Rohit_Kurup

Kanban is a visual tool used to control, manage and review the flow of work within a process/line/lifecycle of a project/operation through the usage of Kanban cards which would contain details such as status of work, from which station it has come, at what time, quantity, who is supposed to take it up for further processing, etc. It's literal meaning in Japanese is roughly "cards, which you can see". It's purpose is to ensure a pull effect on the work-in-progress(WIP) to ensure that capital tied to WIP is kept at a minimum and the MURA( uneveneness- in this case variation in work flow) and MURI (overburden-in this case backlog of WIP) are reduced so that MUDA (waste- in this case rejections/rework) is not generated. Gantt charts are also a visual tool which is used for preparing plans of activities within a project as a horizontal bar graph plotted against a timeline. The progress of the project is tracked against the vertical bars using embedded bars or other bars in different color to showcase the amount of work completed against the planned timeline. It is used to visually observe the actual progress of the project against the planned timeline. Both are powerful tools for visually assessing the progress of the work, the effectiveness of one over the other in case of applicability towards LEAN projects depends on the type of project and on the purpose for tracking of the same. Example: - Suppose, the LEAN project is undertaken aimed towards addressing the losses and leakages occurring through the sub- steps/sub-operations of clear and well defined processes which have been stable over a period of time in order to improve the same or take it to next level of optimization. In such cases Gantt charts wouldn't be able to track the losses but a Kanban system would be able to capture waiting time/quality losses within it's cards and throw up opportunities for improvement. Thereby, making Kanban a better option in this case. However, the progress of the projects also need to be tracked against a timeline in order to ensure the resources spent on these projects are optimized and benefits are actually obtained. In this case utilization of the Gantt chart makes more sense. We can say that, though not necessarily always, Kanban is usually more effective for LEAN projects which are : - 1. Which have a well defined setup with lot of variabilities in demands, priorities, interlinked sub-operations, lot of work-in-progress and repetitive operations . In such cases Kanban can help identify and optimize the bottleneck operations. 2. Where the goal is to identify the gaps in the flow of work, details of losses are to be identified, And Gantt charts are more effective for LEAN projects which are : - 1. Have set defined timelines for completion, variations within operations are low, lesser no. of parallel & complex activities are involved. In such cases Gantt charts can help identify the operations which is taking more time to complete against the timeline. 2. Where the goal is to identify the gaps in the project timelines and focus is to improve the timelines.

A Control chart is a graphical tool that helps us study how a process is changing over time. Data are plotted in time order(i.e. X axis progressing in time), and consists of a central line representing the mean and the the upper/lower control limit(plotted along the Y axis). These lines, based on historical data, lets us compare current data to see if the process mean has shifted or not or the variation has increased/decreased. It's a great visual representation of the process data over time and allows us to visually see how our processes are performing over time. It is one of the most commonly taught QC Tool (part of the 7 QC Tools) across industries segments. And a lot of LSS projects are focused on either :- 1. Shifting it's mean 2. Reducing it's variation Is it absolutely necessary to use Control Charts in LSS Projects? Like most things in the real life scenarios, there are quite some nuances which has to be understood. Like, 1. What kind of project we are doing Whether its' a DMADV or DMAIC project. Using Control charts makes more sense when there is historical data available. As it allows us to reliably calculate the process mean and control limits. In a DMADV project, we may refer control charts of older designs/processes for identifying opportunities for improvement, but cannot compare the newly designed processes with the old data as most DMADV projects target multiple improvements and focus on a lot more than a single process. However, In case of DMADV, Control charts can be used during the Verify stage and during pilot runs of the new process which is addressed at following points 2. What sort of operation/process we are analyzing & Data Sources available A lot of LSS Projects done by enabling functions such HR, Finance, Procurement etc., may have a lot of variables and non continuous data. In such projects we may be dealing with processes that aren’t continuous or where we don’t have sufficient data points to build a meaningful control chart. In these cases, simpler tools like Pareto charts, histograms, or fishbone diagrams may be more appropriate. 3. Timeline of the Project/Resources available Many a times, we come across projects which have a short timeline with the focus to improve one metric or one process. In such projects, it makes more sense to drive the improvement check whether the process has improved and handover to the Process Owner. Though such projects can be labelled as strictly LEAN Projects, most of the time these projects are run under the larger Lean Six Sigma umbrella. Also, it is common for the LSS professionals to be given ad-hoc projects to improve some particular operations or address some obvious special cause variations in a stable operation then the project can be resolved through proper Root Cause Analysis and a new Failure Mode Effect Analysis. In such cases also usage of Control charts is also an overkill which could divert resources. These are just a few cases where the usage of Control charts may not be advisable. So, in conclusion to answer this question, it is absolutely not necessary to use Control Charts during LSS projects, though they are one of the most common tools used in LSS projects.

