Pradeep Kandpal

A Kanban board should take care of unclear priorities, delayed feedback, and miscommunication leading to milestone challenges surrounding a project. A typical kanban board has the following heads: To Do In Progress Done This area could feature a prioritized list of tasks/project deliverables. This would rule out any certainty around prioritization. The list of tasks should be constantly refined and updated so that tasks with the maximum business value are always on the top. This section can have all tasks that are in progress. WIP Limits should be assigned. Number of tasks in this section should not exceed the agreed-upon WIP Limits. All tasks that meet the Critcal To Acceptance criteria should be moved here. The following Kanban metrices could be part of the KPIs. Tracking them in real time would improve the delivery timelines by improving the team's efficiency. Lead time: Lead time would calculate the time from when you add a new task/deliverable to the Kanban board to the point where the team marks the task as complete. In essence, it gauges the total time a task takes to travel through the entire Kanban workflow. Cycle time: Actual time taken from starting a task and completing it. Work in Progress: This gives a snapshot of the number of tasks that are being worked upon at a given point in time. The number of tasks that are in this stage should be restricted to a pre-agreed number of tasks depending upon the team's capacity. This would eliminate any overload and bottlenecks. Throughput: Throughput measures the number of tasks or work items your team successfully completes within a set timeframe, such as a day or a week. It reflects the team's output and productivity during that period. This metric is vital as it provides a clear indicator of productivity, helping you make informed decisions on task assignments and project scheduling. Cumulative Flow Diagram: A cumulative flow diagram would provide a visual snapshot of the entire kanban board and can provide actionable insights. It offers a comprehensive view of all four key Kanban metrics at once. The diagram features multiple colored bands, each symbolizing a different stage of the workflow, stacked over time. A Daily Stand-Up meeting should be done to assess the progress of the tasks on the Kanban board so that impediments, if any can be addressed. This would provide the much-needed direction and support to the team, and they can focus their time and energy on increasing the throughput and reducing the cycle times of the tasks.

Nemawashi is a consensus-building approach which involves discussing strategic initiatives with key stakeholders using various stakeholder engagement tools so that their interest levels and understanding is gauged well in advance and appropriate steps are taken to obtain their buy-in. This way it is guaranteed that by the time the initiative reaches the final stage, it already has the required support and least resistance increasing the likelihood of approval. In the context of business excellence, for any improvement initiative, be it Lean, Six Sigma, Kaizen, Automation, AI enablement etc. if only a top-down approach is adopted, it loses its rigor and sheen once it reaches the employees at the lower levels mainly due to their lack of awareness of such initiatives and their inability to establish a synergy between such improvement methodologies and their day-to-day operational activities. On the other hand, if only bottom-up approach is adopted, then the employees at the lower levels, despite being sensitized and trained on lean and six sigma initiatives, use lean practices and six-sigma tools on a piecemeal basis with a focus on local optimum than global optimum. This limits the potential organizational-wide impact of six sigma and lean as the top management's commitment to such efforts is usually limited to delegating activities surrounding these initiatives to others. Nemawashi to an extent establishes a balance between both, enabling top-down direction along with bottom-up engagement in a simultaneous manner. It encourages each and everyone to contribute by being transparent. An example could be where an organization when deciding to increase AI Penetration across verticals, first needs to have a considerable amount of buy-in from the key stakeholders for such an initiative. Once all reach a consensus and are in agreement, they need to first familiarize themselves with the AI technology that is being discussed. There should not be any disconnect between what they expect AI to do and what actually AI can do. Only after they have an absolute clarity on the time, effort and cost on such an initiative, and the benefits they could expect, a decision on whether to hire an external consultant or a full-time resource to train employees at various levels it to be taken. The employees thus trained in advanced AI tools can now drive improvement efforts in their respective business units. Conclusion: For any improvement initiative to deliver results, the commitment, engagement and consensus of key stakeholders is paramount and Nemawashi to a large extent addresses the proverbial "What's In It For Me" by ensuring effective and conflict-free execution.

Diagnostic Analytics is one of the data analytics techniques that analyses a dataset to arrive at root causes of events, behaviours, and outcomes. It is primarily conducted to provide insights on various factors that are responsible for a problem at hand and tends to uncover the “WHY” behind the data. The data source, quality and reliability is paramount while conducting a Diagnostic Analysis. Diagnostic Analytics primarily represents the Current State in a problem-solving domain which connects the dots between Descriptive Analytics (what is wrong?) and Predictive Analytics (what is likely to happen?). The findings of these provide further insights on Prescriptive Analytics (Future Course of Action). In a DMAIC framework of six sigma, maximum value can be derived from Diagnostic Analytics in the Analyze phase. Examples & Use Cases: RCA Techniques: 5 WHYs & Pareto analysis to find out of the root cause - For e.g. A 5-why analysis revealed increased usage of UPI transactions to be the root cause of CASA ratio decline in a leading bank. A pareto analysis showed that discounted products which correspond to 20% of the overall merchandise are contributing to around 80% of the sales. Clinical Diagnostic tests use patient's tests results data to generate a complete summary based on the insights derived post comparing it against the standard and also against patient's past data. The physician in turn could do RCA to derive meaningful conclusions as to why this is happening. Hypothesis testing: To test an assumption that better wages outside is contributing the most to the attrition in a leading organization. A sample of exit interview data was subjected to a statistical test (1 proportion test). The test result was found to be statistically and practically irrelevant and rejected the assumption. Correlation & Regression Analysis: Many stock broking platforms have built-in algorithms based on pattern recognition, correlation & regression analysis to derive meaningful conclusions so that their investors can make informed decisions. Anomaly Detection: Network analysis make use of built-in control charts to detect any anomalies that may shed further light on the assignable causes of frequent downtimes and network jams.

