campaign is tracked using (say) people not getting drinking water, a Type 1 error here could actually be a Type 2 error for those organizations, who are impacted adversely by this success. Others In addition to the above, whenever there is a fundamental difference in the motives of two different entities, this “phenomenon” can be observed.

Quite often, the use of a methodology is indirectly and perhaps unintentionally governed by its history and the original purpose for which the approach was created. Such is the case with Six Sigma and 8D also. According to Eileen Beachell, one of those involved in documenting the 8D approach originally at Ford, “the 8Ds are a well-defined linear logic methodology to address chronic problems with the purpose of changing the management procedures that allowed the problem to occur in the first place”. On the other hand, Bill Smith evolved Six Sigma at Motorola, from a study of the relationship between manufacturing defects and field reliability, which resulted in a thrust to improve process capability to the point that no more than 3.4 defects per million opportunities would be created when combined with their respective design specifications. The method of course involved use of statistical tools. The one key difference between these two methodologies in spite of other similarities is the “Implementation and Verification of Interim Containment” in 8D which is not part of the Six Sigma Methodology. In certain problem-solving situations, there could be some really burning problem and a need for some very quick, yet considered action on this, which should involve multiple skill inputs and multiple stakeholder representation. The action should also include damage control in addition to a permanent solution. There may not be sufficient time to form a cross-functional team, train key people in Six Sigma, go through the DMAIC phases, complete tollgate reviews, run a well-controlled and monitored pilot and complete a full-fledged Six Sigma Project. In such cases, the 8D approach may be easier to follow, arrest the adverse impacts of the problem, resolve the issue quickly and keep stakeholders satisfied. It need not be necessarily better than the Six Sigma approach, but in this situation, may be that little bit easier to do. A smaller team could be formed quickly within the closest circle of influence and contribution. S the team members are already familiar with one another and with the process also, they can get cracking as a team pretty quickly. To begin with, the problem could be described in detail and a quick correction could be implemented to begin with. This would satisfy the stakeholders for the time being as the adverse impacts of the problem have been contained. Then this team could do a thorough analysis of all potential root causes, identify the relevant ones, identify likely escape routes, design and implement corrective actions and preventive actions. Additionally, the last scenario of the enhanced Kano Model is the “Reverse” trend. In this, customers who are out to prove their capability in demanding product features that cannot be provided get dissatisfied if their requirements are fulfilled. Something similar to this can happen in implementing Six Sigma in certain organizations. The completeness of the “Six Sigma” approach, the structure in every phase, the need to be aware of and use some basic statistics and sometimes its sheer success and its popularity can occasionally create some irritation in people. They may not really want to be involved in a Six Sigma Project and would be interested in other alternative structured improvement approaches. Such people may be satisfied with the 8D approach, which does have some positives that the Six Sigma approach also has.

The role of a Lower Control Limit in the case of defects or defectives control chart is a very relevant question as who would not like having a process with a defect or defective rate as low as possible. Anyone would probably be happy if some data points fall below the Lower Control Limit. It would be a “Out of Control” problem, which would be good to have. It would create an opportunity for the process owner to investigate the reasons why the process’ defect or defective rate went below the control limit, identify if any best practices had been effective and then replicate these practices elsewhere. Yet, there could be situations where the Lower Control Limit becomes relevant for other reasons. Outsourced Process – You have to only meet the requirement, cannot exceed it There could be an outsourced process, where the customer requires the vendor to inspect and remove defects or errors to the levels the customer has agreed to with the end user. By good process control practices and by using the right methodology and equipment, the vendor may be able to bring down the error rate even below the agreed limits. The customer could react in two different ways. He may accept the output quietly and leave it. Or he may have some other points to worry about. He may feel that if the vendor’s good work in bringing down the error rate far below the agreed limits is accepted and acknowledged let alone appreciated, the vendor may use such events to try to negotiate a higher rate and increase costs, which as far as the customer is concerned, may not add any value as his contract with the end user is at a higher defect rate. Therefore, the customer if he control charts the vendor’s performance, he would need the Lower Control Limit to tell the vendor that his process is “not in control” and that his “performance has to improve”. The customer may also be worried if the vendor, buoyed by this acknowledged performance, may quote this performance with other potential customers, who would be his own competitors, get more business, become less dependent on him and so on. Furthermore, the customer may be worried that if he “spoils” his end-user with such “Super-Quality” deliveries, the end-user may get used to this and then start cribbing if the deliveries are within the agreed defect rates, but not significantly better. To avoid this, the Customer may prefer to stick to the agreed norms. “Negative” Lower Control Limit In another scenario, the Lower Control Limit when calculated could be negative. Obviously it is not possible to have negative defect or defective rates as this would mean that when the process is run on the input material or information, defects in the input are removed. Negative Lower Control Limits could mean that the process is occasionally capable of operating at zero error or zero defect levels. When a process has a positive Lower Control Limit, this may mean that the process in its present form is not capable of zero defect production and will need to be improved upon considerably. While in the above two situations, the Lower Control Limit may become more relevant than usual, the following Quality Story would make interesting reading in the context of “Lower Control Limit”. Quality Story An American firm scouting globally for buying an automotive spare signed a deal with a Japanese firm @ 25 cents per piece including all packing, transport, taxes, duty etc. and placed an initial order for three million parts. Wanting to impress their vendor on how strict their quality standards were, they added “We accept just three defects for every thousand parts”. The order was delivered as per the agreed schedule, but accompanied by a bill for $750,900. It did not take the American accountants a very long time to figure out that the bill was $900 more than what was agreed to. A bit perturbed about this (especially since it was believed that sticking to deals was part of both Japanese tradition as well as Japanese management practice), the firm rushed a cable to the Japanese supplier, requesting for an explanation. Back came a letter from the supplier, “You had ‘asked’ for 3 defectives for every 1000 parts. At this rate, you will require 9000 defectives for three million parts. We have made extra efforts to produce the 9000 defectives, which works out to an additional 10 cents per piece. The extra $900 is on account of this!”

Long Term and Short-Term Sigma levels can be calculated from Ppk and Cpk, which in turn can be calculated using Long term and Short-Term Standard Deviations. Short term Sigma considers primarily common causes, while Long Term Sigma considers special causes. Very often, it is difficult to assess Long Term Standard Deviations as gathering data over a sufficiently longer duration of time can be challenging. The time may extend to many months, during which time, many things can change including, the market demand, business scenario, departmental and organisational leadership, observers, sponsorship of the study and so on. Therefore, to cut the lead time for the Long Term Sigma Assessment, the relationship between Short term and Long-Term Sigma can be used. Moreover, when setting a target for any process, the following need to be considered. One would be the target under standard environmental conditions. The other would be changing environmental conditions which may result in variation. Even highly stable processes, over an extended period of time may feel the impact of changing environmental conditions, which causes variation. These environmental changes need to be balanced by a compensation factor in order to account for these changes to ensure that the long term target is met. Therefore, the Short-term target would be the Long-term target plus a compensation factor. This compensation factor has been empirically arrived at by Motorola as approximately 1.5, originally referred to as “Long Term Dynamic Mean Variation”. This was arrived at under some assumptions. Thus, a process operating at 3.4 DPMO would be at a short-term Sigma level of 6, but in the long term would be only at a Sigma level of 4.5. For a process to be at a Long-Term Sigma level of 6, it needs to operate at 2 DPBO (Defects per Billion Opportunities).