The ADKAR (Awareness>Desire>Knowledge>Ability>Reinforce) is people focused change management tool developed by Jeff Hiatt. It's a structured framework towards implementing organizational change through changing individuals of the organization. The acronym is fairly self explanatory and refers to steadily lead individuals through some steps wherein they would end up achieving the following stages: - 1. Awareness :- Individuals will become aware of what the change is and why it is being pursued. 2. Desire :- Change is more often than not met with some resistance or denial by the people. In this stage they are made to overcome any resistance to the change. People should become forthcoming to participate in the change wholeheartedly. 3. Knowledge :- At this stage they will trained on the knowledge required to bring to effect the change. 4. Ability :- The knowledge gained is to be put to work. Once that happens the ability of the people within the organization improves. 5. Reinforce :- Any improvement if not sustained will eventually wither away. Just like that any ability/skills/change in the way of working if not reinforced will wither away. Therefore, strategic steps such as R&R, promotions linked to participation, etc. are to be implemented in order to reinforce and sustain the change management. It's integration with the DMAIC methodology to effectively manage change in a Lean Six Sigma project can be multifold. They are as mentioned below: - -------------------------------------------------------------------------------------------------------- 1. DMAIC aided by ADKAR 1.1. DEFINE PHASE In Define Phase, through the usage of multiple tools such as SIPOC, Affinity Diagram, VOC/VOB/VOP analysis etc. a project charter is created which is supposed to be signed off by all stakeholders. This is similar to Awareness and Desire phase of ADKAR as proper stakeholder analysis management, risk analysis and management, leadership engagement and communication plan are used to bring awareness and desire in the people. 1.2 MEASURE PHASE In Measure phase, a multitude of statistical methodologies are used to measure the current level of performance & quality through DCPs, Process Mapping, Sampling, MSA, SPC etc. For this Knowledge on the processes are shared and people are trained to use these tools. This can be synonymous to the Knowledge & Ability phase of ADKAR. 1.3 ANALYZE PHASE Post this, usage of tools for Hypothesis testing, RCA, FMEA,etc. are also used analysis. Even for this the Project Lead (BB/GB) would have to share the knowledge to the process owners and other stakeholders for performing these analysis. These too falls under the gambit of Knowledge & Ability phase of ADKAR. 1.4 IMPROVE PHASE Utilization of the tools mentioned above and the improvement plan put forth leads to not only improvement in the process but also leads to the improvement in the ability of all participants in the DMAIC project. This integrates well with the Ability phase of ADKAR. 1.5 CONTROL PHASE The control phase in DMAIC leads to Improvement Sustenance Plans and Control Plans to be implemented in order to sustain the improvements achieved in IMPROVE Phase. This needs the Reinforce phase of ADKAR to ensure that the changes through these plans are sustained. -------------------------------------------------------------------------------------------------------- 2. ADKAR leading to DMAIC Organizations, due to multiple reasons such as rising costs, competition, stakeholder demands, etc. may undertake change management projects to improve their operations. One of the best available methods is to adopt Business Excellence Goals and run Lean Six Sigma Training programs and certifications in the organization. In order to reduce wastages and variations. This falls under the A & D stage of ADKAR. In such cases LSS Yellow Belt, Green Belt and Black Belt trainings can be introduced. And these trainings can be certified after completion of some DMAIC projects. This falls in the K & A stage of ADKAR. Setting up of aspirational targets within the organization. For e.g. Targeting that at all times within the organization there are at least 5% Black Belt, 50% Green Belt and the remaining 45% are Yellow Belt trained and certified workforce. This would ensure that culture of continuous improvements are sustained through constant and sustained continuous improvements(this is just an example, different organizations may take different approaches based on their maturity towards adoption of Business Excellence). These would fall under the R stage of ADKAR.