SOPs enable us to make our processes refined and best optimized to deliver products and services to a level that they are comparable with the best-in-class players within the industry. Once we reach that level, SOPs can act as a lunching pad from where organizations can leverage their market research, inventive problem solving, design thinking, and R&D capabilities to improve their products and services. They should also be bold enough to cannibalize their existing offerings to accommodate new product features and services built upon the standardized best practices so that they always have a competitive advantage. If we restrict ourselves to just standardizing and refining our existing processes via SOPs, and stop there, it would lead to stagnation across industries, predisposing us for eventual replacement by outside innovators.

Process Benchmarking: Process Benchmarking can be defined as measuring your process performance against the best-in-class processes by gathering data, analyzing it, determining how the best-in-class processes achieve those performance levels, and utilizing the information thus gathered as a basis to improve your own process. The best-in-class processes that are being benchmarked could be either internal processes within the organization or external processes outside the organization. Benchmarking relies solely on your capability to gather reliable data of the best-in-class processes that you are intending to benchmark via appropriate benchmark methodology. Benchmarking can be done via best practices sharing forums, partnerships that allow both parties to learn from each other known as collaborative benchmarking. One could also subscribe to “exchange service” that collects benchmark information and makes it available for a fee. Sometimes, processes of other industry provide valuable insights that can be implemented in our process to achieve breakthrough improvements. This is called as functional benchmarking. Most of the benchmarking processes undergo the following phases: Use of Process Benchmarking for Project Identification: The following benchmarking scenarios can be a source of various projects: 1). Efficiency Issues with the Current Processes: The existing process is producing way too many defects along with lead time challenges resulting in quite a few client escalations. Benchmarking it with similar best-in-class processes that are more efficient can help narrow the gap between current and future performance. The lead time challenges can be addressed via Lean Projects and a DMAIC project may assist in reducing the defects. 2). Challenging the Status Quo: The organization wants to reassess how the Critical to Acceptance parameters of key deliverables of their processes fare vis-a-vis their competitors via competitive benchmarking. Customer surveys can provide a high-level overview followed by a more granular approach such as QFDs to further drill it down. Common suppliers can also provide valuable insights. Any significant gaps in the Critical to Acceptance parameters that might pose a potential risk to customer retention could be a candidate for a DMAIC project. 3). Adoption of Emerging Technologies/Digital Transformation: The organization intends to improve the AI penetration in their processes and is looking to benchmark processes of potential organizations that have successfully used GenAI to improve process efficiency. QFD or Design Thinking might assist here, and it could be a candidate for a Process Re-Engineering/DMADV project. Conclusion: Despite being a well-known tool for best practice sharing, benchmarking should not restrict an organization to a level it has attained post benchmarking. Since the best-in-class process that is being studied, is there for everyone to see and emulate, it is hard to imagine how it would provide someone a competitive advantage. It might be a good idea to use benchmarking as a short-term solution to bring your services and products to a level that they are comparable with other players in the same industry but in the long term, the benchmarking process should be flexible enough to accommodate new and innovative ideas built upon the benchmarked best practices. If everyone keeps benchmarking each other, it would lead to stagnation across industries, predisposing them for eventual replacement by outside innovators.