At a routine military inspection at an Army barracks, the Colonel inspecting the unit asked a new recruit, “What is the first thing you do, when cleaning your rifle?” to which the recruit answered, “Make sure that the rifle is mine”. Behind the humour of the above-mentioned quip, is an important lesson to be kept in mind before embarking on a project, big, small or miniscule. One needs to make sure that he is on the right job. Identification of outliers in a data set is akin to what the army recruit rightly said. If processes, be they in a factory or in a laboratory or in an office were all running as intended and planned, there would not be any reason to have problem solving measures as there would not be any problems at all in the first place. It is only because that this does not happen all the time, that data needs to be collected and problems need to be solved. Problems are not exclusive only to the process being studied. They can also happen in the monitoring, measurement and data collection processes themselves. Due to problems in any of these, some transactions or parts can get impacted. It could be possible that there was a problem with the measurement device or gauge or software. Due to one or more of these reasons, the value of the metric for the one or few transactions or parts may go out of a normal expected range or be zero or the maximum value on the measuring device. Also, there could be errors in transcription or reporting. Before embarking on an analysis of the data collected, or as a first step of the data analysis, it makes sense to check for possibility of any occurrences of the above. Mere occurrence of a low or high value of a metric need not make it an outlier. The situation and the other data need to be considered. For example, when collecting data on weights of normal male adults, if a person’s weight is recorded as 8 kilograms, obviously this is an outlier caused by a digit missing on any one side of the “8”. But when collecting data on weights of new born babies, weights even around two kilograms may not be an outlier. If the data analyser does not identify, investigate and remove outliers from the data to be analysed, any measures computed from the data with outliers can be incorrect as most measures are sensitive to every data point in the data set. Further, any advanced analytical tools if used on data with outliers can return incorrect results and mislead the investigator and send him on wild-goose chases. Worse still, the investigator can incorrectly conclude that there are no problems with the process and thus discontinue the investigation. There are popular formulae for identifying potential outliers in terms of the Inter-Quartile Range (IQR) and Quartiles that can help in a first level screening. For example, points lower than the first quartile by more than 1.5 times the IQR and points higher than the third quartile by more than 1.5 times the IQR are considered as mild outliers, while points lower than the first quartile by more than 3 times the IQR and points higher than the third quartile by more than 3 times the IQR are considered as extreme outliers But all points identified by these formulae cannot be blindly called outliers and left out. The reasons for these outliers need to be investigated before a decision to ignore them or include them can be taken. Potential outliers can occur due to the following reasons. Improvement Opportunity As this is a valid data point due to an hitherto unknown cause, to be considered and further investigated Genuine data error Investigate the data point and correct the data Malicious intention Subject to reach and time available, interact with those who have misreported, motivate them to report the correct data and use it; if not feasible, can be identified as an outlier Lack of standardisation After confirming the reason, can be identified as an outlier Uncontrolled sampling error As the sample itself is incorrect, can be identified as an outlier

Definitions of Maturity The definitions for some of the various types of maturity are as under: Psychological Maturity Acquired ability to respond to environment in an appropriate manner Financial Maturity Stage, an investment reaches a state when it can be liquidated Fruit Maturity Time when the fruit is ripe and fit for consumption The commonality in the above definitions is that there is something beneficial that comes out of the state called maturity, be it intangible like a response or something tangible like money or fruit. Amongst these, the best analogy for a “Mature” process would be that of a “Mature” person. What does a Mature process do? Just as a mature person is able to respond correctly to the environment and the changes therein, a mature process is able to take in its stride the changes that can impact it adversely and continue delivering to specifications. Stability is just one sign of a matured process. But it is not just stability that makes a process mature. Prolonged spells of stability under different and varying environmental conditions are better indicators of the maturity of a process. The process continues to remain in control under different conditions and at the same time is also capable of meeting different sets of specifications with appropriate settings. For a mature process, those causes that are traditionally considered as “special” will have only the same impact as a “common” cause. This is because the process has become sufficiently robust to withstand the adverse impact of special causes. These special causes include but are not restricted to one or more of the following changes. 1. Origin of one or more of the input material or information 2. Type, Quality and Quantity of one or more of the input material or information 3. Type and Quality of tools used if any 4. One or more of machine or process settings either planned or accidental 5. One or more machine parts 6. One or more Operators or staff 7. Maintenance schedules 8. Training schedules 9. One or more output specifications 10. End users or end uses of the output 11. Duration of working – shift times and number of shifts, number of days in a week 12. Shift supervision, Management 13. Vendors related to the process 14. Initiatives launched in the Department or organisation like QMS Certification, Structured Improvement approaches etc. The mature process is able to withstand these and other special causes which would be expected to put the normal, “In control” process, out of control. How does a process mature? Maturity of a process is not a chance occurrence or an accident. It is a set of well-planned and executed actions over a period of time. Each step in the maturity progress of a process is a milestone and also an opportunity for lessons to be learnt for making the process more robust. A process would start off with a basic level of process management in the form of a controlled pilot. After the pilot is successful with or without corrections and iterations, the process reaches a stage when it can be managed by characterising the various aspects of the process and any issues are immediately attended to and the process is swiftly brought back within control. Various small improvements are implemented or improvements already implemented in other areas of the organisation are extended to this process also. The process is capable of meeting the specifications and the purpose for which it was created. The process is maturing. After some time, the process management becomes more proactive and potential problems are anticipated, predicted and attended to, without allowing them to impact the process. More improvements and early warning systems are either implemented afresh or read across from other processes. The process with some tweaking is capable of meeting a wider range of applications and their specifications. The process is further maturing. Yet further down, management of the process becomes more and more measurement based and the fineness of control improves, along with the robustness of the process, which is now at a high level of maturity. Improvements become more data driven. Process stability is practically de rigueur and process capability for a variety of applications and specifications is also improving. All the hard work done, all this while brings the process into an optimised state of operation in which, when anything is changed, the process remains not just stable but also capable. Improvements are further fine-tuned and the process can now be said to have matured. Process redesign and Improvement The changes in the market and as a result, changes in the business strategy will obviously result in changes to an organization and the processes that run in the organization. This will include these mature processes also. In fact, these mature processes will be the easiest to change and also the fastest to mature again under the new design and new operating conditions. All the improvements that have been implemented and that have matured the process have now cleared the deck for a transformation or a quantum, breakthrough improvement, including possibility of redesign. Redesigning a process that is not matured will involve a lot of additional work, experimentation and data collection which is tantamount to almost reinventing the wheel. Process maturity is not voided by process redesign or improvements. On the contrary, the process’ maturity has facilitated and simplified redesign