Any exercise which utilizes elements of games (situations/step-by-step escalation/challenges/awards/comparative dashboards/points/levels etc) in order to enhance the experience of the participants similar to that of the wide myriad of games can be called as Gamification. It is intended to create a gaming-like experience of achievements and rewards in a real life situation so as to build motivation, sense of competition, build engagement, etc. We get to see gamification being used to achieve a lot many things such as :- a. Increased sense of competition: - Comparison dashboards or leaderboards for different sites/teams in an organization b. Training & Skill Development: - Different levels of achievements through Badges, Belts(LSS YB, GB, BB, MBB is also after all a way of building skills taken from the level of mastery in Judo) & learning apps which gives have built in skill development through multiple levels of achievements and rewarding users with recognition for achieving the same c. Maintaining a streak of good performance: - At the entrance of the many manufacturing organizations we can get to see streaks mentioned such as 0 Near Misses, 0 Accidents Reported since so and so dates. d. Engagement: - Competition built within different departments of organization through competitions e. Healthcare & Habit building Tech.: - A lot of wearable tech and fitness apps also gamify exercises and health routines giving the users an external stimulus to maintain their streak of achievements for maintaining their practices of exercise, habit building such as waking up in a particular time, journaling, etc. f. Marketing: - Almost any store, payment app, credit/debit card we use, we can see them send some points, credits, awards which can be collected and then used for taking part in some contest/ lottery/ any other uses in order to build customer retention. ---------------------------------------------------------------------------------------------------------------------------- Some of the risks associated with gamification which we can see are: - 1. Obsession leading to harmful consequences: - There can be risks of obsession fuelled decision making in order to gather more points at the expense of their well-being. Buying unnecessary goods only to improve tally of points by customers, using unfair practices by people in organizations to reach and remain at top of the leaderboard, etc. are all true events which have become very common. 2. Discontentment: - Poorly designed gamification can lead to discontent and disillusionment to creep amongst participants just like any poor decision making. For eg. Counting the no. of hours someone spent reading a content. People may simply keep the window open for hours and go about doing something else. They will surely benefit over others who actually read through diligently and closed the window). ------------------------------------------------------------------------------------------------------------------------- We can utilize gamification in the following ways to improve LSS Training : - A. Point Systems During trainings we see that the interaction of the trainer and only a minority of trainees happen frequently. Majority end up remaining silent spectators or are too shy/disinterested to interact though they may a lot to add. For getting more interaction the below ideas can be implemented, such as:- 1. Building a points systems for meaningful interaction. Giving points for active participation which do add some value to the training program and having a leaderboard for the same. Off course this needs to be thought out well else there would a cacophony of pointless talk just to score some points. Something which is hilariously visible in Group Discussions. 2. Points systems for the amount of time the camera had been switched on. Only if the participant is having the camera on and focusing on the content for a minimum amount of time should he/she be given attendance or some points. This can address the rather troublesome challenges we see in online classes were people will join the meeting but will be simply working on something else. 3. Profiling an Ideal Trainee. An image of an ideal trainee should be created who will be having some minimum no. of pts. for abovementioned activities, on-time arrival/joining, etc). This would act as a benchmark for the trainees. Creating a real time profile of the participants based on these parameters will help create a sense of comp etition and also motivate the trainees to reach the ideal level. Offcourse, this has to be enabled by using some AI tool to track the sessions. ----------------------------------------------------------------------------------------------------------------- B. Content Delivery We can also improve the training through gamifying delivery of Content 4. Phase by Phase Buildup, gamification of the skills developed being part of a puzzle which eventually leads to the completion of an image/art of the training. This will give a sense of achievement and satisfaction to the trainees. 5. Gamification of Sub-section quizzes. Most of the gamification in learning platforms are limited to different phases of LSS training i.e. D-M-A-I-C. However, a layer of gamification can be added to the different stages within the Project. Such as different exercises within each phase. For eg. micro-quizzes for CTQ identification, VOC/VOP/VOB, QFD, SIPOC,As-Is, Resistance Analysis, Project Charter etc. during the Define phase. This will help in keeping the training engaging. 5. Role-play gamification of a case study. Where, branches of scenarios and decision trees are built. Each decision would lead to a scenario which would lead to another scenario. This will help the trainees understand the concepts better and also help explain how and where common mistakes can occur. 6. Toll gating/ Mile stoning with a roadmap of different stages of the training rather than just a %age completion of the course. This would be a visually appealing added feature that would keep the trainees engaged. 7. Revealing the content level by level. While handing over reading materials and other contents to the trainees will be helpful to them to prepare themselves for the training, however it can be seen that it can also overwhelm trainees with a lot to absorb. Revealing the Content Page and progressively enabling access to them would allow for bite-size consumption of the material thereby improving retention of the same. This would also build an anticipation for the next part of upcoming sessions which would also fire up the curiosity of the trainee. ----------------------------------------------------------------------------------------------------------------- C. UNPREDICTABILITY/ANTICIPATION 8. Surprise rewards within the training. During the training certain elements of surprise gifts should be included. This would hook the participants to focus on the training by giving them a short term reward. This can be anything from a cashback, licensed access to a paid-library, discount for other trainings, premium subscription to online tools etc. This will ensure that the trainees are given a short term achievement by focusing on the training.

