Rajesh Bhayankaram

Nemawashi is a Japanese practice that involves informal consensus building with all the stakeholders before making formal decisions to ensure all stakeholders are on same page, which is crucial for successful deployment of strategic initiatives. By engaging stakeholders early in the process, address concerns and gather feedback before formalizing decisions. This helps in building trust and ensuring that everyone feels heard and valued. This can prevent conflicts and resistance, through informal discussions you can identify potential issues and obstacles that might not surface in formal discussions. It is a vital means of obtaining the buy-in of everyone involved in a decision-making process before a final decision is reached regarding a proposal or idea that requires scrutiny or review in order to uncover potential problems or blockers. For example Nemawashi is one of the practice utilized before implementing Lean production system in Toyota by managers engaging in Nemawashi to ensure that all team members understand and support the changes. This has been key to Toyota's success in maintaining high efficiency and quality. In Pharma industry, Nemawashi is used before launching a new drug, to discuss the potential impact, gather feedback from key opinion leaders, and ensure regulatory compliance. This helps in identifying potential issues early and building support for the new product.

Boundary spanning is an organizational strategy that involves reaching beyond the traditional boundaries like company departmental or geographic boundaries to create connections and partnerships. This approach leverages the interdependence between various stakeholders (both internal and external. Boundary spanning refers to individuals who play a crucial role in connecting to create the partnership. These individuals facilitate the flow of information and resources across different parts of the organization and beyond, fostering collaboration, innovation, and drive growth. By connecting diverse groups and facilitating the exchange of ideas, boundary spanning help organizations tap into a wider pool of knowledge and expertise. It encourages collaboration by breaking down silos and promoting communication between different departments, or geographic regions. It helps organizations stay agile and responsive adapting to dynamic business environments. Boundary spanning is particularly relevant in breaking down silos within organizations, by promoting cross-functional collaboration and information sharing, which helps to create a more integrated organization. In my pharma company a cross-functional program was driven in the Technical Research & Development which promoted cross-functional collaborations across various functions like Research, Development, Operations, Regulatory Affairs etc and innovation through various initiatives. By encouraging collaboration across different functions and geographies, the program led to improve organizational agility and achieve R&D performance.

Integrating blockchain technology with Lean Six Sigma (LSS) can significantly enhance process transparency, data integrity, and efficiency. Blockchain's technology can ensure that all transactions and process steps are recorded in a transparent manner. This transparency can help in tracking the progress of LSS projects and ensuring that all stakeholders have access to the same information. The technology ensures that data cannot be altered once it is recorded. This is crucial for maintaining the integrity of data collected during LSS projects. By automating the recording and verification of transactions, it can reduce the time and effort required for data collection and analysis further enhancing efficiency. But the challenge is the complexity in implementing blockchain technology, the high cost in the setup and depending on the industry, there can be regulatory and compliance challenges associated with using blockchain technology. Example in the healthcare sector, successfully blockchain and LSS have been used to improve patient data management and streamline processes. The integration ensured that patient data is secure, accurate, and easily accessible to authorized personnel.

Thinking, Fast and Slow introduces two systems of thinking that significantly influence decision-making and leadership styles. System 1 [Fast Thinking]: This is automatic, and quick, relies on instincts, making it useful for rapid decisions in high-pressure situations where time is limited. However, it can be led to biases and errors. System 2 [Slow Thinking]: This is deliberate, analytical, and logical method. It is used for complex problem-solving and long-term strategic planning, however it is slower and can be exhausting. System 1 can help leaders make quick decisions in emergencies or when immediate action is required. For example, a CEO might rely on their intuition to navigate a sudden market shift. System 2 can be useful thorough analysis and strategic decisions, such as entering a new market or developing a long-term business plan. Business leaders can balance System1 and System 2 by recognizing when to use System1 vs System 2 and having the awareness to switch between the two as needed. Like business leaders can use System1 during crises with past experiences to make decisions, for long-term strategic planning, leaders can gather data by structured thinking and consulting with experts. By balancing these two systems, business leaders can adapt their approach to suit different situations, ensuring both agility in high-pressure moments and depth in strategic planning