All "known risks" before manifesting themselves as issues/problems would've been unknown once. Such known risks after obtaining valuable insights from key stakeholders, domain experts and SMEs become part of a document known as Risk Register. In project management, this Risk Register is an output of a process called as "Identify Risk". The Risk Register is generated to document all the identified risks both positive (opportunities) and negative (threats) along with other details which more or less fall under the following heads: Criteria to Enter a Risk Item: It is important to identify as many risks, as early as possible at the beginning of a process or a project lifecycle so that you are better prepared to handle any unforeseen circumstances that might announce themselves as critical issues in the later stages. Any uncertain event that if occurs results in a negative or positive impact on one or more parts of the project or a process can be a part of the risk register. Criteria to Remove a Risk Item: Irrespective of the number of risks identified at the beginning of the process or a project, the old risks might lose their sheen and are not relevant enough, probability and impact wise to be a part of the risk register. Such risks that do not show any signs of manifesting themselves as issues or problems after having reached their corresponding risk triggers can be removed from the risk register. Application of Risk Register in Process Management: In a process management, the Risk Register can be a source of all the identified risks so far. These are then evaluated and rated with domain experts and SMEs on the following parameters: >> Urgency - The speed with the risk response is to be applied. >> Proximity - How imminent the risk is? >> Dormancy - How soon do we feel/discover the impact of the risk once it would occur? >> Manageability - How easily the risk response can be implemented? >> Controllability - The degree to which the outcome of risk can be controlled? >> Detectability - Ease with which it would be known that the risk is about to happen. >> Connectivity - The degree to which a risk is connected to other risks. More the connectivity, higher the criticality. >>Strategic Impact - If the risk has an impact that would affect the overall strategy of the organization. >> Propinquity - Perception of the end user - Criticality of the risks perceived by a key stakeholder/end user. After all the risks are passed through these parameters, a list of prioritize risks is generated. Of these prioritized risks, negative risks can be addressed by using popular Risk Management tools such as FMEAs and Probability and Impact Matrix with a view towards reducing the probability and impact of those risks. The more detailed and up to date your risk register is, the more value your can derive from these tools. For positive risks, all efforts should be made to increase the probability and impact of these risks. Once these risks manifest, then they should be exploited to derive maximum value out of them. For Example: 1). Risk register containing a list of identified risks before rolling out a completely new process. 2). During process re-engineering all possible areas where things could go wrong can be listed as risks in a risk register. 3). In an established process, a risk register should be constanly refined by constantly re-visiting the risk items. New risks should be included, if any and old risks should be removed. 4) During the pilot process in the improve phase of a DMAIC project, all risks can be included in the risk register. 5) The assumptions and constraints that are listed before rolling out a new process should be constantly revisited to check if any of them are leaning towards becoming a risk can be thus included in the risk register. Conclusion: From a process management perspective, it is also important to note that risk identification in and of itself is an iterative process where new risks might get added to the risk register if the process steps are modified due to updates from the client or as a result of a process improvement. Similarly, old and irrelevant risks might be removed from the risk register as the process matures.

OEE is given by the formula: OEE = %Availability x %Performance x %Quality - Where each Availability, performance and Quality can take up values from 0 to 100%. Availability in this case is expressed as Availability = Run Time / Planned Production time. Where Run time = Planned Production Time Minus breakdowns, changeover times, etc Many businesses DO NOT view most of the downtimes as downtimes, but rather as a part of the process. For instance, small stops, part loading and unloading, inspection, machine setup time, changeover times is not considered as a downtime. This defeats the entire purpose of calculating OEEs and would often end up producing OEE numbers that are not correct. OEE inflation can be avoided by factoring in all the time spent in addressing any and all time-traps, bottlenecks, changeover times, set-up times, etc during the planned production time. Examples of Downtimes which are often not considered while calculating Availability: 1) Frequent inspections in between the process steps which despite being a part of the process eats up a considerable amount of planned production time. 2) Changeover times in between the processes if production levelling is done by product type. 3). Frequent part changes in between. Conclusion: Inflated OEE results in so many hidden wastes and deprives us of the opportunity to continuously improve our processes. A magic OEE number is yet to discovered. While 85% OEE is considered an ideal and desirable (95%A x 95%T x 95%Q), what matters more than just the OEE figure is attaining a gradual improvement in OEE overtime as a result of continuous improvements thereby increasing the overall equipment effectiveness. More than chasing the OEE benchmarks, an accurate self-assessment of your performance should be the first step in determining what is feasible and where you might look for further improvements.

Thanks a lot, Mayank.

In Minitab under "power and sample size for 1 proportion or 2 proportions", is the comparison proportions same as margin of error while calculating sample size for proportions. If ETOP or the hypothesized proportion of a population of interest is 20% then would we be putting 5% in comparison proportions assuming 5% margin of error and 90% power. Please advise.

Thank you, Mayank!!!

In an attempt to automate several steps of a process, an AI based tool is being considered. I’ve been tasked to verify the robustness of same. The output would be expressed in accuracy. The critical factors that would be at play while using the AI tool have been narrowed down to 3 factors having 2 levels each. With 2 replications, I am currently in the process of conducting a full-factorial experiment with 16 runs. With no time and cost constraints, how many replications should I go for so that the factors are best optimized and I have more confidence in the output that is generated?

