Jay Nanwani

Control chart monitoring frequency is determined based on various factors such as the type of process, stability of the process, sensitivity requirement, and other practical considerations related to data collection and analysis. Listed below are some considerations for determining the frequency of monitoring control charts: Process Variation: If the process is stable and consistent over time and there are no significant changes expected in the process then monitoring with less frequency would suffice. The Opposite is also true, if there is high variability in the process or it is prone to frequent changes then the monitoring frequency required may be higher to detect trends promptly. Process criticality: The impact of any process on product quality, and customer satisfaction will influence the control charts monitoring frequency. The higher the impact, the more frequent the monitoring, and vice versa. Data accessibility: Availability of data also influences monitoring frequency. If data is readily accessible in real time then more frequent monitoring is possible. However, if data collection requires significant time or resources then less frequent monitoring may be a more practical option. Statistical consideration: The desired level of sensitivity can also guide the selection of frequency monitoring. For example: for narrower control limits or small sample sizes control charts will require more frequent monitoring and for wider control limits and high sample sizes less frequent monitoring will also suffice the requirement to maintain sensitivity to process change. Operational bottlenecks: Various process constraints such as resource availability, and operational priorities also influence the frequency of monitoring which is why organizations balance the need for timely monitoring with resource availability and competing priorities. Statutory requirement: In Industries that are highly regulated such as Pharma, Chemical, Oil 7 Gas, etc. there are statutory requirement or standards that prescribes specific monitoring frequencies for certain processes and quality metrics. In order to comply with these requirements organization ensures the frequency of monitoring as per the prescribed specification. Data analysis: By analyzing historical trends or process data could provide insights to derive appropriate frequency for monitoring control charts. Various patterns of variability, seasonality, or past process changes can guide in deciding optimum monitoring intervals. Frequency levels in control charts are very critical aspects as they enable the data engineer to understand process variability, maintain quality standards, and provide insights to select appropriate corrective action. There are certain repercussions associated with the wrong consideration of monitoring frequency level, such as: 1. If the Frequency level is too high then monitoring control charts would require excessive resources in terms of time required, no. of people deployed, and efforts required to collect data. Frequent monitoring can also lead to an increase in the chances of detecting random variability in the process also known as noise. It can also lead to a decrease in the control chart's sensitivity to detect impactful process variations. With continuous monitoring, it becomes difficult to differentiate between process shifts causing process instability. Continuous or frequent monitoring can also lead to information overload and if the collected data does not make any sense to the decision-makers then they tend to lose focus on key performance indicators and priorities. 2. Low Frequency monitoring level poses the risk of delayed detection of process variability or abnormalities, There will exist the possibility that significant process changes may go unnoticed and could cause issue escalation resulting in the requirement of higher resources to perform corrective action. Decision makers could also miss the opportunities to check and address process inefficiencies, defects, or quality issues this could lead to the suffering of improvement initiatives due to lack of timely feedback. Because of reduced monitoring frequency decision makers can also experience a risk of slow response time to process variability. Eg. If the customer is not satisfied due to any particular process then having a lower frequency interval can lead to an increase in time to detect the process variation cause of customer dissatisfaction and can increase the time span till which customer remains dissatisfied and can also impact brand image.