Data preprocessing is a critical step in data analysis, and it does takes up a significant amount of time because of raw data collected from various sources is usually incomplete or inconsistent which can lead to inaccurate analysis. It is a essential step in data analysis because it ensures that the data is accurate, complete, and consistent, which is crucial for making informed decisions. It involves transforming raw data into a format that is more suitable for analysis and modeling. Without proper preprocessing, the final output could be inaccurate with faulty insights. Following are the list of things typically get checked in data preprocessing like - Missing data, Outliers that can distort the analysis, consistency of data and/or make it more compatible for analysis like converting the categorical data to numerical data and reducing the number of features to simplify the analysis. Some of the tools which can be used to speed up data preprocessing are KNIME: A visual data analytics platform, Apache Spark: Distributed data processing framework, Pandas: Data analysis library in Python that provides performance data manipulation and cleaning functionalities etc..

As a Project leader I would argue that the focus should be on driving forces, as it is the catalysts for change, and it will bring the momentum and energy needed to push a project forward. By emphasizing the driving forces helps to build a compelling vision for the future and gain support of the stakeholders by showing the benefits and potential outcomes of the change. On the other hand, it is also important to be aware of resisting forces and balancing both the driving and resisting forces is crucial to ensure the successful change management. Few key steps to follow which can achieve this balance are : 1. Understanding the motivations behind each force helps in addressing them effectively, 2. Strengthening the driving forces by highlighting the benefits of the change. 3. Provide support and resources and address the concerns associated with resisting forces, 4. Develop a change management plan that should include clear actions, timelines, and responsibilities and 5. Involve stakeholders in the decision-making process and empower them to contribute to the change. For example, my organization (Pharma) is continuously investing in new technologies, implemented a new system aimed at enhancing the efficiency of clinical trail management. There was certain resistance due to uncertainty about how new system will affect employees roles and responsibilities, resistance due to new ways of working especially to adopting new technologies, Concerns about potential risks and negative outcomes with implementation of new system as seen as risky and disruptive. The leadership have overcome the resistance by ensuring transparent and open communication with employees about the reasons for change, the benefits will bring, involving employees in the change process by seeking their input and feedback, providing training and support to employees to adapt to new systems, actively listening to and addressing the concerns, Building trust through consistent actions and motivating to embrace the change.

Value Stream Map (VSM) is a powerful approach to identify inefficiencies in a process. It involves mapping out the entire current state process flow including the flow of materials and information, gathering process times, lead times and relevant metrics which helps visualizing the current workflow and data provides quantitative basis to identify the inefficiencies. Within current process flow, we can identify Value-added activities, the wastes (seven types of wastes - Inventory, motion, defects, transport, waiting and overproduction) and Non-value added activities which are often the root causes of inefficiencies. VSM is a team exercise involving the team members, subject matter experts and other key stakeholders. VSM can be used to answer the following questions: 1. What is the flow of material and information?, 2. Where are the linkages between the information and material flows? 3. Where are the sources of waste? 4. What should our flow look like? (when desired process flow mapped). 5. Where should our improvement efforts be focused? Once the current flow is mapped and waste is identified, A future state process is mapped basis of lean improvement implementation plan which is defined in alignment with organizational strategic goals that the optimized process should achieve. These goals could include reducing lead times, improving quality, increasing customer satisfaction, or reducing costs. For example: In my current organization (Pharma company) which Transfers the Value (ToVs) to Health care professionals (HCPs) for the service the HCPs provides, required to be disclosed to government regulation. Based on the country the regulation requirement can be different and the disclosure document should meet the requirements. The process involves the Data collection of ToVs (payments) made to HCPs from various countries where the company operates, and HCP services were taken. Followed by data validation and data submission with disclosure to regulation department. A Value Stream Mapping was conducted and identified the process times, lead time, value-added and non-value added process steps where majority of process is conducted in manually method. The VSM helped us to identify the inefficiencies in the process and also the opportunities for Automation. Based on solution brainstorming and aligning with team strategic goal to optimize this process, an improvement opportunity was proposed using RPA automation and implementation plan was developed.