Both lean and six sigma approaches can be top down, bottom up or a combination of both depending on the organization’s culture, its readiness to embrace changes, and its process maturity level based on the Process Maturity Models such as CMMI. Organizations with lower process maturity levels (Level 0-2) have unpredictable activities, no proper SOPs, less cross-functional coordination, and are usually reactive to any uncertainties. Such organizations when deciding on implementing six sigma or lean, should adopt a traditional Top-Down approach since it would require a stronger strategic push, support and drive from the top management before they are assimilated by the middle and lower rungs of the organization and realizes its full potential. Having said that, a successful implementation of lean and six sigma in such organizations would be far more challenging as everything would have to be started from the scratch. Bottom-up approach is more suitable for organizations with a very high process maturity levels (Level 5) which is much more receptive to improvement methodologies such as lean and six sigma as it is already a core part of their culture. Any grassroot initiatives with respect to lean and six sigma implementations have a high degree of acceptability, support, and buy-in from the top management as they are always committed to the success of these initiatives. The employees at the lower levels or shop floors, being trained in six sigma and lean are well aware of their goals and their respective alignment to the organizational strategies and vision and are hence empowered to take lean and six sigma initiatives bottom-up. For e.g. Toyota, Motorola, GE, Ford, Sony, ABB, Lockheed Martin and Kodak are examples of such organizations. Most of the organizations are at a Level 3 and 4 on the basis of process maturity hence the most suitable approach for them is a hybrid of both the top-up and bottom-up approach. In such organizations, if only top-down approach is adopted, it loses its rigor and sheen once it reaches the employees at the lower levels mainly due to their lack of awareness of lean and six sigma values and the unfamiliarity to the potential synergy between such improvement methodologies and their day-to-day operational activities. On the other hand, if only bottom-up approach is adopted, then the employees at the lower levels, despite being sensitized and trained on lean and six sigma initiatives, use lean practices and six-sigma tools on a piecemeal basis with a focus on local optimum than global optimum. This limits the potential organizational-wide impact of six sigma and lean as the top management's commitment to such efforts is usually limited to delegating activities surrounding these initiatives to others. In such organizations, successful implementation of lean and six sigma requires top-down direction and guidance along with bottom-up engagement in a simultaneous manner. Merging both top-down and bottom-up approaches to implement lean and six sigma would likely result in a faster, larger and more sustainable organization-wide impact. Employees in the middle management have a very crucial role to play here since they are the ones that bridge the gap between the top management and the employees at the production floor. An example of such a hybrid approach could be where such an organization when deciding to implement lean and six sigma across verticals, first need to have a considerable buy-in from the the top management and key stakeholders for implementing six sigma and lean initiatives. Once all reach a consensus and are in agreement, they need to first familiarize themselves with lean and six sigma methodologies before making an informed decision. Only after they have an absolute clarity on the time, effort and cost such initiatives would require, and the benefits they could expect, they can then either hire an external consultant or a full time resource to train employees at various levels in lean and six sigma methodologies so that they can drive improvement efforts in their respective business units. As soon as the employees are trained on lean and six sigma concepts, they start driving high impact improvement projects the resources for which are anyways pre-approved by the top management since they are highly committed towards such efforts. Once these projects are shown to provide the desired output more and more employees opt for getting trained on six sigma and lean thereby driving more improvement projects making six sigma and lean a part of the organization’s core culture. Such project leaders get active support and cooperation from across verticals since all are now sensitized on the effectiveness of such efforts and how it would help the organization to reach the next level by always having a competitive edge. Conclusion: Traditionally, irrespective of the organizational culture and their maturity level, both lean and six sigma were known to give better results when implemented top-down but now with most of the organizations moving towards agility by empowering employees to become self-sufficient and self-organized, such traditional approaches are being challenged and hybrid approaches have gained more momentum to eventually reach a stage where such initiatives could be driven bottom-up. To sum it all up, six sigma or lean can be initiated by trained individuals at either top, middle or lower levels based on the organization culture and its process maturity but an active top management engagement, guidance, support and direction is key to the successful implementation of lean and six sigma.

A Placket-Burman design exhibits the following differences when compared to a regular 2-level design: Plackett-Burman Designs Regular 2 -Level Designs 1. These are highly fractional resolution III designs where the main effects are confounded with the 2-way interactions. 1. These could be either full factorial designs or fractional factorial resolution IV or V designs where there is no confounding of main effects with 2-way or other higher order interactions. 2. Only used to screen main factors from a long list of potential factors. 2. These are used to screen as well as to optimize the main factors. 3. The number of experimental runs in these designs are in multiples of 4 starting from 4 to 128 and the number of factors has to be one less than the number of runs. For e.g., A design with 32 runs can screen main effects of up to 31 factors. 3. The number of experimental runs in these designs is expressed as 2k (full factorial) and 2k-n (fractional factorial) where k denotes number of factors and n could be 1, 2 or 3 for 1/2 factorial, 1/4 factorial or 1/8 factorial designs. 4. 7 factors with 2 levels would warrant just 12 runs in a PB design as show below: Design Summary: Factors: 7 Replicates: 1 Base runs: 12 Total runs: 12 Base blocks: 1 Total blocks: 1 4. 7 factors with 2 levels would require 128 runs for a full factorial and 64 runs in a fractional factorial as shown below: Design Summary - Full factorial Factors: 7 Base Design: 7, 128 Runs: 128 Replicates: 1 Blocks: 1 Center pts (total): 0 Design Summary - Fractional factorial Factors: 7 Base Design: 7, 64 Resolution: VII Runs: 64 Replicates: 1 Fraction: 1/2 Blocks: 1 Center pts (total): 0 Benefits of Plackett-Burman Designs: · Plackett-Burman designs are usually helpful when we have a significantly large number of factors, and it is not economically feasible to conduct even 1/4 or 1/8 factorial designs. · Not only does it saves time and money but provides us with vital few from trivial many which can then be optimized further via full factorial or resolution IV or V fractional factorial designs. Limitations of Plackett-Burman Designs: · These are only helpful early on in a study when we have limited knowledge about the overall study and there are large number of factors to choose from. · Due to them being highly fractional, it is not advisable to use them for studying interaction effects. · Recommended to be used only when there are negligible 2-way interactions. The results of these designs to identify main effects are not reliable when two-way or other higher order interactions are present. Examples of Plackett-Burman Designs: · Clinical Research – There are several potential factors that are at a play at the initial stages of a drug development against a diesease and PB designs come to its rescue by weeding out the insignificant factors. Once the main interactions are identified, the drug resistance is measured against those by deploying a full factorial or a fractional factorial design. · FMCGs – PB designs are used in the initial stages of a product development for screening significant factors from a list of potential factors that can be further studied and optimized with an eye towards enhanced customer experience and increased market share. · Agriculture: Plant fertilizer industry always strives to enhance the effectiveness of their plant foods by investing a considerable time and effort on optimizing the factors that are crtitical to the crop yield and quality. PB designs helps them to narrow down to these vita few from trivial many.