VOB and VOC – Unity in Diversity? VOC and VOB contradicting one another is almost normal and par for the course for most organizations. Only in those organizations in which a different approach to work is not just known or implemented, but institutionalized in the real meaning of the word and embedded in their DNA, does the VOB have even a slim chance of being close to the VOC. This is simply because, by definition, any business or organization wants its own lot to get better – be it market value or revenue or profits or reputation , which is exactly what their customers also want, but for their own organizations. Thus, the supplier organization works for itself, while the customer organizations work for themselves. In this scenario, is there really any surprise that the VOB and the VOC are at loggerheads or at least not quite synchronized? After all, how many organizations have their Mission on the lines of “Helping our customers achieve their Mission” or “Grow with our Customers” etc. · How many organizations practice this philosophy? · How many organizations institutionalize this approach and strategy? · How many organizations train all their staff to think this way? · How many organizations embed these thoughts, words and deeds in their DNA? A closer look at the inevitable dichotomy What the supplier organization wants and what its customers want can be generally summarized as under. It can be noticed that the two are mostly contradictory. Sr. No. Supplier Customers Remarks 1 Higher Sales Revenue Reduced costs including outsourcing costs Supplier’s revenue comes from customers who want to reduce their cost by paying suppliers less 2 Reduced costs Best service from Suppliers Suppliers reduce costs by eliminating perceived NVA which could actually be a differentiator for the customer 3 Increased profits Increased profits Increased profits for their own organizations 4 Increased volume commitment from Customers Balance risks by having alternate suppliers Suppliers need assurance of an increasing volume, while customers prefer to split their supplies and thereby the risk between more suppliers 5 Steady volume from their Customers for leveled production and cost optimisation Supplier capability to ramp up and / or down depending on the market demand without any extra cost To optimize costs by leveling, Suppliers need a less fluctuating order from Customers, who expect their supplier to be capable of increasing their capacity at short notice if the market requires it without any cost implications 6 Stable requirements for planning, training and executing production and quality assurance Flexibility for multiple product types depending on the market demand, new products etc. To avoid changeover delays and costs, Suppliers want their product specifications to be frozen by their customers, who want their suppliers to supply a variety of products with no or little cost impact 7 Periodic price increase from Customers to counter inflation Periodic price reduction from Suppliers as they should have become more efficient with time To manage the increase in costs which are beyond their control, the suppliers want a consideration from their customers, who on the other hand expect their suppliers to become more efficient with time and pass the benefit to themselves 8 Controlled investment in production facilities like equipment considering market fluctuations Use of the best facilities for delivery all the time With markets being unpredictable, suppliers are hesitant to make any quick investment decisions w.r.t. (say) equipment, while customers expect the best equipment to be used for the products delivered to them 9 Pricing structure for value-adds which they supply themselves or when they exceed requirements Value-addition is expected and an implied requirement and needs to have its cost blended with that of the base-product and not as an extra cost Suppliers expect the value-adds they give their customers to be remunerative while the customers expect all such costs to be absorbed within the base rates agreed 10 Some reasonable tolerances in specifications of products Strictest adherence to specifications Suppliers want a tolerance in product and component specifications commensurate with the rates, but customers expect complete adherence to specifications which they feel are driven by the end users Reconciling the differences We saw earlier why the VOC and VOB are inherently in conflict almost by rule rather as an exception. The reasons for the same are also generally well understood by all organizations but unfortunately accepted by these organizations as fait accompli and something that cannot be negotiated. To break this invisible layer of ice, both the supplier organization and its supplier organizations need to make certain bold changes in their approach. The supplier organization that wishes to synchronize its Voice of Business to the Voice of Customer needs to take the first step. The organization needs to, in a controlled and perhaps, exploratory manner, be willing and also be seen as willing to link its future with that of its customer. That the supplier organization states its intention to align completely with its customers need be seen neither as a “surrender” to its customer nor does it restrict its independence. It merely signifies the maturity of this supplier organization. Now it is the turn of the customer organization to hold and seize the opportunity afforded by its supplier to become more than a supplier and be its supporting and reliable partner in its own growth. When the two tango, all points of differences become opportunities for total alignment as described below. Then VOB will echo the VOC. Sr. No. Aligned Supplier – Customer Win-Win Combo 1, 2, 3 and 4 Revenue, Costs, Profits and Volumes The Customer publishes his business plan with appropriate representation of the supplier, who then aligns his business plan accordingly. After discussions and changes where required, the volumes to be supplied, the cost of the work outsourced or parts purchased, the revenue share of the supplier, the profit margin, the BCP arrangements by the Supplier to reduce the risk to the customer etc. are all agreed to mutual satisfaction. The Supplier is free to further implement improvements, reduce his costs and increase his profits and also attend to other customers as required. 5 Leveled production From the business plan of the Customer, a clear understanding of the end-user market is obtained by the Supplier, including possibilities of spurts and drops, seasonal variations, event-based variations etc. The cost of the additional investment the Supplier needs to make e.g. overtime, sub-contracting etc. while still remaining Lean and clear of the Seven Wastes is agreed to and the annual plan is leveled. 6 Stable Product Specifications An improved understanding of the end-user market and potential new products, global market trends etc. keep the supplier in a better position to fine tune his changeover strategy and remain alert and prepared for new requirements at short notice. The investments required for this are adjusted against the additional business the customer agrees to give after the new product is released. 7 Cost Trends with Time A medium term memorandum of understanding factoring in known causes of increase in cost and leaving options open for unknown causes in case they happen as well as the improvements in planning and order execution, leading to improved efficiency and costs is signed by the Supplier with the Customer. Basis this, the pricing strategy in the medium term is agreed to that satisfies both possibilities of increase and decrease in costs. 8 Investment Commitments and Volume Commitments With a better understanding of the market in 5 and 6 above, it is easier for the Supplier to come to an agreement with the Customer on the additional capacity requirements, modernization of production facilities, etc. and the investments therein and also considering the agreed pricing strategy. 9 Value-addition and charging for them The edge given by the value-adds to the customer in satisfying the end-user is discussed and those priced value-adds and the complementary value-adds are both signed off. 10 Specification tolerances The exposure of the Supplier to the final uses and application of the product helps the Supplier propose a realistic set of performance and functionality tolerances that do not impact the customer’s market reputation and or the end-user’s satisfaction. Moreover, with the new equipment to be invested in agreed to in 8 above, improved process capability would become a reality.

