Smithesh Pankaj

BRD is a formal document which outlines the requirements of a project for its success. It provides a roadmap of the project and helps the involved teams to understand the different aspect. For instance, the goals, expectations etc. In my opinion also, many failures in Tech product or tool can be attributed to BRD. Although it may not be the only reason of the failure. Below mentioned are some of the examples: Incomplete requirement gathering: If BRD does not capture all the requirements or business goals, then the product will not meet the business expectations. Inaccurate requirement captured: If incorrect requirements are captured, it would lead to the development of features which are not required. Agreeing to requirements which may not be feasible: If BRD captures requirement which are too complex and may not be delivered. Failure to involve key stakeholders: If the Key stakeholders are not consulted, then critical functionality might be missed.

Replication method involves identifying scenarios or issues where we can utilize the key learnings from another project. We can apply the same solution on similar issues. Challenges or Limitations: Some of the challenges or limitations are mentioned below: Legal Challenges: As the patented process are protected by law, replication without proper licensing or agreement would lead to legal issues. Incomplete information: The detailed information of the patented is not available or accessible. Licensing Cost: Licensing of the patented process is often costly. A Patented process cannot be replicated, below mentioned are strategies or approach to overcome the challenges: Associated processes which do not come under the purview of the patent. It could be: · Activities prior to the Production, like materials handling, procurement. Etc · Activities post production, like packaging distribution etc. · Activities supporting the process, maintenance, calibration etc. Replication of the principles and not the process: o We can look at replicating the principles or methodology We can look at the possibility of getting the license of patent from the owner or have a collaboration with them.

Conformity Bias is the likelihood of change in opinion or behavior to blend in with the group. It can have a number of effects on decision making in project management: There can be both positive and negative effects of conformity on decision making in project management, the negative effects outweigh the positive effects: Positive effects: · Since the thinking of the team is aligned, it strengthens the unity and collaboration within the team. · Quick decisions can be made as there is agreement on decisions. · Helps in reduction of internal conflicts in the team/group. · Since there is agreement, it ensures compliance of the agreed process without deviation. Negative Effects: · The team members are hesitant to share ideas and that could lead to reduction in innovation. · Conformity could lead to some of the critical risks to be left unnoticed. · Erroneous decision making, as the decisions might be basis majority in opinion. · Teams might settle for safe decision, rather than an ideal one. · Might disregard the requirement of the smaller group, as majority is considered. How can project managers mitigate these to ensure more independent and effective choices? · The project managers should make the team comfortable, so that the team can voice their unbiased opinions. · Anonymous feedback should be taken to get a view of the concerns in the team. · Should segregate the team in smaller groups, which is appropriate for open discussions and debate. · People from different departments, external stakeholders etc. should be included in the discussion to get a diverse viewpoint.

DOE helps us to identify the relationship between cause and effect. It provides an understanding of interactions between causative factors and helps us to determine the levels at which the controllable factors need to be set to optimize reliability. It is mostly used by the engineers in the manufacturing industry to maximize yield and decrease variability. Botched Run is when the experiment is not conducted properly. This results in datasets which are inconclusive and not possible to analyze. Effects on the output of Botched run: · Inaccurate data · Unexplained variability · The output of the overall experiment may not be accurate Steps to prevent Botched run: · The experiment needs to be planned thoroughly, considering all possible variables · SOP’s should be created for all experiment procedures · All the people involved in the experiment should be well trained · Monitor the experiments continuously and document any variations