All companies are evaluated based on the performance on some of their key metrics such as Market Share, Earnings Before Income Tax, Depreciation and Amortization), PBT (Profits Before Tax), Earnings Quarter to Quarter Growth, etc. This is a good way to evaluate and compare the performance of the organization against some standard or benchmark metrices. This is referred as Managing by Metrics. And in today's age of ultra-sensitive market dynamics and volatile market conditions aggravated by issues of bonuses/payouts of key decision makers linked to the achievement of short-term goals(humorously referred to as the Quarter-Se-Quarter-Tak attitude, punning the name of the popular Bollywood movie Qayamat Se Qayamat Tak) the key team responsible for decision making in organizations chase the numbers of such metrics by hook or crook at the detriment to the larger interest of the organizations. This is referred as Managing The Metric. This also explains the evident jump in the closing dates of every month, quarter and annual calendar of metrics like deals signed, sales no., OEE nos. etc. And dip in metrics such as Consumption of Raw Materials, Incoming Inventory, Losses, etc. The impact of this at the CSAT(customer satisfaction) and business growth is the following: - 1. Growth of dissatisfied customers In order to paint a rosy picture or a less painful picture of the current state organizations attempt to manage the metrics, organizations falsify or close customer complaints, feedbacks without addressing it properly. This would though in the short-term bring down the resolution time & pending customer complaints it would leave customers dissatisfied and damage the customer relations in the long term. 2. Risky decisions to meet targets The 2008 financial crisis started off due to the practice of US financial institutions of giving out risky loans without any due diligence in order to show growth in businesses and for fatter bonus payments. This eventually led to the closing down of many such banks. Similarly many established organizations had to close down or downsize their operations due to the mismanagement of their businesses just in order to meet business targets. Very good example of this is GE which had actually popularized the use of Six Sigma to improve their operations but later had to sell their once most profitable business. 3. Diminishing Brand Value In order to meet short term goals, many organization may peddle in nearly unlawful or outright unlawful practices. When such practices are caught they eventually lead to loss of trust amongst stakeholders and lead to diminished brand value. This can affect the good will created by the companies over decades of responsible behaviour. Thereby causing business to degrow. 4. Negative Company Culture & Unintended Consequences Once the practice of Managing the Metrics culture catches on in a single team, it very quickly spreads like wildfire across the organization with over-enthusiastic participation and competition in order to manage the metrics. So much so that it would become near impossible to actually know what is current condition of those metrics are leading to unintended consequences. This would also generate animosity amongst departments, and further worsen the organizational culture. Departments and functions would work in Silos and eventually the customer and the business suffers. 4. Kill Innovation When teams focus on managing the metrics, they will go blind to available avenues of innovation and risk obsolescence. In pursuit of managing the metrics they will lose sight of the big picture and miss opportunities of growth and loss market share. The methods to control these are: - 1. Integrated Balanced Scorecard Having a integrated balanced scorecard, would allow organizations to measure how each department and each entity would be working towards providing value to the customer. And linking those with the customers' satisfaction metrics. This would ensure that there is a coherence in the leading and lagging metrics of the organization. 2. Business Excellence Culture By instilling a business excellence culture and through participation in thorough assessment of the organization be it for internal continuous improvement culture or for challenging different Business Excellence Awards such as the CII EXIM Bank Business Excellence(Based on European Foundation of Quality Management), the Ramkrishna Bajaj National Quality Award(Based on Malcolm Baldrige National Quality Awards Framework), Deming Prize for Total Quality Management and many other more. Through such exercise they can keep themselves abreast with the changes in the industries and assess their businesses holistically. 3. Conduct regular 3rd party audits Having regular 3rd party audits beyond the statutory requirements helps in desisting the malpractice of managing the metrics as most of the metrics which are managed are those of statutory nature. 4. Comprehensive Incentive Plans Many in HR functions believe that comprehensive incentive plans are complex good-to-haves rather than must-haves and follow outdated single or limited KPI(Key Process Indicators) incentive plans. For e.g. in Sales functions even now incentives are based on Q-on-Q business growth or absolute sales figures. This incentivizes fake invoicing, inflation in some metrics at the expense of others, poaching/encroachment of team members customer segment etc. Instead of this it should make the metric comprehensive through an integrated score based on customer retention, market condition, etc. 5. Real time-automatic data acquisition By automatization of data real time the opportunities for managing the metrics can be reduced and ensure that the actual picture of the business is captured without any embellishment.