Knowing and looking for any of the seven wastes and just working to eliminate them will not be as effective as making waste elimination part of an organization-wide Lean Transformation Programme. Having been questioned by the Leadership on the best uses of the concept of Seven Wastes, would use this opportunity to also present my proposal for Lean Transformation in my organization. Lean Transformation Program 1. Mission Statement a. Derive Lean Transformation Mission Statement from the Organizational Mission Statement b. Achieving the Lean Transformation Mission will take the organization closer to achieving its Mission 2. Objectives a. Derive lean Transformation Objectives (Quantified, Tangible) from the Mission Statement b. If the Objectives are fulfilled, the Mission will be achieved 3. Governance Structure a. Steering Committee b. Working groups c. Frequency of reviews d. Roles and Responsibilities e. Creation and Approval of Lean Roadmap 4. Steering Committee a. Responsibilities b. Authorities c. Targeted Savings d. Budget approvals e. Training plan f. Socialization Plan g. Plan for leveraging Technology h. Base-lining of current costs i. Target setting guidelines for Working Groups and Projects j. Norms for Rewards and Recognition 5. Working Groups a. Responsibilities b. Authorities c. Expenditure approvals d. Target finalization e. Norms for identifying Lean Transformation projects 6. Training of Trainers a. External or Internal training of In-house trainers b. Preparation of gamified training material, props, artifacts, audios and videos by these trainers for training other staff 7. Lean Awareness Sessions a. Plan for introducing all staff to basics of Lean Management b. Training to be done by trained trainers c. Weekly and Monthly Awareness Training coverage report vertical-wise and for the organization 8. Training in Lean Methodology a. Training of staff on Lean methodology including waste identification and Value-Stream mapping in batches b. Games and Exercises inbuilt in the training session c. Lean Project completion mandatory for certification after training 9. Socialization a. Roadshows, Standees, Posters, Videos b. Competitions and Quizzes with Prizes c. Sensitize all staff to become intolerant to any waste 10. Project Identification a. Lean Transformation project identification in all verticals b. Cross-Functional Lean Transformation Project team formation c. Identified projects to be reviewed by Vertical Working Group and cleared for kickoff 11. Project Execution a. Progress of Lean Transformation Projects in a structured manner b. Structured approach to executing Lean transformation projects by Waste Identification and Elimination including i. Scoping the project ii. Base-lining current costs for process being improved iii. Documentation of existing process steps in detail using appropriate tools like SIPOC, Process Maps iv. Preparation of Current State Value Stream Map v. Analysis of Current State VSM vi. Waste identification in Current Value Stream vii. Preparation of Future State VSM viii. Risk analysis through FMEA and other Tools, Control evolution and implementation ix. Kaizen blitzes to eliminate identified wastes x. Implementing the improved process after selective, controlled piloting xi. Preparation of (Now) Current VSM, comparing it with (Earlier) Future VSM and identifying further opportunities xii. Handing over the improved process to the Process Owner 12. Project Reviews a. Review by the Working Group at different stages and after different milestones b. Review by the Steering Committee after project completion c. Identifying opportunities for reading across and replicating Best practices in other verticals 13. Auditing of benefits a. Auditing of the benefits by Finance and validation of the same b. Lean Transformation Project Benefit validation report by Finance 14. Rewards and Recognition a. Recognition of project team members depending on benefits obtained and impact created b. Annual Lean Transformation Celebrations c. Embedding Lean Transformation in the organisation’s DNA 15. Continual Lean Transformation Programme Improvement a. Periodic review and audit of Lean Transformation Programme Methodology vis-à-vis audited benefits b. Leaning out the Lean Transformation Programme c. Continually improving the ROI of the Lean Transformation programme

Those who have already heard this very old joke must be old themselves. A tourist passing by a picturesque lake feels like having a refreshing bath inspite of not knowing swimming. To check, he asks a local relaxing on the bank, “Hi, What is the depth of this lake”, to which the local casually drawls, “Around three feet”. Reassured, the six footer tourist happily descends into the lake and is shocked to feel himself sinking slowly in an apparently bottom-less lake. With an effort, he screams, “You said three feet, but I am sinking”. The guy on the bank stops chewing the grass in his hand and says, “The average depth is three feet. At the point you are in, it is 20 feet”. Apart from the above apocryphal situation, any one of the following situations or a combination of two or more of the following situations could also see variation having more relevance than central tendency. 1. Relatively easy target Sometimes, perhaps due to the technology used, the target centre in a process would be relatively easy to achieve. There would not be any effort required to meet the target. Further improvement in performance is possible only in reducing variation. Therefore, the focus now shifts to minimising the variation. 2. Narrow specification range For some processes, the target specification range could be very narrow. With such a low tolerance, variation needs to be very low. So the focus changes to minimize variation. 3. Many processes upstream based on the output If based on the output of a process, many upstream processes are to be run, the focus would be to restrict the variation of the process output as planning and running the subsequent processes would be easier and less expensive if the inputs are within control. If the output of the process under question were to vary beyond control, then there would need to be some rework and / or scrap, both of which are wastes, before starting the next process. This would repeat for every subsequent process. To reduce these wastes, the only option available would be to ensure that the process spread is minimal. 4. Use of less robust machines for further processing If the machines further processing this output are not very robust and require their inputs to be within a small tolerance, then the focus would be on reducing variation rather than on central tendency. If control of variation is ignored, the machines in the next process will not be able to handle the input and either breakdown or produce sub-optimal output resulting in waste. 5. Batch Processing When the next process is a batch process and the settings are for the entire batch which requires inputs to the batch to be varying within certain specifications, the focus will be to contain variation because as long as the input variation is within control, the average would not matter as the settings can be accordingly changed. If variation is uncontrolled, then the batch may have to be split into two or more batches and processed under different settings which would involve additional cost and delays.

Discrete data are very much preferred when the event that is being measured is itself discrete in effect. Discrete data could be preferred if the information required from the measurement is qualitative. There could also be a continuous measure possible and being done, but the objective of the exercise would be only whether a transaction has met the requirements or not. By how much has a transaction failed to meet a requirement or by how much it has exceeded the requirement may not be of immediate concern. One recent example that has made it to the newspaper headlines atleast on the sports pages is the use of the Danish football physiologist Jens Bangsbo’s Yo-Yo test as a criterion to decide if a cricketer, found to be adequately skilled in different departments of the game, is also physically fit enough to be considered for national selection or not. It does not matter if a player has failed to meet the required score of (say) 19 by a very small margin, he is out of the team and out by how much is irrelevant. Similarly, if a player passes the test, he is in the team and whether a player has exceeded the requirement by a high margin or he has just scraped through does not matter as both the players are equally in the team. One other situation could be grading of students in an educational institution, be it a school or a college or any other qualifying examination. Typically, it could be felt that there is not much difference between students who get marks that differ from one another by a very small number. In other words, the difference between two students with scores of 95 and 96 could be only considered as only due to chance causes. Therefore the examining authority could create multiple slabs of marks and map these slabs to grades. All students who get marks within a slab would be awarded the corresponding grade and the actual marks scored would not even be reported as they are not considered relevant once the slab and thereby the Grade has been decided. Ofcourse there could always be a cut off for passing the exam. In the same educational institution, the faculty could be rated by the proportion of students who have passed the exam in the class handled by the faculty member. Here, the average marks scored by the students, although calculable is not used as a measure of performance, because helping students pass an exam is considered a very basic requirement and more critical than helping more students get higher marks. Such situations are also common in tracking the effectiveness of controls, especially manual controls. These controls could be just process controls or security related controls. The objective is to understand the effectiveness of controls for which the pre-requisite is to measure the proportion of instances in which the control has been implemented out of the total opportunities. The control can be just an entry in a register or a screen or a phone call or mail to communicate something. As the implementation of the control itself is a discrete action, that is, either it is implemented or it is not, the measure of the control itself is preferred in discrete form. The time interval within which the control is implemented or the quality of the mail or phone conversation is irrelevant and the KPI tracked here is the proportion of transactional opportunities in which the control was applied.