ACPT is a structured approach to analyze and derive the root cause behind customer dissatisfaction. This RCA tool with a customer-centric framework is usually used in contact centers where customer satisfaction is paramount. There is an analytical methodology in this framework that helps to understand the root cause of customer dissatisfaction whether it stems from the action of an Agent, Customer behavior, Process constraints, or technological challenges. Agent refers to Agent Analysis: associates responsible for handling and interacting with customers. Customer dissatisfaction can be traced back to agent-related issues such as agent bad behavior, inadequate knowledge, false claims against customer queries, etc., categorized under the “Agent” structure of the analysis. Examples of Agent-related issues may include lack of training, low morale, and lack of motivation to resolve customer issues. The positive part about this aspect is that it is controllable, and it can be improved through agent training, coaching, or counseling. Customer analysis refers to examining customer behavior, feedback, responses, and reasons for dissatisfaction. It is sometimes, the customer behaves in a very rude way making unreasonable demands and despite the best effort put in by the agent, the customer still provides a negative rating. It is important to accept the fact that 100% customer satisfaction is an unrealistic goal and pursuing it relentlessly can lead to agent fatigue. Example: Customer reaches out to the contact center carrying a bias in his head for the brand and irrespective of the quality of solution provided by the agent, customer still provides the negative feedback. Analysis of Process structure will indicate the limitation in the designed process itself resulting in the dissatisfaction of the agent or customer. Sometimes the designed process will have a misalignment with customer expectations and even though the operator follows the SOP the customer will not be satisfied which will lead to a low customer satisfaction score. For example, A company is marketing its product with a question-asked refund policy in case of a defect but when a customer calls the contact center to initiate the refund, the agent has to follow a very complicated process to initiate refund this can create dissatisfaction in both agent’s and customer’s mind. Technology analysis would highlight the performance and reliability of the system and technological tools used for interaction between customer and agent. This identifies the issues related to the complete IT infrastructure including software, hardware, etc. for its capabilities, responsiveness, uptime, and functionality with other integrated systems, the malfunction of which had led to lower customer satisfaction. For example: During a call, there are some glitches due to which the customer is getting reassigned again & again to a different agent and where the customer has to repeatedly share the matter with various assigned agents. This could lead to customer frustration and can result in lower satisfaction scores. ACPT analysis is usually used in contact centers, its principles can also be applied to other organizational contexts as well for eg: 1. In healthcare, Principles of ACPT analysis can be used to improve patient satisfaction and outcomes by analyzing patient interaction with clinical workers, clinical workflows, electronic health record systems, medical devices, etc. 2. ACPT analysis can also be utilized in the hospitality industry to provide an exceptional guest experience by analyzing the performance and service delivery of hotel staff to the customer, understanding guest behavior, feedback to personalize services, evaluating the ease of interactive processes such as check-in/check-out system and also by assessing the effectiveness of hotel IT infra to provide seamless guest experiences. 3. In the Retail industry customer experience can be improved by adopting ACPT principles. It can help in identifying improvement opportunities by assessing the performance and engagement levels of store employees with the customer. It can also provide some insights to the store owner to help him tailor product offerings, layout, store operations, and interactive processes based on customer's behavior, feedback, and shopping preferences. Similar use cases are there in various other industries such as Financial institutions, education institutions, Transportation services, Non-profit organizations, etc. This illustrates the usefulness of the ACPT framework making it an effective tool across the industry to identify areas for improvement and optimize interactions between agents, customers, processes, and technology.

Objectives and Key Results (OKR) is a framework for goal setting that was popularized by John Doerr, one of the earlier investors in Google. This framework is designed to align organizational objectives (Qualitative) with individuals and teams throughout the organization with measurable (Quantitative) outcomes. This framework is used when the objectives set by an organization are very ambitious and aspirational and that needs to be achieved in a very short time frame. Examples of such objectives could be, to improve customer satisfaction, increase market share, reduce go-to-market TAT, etc. Such objectives will answer to the question “What do we want to accomplish”? Key results are the SMART outcome that indicates progress achieved towards the set objective. It is a quantifiable metric that reports feedback on the results of the execution plan derived after the objective setting. These Key results answer the question “Have we achieved our objective?” Key results along with Objectives are aligned and cascaded throughout the organization with all the employees and team, focused at all the levels of the organization for aligned collaboration and aim towards achieving objectives in time bound manner. Comparing the OKR framework with Hoshin Kanri will make us understand the fundamental distinction between both the goal-setting approaches. Hoshin Kanri is a Japanese management methodology of policy or strategy deployment focusing on principles of long-term vision, Catchball process, and PDCA cycle. OKR and Hoshin Kanri are distinct in terms of the Scope of an objective. In Hoshin Kanri objectives considered are of a long-term nature which sets up the direction for the organization. OKRs are usually focused on short-term, more tactical goals typically set on a quarterly basis. These are more agile and flexible in nature enabling organizations to adapt quickly to changing market conditions. OKRs emphasizes alignment and transparent execution with clarity into org. goals and progress it makes. It promotes CFT coordination and collaboration and fosters a sense of ownership and accountability. Hoshin Kanri utilizes Catchball process to establish alignment and communication in an organization. It promotes constant dialogue between the strategic team, middle management/Tactical team, and operational team to ensure goals are understood and supported at all levels OKRs are more flexible and encourage teams to experiment and innovate in the process. It allows for iterations in goal-setting and goals are continuously refined incorporating learning from failures. While Hoshin Kanri has provisions for some adjustment, it follows a very structured and defined approach for goal deployment and execution. It may be less agile and responsive compared to OKR’s