SWIFT Analysis stands for Structured What-if Technique and is a Risk identification technique. It is used to identify and evaluate potential risk. It uses Brainstorming sessions. The team discuss the possible risks, their causes and impact using What-if scenarios. The brainstorming is done using a diverse group and are Facilitator led sessions. Advantages: · Systematic Approach · Diverse viewpoint as we use a multidisciplinary team · Swift Analysis can be implemented in various types of process and industries · Encourages to think creatively Disadvantages: · It is a Qualitative approach and may not provide numerical data on risk probability and impact · The outcome of the discussion depends on the expertise of the team Example: Scenario : Deploying Automated Patient Selection Automation Objective : Identify potential risks of deploying an automated Patient selection automation Steps in the Swift Analysis Scope : Is the deployment of automated patient selection in the prescription. Team: · Pharmacist working on the manual cases · Technicians working on the manual cases · Manager · Leadership What-if questions and answers : Q1: What if the automation selects incorrect patient? · Identified Risk: Medication sent to the wrong patient, Legal implications, Customer dissatisfaction, Health risk to patient. · Potential Causes: Not including all the possible scenarios of Patient selection in automation, Twin scenarios · Mitigation Measure: Extensive Testing to be done before deployment to ensure all the patient selection criteria are defined and captured in the automation. Q2: What if there is a change in the UI of the screen where automation performs the tasks? · Identified Risk : Possible incorrect selection of patient, not able to complete the task and build up of work for manual handling · Potential Causes: Change in the screen/fields or upgrade in the system which was not intimated to the automation team to assess the impact. · Mitigation Measure: Coordinating with the Application team and ensuring that all the updates to the system are shared well in advance with the automation team for impact assessment. The team has identified the risks, their causes and Mitigation measure and documented as well. The team creates an action plan to implement the mitigation measure, assigning responsibilities and setting timelines for the completion. Regular meetings are scheduled to check the progress.

What is a Mixture Design? Mixture Design is a technique in the field of Design of Experiments (DOE) which is used when the response variable of interest depends on the proportions or components of a mixture or combination of different factors. In a mixture design, the sum of the proportions of all components adds up to a fixed value, usually it is 100%. What are advantages of Mixture design over the traditional methods in the Design of Experiments? Below are some of the advantages of Mixture design over the traditional methods: Efficient Use of Resources: In Mixture design there is more efficient use of resources as it requires less experimental runs as compared to traditional designs. In traditional methods the researchers would need to examine all possible combinations of factors, which can become impractical or expensive for multiple component scenario. In Mixture designs the focus is on proportions of components, reducing the experimental space and allowing researchers to study a wider range of conditions with less experimental runs. Addressing Component Proportions: Mixture designs are designed to handle scenarios where the response variable depends on the proportions of the components in a mixture. Due to this reason it is ideal for situations where the outcome is influenced by the relative contributions of each component rather than their individual levels. Traditional designs might not be suited for studying such mixtures as they focus on independent factors. Handling Constraints: In a lot of real-world applications, components in a mixture might have constraints, like limited availability or compatibility restrictions. Mixture designs can easily accommodate these constraints into the experimental setup, which ensures that the final results are practical and feasible. Traditional methods might struggle to deal with such constraints. Examples where mixture designs are more valuable than traditional methods in the Design of Experiments: Drug Formulation: In pharmaceutical research, a drug's efficacy and safety often depends on the proportions of different active ingredients in the formulation. Mixture designs can explore the effects of varying proportions of these ingredients, optimizing the drug formulation to achieve desired therapeutic effects while minimizing potential side effects. The Traditional factorial designs would require a large number of experimental runs, making them less useful for this scenario. Food Product Development: Food product development requires combining various ingredients to achieve a specific taste, texture, and nutritional properties. Mixture designs allow researchers to experiment with different ingredient proportions to create the desired product characteristics while adhering to cost and nutritional constraints. Traditional designs might not consider the importance of component proportions, leading to less accurate optimization. Material Composite Optimization: When creating composite materials, for eg. in the aerospace or automotive industries, the mechanical properties of the material depend on the proportions of different components (e.g., fibers, resins). Mixture designs can determine the optimal composition, balancing the desired mechanical properties while minimizing weight and cost. Traditional designs might not be able to identify the interaction effects between the components, leading to suboptimal material composites.