Many a times we come across terms as Average Joe, the common man or Aam Aadmi. The moment this term comes up we have an image come up in our mind. This is sorts of a subconscious profiling. A survey of the imaginations would throw up wild variations but also quite some commonalities. These commonalities are built up through a generalization of what a typical set of people is known to tend to be like. This could be anything from buying patterns, voting patterns, eating preferences, clothing, lifestyle etc. This is an example of persona-profiling. Persona profiling is a technique used by marketing, designing, customer service, or for that matter any direct customer/consumer/client facing organizations to build a fictional customer/consumer/client who they are either targeting or more likely to come across. The broad steps to creation of Persona Profiling would involve: - 1. Market research through surveys/studies 2. Segmentation through patterns 3. Building sets of likely personas 4. Creating a backstory for each persona 5. Survey the market for relatability with each persona 6. Refine the persona 7. Create Strategy 8. Design offerings to cater to the persona 8. Revise and update the persona based on market conditions Benefits: - 1. Designing a new product A good example of this comes from the FMCG industry, wherein they typically conduct surveys of the potential market/the existing buyers through 3rd party organizations or themselves and then try to design products to cater to unique needs. These surveys try to collect data on the consumers that not only identify the demographics but also understand their driving factors, behavioral patterns, etc. Using this information they create a model of a typical ideal customer for whom their products are designed/remodeled. For e.g. Shampoo sachets were designed by identifying the unique customer who has just become economically mobile and would like to use the shampoo for Rs. 2/- but wouldn't want to spend a major chunk of his/her daily earnings and store it. 2. Prioritization & Customization Persona profiling also helps in prioritization of service offerings to their customers. Job portals have different pricing plans for different tools and services based on the unique requirements for people with different educational background and no. of years of experience. 3. Building Brand Loyalty This allows the organization to create offerings which specifically target the customers. An interesting case study for this is how Pepsi and Coca-Cola have identified their customers and create ad campaigns to build brand loyalty and have a sublime preference for their products in the early 90s. Based on the persona profiling of their existing customers Pepsi had ad themes that had spunk, rebellious and individualistic, and Coca-Cola had ads themes that had more festivals, social gatherings themes, in-line to social norms. Off course, it keeps evolving over time and is usually never the same. There are certain risks too involved: - 1. Ignoring of other potential risks While focusing on a critical few delighters for the customer, marketers and designers can overlook some basic features. A good case in example for this is how a majority of Indian cars fare poorly on safety tests because they prioritize cost, mileage, maintainability etc. This is severely shortsighted in a country were road accidents claims 1.5 Lakhs lives every year. (https://morth.nic.in/sites/default/files/RA_2022_30_Oct.pdf) 2. Personas built on flawed data sources Many data sources are likely to flawed due to incorrect methods used, unreliable sources, biased sources, etc. Such data sources would likely build a persona that has conflicting characteristics thereby creating a wrong persona. Thus leading to products and service which attracts no one in particular. 3. Obsolescence Persona profiling is a long tedious activity which usually takes a lot of time and deliberations to be completed. During this period, a major innovation/disruptor or a competitor offering can likely change the market dynamics thereby making the persona profiling obsolete

Post purchase rationalization, a variety of Confirmation bias is the rationalization done for supporting a decision which has already been taken ignoring all sorts of scientific or logical reasoning and ignoring new information. This is also something which also leads to a lot of the highly irrational voting patterns, irrational practices, lifestyle choices, purchasing decisions, selection of route maps, etc. A lot of people postpone healthy habits rationalizing their current lifestyle (which as per experts is not good enough) as being sufficient and that they have plenty of time to implement healthy habits later. One another example is how decisions on AI generated answers and Plagiarism tests are conducted using AI and algorithm tools to identify Plagiarism and algorithm-based AI content identifiers may erroneously mark formally-worded, neutral-3rd-person-language and grammatically-correct human created texts to be marked as AI created. Here also the fact that a human created content from someone who has observed how professionally written reports are worded, will be marked as failing AI tests.. Thus, necessitating changing a practice of formal writing to writing answers in a more informal tone and addition of