To check the stability of process for Defective Data, we use NP chart (subgroup size fixed) and P chart (subgroup size variable) and for Defects Data, we use C chart (subgroup size fixed) and U chart (subgroup size variable). Based on the criticality of the defects i.e. minor, major and critical as in this case, Special Cause Tests also known as Nelson’s Rules for special cause are used along with the relevant control charts. Out of the 8 rules, the following first 4 are applied here due to it being a discrete dataset: 1. 1 point greater 3 std dev on either side of the center line – Indicates strongest evidence for lack of control and a special cause. 2. 9 points in a row on same side of the center line – Indicates shift in the process mean. 3. 6 points in a row, all increasing or all decreasing – Indicates a trend or gradual shift of mean. 4. 14 points in a row alternating up and down – Indicates a non-random pattern. If it’s a minor defect, we may just want to use the 1st test to understand the special cause. If it’s a major defect, then any one or two tests from the remainder 2nd to 4th along with the 1st can be used depending on shifts, trends or patterns whichever is of interest to us. If it’s a critical defect and we want all the warnings and alarms of special causes well in advance, then all 4 special causes tests are to be conducted. Example: In a typical publishing house, the content of several monthly and weekly magazines is evaluated by subject matter experts against various attributes that are critical to quality and acceptance by the customers. The attributes are categorized into minor, major, and critical defects and checked for stability from time to time. 1). Minor Defect –Punctuation Errors Per Page is considered as Minor Defects. The publishing company might just want to know the special causes, if any, and would go ahead with the 1st special test along with the relevant control chart. 2). Major Defect – Spelling Errors or Improper Verbiage per page is considered a Major Defect. In this case, along with the Special Causes, the company may also want to know if there is any shift of the mean or if there are any trends or non-random patterns and might consider applying either one or two tests from 2nd to 4th along with the 1st test depending on its area of interest along with the relevant control chart. 3). Critical Defect – Any defects in the magazine that arise out of a content that lacks integrity (unverified numbers and information), promotes discrimination, is politically or racially motivated, have shades of blasphemy and have provocative verbiage is considered a Critical Defect by the publishing company and if not controlled may warrant scrapping of the entire weekly or monthly edition. In such a case, the publishing company may want to use all the 4 special tests along with the relevant control chart so that it is aware of any unusual shifts, trends or patterns well in advance and can take necessary actions to eliminate such defects.

Using various approaches and then integrating them based on several variables stemming from org culture, product/service mix and employee competence would be a far better idea. I believe that for any transformation to begin in an organization, we have to first understand the "organizational readiness" to emrace the change that we are about to initiate. Some organizations may check all the boxes and implementing those changes would be far easier there but in other cases it would be a better idea to get it done in a phased manner.

Both Transportation Waste and Motion Waste being two of the eight wastes of lean, may exhibit the following differences: Transportation Waste Motion Waste 1) Any movement of material and information between process steps, workstations and plants that does not add or create value. 2). May cause information dilution resulting in loss or misrepresentation of vital facts. Damage of materials while in transit might also ensue thereby adding to further costs. 3) Examples: Service Industry: a). Interdepartmental e-mail chains cc’d to more than required individuals with unnecessary attachments. b). Multiple processing levels in a transaction processing unit. Processors, evaluators, QA1, QA2s etc. c). Multiple escalations in a call centre rather than FTRs. Manufacturing Industry: a). Unnecessary movement of goods from warehouse to production floor and vice versa. b). Ordering of raw materials from a distant vendor when nearby options were available. c). Delivery routes that add to inefficiencies. 1). Any movement of people and equipment within process steps, workstations and plants that does not add or create value. 2) Occupational injuries might ensue due to stress and strain caused by overuse of certain muscles and body parts leading to too many sick leaves and absenteeism. Early wear and tear of machinery could also be a consequence of this. 3) Examples: Service Industry: a). Using way too many keystrokes and mouse clicks for simpler daily tasks rather using shortcut keys. b). Taking time to search for vital information across multiple folders or systems. c). Way too many unproductive meetings and discussions within teams. Manufacturing Industry: a). Taking time to find the right tool every time the need arises. b). Unnecessary human and machine movements than required to finish a task. c). Reaching for tools that are at a distance. Identification of Wastes Arising from Unnecessary Motion and Transport: The first step to eliminate inefficiencies arising out of these wastes is to identify the reason behind these wastes. Poor layout of equipment and machinery, large batch sizes, absence of updated SOPs/Standardized Work, inefficient staff, non-calibrated machineries are one of the few primary reasons for both transportation and motion wastes. Both these wastes can be detected through effective Value Stream Mapping and Gemba Walks. How to Prevent Transportation Waste: 1. Once inefficiencies are identified, implement 5S. This would eliminate and minimize most of the wastes cropping up due to transportation. 2. Optimize the usage of remote assistance tools for troubleshooting rather than physical addressal of mundane technical issues. 3. Minimize or eliminate use of paper trails. Maintain a central repository for knowledge sharing and lesson learned. 4. Empower team members so that they are self-organized requiring minimal supervision and strive towards developing a team with T-shaped skills. This would maximize the FTRs. 5. Emphasize more on cellular layouts than functional layouts. 6. Reduce batch sizes by implementing SMEDs. 7. Use continuous flow where possible. How to Prevent Motion Waste: 1. Implementing 5S can eliminate wastes due to unnecessary motions. 2. Develop Standardized Work and ensure team adheres to same. This would eliminate inefficiencies arising out of unnecessary motions. 3. Use VA/NVA analysis within processes to eliminate NVAs thus eliminating unproductive movements. 4. Identify opportunities for automation and use appropriate automation tools. 5. Ensure that the individuals and machineries are calibrated to avoid unacceptable work or scrap. 6. Ensure that the team meetings and briefings are time-boxed effectively. Conclusion: Despite having these basic differences, both motion and transportation wastes to an extent go hand in hand. The unnecessary movement of material and information caused by transportation waste more often than not results in unnecessary movement of workers and individuals too resulting in a motion waste.

