AnishMohandas

R squared = Measures the proportion of variance in the dependent variable(y) that is explained by the independent variables (x). It ranges from o to 1 and higher R-Squared value indicates that model is a good fit. The regression model is created based on training dataset. R Squared prediction = used to assess the predictive performance of a regression model. This is usually done using the test datasets (unseen data) to know how well the model could predict in the real world. Example: R- squared is a good indicator of how well the model fits based on the training data whereas R-square prediction will actually show how well the model fits the unseen data in the real world. This prediction is usually done with test data. One good example is the loan default prediction model created by a bank where they want to predict if the customer will default on loan based on various parameters(X factors such as age, gender nationality, loan amount, occupation, purpose of loan etc). The regression model is created based on the historical data using training data set. R-squared value = 0.93. This indicates that the model fits well.The regression model was then used on training dataset to predict how well the model fit for unseen data. R-squared prediction value =0.87 which also indicates that the model is a good fit for unseen data.

Process benchmarking involves evaluating your organization's processes in comparison to those of leading organizations known for their excellence in one or more aspects of their operations. It offers crucial insights to gauge how your organization stacks up against similar entities, even if they operate in different sectors or cater to different customer bases. Additionally, benchmarking aids in pinpointing areas, systems, or processes that warrant enhancements, whether through gradual, continuous improvements or transformative, large-scale business process re-engineering initiatives. Sequence of activities in process Benchmarking : Step 1: Identify the process to Benchmark Select the process Fully understand the process and identify non-value adding areas or scope of improvement Find defects, opportunities, and sigma level Step 2: Select organization to Benchmark Find out industries/ functions which perform the process Identify the leaders or best performer (different industry or same industry) Step 3: Research the process to be benchmarked Step 4: Develop a questionnaire based on your requirements Step 5: Exchange Ideas Conduct visit, if necessary Step 6: discuss the comparison internally with the team Step 7: Devise an action plan and implement best practices Example: UAE based Bank identified the need to enhance its digital account opening process to provide customers with a seamless and efficient customer experience while onboarding. The bank aimed to achieve the following: Reduce the lead time to open an account Enhance customer satisfaction by simplifying the account opening process and provide clear guidance. Optimize resource utilization by streamlining internal procedures and reducing manual interventions Recognizing that other Banks who are considered as Digital leaders might have more advanced digital account opening processes, our Bank decided to leverage benchmarking exercise to expedite improvements without reinventing the wheel. A cross functional team was established, and we began by mapping out the current digital account opening process to establish a baseline, paying particular attention to the steps involved from application submission to account activation. The team conducted surveys and interviews with both existing and potential customers (user testing) to gather insights into expectations regarding the digital account opening process, as well as any pain points, they encountered. Following the survey, team experimented with the digital applications of leading Banks (within UAE) and reached out to Digital Enhancement team to understand the technologies and methodologies utilized, engaging in a dialogue to gain insights into the process. Based on the findings, the team complied a benchmarking report outlining the best practices observed at leading Banks and proposed recommendations tailored to our requirements. Finally, our Bank implemented several of the recommended changes, such as simplifying the online application form, integrating automated verification process and OCR technology. As a result, we witnessed a significant improvement in account opening process by reducing the lead time and providing a seamless experience to customers.

Machine learning researchers and data scientists often use in-sample and out-of-sample testing to refer to training and test sets respectively. In-sample data refers to the set of data which is used for training or fitting a model. When analysts try to build statistical or machine learning models they usually make use of historical data which enables the model to be taught about how predictions or classifications can be made. During this process, inputs are given, along with their corresponding outputs to enable it to learn the underlying patterns and relationships that exist in the dataset. This is essentially what is meant by in-sample data; it is the dataset upon which the model learns from. On the other hand, Out of sample data refers to unseen data by a model when undergoing training phase. After a model has been trained, measuring its performance on new unseen information is important for assessing its ability of being generalized. For this reason, out-of-sample testing is employed. By trying out a model using real time cases that have never encountered before, analysts can make an estimation as regards making predictions or classification of unseen instances inside such models as well. This stage helps confirm its applicability within practical contexts (Data modelling and learning steps are illustrated and attached) In-sample (Training data) Out-of-sample (Testing data) Advantages: It facilitates model evaluation based on the same data used for training, It gives us insight on how well the model fits the training data. Computationally well efficient Disadvantage: It is prone to overfitting Advantages: Provides a more accurate estimation of model’s performance in unseen data. Validates the model effectiveness in real world scenario Disadvantage: It requires a separate dataset for testing. It can be computationally intensive if multiple iterations or cross validations are performed Example of in-sample and out-of- sample data in real world scenario Assumption:80% in-sample and 20% out-of-sample The examples demonstrate how in-sample and out-of-sample testing are applied across all domains from Finance, Healthcare, cybersecurity etc. Credit decisioning model Training machine learning models on historical data to predict stocks Developing a spam email classifier Fraud detection algorithm Evaluating the performance of medical diagnosis model