images and emojis to pass these tests. Analytical Methods to identify and rectify it are: - 1. Pattern Identification through Blind testing (kind of similar to Measurement System Analysis): - Identifying whether feedback for products/services/experiences are being repeated post anonymization of the same. Considering that post purchase rationalization is primarily a folly of the human mind. Examples of these can be seen in organizations with a strong push for Diversity, Equity and Inclusion have anonymized resumes being viewed by hiring managers in order to avoid confirmation bias. Similarly, Social media platforms such as YouTube is awash with a lot of experiments where people when asked to use products, services or even while discussing ideas share very different feedback and are either stumped or try to change back their feedback. 2. Critical Evaluation Critically evaluation post the purchase can lead to coming to the right analysis but can also lead to buyer's remorse. Wherein again the feedback may come from the fear of a wrong decision being made. This can be avoided by systematically using Business Modelling techniques such as Monte-Carlo simulation and group decisions/strategy frameworks Analytical Hierarchial Processes. These are useful tools to carefully analyse the data and address biases of any sorts. 3. Constant Vigil/Situational Awareness/Retraining Constant vigil/mindfulness/situational awareness are certain skills which are developed through much practice in order to catch oneself from being complacent and falling to certain biases. In some of the organizations attempts to address such types of biases have been successful through regular training and awareness campaigns.

In Six Sigma projects we usually try to improve the capability of the process in order to achieve a better DPMO(defects per million opportunities) than the current DPMO. In simpler words, how much the input material/effort is converted to quality output. And since, variation is a given in the near perfect world, it is challenging to always achieve 100% good output. That's where most of us LSS practitioners delve in order to improve quality. In general, the formulas for : - % Yield = Actual Good Output/Maximum Good Output * 100. % Defective = Actual Bad Output/Maximum Good Output *100. These 2 metrics could be used for anything ranging for products manufactured, quality level of any particular sub-operation, raw materials received from a partner, etc in the manufacturing industry as well as quality of service provided, no. of bugs detected, no. of failed transactions, no. of dissatisfied customers etc. in the Service Industry. When this metric of %Yield or %Defective is used across various industries, various operations, various metrics, various people at various levels of maturity towards their quality journey it becomes very challenging to measure it for their projects as there could be multiple inputs, multiple processes & reprocessing, multiple interpretations of the data etc. Some of the Challenges are: - 1. Complex or Multiple Operations When the operations are complex or has got multiple operations going on simultaneously or in sequence there are challenges in finding the reason why a defect or variation has occurred. For e.g. in the brewing industry, the % conversion of the sugars to alcohol has multiple steps and is based on multiple factors such as source of starch/sugars, filtering conditions, microbial culture, pH of water, and a whole lot of other variables and it is still now an empirical calculation that translates to the yield % for the processes. This causes difficulty in identifying why a particular batch had low yield. Rolled Throughput Yield supplemented by cross-functionally accepted definition of Operations Parameters can be an effective tool to use in such cases as it would further breakdown the process steps and help identify the operation/process where the loss actually occurred. 2. Varying Skill Levels and Maturity in Business Excellence Very few organizations have matured in Business Excellence principles. Thereby, still having some elements of functional disagreements and siloed work processes. In such cases, it becomes very difficult to calculate the yield as conflicts would arise on the method and parameters to calculate the %yield and %defectives and spark off a blame game. For e.g., In many industries the output of a particular process of the Production department is dependent on availabilities of utilities which may not be aligned with the goals the Maintenance & Engg departments. In such cases, it is important to get a sign-off on the parameters and methodologies to calculate the yields and the definition of the Defects , In-process parameters and Service Level Agreements right at the Define Stage itself or at the Toll Gate reviews from the Team Members/Project Champion/Subject Matter Experts. 3. Unreliable/biased/inconsistent/Insufficient/Wrong Data Sources There is always a challenge in getting accurate, consistent, unbiased or correct data especially when there is a human element to the data collection techniques when initiating a LSS Project. It's important to clean and categorize the data and undertake a Normality study or any other applicable data reliability study & MSA in order to ensure that the data can be used for analysis.

