RadhikaG

An awesome question. Let us understand this by understanding the meaning of the phrase "Audit by design". In simple terms, it implies that you audit the performance of the product or service as a integral part of building that product or offering that service. Hence, rather than calling for an audit as an ad-hoc process; it is built into the DNA of the product or process build/ offering; thereby giving the individual, or the team, or the organization a continuous way to understand the improvement opportunities. Having understood this term let us try to understand how this is a game changer? When we continuously seek to improve the current condition, and move towards the target condition or redefine the newer challenging target conditions; that is what we call continuous improvement and achievements. " Without continuous improvement, words like achievement and success have no meaning. - Benjamin Franklin" It is hence quite natural for this to link to the business excellence or control & verify phases of the Lean Six Sigma methodology. Having said this, there are many organizations that deliver software and fail to use Lean Six Sigma methodologies. It will hence be advisable to use this as an approach irrespective; and hence I highly recommend all organizations to deploy Lean Six Sigma approach or accept audit by design as a standalone way of developing/offering a product or a service to the customers or set of customers. Now, let us see what happens in a regulated environment? An environment with requirement to audit trail documents or series of actions taken as a part of the service or process as natural, and driven by an external regulatory body, is a regulated environment. Hence, it becomes natural for such organizations to be top of their game when it comes to document keeping and managing a central repository of data in multiple spheres of work. Therefore, having "Audit by design" mindset built into the culture of the firm will prove extremely beneficial.

This is a wonderful question because many a times we confuse being innovative with being inventive, let us see how. #1 Invention is easier to understand. It is coming up with something tangible that is totally new, that can be sold as a product and service to others. Innovation, on the other hand, is mostly looking at newer ways of doing a process. This could use a newer invention, but is not necessary. It is for sure moving away from current state to attain target condition, and typically you would want to be progressive in your way of work, hence using latest economical invention of that time would be recommended. Let us understand this with an example. Wheel was invented long time ago. if a heavier version of Off Highway truck uses a bigger wheel than the corresponding lighter version; that is just an innovative approach to creating a product line for customers. Heavier version OHT will be an invention; but usage of wheel in it will not be. #2 Innovation is typically a LSS project. You can easily move from current state to meet defined goal and future state through Lean or Six Sigma approach depending on the topic itself. Various other methodologies like TOC, Design Thinking, DMADV etc can also be applied. But, for Invention, though you can apply high level DMAIC framework; but key usage will be of TRIZ principles to help you rethink your solution, and create inventions. So, in conclusion, yes you can apply LSS & TRIZ to both innovation and invention; but they apply respectively for innovation and invention in a better way.

Paradox of excellence, made famous through the book by David Mosby and Michael Weismann, talks about slight deviation in excellent performance of companies leading to poor customer retention, and hence links to poor performance. I will like to look at this statement slightly differently and will explain why it makes sense, highlighting that it is not the paradox, in fact it is the nature of customer requirement. How many of you have heard about the famous Kano's model? We understand "dis-satisfiers" as the bare minimum features that we expect from a product/ service or a brand. Sometimes, it is sum-total of the features expected from product/ service & brand. As soon as we become mindful of this concept; and add the biased human mindset; it can be easy to understand that the bar is higher for bigger reputed brands, but smaller for non reputed ones. Similar bias exists not only from companies, products, services; but can easily be extended to teams, people etc. So, in summary to "What is Paradox of Excellence", we have understood from an angle of biases and Kano's model that why it exists. Let us look at how it affects companies performance? - This is easy to check too. Less repeat customers imply effectivey plateau growth, which can only mean all bad things for a company. The company needs to hence look into mitigating its effects. Let us try to answer, how can companies mitigate its adverse effects? - Try to consistent in performance & offering of a product & service - Try to go over and beyond and do your best - Couple with delighters to ensure you are forgiven or ignored for the missing dis-satisfier.