Cross-docking is a technique that speeds up delivery and improves supply chain efficiency. When goods arrive at a hub, they're quickly moved from incoming trucks to outgoing trucks without any storage. Businesses use cross-docking to combine items from different suppliers, break up big shipments into smaller ones, and prepare goods for delivery to stores, warehouses, or customers. Implementing cross-docking typically results in various benefits for companies. They can improve product delivery speed, reduce the requirement for physical storage space, enhance inventory management, and minimize both transportation and workforce expenses. Types of Cross- docking: Different operational requirements are met by a number of strategies. One form involving the continuous cross-docking approach to speed up delivery time is where products are moved through a distribution canter on an ongoing basis. However, consolidation and deconsolidation techniques for cross-docking revolve around amalgamating or segmenting shipments within the facility in order to save on transportation expenses and make sure that goods reach their destination on time. Continuous Cross-docking: Continuous cross-docking entails uninterrupted movement of stock within a cross-dock site with periods of non-storage. On arrival, the commodities are quickly offloaded from incoming vehicles and loaded into outbound containers that will be shipped to final destinations. This technique emphasizes quick movement of items throughout supply channels necessitating precise coordination among suppliers, carriers, and company operators. Moreover, this method is suitable for perishable goods such as high-demand high-volume products because it offers timely delivery yet reduces inventory holding costs Consolidation Cross-docking: On the other hand, different inbound shipments are merged or consolidated into a single outbound shipment with an objective of improving shipping efficiency and reducing transport costs. Unlike continuous cross-docking, this model involves storing goods in the warehouse until full truckload is assembled for outbound delivery. Inventory management can be streamlined through the adoption of warehouse management systems that coordinate with supply chain partners. Examples are international freight forwarders who widely adopt consolidation cross docking to achieve maximum costs benefits in shipping. De-consolidation Cross-docking: This method is opposite to consolidation. The incoming load is broken down into smaller shipments at the cross-docking facility and then delivered to customers. For instance, parcel carriers may ship items across the country in one big consignment and later break it into small loads for final customer delivery purposes. Retail stores have their distribution centers where they receive large deliveries from suppliers and then break them down into individual store lots before delivery. I’m not sure about what it means by ‘Structured’ except if you mean organized which would not make sense because any rewrite should be structured properly irrespective of whether one has been involved in the field for long or not. Advantages: Cross-docking offers a multitude of business advantages, ranging from expedited shipping to heightened supply chain efficiency. This system significantly diminishes costs associated with storing, handling, and transporting inventory, yielding several key benefits: Accelerated Shipping: By minimizing or eliminating storage time in warehouses, cross-docking expedites the delivery of goods to both business partners and customers. This swift turnaround is particularly advantageous for retailers and B2B sellers striving to meet increasingly stringent delivery timelines and customer expectations. Cost Reduction in Inventory Storage: The implementation of cross-docking diminishes the need for costly warehouse space to accommodate products during transit from suppliers to end-users. Moreover, it alleviates additional warehouse management expenses, such as inventory tracking and management, by bypassing prolonged storage periods. Decreased Labor Expenses: With reduced reliance on warehouse storage, cross-docking mitigates labor requirements. Workers are primarily tasked with transferring goods between inbound and outbound trucks, eliminating the need for intricate routing of products within warehouse confines and subsequent retrieval for outbound shipments. Optimized Shipping Costs: Consolidation and deconsolidation cross-docking methods afford companies opportunities to optimize shipping expenses. Through the consolidation or splitting of loads, businesses can maximize efficiency in vehicle utilization, resulting in decreased shipping costs. Minimized Risk of Product Damage or Spoilage: The frequency and duration of product handling directly correlate with the risk of damage. Cross-docking mitigates this risk by streamlining the handling process, thereby reducing the likelihood of product damage. Additionally, perishable items are not subjected to prolonged storage in warehouses, mitigating the risk of spoilage or expiration.