Tribal Knowledge is something almost all of us have been practising in our homes and families for many generations. How often have we heard our relations and friends appreciate one particular dish of our mother and enjoyed seeing her blush when the guests proclaim that this is the best version of that dish they have ever tasted in their lives? Once this tribal knowledge is identified, there would be almost a competition amongst the daughters or daughters-in-law or even others to be the first person to learn this secret knowledge from the mother. Even after learning this, applying this acquired knowledge to create that dish would still remain tough, but half the battle is won if this knowledge is obtained. This knowledge may not be available in documents or procedures. In a commercial organisation, this tribal knowledge has a lot of value due to its intrinsic trouble-shooting, problem-solving, customer-delighting potential. Some staff by their sheer involvement and passion in their profession and past experience pick up small, subtle yet impactful tit bits or nuggets of information about their work, perhaps about a customer, a machine, a tool, a transaction, a process, a drawing, a person or certain work situations that often turns out to be the missing piece of a complex jigsaw or the icing on a solution cake that multiplies its effectiveness multi-fold or the key to solving a problem that has flummoxed all known SMEs. Now the immediate need of the moment is fulfilled by using this tribal knowledge, but what would happen if another problem, be it same or similar turns up after an uncomfortable time gap that is just long enough for people to forget how the situation was tackled the last time it happened, if anyone even remembered, that it ever happened before. This is exactly why this Tribal Knowledge otherwise known by various other terms including but restricted to Residual Knowledge or Tacit Knowledge or Hidden Knowledge, needs to be diligently identified, unlocked for sharing with all concerned, captured for posterity and harnessed for maximising organisational benefit from the Tribal Knowledge. This is never an easy task and cannot be achieved by any knee jerk reactions to a problem that was found difficult to solve till someone shared his or her knowledge. Knowledge Management (KM) as a larger subject includes managing Tribal Knowledge and should be part of the organisational strategy. For successful Tribal Knowledge Management, the focus should be on “De-Tribalizing” the Tribal Knowledge. In other words, the organisation should aim to bring as much as possible of this knowledge into the open and retain the knowledge by digitising and documenting the same in an easily understandable manner, train others on this and use it wherever required thereby developing enough additional experts who could even improve further on what they have learnt. The following are some of the steps that could be followed for unlocking, capturing and harnessing Tribal Knowledge. Sr. No. Level To be done 1 Strategic · Process Framework for Knowledge Management to be implemented and followed 2 Strategic · Governance structure to be implemented with an organisational Knowledge Management (KM) Steering Committee and a Budget for KM 3 Strategic · Quarterly meetings of the Steering Committee to appreciate achievements and guide problem resolution 4 Strategic · Tribal Knowledge Management Working Groups to be formed 5 Strategic · Monthly Meetings of Working Groups to review achievements and resolve problems 6 Tactical · Creation of Forums for sharing Tribal Knowledge – KM Blogs, KM Mail Box, KM Newsletter, KM Portals etc. 7 Tactical · Creation of an SOP for KM including Tribal KM · If required, customisation of SOPs for different Verticals and Horizontals 8 Tactical · Identification of different groups of high potential contributors – Senior Associates, Quality Auditors, Quiet Achievers not ready for Service Delivery jobs like People Management, Subject Matter Experts (SMEs) not interested in routine production jobs and so on and encouraging them to be more forthcoming in sharing their Tribal Knowledge 9 Tactical · Institution of a Rewards and Recognition (R&R) Scheme for contributions to KM · Making contributions to KM and TKM one of the criteria for performance awards like Employee of the Week, Month, Quarter, Half-year, Year etc. and also for half-yearly and annual performance appraisals 10 Operational · Awareness sessions on KM and Tribal KM for all staff in the organisation · KM Session to be included in the Induction program for new recruits 11 Operational · Appointment of a Committee within each Vertical or Domain to quickly validate the contributions to the various forums, examine them for sensibility, correctness, fitness to post etc. and then post them on the KM Forums · Periodic Review of the KM Committee’s (KMC) performance by the Vertical Head 12 Operational · KMC to open up the posted Tribal Knowledge for comments and blogging by others who can agree, disagree or further fine tune the post 13 Operational · KMC to make a selection from various Tribal KM posts with some weight given to the blog comments and trigger modification of the formal process documentation like SOPs, Specification References, How-To-Guides (HTGs), Operation Manuals, User Manuals, “Tips and Traps”, FAQs, Ready Reckoners and so on · Where relevant, the changes need to be validated by the customer before inclusion in the documentation · Now some of the Tribal Knowledge has been made Common knowledge 14 Operational · KM R & R to be implemented and awardees to be given due publicity · Rewards can be given away at the Floor level, at the Vertical KM Working Group level or at the Organisational KM Steering Committee level depending on the contribution and the award 15 Operational · When there is a problem effectively solved or an exceptional performance done or a crisis avoided, the key resource can be encouraged to contribute to the KM Forums 16 Operational · Whenever there is a Delivery or Quality issue or a Customer Complaint, the lessons learnt from the same can be also added to the formal documentation Duration of KM Forums, Schemes etc.: The creation and documentation of Tribal Knowledge is at a peak for approximately 12-18 months after the beginning of a transition or project or a new product, after which a steady state is reached and the rate of contributions start tapering off. The focus can then shift to relatively newer projects or products. However, if there is a disruptive event in a long-running project like a spell of heavy attrition, leading to a lot of newcomers on the floor at the same time, or a change of technology platform, or change of customer specifications etc. there could be a flurry of KM activities, which will further improve performance.