Let us try to understand a few concepts before we begin. Rolled Throughput Yield = Multiplication of Yield of Steps 1 to Step n = Yield 1 X Yield 2 .... Yield % of Defective % = Defects / Total ideal output = 1- RTY So ideally, we are talking about Absolute Defect % which is a useful but complicated variable to get in a E2E business process. Let us understand why it is difficult? 1- A sum total of process is built across various departments, sometimes vendors, and details of interest are often not reported, or not a part of concern. Let us see when this can happen? 1.1- Headcount approval process application might need to pass 8-10 stakeholders. When it goes to HR, they might just look at the applications from Department X, and not track number of times rework is needed on the application. In this case, even if Department X is looking at # rework loops, overall getting this metric will be next to impossible lest it is agreed across all stakeholders. 1.2- Let us look at the example of a vendor who must update the details of this application on a portal within a maximum of 8H or same working day. Now they do not care about # rework loops/ interaction they need to make with the HR department. 2- Multiple process steps add defective %, because as a rule of thumb, each manual intervention brings in varied opportunities of creating a defective output. Having understood this, let us see how we can get this metric in the LSS projects. A- Alignment of the stakeholders at the time of kick off and highlighting the KPIs that the project is trying to address; or associated KPI tree that supports the project helps get everyone to the same page. B- Assigning responsibilities/ Process owner across departments will also help in getting necessary data and hence proceed with the project in a streamlined fashion.

Payback period highlights in years, the time taken for the investment to payback itself, ie to breakeven. Beyond Payback period, businesses can start accruing profits, and it is important to know how soon this time can start. As the name indicates, Payback period = Initial Investment/ Annual Cash flow. As a means of comparison, higher the payback period, less lucrative the investment. The biggest advantage of this metric is the ease of its calculation, and hence it comes in very handy to compare various options, in our case, project selection. On the other hand, the biggest disadvantage of this method is that it ignores time value of money. Simply put 100 units of currency are worth more today than in future. This can be easily accounted in NPV thereby making it a better metric to use.

To answer this question, we need to understand that the DMAIC methodology is nothing but mindfulness and data driven solution identification for a problem. Thus, ideally this must have data to understand the problem, analyze it and identify root causes, improve through suggestions and validate once executed. Statistics is very helpful in understanding the problem and root causes. And wait a minute, this can be taught to the machine. Yes? Now a project is typically done by constituting a team of experts, who need to resolve this problem over and above their day-jobs. This manning of staff on a project makes it a time consuming, and expensive problem identification and resolution process. If we understand these key aspects of the DMAIC project, it will be fairly easy to understand that Measure, Analyze, and Control Phase can all be done better with technology, with AI/ML driven algorithms to understand and analyze newer trends and patterns in real time and ensure that we deliver on the overall aspects of cost savings, productivity enhancements, and sustainability benefits of using lesser earth resources; and without manning these projects separately. We will still need Subject matter experts to help with approving the improvement, or execution in sub/super systems etc. That is also because as of now, in learning phase, we need to train AI to ensure our learnings are embedded into the systems. All right, going to use cases now: Case 1 - Predictive maintenance : 1- In aviation industry, we have tons of data driven systems, some of them have hard life for change, others have continuous monitoring and we need to replace parts and systems as per their behaviour. In this case, having a digital twin, and analyzing how system is behaving at all points in time outside the actual monitoring on the aircraft, to understand trends and patterns and predict the possible next steps is crucial. 2- Likewise, think about the same on a train or metro. 3- Think about this on construction site where we have tons of expensive assets helping us day in and day out. If we can understand the deterioration better, we can do predictive maintenance and prevent the possible downtime on account of arranging the maintenance work and loss of productivity. 4- Think of the same in machining environment for producing sheet metal parts, doing milling, CNC machines etc. Case 2 - Inventory management & Control: 5- Across industries, inventory management and control is a negative sum game. No matter how many suppliers are on contract basis, no matter whether we have SAP, or other ERP systems, we fail when we have an unseen change in supply-demand. Each case brings in its own challenges, and teams can't face these challenges with any historical reference. Assume a system that has understanding of various epidemics, financial crashes, seasonality trends, minor changes in supply chain issues etc and how much really we should stock now to prevent a furture stock out; and how much less should we order than what we are currently ordering. ML/AI will have a great role to play here. We already see ML driven Inventory ordering systems in use across industries. Case 3 - Troubleshooting 6- In industries, it is a challenge to understand what part finally fixed the problem. With ML/AI driven solutions, we can have a more realistic understanding of what % of problem is solved by Path A or Path B or Path C if each of these paths can be taken to resolve the same fault. Now extend this across industries - whether it is television, or your washing machine or an automobile, or F1, or train or aircraft. The progressive drive to improve has the next action item of ensuring the data visibility is not on need based, but is continuous. AI/ML support is not a choice, it is already becoming a default. The key is for various teams to imbibe these skill sets together, and understand how to optimize resources better and deliver faster and more efficient outcomes.