Traditional analytics is a conventional process of analyzing a batch of data sets collected over time. Usually, processing of the data in conventional processes occurs offline. This method involves longer processing times and delays in getting meaningful insights from the data set. Decisions are taken in retrospect as the data is processed offline and it is similar to work with historical data. Real-time analytics is a discipline in which analytics is completed as soon as new data arrives in the database. This method provides rapid insights and allows stakeholders to make timely decisions. This enables organizations to quickly respond to dynamically changing conditions, seize opportunities, and mitigate risk more effectively. A key distinction between traditional and real-time analytics is in terms of scalability. In the conventional approach, it becomes complicated to accommodate sudden data surges and the required volume to be processed and it will call for expensive resource deployment. Real-time analytics platforms are designed for scalability and these platforms can dynamically utilize resources to accommodate sudden surges in data processing demand, making the analysis consistent and reliable. Common challenges that data engineers face in real-time data processing are: a.) Handling large volumes of data: Analytics would yield an optimum result if a large set of data is processed for any given objective. Processing this high-volume data sometimes creates a bottleneck for engineers as they try to figure out how to manage and make use of this large amount of data. b.) Managing high variety of data: Usually every data source does not always follow a standard template hence data collected from these sources would have a high variety of structures, formats and it becomes difficult to process and transform this unorganized data and make sense of it for the stakeholder c.) Quality of data: There is a saying that “garbage in is garbage out”. Data will only be useful to derive insight s if that data is accurate. It is imperative that while processing inaccuracies present in the data are identified and reported for the user for effective decision-making. Identifying such noise in real time is also a key challenge for real-time analytics. d.) Infrastructure requirement: Real-time analytics requires processing complex and high-volume data as soon as it enters the database. This would require creating and managing such advanced infrastructure that can handle such kind of speed and velocity of data processing. The cost of establishing such a level of infrastructure would be very high. e.) To maintain low latency and high performance: Real-time analytics aims to provide quick meaningful insights and analysis to the user. This can be a key challenge to maintain such low latency and quality of insights in real-time by minimizing processing delays, optimizing data pipelines, and rapid query performance.

Before getting into identifying the distinction between modeling and simulation we should first understand both the terms. A business model is a set of defined boundary conditions of an organization that describes what all sorts of values are provided to all the stakeholders. It also includes a framework outlining how the value provided by the organization will be transformed as revenue flows back into the organization. Every business model has 5 main elements viz. customer, products/services, value propositions, stakeholders, and finance. Interlinking of all these elements represents how a business operates and how these various elements interact with each other in real time, exhibiting an organization's business model. Simulation is experimenting with computer-generated mathematical models that mimic real-world scenarios to visualize the impact on real-world systems and processes when boundary conditions are modified or adjusted. It helps stakeholders to gain insights into complex systems and make data-driven decisions. A business model (BM) is different than a simulation for the following reasons: a.) BM is a conceptual representation of a business, while simulation involves creating dynamic models that simulate real-world processes and systems. b.) Simulation is used for experimentation, hypothesis testing, prediction, and decision making while BM is primarily used for conceptualization. c.) BM consists of static elements such as diagrams, process flow, and more of a simplified view of the business environment, while simulation involves creating dynamic models capturing the complexities and intricacies of world scenarios in great detail. BM (Business Modelling) and simulation are similar at various levels: Both BM and simulation assist stakeholders in decision-making through understanding and analysis of complex systems within an organization. BM can provide insights by capturing the high-level granularity while simulation captures detail-level nitty-gritty and nuances. It also enables management to make decisions by predicting future outcomes within predefined boundary conditions as well as variable boundary conditions by defining various assumptions and forecasts in the model. Modelling and simulation have a very wide range of applications in various industries and a few of those are highlighted below: 1. In Manufacturing a.) Throughput Improvement b.) Profitability analytics c.) Expense projections d.) Sales predictions 2. In IT sector a.) Project management b.) Throughput improvement c.) Prioritization for Agile 3. In Pharma & Chemical Industry a.) Product mix optimization b.) Improving DOE c.) Yield prediction 4. In the Banking and Financial sector a.) Lead time reduction b.) Business growth c.) Profitability