Algorithmic bias is the systemic and unfair discrimination that results from algorithms and automatically powered systems. Such biases can occur in the early stage of algorithm development: data gathering, design of the algorithm, and during implementation. There is a danger that where Lean Six Sigma projects make use of AI and other tech-led solutions, the outcome could end up being unfair or inaccurate, thus reinforcing or aggravating an already existing bias. 1. Bias in Algorithm Effects 1.1 Hiring Systems Example: Algorithmic shortlisting based on biased previous hiring practice which would end up giving bias to female candidates compared to male candidates. It was identified in Amazon that, AI-based recruitment tools showed bias against female candidates. The algorithm, trained on historical hiring data, learned to prefer male candidates because the past data reflected a male-dominated hiring trend. Impact: High-quaified female candidates potentially get erroneously filtered or screened out; bias tilts the scales towards male candidates. 1.2 Scoring for Credit Example: AI credit scoring models – if training data is being modeled on biased human decision-making scenarios of previous erroneous practices by financial companies—will reinforce bias, penalizing racialized groups. Impact: Members of marginalized communities might be denied equitable access to loans or receive worse terms, perpetuating economic inequalities. 1.3 Route Maps for Online Navigation Example: Routing of cabs through highly congested roads even though there’s a longer but more suitable route available elsewhere. It’s a common phenomena still to get stuck in traffic during peak hours congestion due to some accident/ repair/ special occasions, etc. because the Algorithm is not tweaked to identify patterns. Cab drivers also do not take the risk of the penalty for taking longer routes and thereby causing not only a discomfort to the customer but also contributing to the traffic chaos. Impact: Traffic jams and delays 2. How to Combat Algorithmic Bias To prevent algorithmic bias in these tech-led solutions, especially in Lean Six Sigma projects, consider the following tactics: 2.1 Diverse and Representative Data Ensure the training data set is diverse and representative of all groups. It can best be accomplished by proactively looking for inputs from populations not easily identified and using auditing systems to the datasets to filter out bias. Action: Regularly audit and update datasets to ensure they remain representative. 2.2 Bias Detection and Mitigation Techniques Detect and mitigate biasing techniques in the algorithms. This refers to the use of fairness metrics and bias correction algorithms during model development. Action: Apply fairness-aware machine learning, like re-weighting, re-sampling, or adversarial debiasing. 2.3 Transparent and Explainable AI Develop transparent and explainable AI models to understand how decisions are made. This will help in understanding the identification and mitigation of any biases. Action: Use explainability tools to interpret model decisions and provide transparency. 2.4 Continuous Monitoring and Auditing Constantly monitor and audit the performance of AI systems to ensure they remain fair and unbiased over its operation. Action: Develop standard review mechanisms to assess the output of the algorithm and modify it accordingly. 2.5 Inclusive Design and Development Teams Make sure the teams involved in designing and developing AI-systems are diverse and inclusive. This can help diversify perspectives and reduce the risk of unintentional biases. Action: Increase diversity in hiring and ensure an inclusive work environment. 2.6 Ethical Guidelines and Accountability Lay down the ethical guidelines and ensure that the developers are responsible for the outcomes of their algorithms. Such ethical considerations are encouraged from the outset of design. Action: Create governance bodies, such as ethics boards, to oversee AI projects and ensure compliance with ethical standards. In conclusion, Algorithmic bias arguably represents one of the most significant challenges in AI integration and tech-based solutions within Lean Six Sigma projects. However, proactivity in taking well-rounded and multi-pronged measures shall ensure a check on adverse effects, thus making the systems fairer and more equitable.