Quantile based analysis is a tool used for understanding data distribution by dividing data into equal sized intervals (quantiles). The analysis provides more comprehensive view of data distribution by highlighting the spread and skewness, less affected by outliers and can use the datasets with extreme values. The analysis allows for easy comparison of different datasets. The limitation with this analysis is for small datasets it can be less accurate in quantile estimates and for large datasets it can be more complex in interpreting the multiple quantile estimates. In real-world data the challenge is getting the right data quality as inaccurate data can skew quantile estimates and can mislead the results. The other challenge can with time-series data where continuous recalibration is required as quantiles may change over time. Quantile analysis can be used in various industries, For example in a pharma organization this Quantile analysis can be used to gain insights into the distribution characteristics of blood glucose levels for different groups. Let’s say we use a dataset related of Type 1 Diabetes (T1D) analysis. This dataset includes information about two different conditions: Type 1 Diabetes (T1D) and Healthy Donors. We can use quantile analysis, by capturing the blood glucose level measurements for T1D and Healthy Donors, ensuring that each sample is labeled with its respective group (T1D or Healthy Donors), calculate the quantiles for a blood glucose levels measurement and divide the data into quartiles (25th, 50th, 75th percentiles), analyze the distribution of blood glucose levels within each quantile to understand the spread and skewness of the data and compare the quantiles between T1D and Healthy Donors to identify any significant differences in blood glucose levels. In this way we can compare the two datasets and gain insights on distribution. Overall Quantile-based analysis is especially useful when you need a detailed understanding of the distribution characteristics of your data.

Parametric analysis is a statistical technique used to estimate the population parameters of the distribution from which the sample data are drawn. Parametric methods more efficient as they make use of all the available data, provide more precise estimates, simpler to implement and interpret. Parametric analysis is used in various industries like Pharma & Healthcare, Manufacturing & Engineering and also in Finance & Economics. For Example in pharma industry the Parametric analysis method is used for clinical trials to analyze survival data, estimate treatment effects and compare the different treatment groups during the development of the new drug. For instance use of time-to-event models in clinical trials where the timing of events (such as disease progression or recovery) is critical. These parametric models can provide more precise estimates of the time to an event, which helps in planning and conducting trials more efficiently. Parametric analysis also used for predictive modeling. This involves forecasting the outcomes of various drug development scenarios, predicting the success rates of different clinical trial designs and estimating the time to market for new drugs. By using parametric analysis researchers can optimize drug clinical trial designs and improve overall efficiency.

Decision Intelligence is a discipline which combines the data science and managerial science to improve the decision-making process. It utilizes advance analytics/ML/AI to provide a framework for data-driven decisions. All four types of analytics do contribute to Decision Intelligence, but Prescriptive Analytics plays the most significant role. As prescriptive analytics not only analyzes past data and predicts future outcomes, provides actionable recommendations to achieve desired results. Decision Intelligence integrates insights from descriptive analytics to capture data and examine what happened from past, it integrates diagnostic analytics to examine why something happened in the past to gain insight on the causes, it integrates predictive analytics to predict likely outcomes and guide decision-makers towards the best possible actions. For example, in my current organization (Pharma), a product was created which pulls data from our customer data hub and uses different analytical models to generate actionable recommendations, tailored to different key roles and designed to help them decide on the best actions to take. This helped teams like sales representatives and brand managers leading to higher impact discussions with Health Care Professionals and increased commercial performance.