ACPT stands for Agent, Customer, Process, and Technology, representing a systematic method for identifying the root causes of customer dissatisfaction. By employing this approach, one can assess whether dissatisfaction stems from the actions of an agent, the behavior of the customer, constraints in the process, or technological challenges. Agent Customer Process Technology “Agent” refers to customer service representatives specifically, who handle cases and organize interactions between the client and the company. This is the source of dissatisfaction since the agent can be rude, unknowledgeable about the product range, provide wrong information, or promise things that can never be fulfilled. This issue is inherent but manageable, and there are measures in the form of coaching, training, and response procedures to take out uncertainty. The "Customer" aspect recognizes that dissatisfaction cannot be fully assigned to the agent’s actions. The worst kinds of feedback are due to other reasons like prior bad experiences, vilification, low ratings due to unrealistic expectations, and inappropriate rudeness or erroneous low ratings. The “Process” component highlights that dissatisfaction might be due to procedural limitations. Some processes routinely do not fulfil customer expectations, leading to lower satisfaction scores. It is essential to identify and improve such weaknesses. This should be applied to improve the process. . The "Technology" component highlights the impact of technical issues on lowering satisfaction levels. Even if customers appreciate a brand's service, unforeseen technical problems can spoil their experience. Addressing and reducing frequent technology-related problems is key to enhancing overall customer satisfaction. Here is an example of ACPT analysis for a contact center in which customers complain about the response time and quality of service provided. Agent (A): Insufficient Training: Agents are not adequately prepared to handle complex queries, thus prolonging resolution time and providing unsatisfactory responses. Turnaround Rates: Frequent changes in staff numbers lead to the recruitment of workers who are often unaware of processes, rules, negatively affecting service consistency as well as its value. Customer (C): Unrealistic Expectations: Some customers have expectations that are above what the contact center provides because they have been influenced by deceptive advertising or unclear product offerings. Process (P): Cumbersome Processes: The existing procedures set up to handle customer queries are quite complex, with lots of steps and approvals in them that cause the long time it takes to find solutions. Feedback Channels Inadequacy: There are no effective ways through which complaints made by customers can be captured and utilized for continuous service delivery enhancement. Technology (T): Archaic Systems: The response rates have been slowed down by the technology platforms used while limiting the access that agents have to relevant information promptly. Non-Interoperability: Agents have to go through a multitude of systems since there is no integration among them thus elongating the process of resolving a single query. The ACPT analysis (Agent, Customer, Process, Technology) is versatile and it can be used outside contact center as well. It can be used in any industries such as Banking, Finance, Hospitals, Insurance, Airlines, Hospitality sectors, retail sectors etc. Here is another example of ACPT analysis in the banking sector. In this case, customer has applied for an Easy Payment Plan to his credit card using the contact center channel, eventually dissatisfied with the whole journey and provided a rating of 3 out of 10 in the Customer Satisfaction survey. Agent issues (A): The customer has received incorrect information because the agent was a new employee. The agent did not have a comprehensive training on this new Easy Payment Plan and this fact directly led to the incorrect details being provided to the customer. Customer Limitations (C): It should be noted that the bank’s policy regarding the application for the EPP directly states that the customer can apply only through the contact center. This policy is restrictive and has directly affected the customer’s choice. Process Inefficiencies (P): Incorrect applications were processed due to manual processing errors and unclear procedural guidelines. The process of applying for Easy Payment Plan was cumbersome and they were physically processed by backend team. Technology gap (T): The bank lacked self-service options for applying for the Easy Payment Plan through their mobile app. This absence of a digital self-service channel forced customers to rely on the contact center, further exacerbating the inconvenience and contributing to the dissatisfaction experienced during the application process. ACPT analysis serves as a valuable tool for understanding and addressing the multifaceted nature of customer dissatisfaction in the industry. By acknowledging the different factors contributing to dissatisfaction, organizations can work towards creating a more realistic and achievable goal of delivering consistent, high-quality service to their customers.