A person who needed to complete a long journey within a short time was in such a hurry that he could not spare the time to fill his car with fuel, with the result that the journey could not be completed at all as his car ran out of fuel, leaving him stranded midway. Similar is the fate of the Business Excellence professional who ignores rational sub-grouping in problem solving. The need or the opportunity to use rational sub-grouping occurs almost in all problem solving attempts. In a typical such case, the variation in a performance parameter will need to be identified, then root-cause analysed and corrective actions need to be implemented. The real, disruptive variations are caused mainly by special causes. These special causes normally do not carry a label and put their hands up to be identified. They have to be identified by meticulous study of the process data. For the special causes to be easily identifiable from the data, factors that can distract or obscure the special causes need to be got out of the way. One of the most frequent such factors that can obscure special causes are their own “cousins”, i.e. chance causes. Therefore, even before looking for special causes, the problem-solver needs to take steps to ensure that special causes are not masked by chance causes. This is most effectively done by being aware of, taking cognisance of, applying and using, “Rational Sub-grouping”. Rational Sub-grouping, by definition and practice, does an effective job of preventing the obscuring of special causes by chance causes. This it does by clustering together or grouping together, those data points, each of which could represent a product or transaction produced under same or similar conditions. By this clustering, the variation between “homogeneously” produced products, mostly due to chance causes are also clustered, thereby allowing the observer to look for special causes that cause variations between sub-groups. As the chance cause variations are wrapped up in their sub-groups, they do not get in the way of the observer’s line of vision when looking for special causes. Rational subgrouping thus, organizes data into groups that were produced under similar conditions in order to measure the variation between the groups rather than between individual data points. If when creating control charts to investigate a process or a problem, an excellence practitioner ignores or does not use a rational sub-grouping strategy, these control charts may not provide the correct answers required to identify the source of variation of a process. The practitioner may not be able to, atleast immediately zero down on the special causes, which were the primary targets of his investigation. The practitioner may end up searching for among the chance causes, the proverbial needle called special causes in the haystack. There may be a high probability of going on the wrong track and investing time, energy and money in irrelevant actions and solutions to try to eliminate what really are just chance causes, which may not be effective to really solve the problem. The special causes would not even have been identified, let alone be eliminated. In conclusion, without the fuel of “Rational Sub-grouping”, the practitioner would get stranded in the “Problem Solving” journey and not reach the targeted destination of implementing an effective solution to solve the problem under focus.

The qualities required of a good sponsor of Business Excellence would be drawn from the Sponsor’s responsibilities. The typical qualities required for carrying out those responsibilities effectively could be summarised as under. A. Strategic Level Qualities 1. High Professional standing in the organisation’s Board or CXO level 2. Domain Knowledge of the relevant Verticals 3. Awareness of Market Trends 4. Reasonable level of Tech savvy-ness 5. Understanding of Structured Improvement methodologies including Lean and Six Sigma 6. Ability to align organisational programs and initiatives to the organisation’s Vision, Mission, Strategy and Objectives B. Tactical Level Qualities 7. Ability to cascade the organisational objectives into goals for various programs and deploy the same in various Departments and levels across the organisation 8. Ability to articulate the benefits of structured improvement approaches to different opinion leaders in the organisation 9. Stakeholder Management to keep all interested parties satisfied 10. Knowledge and ability to use Financial KPIs to measure and improve effectiveness of Business Excellence initiatives C. Operational Level Qualities 11. Ability to remain at a 30,000 feet level to view the larger picture and when required come down next to the an Operator on the floor and identify issues 12. Ability to trouble shoot projects that are stuck, liaise with peers across the organisation and clear roadblocks 13. Conflict management – typically between an Improvement Project leader and Process Owner 14. Commitment to remain totally neutral in tollgate reviews D. Other Soft Qualities 15. Verbal and written communication skills 16. Ability to identify trainable resources for training in structured improvement methodologies 17. Perseverance in not being put off by obstacles 18. Multi-tasking in monitoring and following up multiple projects 19. Flexibility in adapting structured improvement methodologies to achieve Business Excellence Objectives 20. A passion for Excellence that overrides everything else

Use of a baseline is somewhat dubious in the following situations. 1 Project to design a process where there is none There is no process at all to baseline against. 2 Upgrade of technology The technical environment has so completely changed either through the project or otherwise, so much so that comparison cannot make any sense

Frida Kahlo, the Mexican painter once said, “Nothing is absolute. Everything changes, everything moves, everything revolves, everything flies and goes away”. The first part of this statement is applicable to all subjects, including, “Rework”. While, “Rework” is traditionally considered a waste, in the following situations, “Rework” may be less unwanted. 1 Innovation By definition, innovations or the first attempts at it cannot give exactly the desired results. Unsuccessful innovations are required to eliminate failure modes and rework leads to a marketable product 2 Product Development When factors outside the control of the organisation change, like tighter laws, rapidly evolving customer requirements etc., rework is almost mandatory to sell the product 3 IT Coding and Development Especially when large applications are developed over a period of time, the circumstances under which decisions related to development would have been taken would have changed with time. While under the set of then prevailing conditions, the decisions would have been correct, in the present circumstances, the same decisions may warrant review. Upon review, if these decisions are found to be not appropriate to the current circumstances, then they may need to be changed and the work impacted may need to be redone. This is preferable to trying to avoid rework and ending up releasing a poor application with known issues. 4 IT Testing As part of testing a unit or a module of an application, if say Mixed case incorrect input is not detected as an error by the code, it is advisable to rework the code to further increase its robustness 5 Ignorant Market When no one including the potential users know what they need, the only way to create and supply what is needed is by experiments, testing the market with prototypes, feedback on failures and rework 6 Painting or creating other works of art Perspectives are available only after creation of the entire work of art or atleast part creation. Rework for improvement is the route to the success of that work of art 7 Cooking When testing the soup to be served at the beginning of a huge dinner, if the soup is found to be too salty, rework in the form of dropping a few quartered potatoes over a simmering flame is essential to de-salt the soup and make it consumable 8 Wine making Wines are known to get better with age. If a wine has not been sold after the targeted ageing (say) ten years, its price may further increase as in the additional waiting time the wine would mature more and thus become better. This additional ageing is a kind of preferred rework.