Definition Johnson transformation is used to transform non-normal data to an approximate normal distribution. It helps on finding a suitable transformation which makes the data approximately normally distributed. Conditions where Johnson transformation will be preferred Non-symmetric distributions: The Johnson transformation is used when the data is not only skewed but is also exhibiting asymmetry beyond what the Box-Cox transformation can handle. It handles both positive and negative skewness, also complex distributions. Heteroscedasticity: If significant heteroscedasticity is present in the data, Johnson transformation could be preferred. The Johnson transformation can help stabilize the variance of the data. Specific distributional assumptions: In Johnson transformation we can choose from a range of parametric forms, like Johnson SB, SU, SL, and SN distributions, which corresponds to different shapes of transformed data. In presence of specific distributional assumptions or theoretical considerations which guides the analysis, Johnson transformation provides flexibility in fitting different distributions to the data. What kind of statistical analysis can and cannot be performed on this transformed data Johnson-transformed data can be used for parametric tests that assume normality. Examples: t-tests, ANOVA, linear regression, and other parametric models. The Johnson transformation does not restrict the types of statistical analyses which can be performed on the transformed data. Some Examples as to Why Statistical Analysis cannot be performed with Johnson transformed data : Model assumptions: Certain statistical models have specific assumptions, Johnson transformed data might not meet those assumptions even after transformation. Example, if a statistical model assumes homoscedasticity (equal variances), but Johnson transformation does not sufficiently address heteroscedasticity in the data, the transformed data may violate the model assumption. Interpretation challenges: Interpretation of results obtained from Johnson transformed data may be complex due to the additional parameters in the transformation. Robustness of statistical tests: Some statistical tests may not be as robust or well-validated when applied to Johnson-transformed data. Johnson transformation is designed to approximate a normal distribution, usage of specific parametric form can impact the validity of certain statistical tests or underlying assumptions.

Robust design is a methodology that is used to develop products or processes that are highly reliable and is resistant to variations in the operating conditions. This methodology involves designing and optimizing a system to minimize the impact of variability and uncertainties, such as manufacturing variations, environmental conditions, and usage conditions. By reducing sensitivity to these factors, robust design ensures that the product or process remains functional and meets its performance requirements. Robust design uses several tools and techniques Design of Experiments (DOE): DOE is a statistical method to systematically vary input variables and analyze their effects on the output performance of a system. By identifying the critical factors and their optimal settings, robust design ensures that the product or process remains robust despite variations. Taguchi Methods: This approach focuses on minimizing the effects of noise/variations on system performance. This method involves the use of orthogonal arrays and signal-to-noise ratios to optimize the design parameters and improve robustness. Quality Function Deployment (QFD): QFD is a systematic approach that translates customer requirements into specific engineering characteristics. By prioritizing customer needs and aligning them with design features, QFD helps in developing robust designs that caters to customer expectations. Failure Mode and Effects Analysis (FMEA): FMEA is a risk assessment tool used to identify potential failure modes, their causes, and the effects on system performance. Addressing these failure modes during the design stage, the robust design minimizes likelihood of failures and their impact. Examples of products developed using robust design techniques: Automobiles: Automotive manufacturers use robust design to develop vehicles that perform reliably under various driving conditions, such as extreme temperatures, road conditions, and user behaviors. Robust design techniques help optimize engine performance, fuel efficiency, suspension systems, and overall vehicle reliability. Aerospace Systems: In the aerospace industry, robust design is crucial for aircraft, spacecraft, and related systems. By considering factors like aerodynamics, structural integrity, and varying operating conditions, robust design techniques are used to develop reliable and safe aerospace products.

Approach Suitability Example Top-down Top-down approach is more suitable for Six Sigma implementation. In this approach the lean principles are initiated from the Top Management and eventually implemented to the bottom levels and the whole organization. * The Top management is primarly involved in the implementation of the six sigma approach, the top management would provide the direction and eventually the entire team follows the process as per the guidance from them. * This approach ensures the upskilling of the resources in all the levels on the six sigma techniques and methodologies. * The Project selection is based on the alignment to the strategic goals of the organization. The Top management is actively involved in the selection of the Projects. In a Backend process in BPO the Top Management identifies providing affordable healthcare services to its members as one of the strategic goal. The Top management would align six sigma/process excellence teams to identify opportunity areas of improvement in the overall process to make the services affordable. The Top management would provide guidance, help upskill the teams and prioritize the projects which aligns to the strategic goals of the organization and eventually to achieve the desired results. Bottom-up Bottom-up approach is more suitable for Lean implementation. In this approach the lean principles are initiated from the ground level employees in the organization and is eventually implemented in the whole organization. * The Ground level employees are involved in the identification of problem areas, suggest possible solution. Eventually employees at all levels are involved. * This approach also encourages the employees to go to the actual work area and observe the process to identify opportunities. * This approach encourages inter department collaboration as employees from different departments are involved. In a Backend process in BPO the starting level agents are encouraged to identify non value added steps, duplicate steps or steps which can easily be automated. Since the agents are involved in processing the order/cases, the process knowledge of the agents help them to identify opportunities easily.