OKR Framework: The OKR (Objectives and Key Results) Framework is a strategic tool that numerous organizations across various sectors, including industry leaders such as Google, Intel, Airbnb, Uber, Twitter, and LinkedIn have adopted to drive success and innovation. This framework is structured around two core elements: Objectives, which articulate the goals an organization aims to achieve, and Key Results, which define measurable outcomes that indicate success. Below is an illustration of the OKR framework designed for the Banking and Financial Sector Objectives Key Results Enhance Customer Satisfaction Key Results 1: Achieve a Strategic NPS of 75% points by the end of Q4’24 for Mass and Affluent segment. Achieve a strategic NPS of 82% points by the end of Q4 for High net worth segment. Key Results 2: Reduce customer service time to less than 2 mins in all branches by Q3’24 Key Results 3: Increase credit card customer retention rate by 30% by Q3’24. Increase Operational Efficiency Key Results 1: Automate 40% of manual process in Central Operations by Q3’24 Key Results 2: Reduce operational expenses by 10% through process optimization and technological enhancements by Q4’24. Grow Revenue Streams Key Results 1: Expand mortgage loan volumes by 20% by Q4’24. Key Results 2: Augment Credit Card volumes by 20% by Q4’24. Key Results 3: Launch two new financial products targeting millennials, achieving a market penetration rate of 10% by Q3’24 Strengthen Compliance and Risk Management Key Results 1: Achieve 100% compliance with the latest regulatory requirement before the next Central Bank Audit. Key Results 2: Implement a new fraud detection system reducing External Fraud cases by 75% by Q3’24. Improve Employee Engagement and Satisfaction Key Results 1: Increase employee engagement scores by 20% as measured by the annual employee survey. Key Results 2: Reduce employee turnover rate by 10% by enhancing work life balance options and flexibility. Hoshin Kanri: Hoshin Kanri, a Japanese term, is a top-down, bottom-up, systematic, and structured strategic planning process that engage all levels of the organization, while creating measurable and aligned goals that imbue the concept of continuous improvement through use of the Plan- Do – Check – Act cycle. Here is the generalized flow of Hoshin Kanri process which begins with key inputs from SWOT (Strengths, Weaknesses, Opportunities, Threats) and PEST (Political, Economic, Social, Technological) analyses. These tools offer comprehensive insights from both internal and external viewpoints. Additionally, the process actively seeks contributions from customers, employees, and stakeholders to ensure a well-rounded planning approach. Strategies are built in Planning stage and prioritized in the Do stage. Plans are cascaded downward throughout the organization in a systematic manner. Plans, metrics, and score cards are reviewed in the check stage. Finally, the entire planning process is reviewed, and lessons are gathered and incorporated as improvements into the following year’s planning process. Hoshin Kanri Vs. OKR for goal setting: OKR Framework Hoshin Kanri OKR is developed in the tech industry, popularized by companies like Intel and Google. Hoshin Kanri is Originated in Japan, with roots in Total Quality Management principles. OKR is a goal setting framework which consists of Objectives and Key Results. Measurable goals create alignment. Focus of OKR is to Set ambitious goals to drive performance beyond current capabilities Hoshin Kanri is a goal setting framework which combines long-term objectives with annual objectives and improvement priorities with metrics and KPI. It Focuses on achieving a unified direction through alignment and consensus. OKR is typically set and reviewed quarterly, allowing for rapid adaptation and iteration. Hoshin Kanri is planned annually with monthly or quarterly reviews for alignment and adjustment. Cascades goals top-down but allows for bottom-up feedback to adjust and align objectives. Emphasizes top-down goal setting with bottom-up feedback to ensure alignment and consensus. OKR is Flexible and adaptable, suitable for fast-paced and rapidly changing environments. Hoshin Kanri is Systematic and structured, with a strong emphasis on planning and process. OKR focuses on measurable key results to track progress towards objectives. Hoshin Kanri uses metrics and targets within detailed action plans to measure progress. OKR encourages employee engagement by connecting individual contributions to broader company goals. Hoshin Kanri engages all levels of the organization in the planning process to ensure alignment and commitment.