Blue ocean strategy is a strategical framework popularized by W. Chan Kim and Renee Mauborgne. It focuses on creating uncontested business space called “Blue Oceans”. In this framework, strategies are derived considering competition as irrelevant and non-existent. The differentiating factor in this strategy is to constantly innovate and thereby create new demand and value for the customers. Organizations adopt this strategy to capture untapped market space and opportunities by delivering innovative products or services to address unmet customer needs. There exist some similarities between BOS (Blue Ocean Strategy) and LSS (Lean Six Sigma) such as: Both BOS and LSS focus on value creation for customers. While LSS emphasizes eliminating waste from the value chain and improving operations efficiency to deliver value, BOS aims to create new value by offering innovative products & services. Both strategies are implemented considering the customer at the center of the framework. LSS identifies and addresses customer requirements by encouraging organizations to focus on process improvement initiatives, while BOS prescribes to creation of new markets or reimagining the existing ones based on customer requirement insights. Along with some similar characteristics BOS and LSS also have a few differences in terms of a.) Application: BOS is applicable for high-level strategic planning and market analysis, While LSS is applicable for operational processes across various functional areas to enhance efficiency, improve quality, reducing waste. b.) Innovation scope: BOS emphasizes introducing groundbreaking innovations to create new market space and fulfill unmet customer needs by unlocking new demand and value, while LSS focuses on continuous and incremental process improvements by optimizing current operations rather than creating a new market space. c.) Time & resource requirement: BOS involves long-term strategic planning and implementation as it requires a very high level of innovation and market creation efforts, while LSS focuses on short to medium-term improvement initiatives which usually do not require such extent of resources. Let us consider an example from the automobile manufacturing industry: Where Tesla and other Electric vehicle manufacturers adopted Blue ocean strategy by investing heavily in battery tech innovation, creating a new market demand, and providing transformative value to the customer, other regular automobile manufacturers such as Toyota are still focused on improving operational efficiency, reducing cost by waste elimination through various LSS improvement initiatives. Both manufacturers are fulfilling the customer demand but the fundamental difference between both of them is that one has created new demand and the other one is fulfilling an existing demand. Let us see one more example from the hospitality industry: The Bread & Breakfast (Air B&B) concept was an innovative strategy derived from the Blue Ocean framework where existing market competitors were considered irrelevant or non-existent. The existing demand in this business was reimagined and transformed by capturing customer insights and utilizing that data to fulfill existing customer demand innovatively. Other renowned hotels such as JW Marriot are also improving their traditional operations by continuously implementing LSS improvement projects to provide better value to their existing customer demand.

MBO or Management by Objective, a term coined by Peter Ducker in his 1954 book "The Practice of Management" refers to aligning employee's goals with the organization's objective so that employees feel more connected and motivated. Organizations are integrating the LSS principles framework and MBO framework to reap maximum results in terms of operational efficiency, cost reduction, and customer satisfaction. etc. Tabulated below are a few important aspects in which organizations are benefitting from the integration of the LSS & MBO framework: Aspect Lean Six Sigma (LSS) Framework Management by Objectives (MBO) Framework Examples Goal Alignment Projects aligned with quality improvement, cost reduction, etc. Objectives aligned with strategic goals and organizational priorities GE aligned LSS projects with its strategic objectives and was able to achieve increased operational efficiency and product reliability. The company was able to reduce 50 % cycle time for their MRI machine production in the healthcare division. Data-Driven Approach Emphasis on data analysis and measurement for process improvement Specific, measurable objectives based on relevant data and KPIs GE Employee KPIs are set with SMART methodology and were monitored and measured using the LSS framework. Through LSS measurement and analysis tools, the company was able to derive insightful outputs that helped the organization prioritize its efforts for improvement. Continuous Improvement Structured methodology for identifying and eliminating defects Framework for setting goals, measuring performance, and providing feedback Toyota aligns Lean Six Sigma projects with strategic goals and uses MBO to set objectives for improvement initiatives. This resulted in engagement of all the employees and increased momentum of improvement Employee Involvement Encourages employee engagement and empowerment in improvement initiatives Involves employees in goal-setting and decision-making processes Training and Development Provides training in problem-solving and process improvement techniques Integrates training to develop skills for driving improvement initiatives Kirloskar's training and development organizational objective is derived considering Lean culture implementation. This is helping the HR department to prepare standard modules for all the employees Performance Measurement Tools for tracking progress and measuring the impact of improvements System for evaluating performance against predefined objectives LSS tools can be used to evaluate performance against set employee goals and further their alignment with organizational objective