The history of Management is as old as the history of mankind. Ever since the human species evolved, there have been millions of Management concepts that have come up, been practised and either died a premature death or merged with another concept or evolved into another version. Some of these concepts have been shown up in due course of time as just a fad, with few of them being aggressively promoted by influential but vested, commercial interests. Very few of these management concepts have really stood the test of time and with every decade, the yardstick in terms of lifetime of a concept has dropped further and further lower as change keeps accelerating. It was in the ‘70s (1979 to be precise) that Motorola CEO Bob Galvin asked the question, “What is wrong with our company?”, that triggered the whole sequence of activities that culminated in the evolution of the Six Sigma Methodology. So, Six Sigma has gone through the Seventies, Eighties, Nineties, Noughties and is still running in the Teens. In a period of accelerated change, “Six Sigma” as a Management concept is entering its fifth decade and is still going strong. The Lean Methodology certainly has been around for even longer, for around a century. No fad or poorly designed management concept can run for a such a long time without merit. The merit of the Six Sigma methodology lies in its completeness as a Management Concept. The statistical tools in the Six Sigma bouquet have been around for a very long time before Six Sigma, some of them being around for centuries. The tools were all used adequately till then, but it was the Six Sigma Methodology that further increased their effectiveness when used. It is not just coincidental that this data driven methodology uses data and the output of what has come to be called Business Analytics in all its phases and steps of all phases. Thus, the science of Business Analytics is well embedded in different steps of the D-M-A-I-C phases of the Six Sigma Methodology. Obviously, there would not be an explicit reference to that. But the principles followed in certain steps are the same. The intersection of the Six Sigma Methodology and the output of Business Analytics could be documented as under. Six Sigma Phase Six Sigma Process Step Facet of Analytics used Used for D E F I N E Generate project ideas Descriptive Identifying potential improvement opportunities from past data Select project Descriptive Selecting the most suitable opportunity from past data Project Objective (Y) finalized Current Process Mapped Improvement Project Charter prepared Stakeholder List prepared Define Phase Tollgate Review Completed M E A S U R E Data Collection Plan prepared Descriptive Collecting current data for MSA and Process Capability Measurement System for Y validated Descriptive Using the current data collected to validate the Measurement system; if required set right the measurement system and revalidate with fresh data Performance Standard prepared Current Process Capability assessed Descriptive Using the current data collected to calculate the current process capability Gap Analysis completed Descriptive Identifying the gap between the current and targeted process capability Measure Phase Tollgate Review Completed A N A L Y Z E FMEA Conducted All potential causes (X) identified Critical Xs identified Descriptive Using data collected under different conditions and conducting Hypothesis Tests Sufficiency of critical Xs for the project verified Predictive Confirming with appropriate data that, if the critical Xs identified are controlled, that the targeted Y is achievable. If not, collect more data to reach sufficiency Measurement System for Xs validated Descriptive Using the current data collected to validate the Measurement system; if required set right the measurement system and revalidate with fresh data Analyze Phase Tollgate Review Completed I M P R O V E Alternative solutions generated Alternative solutions evaluated Best solution selected FMEA conducted for selected solution Selected solution piloted Solution validated with or without changes Prescriptive Confirming with appropriate data that the targeted Y is achieved. If not, improve the solution further, re-pilot and test again with fresh data Improve Phase Tollgate Review Completed C O N T R O L Control Plan for Xs prepared Control Plan for Xs implemented Documentation reviewed and revised Benefits documented Prescriptive Using post improvement data, computing benefits, projecting benefits assessed for the future Improved process transferred to process owner Control Phase Tollgate Review Completed

In any business, resources are expensive and need to be judiciously expended. Customer is important, but the importance lies in the fact that the customer is the source of profits. Customer satisfaction is very important, which is why the correct needs need to be identified and the right resources need to be expended on fulfilling these needs and keep the customer satisfied. Typically, there are many hundreds of customer needs that can be identified through various methods. It would be an exercise in futility to try to prioritize each of these needs. Therefore, the sensible approach would be to categorize these needs into a smaller number of groups, prioritize firstly the groups and then the needs within each of the groups. The Kano model is a useful tool in categorizing these needs as dissatisfiers (Basic requirements), satisfiers (Performance requirements) and delighters (Excitement requirements). Non-fulfillment of dissatisfiers, results in dis-satisfaction, while fulfillment does not increase satisfaction. Fulfillment of satisfiers results in proportional increase in satisfaction. While non-fulfillment of delighters does not result in dissatisfaction, their fulfillment delights the customer. The irony of customer satisfaction vis-à-vis customer needs is that the most important needs turn out to be dissatisfiers as they are very basic and practically taken for granted. As an extension of this very same irony, a less important need can surprisingly turn out to be a satisfier as these are needs the customer wants to be fulfilled and is willing to pay for it. Therefore, there may be more customer satisfaction obtained by improving fulfillment performance needs rather than that of basic needs. In many cases, the fulfillment of dissatisfiers would be mandated by various regulations related to safety, global product standards etc.. After using the Kano Model to categorize and then prioritize customer needs, the next step would be to convert these using a QFD, to Product or Service Functionalities, Product or Service Design Features, Product or Service Design Specifications and finally Process Specifications. Providing features in the product or service always involves a cost and an estimated additional revenue. To produce the product within the budget allotted, use of resources will need to be on those features that have been identified as satisfiers through the Kano Model. Obviously, the basic needs will need to be fulfilled first because the product does not exist without the, But when it comes to improvement of features, satisfies should get prioritized.

In any business, performance is typically expected to vary over time and w.r.t. inputs. When comparing two performances, it would not be completely correct if a decision that the performances are different were to be taken based on comparison of just one or few data points from both the performances. Sampling errors should not influence the decision. Therefore, it is essential that the correctness of the decision taken should be sustainable over time. For the decision to be sustainable, data that reflect the sustainability of both the performances will be required. Once this data is available or is collected, the decision based on this data is also expected to sustain over time. The decision that is taken based on samples must hold good for the populations also. In other words, even after some unavoidable overlaps of both the performances, perhaps due to chance causes, the difference in the performances of the two populations must be visible, conspicuous and clearly discernible. In other words, the difference in the two performances need to be significantly different. But “significance” is quantitative and statistical. The significance of the difference is assessed from statistical data of the two performances. Statistically significant difference represents the clarity or discernibility of the difference between the two performances and the sustainability of this difference over time. Performances of two populations with a statistically significant difference will remain different over time unless there are some special causes in play on one or both of them. But how significant is significant? This depends on the objective of comparison and the stakes involved. The margin of error tolerable in taking a decision on the difference between the performances depends on these factors. For different combinations of conditions, this margin of error could be 1% or 5% or 10% or any other agreed number. This is the error involved in the decision to conclude that the two performances are significantly different based on the available statistics. Uses of the concept of Statistically Significant Difference in Problem Solving and Decision Making The uses of this key concept of “Statistically Significant Difference” to solve problems and take decisions are innumerable, a few of which are given below. 1. Comparison of performances between two or more a. Time periods b. Processes c. People d. Suppliers or Service Providers e. Applications 2. Assessing effectiveness of a. Training b. Improvements c. Corrective Actions d. Action taken on suspected root causes 3. Evaluating a. User ratings in market surveys against marketing campaigns b. Performances of new recruits against agreed targets In all the above cases, Hypothesis Testing can be effectively applied to assess the existence of a statistically significant difference.