A model is a simplified representation of a system, process, or phenomenon, often constructed to understand, analyze, or predict its behavior. The most fundamental and efficient model utilized by every Lean Six Sigma practitioner is the equation y = f(x) where y is the dependent variable and x is the independent variable. In this equation, f(x) represents the function that maps values of x to corresponding values of y. In organizations, different types of models are used in various aspects of day-to-day operations to understand, analyze, and make decisions. Here's how Financial, Customer, Operational, and People related models are commonly utilized: Financial Models Financial models are essential for budgeting, forecasting, and making investment decisions. These models typically involve equations and algorithms that analyze financial data to project future revenues, expenses, profits, and cash flows. Customer Related Models Customer models are used to understand consumer behavior, preferences, and trends. These models often incorporate data from customer interactions, purchases, demographics, and market research. Customer segmentation models categorize customers into groups based on similarities in behavior or characteristics, allowing organizations to tailor marketing strategies and product offerings. Predictive models forecast customer lifetime value, churn rates, and likelihood of purchase, enabling businesses to optimize customer acquisition and retention efforts. Operational Models Operations models focus on optimizing processes, workflows, and resource allocation within the organization. They help streamline operations, improve efficiency, and reduce costs. People/ Staff related Models People models, also known as human resource (HR) or workforce models, are used to manage and develop the organization's human capital. They may include workforce planning models that forecast future staffing needs based on business goals and projections. Simulation is a powerful computational technique used to mimic real-world processes or systems in a controlled virtual environment. It involves creating and executing models that represent the behavior of the system over time. Put simply, every simulation necessitates a model. Steps followed in simulation: · Clearly define the problem. · Gather expertise from relevant sources. · Validate and confirm the accuracy of the model. · Fine-tune the model as needed for precision. · Conduct predictive analysis based on the refined model. · Perform prescriptive analysis to derive actionable insights. Similarities between a Model and Simulation: · Both models and equations serve as representations of relationships or phenomena. · They provide structured frameworks to understand and analyze complex systems or processes. · They facilitate analysis, problem-solving, and prediction across various fields by providing a systematic approach to understanding relationships and making decisions. Differences between a model and Simulation: Model Simulation Represents real-world systems using mathematical equations or conceptual frameworks. Actively simulates the behavior of real-world systems over time, incorporating changes and interactions Typically, static representation, often expressed through mathematical equations or conceptual frameworks. Involves dynamic execution over time, where the behavior of the system evolves based on predefined rules and interactions. Does not inherently allow for experimentation, serving primarily as a tool for analysis and prediction. Provides a platform for experimentation, allowing users to test hypotheses, explore scenarios, and observe outcomes under different conditions. Few use cases showcasing how models and simulations are employed across various industries: Industry Use case Banking / Finance Customer Behavior Analysis: Predictive models analyze customer data to forecast behaviors such as account churn, product preferences, and creditworthiness. Credit Scoring and underwriting: Predictive models assess creditworthiness and determine the likelihood of default for loan applicants. Operational Efficiency: Process simulation models are used to streamline banking operations such as loan processing, account opening, and transaction settlements. Contact Center Performance Optimization: Simulation is used to identify peak call volumes, call arrival pattern, Average Handling Time, and agent utilization rate. Manufacturing TAT simulation to optimize production processes and reduce turnaround times for manufacturing operations Healthcare Throughput yield simulation in healthcare on reducing medical errors, improving patient outcomes, and increasing the efficiency of healthcare delivery.