Both overproduction and overprocessing are two of the 8 wastes according to Lean. Overproduction: Producing sooner and faster than required and in more quantities than needed is overproduction. Overproduction results in waste of time, labor and materials thereby creating too much inventory which results in extra cost. If the overproduced product is seasonal, it would either end up as a scrap or would add to the storage cost and if it is perishable then it would have to be discarded. Poor estimation of customer demands often results in overproduction. Examples of Overproduction: Service Industry: a) Huge meals in the restaurants. b) Creating way too many reports and records than actually required. c) More number of beds in the hospital than required. Manufacturing Industry: a). Keeping labor and material on a standby. b) Warehouses filled with overproduced unsold goods. How to Prevent Overproduction: 1). Using TAKT time to gauge customer’s demands. 2). Practicing Just-In-Time inventory management. 3). Using production levelling by both Volume and Type. 4). Doing production control by using supermarkets where continuous flow is not possible. 5). Having a production scheduling point called as a “Pacemaker Process” in place preferably towards the end of the production line that signals a pull to the upstream processes for production thus enabling a continuous flow for the downstream processes. Overprocessing: Any additional work that does not add any more value to a product than expected is called overprocessing. Overprocessing often results in a reduced overall equipment and people effectiveness. The primary reason for overprocessing is not having a common and clear understanding on the critical to quality and critical to acceptance parameters of the end product. Examples of Overprocessing: Service Industry: a). Many approval levels warranting quite a few signatures on a document for even smaller requests. b). Recommending too many diagnostic tests to patients. c). Entering same data at multiple places. Manufacturing Industry: a). Using advanced machinery for a product that could have been easily produced using a basic machinery. b) Adding more attributes/features in a product than actually needed. How to Prevent Overprocessing: 1). To have a clear understanding and communication on the critical customer requirements and updates. 2). By doing VA/NVA analysis for each activity and develop a Standardized Work for the entire process. This should be done in an iterative manner for the entire process flow as more activities might get added due to changes in customer requirements. 3). Keeping it simple by eliminating complexities that arise out of excessive documentation, instructions, manuals etc. 4). Automate the process steps where possible to reduce or eliminate the chances of overprocessing. 5). Revisiting steps 1 through 4 periodically to ensure sustenance.