Value engineering is a concept developed in the later stages of the 2nd industrial revolution accelerated by the urgency to meet requirements of defense industry at a time when both materials and quality manpower were being consumed by the World War. It attempts to maximizing value of the particular product or service through achieving the highest quality and functionality by using the least inputs including but not limited to cost, material, manpower, energy and time. It has been considered a very good methodology which has been adopted by reputed organizations such as Toyota, Ford, NASA, GE, ISRO amongst a whole myriad of others to "maximize the bang for the buck". However, in recent years many cases have emerged which give Value engineering a bad reputation associated with reducing quality in order to reduce costs. The most prominent example for such an example of bad apple is of the high failure rates in a major airplane which though was designed using a lot of new technologies but digressed from the basic risk assessment and quality control SOPs in order to meet short term cost and delivery targets. There have been various such cases which on proper analysis usually leads to non-adherence to preset necessary safeguards or a gap in the laid safeguards. There are multiple ways to safeguard Quality when Value Engineering of a product or service is done. They are: - 1. Quality and Performance Parameters to be defined. Define the quality and performance standards at the outset which are non-negotiable. Any redesigning of the system should not affect these well-defined standards. 2. Ensure involvement of all key stakeholders Whenever Value engineering exercises are carried out it is of utmost importance that all key stakeholders from within (Functional departments) and outside (partners, vendors, regulatory organizations, customer representation groups, etc.) the organization is involved in the exercise in order to cover all aspects of risks. 3. Rigorous FMEA for Holistic Evaluation Whenever alternative materials, designs, processes etc are being evaluated it is important to not just look at the cost but also the impact it can have on performance, reliability, safety, environment, customer satisfaction and experience etc. 4. Create systems for continuous risk assessments Create a procedure for risk assessment of every change involving above mentioned stakeholders. Many people may consider this to be practice in red tapism slowing down innovation, but having such systems protect the organization, customers, investors, environment and the community from far greater risks. 5. Create mechanism for testing and validation of changes In most cases in the past, value engineering failed due to insufficient testing and validation of the changes in order to meet cost cutting targets. Always having a mandatory robust and thorough quality testing and validation of changes is the only way to ensure the proposed changes do not negatively affect the quality. 6. Learning and Development (Statutory and Regulations, Technical, Behavioral) People should be regularly given refresher training on the quality parameters of their particular work function and be given clear guidelines of what are their non-negotiables and the statutory and regulatory requirements are. Investment in both technical and behavioral trainings would ensure that Value Engineering is not misused to meet short term targets. Jack Welch, CEO of GE in its heyday was a great proponent of Six Sigma principles and had mandatory trainings for all employees and had influenced GE’s vendors to also impart training and adopt Six Sigma. 7. Invest in Value Systems and Leadership It is important to invest in value systems and leadership in order to safeguard good quality products with Value engineering to ensure that unreasonable goals and pressures are not created for employees and partners. Edward Deming’s 14 principles of TQM is good example. Many organizations of repute adhere to this or similar value systems in order to ensure business continuity over short term show of results. 8. Have a dedicated team of Business Excellence Having a dedicated team of cross functional and multi domain professionals such as MBB, BB, GB certified employees focusing and driving the continuous improvement in the organization who can leverage the workforce and processes in order to drive value engineering.

Nowadays, most of the products which we hold in our hands and consume are manufactured by contract/ 3rd party/ franchisee manufacturers rather than the brand/trademark owning companies be it electronic devices, automobiles, FMCG, pharmaceuticals etc. A plethora of factors play into this decision making by such organizations. The primary benefits are of outsourced manufacturing are:- 1. The cost of manufacturing, upkeep of labor, compliance to complex and diverse legal and regulatory requirements are all outsourced to the contracting party. 2. Many organizations in order to compete in the highly competitive marketplace choose outsourced manufacturing in order to invest on innovation and marketing over investing in costly manufacturing facilities. 3. Tapping into the existing resources of an external organization which specializes and focuses on manufacturing. 4. Scaling up to meet market demands. Many organizations though having their own manufacturing facilities also outsource manufacturing in order to meet market demands and avoid the risk of competition eating up it's market share. However, there are a lot of risks too attached with such a decision. They can be summed up as: - 1. Companies lose control over the manufacturing process which may expose its supply chain to major disruptions and with limited leeway to address those risks. 2. Risks of quality getting diluted remains high for organizations which are outsourcing their manufacturing even though they may well defined contracts. 3. Outsourcing also enhances the risk of intellectual properties getting leaked, appropriated and even stolen. The best way to reduce these risks are:- 1. Have a rigorous 3rd party vetting system, which could evaluate and track the past performance, financial stability, liability, reputation, certifications etc. 2. Having a well-prepared and detailed contract which covers all aspects of the quality, intellectual property protections, non-compete, conflict resolution and penalties for non-compliance. 3. Build long term relationships with outsourcing partners which will be mutually beneficial in the long run. 4. Diversification of the manufacturing and its related supply chain in order to build resilience and spread the risk by alternative manufacturers or strategies in place. 5. Build systems for Quality Assurances such as frequent quality audits by self and 3rd parties. 6. Adopt and propagate a culture of Business Excellence which can be extended to the contract manufacturer too. This would foster a sense of combined ownership of the process and maintain customer focus.