Stable Process and Capable Process In the Chapter entitled, “Common Causes and Special Causes of Improvement. Stable System” of his treatise, “Out of the Crisis”, Deming says, “As we shall learn, a process has a capability only if it is stable”. A process that operates with its control limits is a stable process while one that operates within Specification Limits is capable. For a process to be deemed as capable, it needs to be consistently capable. For the process to be consistent, it needs to be stable. While process stability and process capability are not related, the key connection is that Process capability assessments should be performed after demonstrating stability of the process. Process capability assesses ability to meet specifications. But, with an unstable process, it is difficult to assess or predict its capability. With an unstable process, the estimate of the process capability becomes relevant only at that point of time. The capability of a stable process can be improved, but an unstable process cannot be considered capable. In conclusion, while stability and capability need to be treated together in terms of conclusions about the process, it is imperative that “stable” comes before “capable”. Process Stability – A pre-requisite for every process? As mentioned above, stability of a process confirms that its capability can be predicted better. Stability is a characteristic that is observed over time. In other words, is a process as good now, as it was before and will it be as good later? This assumes a certain repetitiveness of the process within a reasonable time-frame. But for processes which have a long time interval between them or when processes are more of one-off or once-in-a-long-time or even once-in-a-lifetime event, in which there is nothing like predictability because there is no definite future for the process or no likelihood of the process happening again in the conceivable future. In such cases, process stability, although important as a concept may not be quite relevant. Examples of such processes can include construction projects, large machine assemblies, equipment erections, rocket-launches, Software upgrades, ERP implementation, Functions, Various Financial, Process or System audits and so on. In these “processes”, process stability may not be a prerequisite.

Despite the fact that correlation does not necessarily mean causation, exploring and where relevant, identifying correlations still remain a key step in the improvement project life-cycle. Exploring correlations of the observed effect with potential causes is a preliminary step in identifying root causes. This shortlists the potential causes from the huge universe of causes, leaving the project team with a reduced list of potential, most-likely causes to be investigated further. Without this shortlisting, the project team can get drowned in the sheer number of potential causes, the recovery from which can use up valuable project time. Further, when people are introduced to and are being trained in structured problem solving, correlations are a good route for inducting them into a "cause-and-effect" mode of thinking. The logic of a relationship between a potential cause and an effect, summed up as correlation is relatively easy to understand as examples from all walks of life can be quoted and when followed by quantified correlation, further embeds the understanding of correlation in the minds of people. This is also applicable when training people on data based decision making and data driven improvements. Additionally, the coefficient of correlation is a relatively easy-to-understand measure and can be used to illustrate both positive and negative correlation. When combined with visual tools like Scatter Diagram which are easy to create on popular spread sheet applications, the concept of correlation becomes even easier to understand. As an extension, the concept of interactions between potential causes, resulting in varied impacts on the effect can also be well illustrated and understood by correlation analysis. In summary, while correlation does not imply causation, causation typically displays correlation, making correlation an essential step in the root-cause analysis process.

In any initiative, any over-emphasis on any aspect of business is bound to hurt business at some point of time. Voice-of-customer is no exception. The advantages of VOC cannot be over-emphasized and still remains a most effective method of getting customer feedback, both to identify opportunities for improvement and also to confirm the effectiveness of improvements implemented. Nowadays, VOC is used not just by Suppliers but also by Customers themselves. Many of these customers may be themselves using VOC to feel the pulse of their customers and know how a response to a VOC survey would be interpreted. Therefore, the Customer can sometimes tend to be manipulative in the response to a VOC. One of the main reasons for this is to proactively snuff out any possibility of rate or price increase request from the supplier, if the customer were to accept total satisfaction with the Supplier's performance. In such situations, using VOC in the usual way may not be ineffective and worse still get the Supplier to focus on incorrect priorities and waste resources in solving non-existent problems. In conclusion, if it is observed that a customer is not quite transparent in feedback, VOC can be used as a dipstick, but major decisions including judging the successes of projects need not be based only on VOC.

The reasons why the humble flow chart evolved into the powerful process map lies in the analogy between the process map and the geographical map. Just as a location on a map is referenced by its latitude and longitude, a process step in a process map is referenced by a combination of (say) the person / team doing that step and the stage of the process in which that step occurs. The references could be also be different – for example, a timeline could be one of the references. These references or the facility to reference a process step constitute the life of a process map. Now that this facility to reference is here to stay, swim lanes, be they horizontal, vertical or both are also an inseparable part of the process map. It does not matter as to which position in the sequence of detailing the process map is. Swim lanes make the process map easier to read and use. Therefore, it would be advisable to create and update one full set of swim-lane process maps from L0 to L5 levels. In the ITeS sector and in a typical BPO scenario, would use the following sequence of increased detailing. Level Description Details L0 Entity Level Customer, Supplier, Other External Parties L1 Sub-entity Level Different Departments of the Customer and Supplier, Other External Parties L2 Process / Sub-process Level Interactions of different processes or sub-processes with hand-ins and hand-outs L3 Activity Level Activities done by different stakeholders at different stages of the process L4 Task / Sub-task level Various tasks or sub-tasks that constitute activities L5 Field / Key-stroke level Absolute detail of every field touched or every key struck This set of process maps for every process is valuable as a training tool, as a real-time guide or SOP and as a trigger to identify improvement opportunities. To augment the above, would also use an enhanced SIPOC that contains apart from the usual Suppliers, Inputs, Process, Output and Customers, related information like process step times, who does what step, the team size and distribution across shifts, the average volumes of these transactions, the qualifications of staff for this process, the training required and so on. Other maps can be used to explain a specific perspective or to support a specific initiative. A turtle diagram or alternatively a Relationship Map can be used to understand at a glance, interlinks and dependencies. A value-stream map could be used to identify opportunities for leaning out a process by crashing lead-time. Overall, a simple, situation-based approach to selection of process map types for use would help in optimal utilization of this wonderful tool.

It would be possible to make data traditionally considered as continuous appear as attribute by an appropriately worded question. Some examples of the same are provided below. The column on the left has a set of parameters traditionally considered as continuous, while the column on the right has certain questions, which if answered will lead to “counting” of the value of the supposedly continuous variable. Weight of an object How many grams of matter are there in that object? Volume of water in a container How many millilitres of space or occupied by water in that container? Bank Balance How many paise are there in that account? Height of a building How many metres of height are there in that building? If there is an argument that the value cannot be always counted in whole number of grams or ml and so on as they can be fractions thereof, the counter to that would be to narrow down the Unit of Measurement to micro-grams, pico-grams, atto-grams, femto-grams and so on and at some point of time, the value of the parameter can be counted. To resolve the above, one just needs to stick to the unit of measurement traditionally used. For example, bank balances are normally measured in Rupees and Paise, which sustains the continuous nature of the parameter. Similarly the traditional units for weights and heights can be considered, as again the continuous nature of the parameter being measured is retained. Some discrete data like errors can get a continuous "make-up" when averaged. For example, errors or error transactions assessed every hour are discrete but when averaged hourly over a day appears continuous. Further, using the discrete data, “errors”, various related parameters can be derived which can appear both continuous and discrete. For example, a defect rate of 10% which is traditionally considered attribute can be also expressed as “Average defects per product” of 0.1, which would appear continuous. Such data could perhaps be considered, "Quasi-continuous". Additionally, in hard-core Mathematics, mixed random variables and topological sets are conceptually considered neither continuous nor discrete.