Both the approaches are used to address the fluctuating demands of the customers and ultimately aim to eliminate waste, reduce lead time and achieve an overall equipment and people effectiveness. A few comparisons are listed below: Demand Levelling Production Levelling 1). A carefully thought-out proactive approach that influences the demand itself of the customer to arrive at a more stable and predictable demand pattern that drives a levelled production. 1). Popularly known as “Heijunka”, it is a mixture of both proactive and reactive approaches in which the production schedule, based on predicted customers’ demands, is fine-tuned in such a way that there no overburden on the systems, resources and equipment while producing products in a consistent manner. 2). An example in manufacturing industry could be – Build-to-Order approach especially used in automobile industry to arrive at a predictable near-approximate demands. In service industry, concept of happy-hours in restaurants and pubs can be used to level the demand surges in peak hours. Another example, could be passport generation in Passport Seva Kendra, where demand is levelled by allocating appointment slots. 2). An example here could be a shoe company producing shoe types A, B, C, and D averages weekly demands of A (5), B (3), C (2), D (2). A mass manufacturer with apparent changeover challenges, interested in economies of scale would follow the following sequence – AAAAABBBCCDD – known as levelling by volume. On the other hand, a lean manufacturer who want to leverage the benefits of a product type along with volume may want to follow this sequence –AABCDAABCDAB - known as levelling by type. 3). Preferable at the beginning of lean implementation to gauge the demand levels of the customers. 3). Is mostly used towards the later stages of lean implementation once value-streams are finalized and takt time is known. A final production schedule is made visible by the use of Heijunka Box. 4). Not feasible in situations when there is rush of demands due to emergencies, pandemic, and low-price high-volume scenarios. 4). Less effective in situations where there are infrastructure and resource challenges to carry out SMED especially when the manufacturer intends to level the production by the product type. Vital Trade-offs: Despite being two different concepts, they both complement each other in a variety of ways. In situations where there are limitations in implementing production levelling due to various capacity constraints, demand levelling is done to meet customer’s demands by modifying its various product offerings and by triggering a change in the way the customers place their orders. The insights obtained from this subsequently feeds into a production/service schedule thereby enabling Heijunka. Similarly, where demand levelling is not possible, a TAKT time provides an approximation on the customer demands and drives the overall production schedule where customer’s requirements are fulfilled via small batches (levelling by volume and type), single-minute exchange of die (SMED) and standardized work. Limitations: Despite all its benefits, Heijunka to an extent walks a tightrope by trading inventory or lead times for stability and is a short-term workaround intended to smoothen the crests and valleys of customer’s demands. Another limitation is that it is responsive only to moderate demand fluctuations. Unusual variations in demand need more extreme measures. With most of the organizations these days moving towards agility by taking steps towards creating a more responsive production system that is more flexible and could cater to varying levels of customer’s demands, Heijunka more or less limits oneself to an approach that revolves around various constraints. You never know when the swings in demands which are perceived as a constraint in Heijunka might be perceived as an opportunity by an agile competitor that is ready to pounce on it with its advanced tools and techniques. A workforce with T-shaped skills, working in a cellular layout with an adaptive production schedule could be a starting step to exploit these constraints.

Before launching a new product or improving its existing products, companies often are in a state of dilemma when they have several features and components to choose from to have a competitive edge in the market. This type of indecisiveness can be addressed through a Conjoint Analysis. Conjoint Analysis also referred to as a trade-off analysis is a survey-based research technique which organizations use to gauge the interest level of customers when they are presented a product with different permutations and combinations of features. A statistical analysis is performed on the data derived from this survey. Relationship with Design of Experiments: Conjoint analysis crosses over Design of Experiments in many ways and use the same concepts of Runs (Profiles, Cards, Stimuli, & Panels), Experiments (Studies, Investigations), Factors (Attributes, Features), their corresponding Effects (part-worth score) on the response variable. The only difference is that in Conjoint analysis, it is assumed that only main effects are significant and two-way and other higher-order interactions are insignificant, so they use highly fractionated factorial designs. Types of Conjoint Analysis: The two most commonly used Conjoint analysis are as follows: Choice Based Conjoint (CBC) Analysis: - Most commonly used conjoint analysis in which a customer is asked to respond to a combination or levels of features in a product also called as full profile product. This is a preferable method when the number of attributes are up to 7-8. For e.g. the following are the various combinations of features that a smartphone company wants responses to: Attributes/Factors Level 1 Level 2 Level 3 RAM 4 Gb 6 Gb 8Gb Storage 80Gb 128 Gb 256 Gb Battery 5000mAh 4000mAh 4500mAh Display 6.4 inch 6.70-inch 7.0-inch Camera 16-megapixel 32-megapixel 32-megapixel Price Rs. 15000 Rs. 18000 Rs. 20000 Expandable Storage Yes Yes No Adaptive Choice-Based Conjoint Analysis (ACBC): In cases where the attributes are more than 8, adaptive conjoint analysis is performed. In this case the subsequent set of questions in the survey is modified based on the responses to the previous set of questions. This type of analysis helps where responses are required against too many attributes and their corresponding levels. Responses to the previous set of questions provide a guideline for customizing the next half of the questions thereby reducing the number of questions significantly and yet extracting the most valuable insights from the respondents in less amount of time before the respondents gets cognitively tired. This type of conjoint analysis is more interactive and more engaging with the customers. It consists of the following 3 steps: Step 1 – Build Your Own: For a product of interest, against each feature the respondents are asked to select their preferred level. The cost for the selected level auto-populates. Step 2 – Screening: The respondents are presented with a combination of features based on their response to step 1 and asked for their preferred choice. Step 3 –Choice Task: Based on their response to step 2, the respondents are provided with a set of attributes and their corresponding levels and asked for their preferred choice. How are the survey results analyzed? Once the survey results are populated, the data is fed into a statistical software and each level of an attribute is assigned a score based on its weighted preference called as a Part-Worth Utility Score. Higher the score, more the chances of the feature being included in the final product. If we sum up all the part-worth utility scores of a level for each attribute it would give us the Total Utility Score for the entire product. The table below shows an illustration for same. Attributes/Factors Level 1 Part-Worth Utility Score Level 2 Part-Worth Utility Score Level 3 Part-Worth Utility Score RAM 4 Gb 1.7 6 Gb 1.7 8Gb 2 Storage 80Gb 2.0 128 Gb 2.0 256 Gb 2.5 Battery 5000mAh 2.0 4000mAh 3.0 4500mAh 2.0 Display 6.4 inch 1.2 6.70-inch 1.5 7.0-inch 1.7 Camera 16-megapixel 2.1 32-megapixel 2.8 32-megapixel 2.8 Price Rs. 15000 3.5 Rs. 18000 4.5 Rs. 20000 2.5 Expandable Storage Yes 2.2 Yes 2.2 No 1 Total Utility Score 14.7 17.4 14.5 From the above illustration, it is evident that a smartphone with Level 2 Attributes appeals the most to the customers. For Adaptive Choice-Based Conjoint Analysis, a utility score is also calculated for each respondent as well along with the levels of attributes. Conclusion: The Total Utility Score thus arrived at by the above analysis, can be a valuable input to determine the preference market share for the product of interest. It would also provide insights into the most optimal sales offer that the company could go for. The organizations use the results of the analysis to understand the price elasticity of the product, optimal price point, demand forecasting, sensitivity to a brand name and how much a customer is willing to pay for a new service or a new feature. A product thus developed with optimized product features and components drives its value proposition in the market.