Reverse engineering or Backward engineering is the process of analyzing a product or a device's details to understand its design, function, and performance characteristics by a structured method of dismantling, dissecting, inspecting, and studying the inner profile/structure of the product. Organizations invest in the exercise of reverse engineering with the sheer goal viz. to identify the scope of improvement in an existing product’s value and create a better version of it. Reverse engineering supports the business excellence strategy of an organization in the following ways: a.) Innovation: Insights derived from reverse engineering enable organizations to understand the strengths and weaknesses of their existing product or competitor’s offering. These insights will become inputs for designers or engineers to improve the existing weaknesses through innovation and create a better offering for the customer. For example: Tesla has reverse-engineered multiple competitor cars to understand their overall architecture, one of them was the BMW i3 which was a market leader in terms of battery technology. This helped Tesla to improve their electric car’s performance and overall efficiency. b.) Cost reduction: Any/every product is the result of the transformation of a raw material to a finished good through various manufacturing processes. Through reverse engineering, one can backtrack all the steps performed to produce that product. Thus, helps in cost reduction by simplifying the production process, optimizing raw material cost, resource deployment, time to deliver, etc. For example: Mobile manufacturers such as Oppo, and Vivo are constantly reverse engineering their competitor’s mobile devices viz. Samsung and Apple to understand its design, features, and production process. This exercise helps them to offer their product in the market with similar designs and features at reduced cost and gain maximum market share in the Indian market. c.) Competitive edge: To stay ahead in the race and continue to be the product market leader companies around the world keep on analyzing their competitor’s products and build features and functions that help them to always have a competitive edge over their competitors. For example: Nintendo a video game company continuously improves its software and gaming console by reverse engineering competitor’s gaming platforms and hardware architecture. d.) New Product Development: Many times organizations get stuck in the development stage and it becomes very difficult for designers to bridge the gap between market requirement and their product offerings. By conducting reverse engineering on competitor products, designers and engineers can get influential insights that help them to continue and conclude the development cycle of a new product enabling rapid product launches. For example: While designing a new aircraft model the Boeing design engineers reverse-engineered competitor’s components such as engines, airframes, avionics systems, etc. which helped them understand the design requirement as well as regulatory compliance. Thus, reverse engineering helped Boeing engineers reduce their development efforts.

When a company plans to expand its business by venturing into products or services within the same industry. This can be achieved by developing that product or service in-house, incurring development costs in reinventing the wheel or there is another option viz. to acquire a company in the same space. This strategy of expanding business operations, product/service portfolio, reducing cost, increase market share by acquiring a company is also called horizontal integration. Few Examples; Facebook (now Meta) acquired WhatsApp, a social media messaging app. It helped Facebook to increase its user base as well as eliminate competition in the market. Tesla acquired Twitter(now X), a social media app. It helped Tesla to venture into one of the biggest discussion forums and user base exercising free speech. Reliance Retail Ventures acquired majority stakes in Ed-a-Mamma, a kid and maternity wear brand founded by Alia Bhatt. Horizontal integration impacts the business excellence strategy of both acquired and acquiring companies in the following ways: 1. Good Practices & policies: Both company Sr. management could assess their present practices and policies and identify the good cultural policies & the best practices that can be horizontally deployed to both organizations Operational efficiency: Combining & centralizing operations resources and optimizing the supply chain of both the organization, operational efficiency will be improved. Market share: Increased customer/user base would enable the organization to increase its market share and with higher market penetration organizations will have pricing power for their market offering Competitive advantage: The organization can offer a package deal to its enhanced user base which will give the organization an edge over its competition