This is a very valid question, and is more important in today's context when companies are overloaded with plethora of transformations, and it is becoming difficult to understand what should the teams support, and why, and how it is benefitting them? I think I will answer this question in terms of why we need to use these various methodologies. It is important to understand that the reason "Why" any organization wants these in the first place is to assist in being progressive, and providing teams with the easy to use tools and right mindset to evolve and grow. With this understood, companies must keep Lean Programmes at the core of their Operational Excellence centre. This core Lean mindset includes Theory of Constraints, DMAIC, DMADV, Double Diamond, NPD, Agile & Digital Tools & Techniques, Business Process Reengineering, Business performance management. Like Altshuller mentioned that 53% of solutions are L1; likewise 50%+ problems are process related, and need to remain linked to Lean, which links to all of the above. Then there must be a Business Excellence centre that explores newer tools and techniques, to ensure the creativity & innovation is abreast technological & industry changes, and helps read across industries. Hence, it needs to be understood whether the various methodologies that are being taught today, retain that understanding, and ensure they touch on core concepts to build further.

Algorithmic bias creeps in due to systemic limitations in terms of basic assumptions considered right at the beginning of the hypothesis. Many a times, these are AI driven, and might go un-noticed during a development. Interesting to understand, that these are actually human biases, hard coded into the systems. Examples: 1- If a solution is made with white population as sample group, there might be a complete set of used cases that will be skipped in this scenario. Solution might be quicker to market, but will not be able to cater to the entire world population. eg biometric & facial recognition systems 2- Recruitment tool that eliminates women candidates who have had a break of more than 2 years , might be a good example of the algorithmic bias 3- Narrow standards for health fitness for women that men to rate attractiveness Key to eliminate them : 1- Question your assumptions, and understand openly why are we making those assumptions. Having a group discussion with the right sample, seniority in the organization is key to ensure that we do not let our biases come into play. 2- Trust & fairness : It is important to remain fair to all cultural and gender groups, eliminate stereotypes and biases that might creep at various stages of the process.

Bias is a distorted view that leads to inclination towards something or someone. In case of Status Quo Bias, the point of view in someone's head is that whatever the current status is, is the best. They might have their theories or opinions about why they believe so, though that might not be necessary logical or analytical or derivable. When someone carries status quo, they might go with their emotional gut feeling, and just have that as the preference all along. How it impacts decision making? This prevents many professionals, for example, 1- From questioning what they do, and how they do; and hence is very opposite to being progressive and the ethos of continuous improvement. 2- From questioning how take decision, hence preventing any improvement that can happen in the decision making process, questioning the inputs and outputs of that decision. 3- From being curious on how a process can be improved, hence preventing them from exploring technology within their aspects of work. In summary, a team or an organization continues to act in old rudimentary ways of work, and poor decision making, unless someone knocks at their door and asks them to improve. This can be their boss, or can be newcomers in the teams. How to overcome this bias? The only way to overcome this bias, like other biases, is being mindful about yourself, which can only be learnt through practice. Imparting training and having smaller breakout exercises under guidance can help teams come out of this bias. Hiring younger staff with fresher perspective, or consultants who impart such trainings, helps in understanding how out of sync from the thought process of the current world we might be.