Before we explore the Blue Ocean Strategy, it's important to discuss the Red Ocean Strategy. This strategy revolves around traditional competition within existing market boundaries. Companies engage in fierce competition, striving to outshine rivals and secure a larger market share. This often leads to price wars, product commoditization, and minimal differentiation. Companies focus on incremental improvements and cost efficiencies to gain an edge over competitors. The Red Ocean Strategy represents a crowded marketplace where companies fight for the same customers, often resulting in limited growth opportunities and intense rivalry. According to me, Lean Six Sigma serves as a cornerstone in the Red Ocean Strategy, offering organizations a pathway to attain competitive advantage in terms of pricing and value addition. Understanding the dynamics of the Red Ocean Strategy provides valuable context for appreciating the innovative approach of the Blue Ocean Strategy. In contrast, the Blue Ocean Strategy involves creating new market spaces uncontested by competition. Companies employing this strategy innovate to offer products or services that appeal to different customer segments. By combining differentiation and low cost, companies break away from competition and tap into unexplored market opportunities. Blue ocean thinking prioritizes value innovation to deliver significant value to customers while simultaneously benefiting the company. The Blue Ocean Strategy and Lean Six Sigma are both strategic approaches that aim to improve business performance, but they have distinct focuses and methodologies. Here's a comparison of their similarities and differences: Similarities: Aspect Blue Ocean Strategy Lean Six Sigma Focus Penetrating uncontested markets and delivering significant customer value Minimizing waste and maximizing customer value through process enhancement Customer Centric Approach Emphasizes innovation to create new market spaces aligned with customer demands Prioritizes understanding customer needs and tailoring processes to meet them Competitive Advantage Targets untapped markets to avoid direct competition and innovate offerings Achieves a competitive edge through enhanced processes, efficiency, and value addition for customers Differences: Aspect Blue Ocean Strategy Lean Six Sigma Scope and Focus Concentrates on pioneering new market territories and fostering innovation in value creation by recognizing and capitalizing on unexplored market opportunities. Targets the enhancement of existing processes by eradicating defects, streamlining operations, and elevating efficiency levels Methodology Involves strategic ideation, innovation, and imaginative problem-solving to identify nascent market potentials and differentiate offerings. Employs a systematic approach (DMAIC: Define, Measure, Analyze, Improve, Control) to systematically pinpoint and eradicate process discrepancies, fostering continuous enhancement. Risk and Innovation Advocates for bold risk-taking and innovative endeavors to forge new market paths and disrupt established industries. Emphasizes the reduction of variability and risk mitigation through data-centric decision-making and meticulous process refinement. Application Primarily suited for industries characterized by intense competition or those aspiring to pioneer new markets and innovate. Widely applicable across diverse industries, notably in manufacturing and services, aiming to heighten quality, reduce expenses, and amplify customer satisfaction TATA Nano as a Blue Ocean Example: The TATA Nano, introduced by TATA Motors in 2008, exemplifies the Blue Ocean Strategy in action. Recognizing the need for affordable transportation in India, TATA Motors embarked on a mission to create a revolutionary product. Through innovative engineering and design, TATA Motors developed the Nano, positioning it as the world's most economical car. The Nano's design and production processes were optimized to minimize costs while maintaining safety and quality standards. By prioritizing essential features and utilizing lightweight materials, TATA Motors succeeded in offering a basic yet functional vehicle at an unprecedented price point. This affordability appealed to millions of middle and lower-income consumers who previously relied on two-wheelers or public transportation. With the launch of the Nano, TATA Motors carved out a blue ocean of untapped market space within the automotive industry. Despite encountering challenges such as production delays and quality issues, the Nano remains a testament to the power of blue ocean thinking in driving disruptive innovation and market success.

Hi, I am planning to implement lean six sigma project to reduce TAT in complaint management but i am bit confused on my data collection part. Should i collect data to see how many times we have not met the TAT i.e. defectives or should i consider the complete TAT details for the project. My only question here is 1. Should i go with continuous data or discrete? which one will give me a clear picture of the problem Regards Anish

Hi all, What is box - cox transfromation ? Why are we converting the non-normal data to normal data when we are having non -parametric tests to perform analysis. Please let me know if it necessary to convert my data to normal when it is non normal. Regards Anish Mohandas

Thank you Sir....

Sir, I have attached the sample data for reference. I want to know if i should use No: of calls escalated( count of calls that did not meet the FCR - No: of defects / day) or FCR % for my data analysis. Is there any possibility of using MSA here, the data is collected from the online tool.If yes, then how should i proceed. Can i use 2 sample t test here to see if there is a significant difference in the FCR during week days and week ends( by taking count of FCR as continuous data) or should i use chi square by considering my defects as discrete. Thanks in advance Regards Anish Mohandas

Hi , We are planning to implement a six sigma project on improving first call resolution. This is one of our primary metric for help desk performance.Now, my question is about data type. Currently , we are calculating FCR as no: of calls escalated to level 2 support for resolution which means my data wil be ( 3, 7, 8, 11,2 ,1,0).Should i convert this in to percentage( 78%, 38%, 24%, 36%) by considering my defects as no: of calls escalated to level 2 support and my opportunity is no: of calls i.e. FCR % = (No: of calls escalated to level 2 support / total no: of calls ) * 100 Which one is more appropriate, this data is non normal. Should i convert this data to normal. What appropriate tools can i use for analysis. Waiting for your reply