It would depend on whether we are doing a “Screening Design” or an “Optimization Design.” If the criticality of the 5 factors is yet not established, then we would go ahead with a Resolution V fractional factorial design. Out of the 5 factors, the factors with significant main effects can be further considered for a full factorial optimization design. Half Factorial - Screening Design: If the significance of the given 5 factors is questionable and are not yet validated as critical Xs, we can use a resolution V design to screen out non-critical factors. Half factorial experiments for screening are used primarily in two scenarios: 1) The existing historical data is inconclusive. 2) There is no historical data available at all. Impact on Time, Resources, and Complexity: A design summary of a fractional-factorial experiment with 5 factors and 2 levels with and without replication is shown below. Without replication With replication A fractional factorial design even with replication would require 32 runs which is almost half of a full factorial. Complexity also would be comparatively less as we would only be focusing primarily on the main effects and not the interactions. Since the effort is less too, fewer resources would be deployed. Full Factorial - Optimization Design – If all of these 5 factors are found to be critical, then we may want to optimize their behavior towards the response variable by conducting a full factorial experiment with replication. The following is the design summary for same: Impact on Time, Resources, and Complexity: A design summary of a full factorial experiment with 5 factors and 2 levels with replication is shown below. In comparison to a fractional factorial design, a full factorial experiment would be more time consuming as the number of runs would be more. We would have to conduct 64 runs with replication for a full factorial experiment. If any of these 5 factors warrant an addition of a centre point to rule out curvilinearity, then a few more runs would be added to it. Complexity would increase as interaction effects are also to be studied along with main effects and would necessitate utilization of more resources due to a sizeable number of experiments. We need to also factor in the Scope, Time, Cost and Resource constraints while conducting a full factorial experiment. Most of the R&D departments with higher risk appetite usually proceed with full factorial as they have to always come up with a robust design.

Randomization is process in which the subjects are randomly assigned to a particular group and their responses to a new drug, overall experience using a new product, effectiveness of a training, etc. are recorded and then analysed further to make an informed decision that is of interest to the researcher. They are of the following types: Simple Randomization: This is based on a single sequence of random assignments. In this type of randomization, from a randomly selected sample of individuals, each individual has an equal opportunity of getting assigned to a group for a particular study and this opportunity is arrived at by either a flip of a coin or by computer generated sequence. For e.g. To study the effect of two different blood pressure pills 1and 2, they can now be administered to the group of individuals that are selected via simple randomization. Block Randomization: In a block randomization, a randomly selected group of individuals are first divided into different blocks of equal size and then within each block, each individual has an equal opportunity of getting assigned to a subgroup for a particular study and the researchers ensures that the subgroups within each blocks have equal number of individuals. Is used when the study does not have the entire sample before the study starts and meaningful conclusion can be made even with a comparatively small sample size. Stratified Randomization: In this process a randomly selected sample of individuals are divided into subgroups based on the same traits, or peculiarities, or attributes, like economic status or level of education, pre-existing illnesses, etc known as Strata before their response to a treatment is studied. Stratified randomization prevents imbalances between subgroups for known noise factors that influence the treatment responsiveness. As a result, stratification may prevent type I error and improve power for small trials provided the stratification factors have a considerable effect on treatment responsiveness. Adaptive Randomization: Adaptive randomization is a method of making changes to the probability of allocation of an individual to a group that is being studied for treatment responsiveness according to the progress and position of the study at a given point of time. The randomization scheme in this case adapts to accumulating evidence. The first individual is assigned to a study group through a simple randomization, and the subsequent ones are assigned the study group that is producing better result. In other words, the information of the individuals who have already participated in the study is used to assign the newly recruited individual to a study group. Rather than having fixed number of individuals assigned to each treatment, there maybe an advantage to varying these over time. For e.g. if the treatment responsiveness is better in one study then we might consider assigning more individuals to that study. This would guarantee that by the time you’ve clearly shown one treatment to be best, most individuals would’ve received that treatment. Another example could be where we want to study the effectiveness of a training program with the two factors being self-enrolled participants and participants enrolled by their organization. If self-enrolled participants are showing better results we may want randomly assign more number of self-enrolled participants to the study.