A great question, and a perfect time when Parle G case studies are flooding the market. Parle has been one of the key Indian biscuit brands since its inception in 1928, and it is important to understand what the founders perceived as value. This so much is similar to Toyota, and in so many respects. Companies need to understand their core value (like Toyota's "Best quality to society & company"). For Parle, catering to the masses aka being India's favorite has been their core driver of marketing and advertisement strategies since 1980's when Parle Glucon was christened Parle-G. Their core value is "Family, Unity, and simplicity", and comes out so in their campaigns and final product produced for the consumption. They retained the INR 5 per pack of Parle G as their value that they offer to the masses, across the classes, ages, purchasing disparity, geographies, languages, values & festivities. With this value as the key driver, it is important to reach different segments of the population, categorized but not limited to the above. For this, they needed brand ambassadors, intermediaries to ensure content is made to suit the various identified categories, which they did. So, as per their value, it was essential to keep intermediaries to reach to masses and retain presence in all parts of India. Let me answer the question around disintermediation or intermediation as a business strategy through few key pointers: 1- Disintermediation makes sense if companies are trying to save costs. Many a times, this deteriorates the value. Keeping in mind that the same value can't be reached unless you are reaching out to the masses as per various categories, and using variety of channels to reach the masses; intermediation is the only way to go for Parle. 2- Integration is another way to eliminate intermediation, but it comes with an increase in complexity of operations. Example Toyota has a set of integrated suppliers producing components only for them, and have been developed, or handpicked by their procurement teams to build-in-quality in the systems. So there will be decisions made as per what is "Value" to a company, and hence this needs to be looked at case-to-case basis. 3- In the end, no matter what value is core to a company, the shareholder's weath, aka market share remains important. And must remain a KPI to measure the overall success of the company.

This is an interesting question. R-squared should ideally reflect the % of the dependent variable that can be explained via the independent factors. Now think what happens when the independent variable is the factor used to calculate using the dependent variable? Example: Poverty as a function of Income, or Cost as a function of time.

Bandwagon effect can indeed have positive or negative impact on a company’s sales and hence, impact the product mix needed for the end-customer. Sometimes, this effect can be short-lived, at other times, it will outlive many other products in the market. I will suggest the following 3 pointers to keep this cognitive bias away for business decisions, primarily for the new product launch, entirely where “jumping on the bandwagon” will affect the businesses and employees: 1. Understanding KPI trends, along with right market research has always been very important for companies; and a key reason why companies invest millions of dollars to get the right answers. “Figures lie, and liars figure” is a very old adage, and more commonly applicable in today’s data driven world. But complete, reliable, consistent and accurate data is the only truth. Companies, by which I imply senior leadership teams, must rely on impartial accurate data; and project leaders must be sponsored to ensure that they collect the right data to ensure the businesses take meaningful data-driven decisions. 2. Understanding what data (qualitative & quantitative) implies, and rightfully using Kano’s model can help understand what are important aspects for the consumer, especially for a new-product launch. For an existing product or shift in consumer preference, this can be clubbed with #1. Kano’s model has been proven to highlight what business should lay emphasis on. Eg for a luxury brand, there is no choice other than going for delighters, and understanding what is the perceived value by a customer. Using Design of experiments to understand the patterns of different customer segments and using AI to extrapolate to understand the market segmentation, and increasing / decreasing predictions, can indeed help businesses to make the right decisions. 3. Last but not the least, advertisement and propagating the right information about one’s products and services is important. Right forums must be used to ensure that the companies make the right choice to propagate their products for the right reasons. Using all means and methods as used by the competitor (in accordance with one’s pocket) will be essential to ensure propagation of the right information, and expand customer knowledge to avoid fallying prey to false consensus. In case companies are benefitting from the bandwagon effect, there will be a bit of change of approach; and probably seems out of scope for the asked question.