Hirak Raval

1. Robotic Process Automation: RPA is used to perform repetitive human task, It is rule based actions to perform a task like automatic reply to an email. Software based bots which are capable of automating human actions or behavior are considered as RPA.RPA makes routine task quicker, more reliable, efficient and reducing human efforts to lowest possible extent. Workflow automations, Screen scrapping, Data capturing at particular conditions are some of the applications where industry is using RPA . 2. Intelligent Process automation: IPA = RPA+ Artificial intelligence + Machine Learning. IPA are required to perform more complex and end to end process solutions with decision making capabilities. IPA enables business to automate process by removing repetitive, monotonous & routine task from the process and make process more efficient. It uses fundamentals of process redesign mostly. One of the best example of RPA and RPA to IPA conversion used in the industry is managing data base of employee attendance and managing pay roll from that available data. Managing employee attendance was very large scale data handling process and required minimum error as it is linked to employee compensation . We have many RPA based applications where large organizations using these attendance data and leave balance data to generate consolidated report . Further to this, now a days some industry is using employee attendance data to digital workforce allotment on shop floor. Once employee punches his attendance at Factory gate , based on his skill level and subject matter expertise ,he gets area/machine allocation automatically. Here RPA of employee attendance further linked with employee skill map matrix and allowed system to take decision on machine/area allocation to employee on Realtime basis as per production planning and requirement in area. This has improved productivity and removed extra processing of workforce allotment manually to each employee. There are many other examples of hyper automation through IPA like Managing freight cost through optimum route selections, Procure to pay cycle automation with cost comparison, Customer relationship management, Real time Quality management, Financial transactional RPA with fraud detection analytics etc. Regards, Hirak

ANOM compares group mean to overall mean while ANOVA compares group mean to other group mean. ANOM indicates which group is significantly different . ANOVA has null hypothesis of all mean groups are equal and alternative hypothesis that at east 2 of the mean are not equal , it depends on Fratio which is Variance between (groups/Random error variatio)n if Fratio is close to 1 than null hypothesis is true.ANOVA can tell us weather one of the means is different but it cannot tell us which one .Below table gives significant difference between ANOVA and ANOM: Description ANOM ANOVA Assumption Normal Data Normal Data Analysis of variance of several Means Yes Yes 1 way or 2-way Yes Yes Variation Around overall means Among each other Identify which means are different Yes No ANOM calculates overall mean & then measures the variation of each from that. ANOM output displays a confidence interval, Mean outside UDL and LDL indicates statistically significant difference from overall mean. Regards, Hirak

AHP is used to develop hierarchy of decision making criteria and alternative courses of actions. It gives relative weightage and rankings(Priorities) of alternatives through structured approach in both qualitative and quantitively situations. Advantages of AHP : 1. Single and flexible model for complex problems 2. It can handle interdependence 3. Hierarchical structure and provide scales 4. Logically consistent 5. Provide desirability of each alternatives 6.Allows for trade-offs 7.Multi decision criteria making 8. Useful for group decision making environments Situations where PUGH matrix is preferable over AHP: 1. In situations where cumbersome process of calculations of consistency can be eliminated and do not make much impact on decision making 2. Difficult to use when number of criteria or alternatives are high , it becomes very cumbersome and complex process 3. New criteria addition or deletion is difficult as it will differ the decision previously taken Regards, Hirak

Project Artifact is any document ,model or design related to management of project. It is a living document which required to reflect changes in project management. They are created to track changes in project so that information can be retrieved at any time of the project. There are 9 types of Project artifacts: 1. Strategy - Business Case, Vision statement, Project roadmap, Project charters 2.Logs & Register- Change registers, Change control 3.Plans- Scope management, Test Plans 4.Hierarchy charts- RACI Matrics 5.Baseline- Milestones,Performance Measures 6.Visual data information- Dashboards, Flow charts. 7.Reports- Risk Reports, Quality reports 8.Agreements and Contracts- AMCs, Supplier contracts 9.Miscelleneous As per DMAIC we have identified key Project artifacts as below phase wise: 1. Define Phase- •Project Charter •Project Scoping- Through SIPOC •Process flow diagram/Swim Lane Map •Financial Validation Sheet •VOC/CTQ/COQ/COPQ/KANO analysis sheet 2. Measure Phase: •Data Collection Plan •Stratification sheets •Base line data validation/Trend •Gap Assessment Vs Target •Data Analysis Sheet •Base Data Validations •Measurement System Analysis Plan and Results •Metrics' Tracker 3. Analyse Phase: •Cause - Effect Matrix •List of Quick Wins •Cause Prioritization Matrix •Statistical Test Plan and Results •Lean Waste Assessment •Kaizen Tracker Sheet •Co-relation/Multiple Regression /Result 4. Improve Phase: •Verification of Root cause •Action Plan preparation with milestones (TCD/SPOC) •FMEA/Change Control •Re-design of Severity and Detection Scale •Before / After Solution Statistical Validation (where data available) •Before / After Solution Physical/Photo Validation (where data not available) •Engineering study/Pilot result •Cost-Benefit analysis 5. Control Phase: •Poka-Yoke Assessment •Statistical Process Control •SPC charts •Control Plan/SOP Update/Training Plan •Communication •Learning •Renforcement •Updated Project Report- Benefits consolidation •Project and Presentation Deliverables Tracking Sheet

Student's T Distribution- 1. This distribution will have heavier tails and it's midpoint value is most likely 2. Criteria- Degree of freedom are equal to or greater than 30. 3. Application for ecometric data like exchange rates Logistic Distribution: 1. This distribution will have slightly longer tails than normal distribution , 2. Criteria- It is used when conditions and parameters are complex, It is used to describe growth 3. It is used to see growth of population over period of time, Analyse some critical chemical reactions etc. Binomial Distribution: 1. Criteria-This distribution is used when for each trials there are 2 possible outcomes, trials are independent with same probability(trial to trial) 2. It applies to describe no. of times an event occurs in a fixed number of trials 3.It is used in likelihood of success or failure in 10 flips of a coin

Autocorelation in regression is degree of same variables between two successive time intervals. It is also known as Serial co-relation. It helps to find repeatating periodic patterns like temp. on different days, Suction pressure at different tank levels, Process parameters at different environmental conditions etc. Generally Durbin-Watson statistics is used to test auto corelation, it ranges between -1 to 1 where 1 means strong positive co-relation, -1 means strong negative corelation and 0 means low level of auto corelation. It helps project leader to understand weather data collected is random or not . It also helps to understand the model fitness if data collection require more details and efforts.

1. Design Phase- Review As is condition , Discuss Desire outcome with Management- We can use VOC, SIPOC,COQ and COPQ analysis from define phase as Lean Six Sigma Tools. 2. Model- Identifying and defining future process(To be process) as representation to all stakeholders. We can use VSM,SWIM lane diagrams, PFDs can be uses 3. Execute- Pilot Execution and validation, open for all users, Engineering study plan, 4. Monitor- Establish KPIs for performance check, Various statistical tools like Statistical process control, Control Chart can be used as statistical tools 5. Optimize- Redesign the business processes and improve process efficiency. Six Sigma can be used first to improve process and after that we can use Business process management to automate and improving cross functional colaboration. We can deploy statistical analysis and disciplined approach of six sigma to resolve issues and BPM measures can be used to monitor performance of business processes . We can apply BPM as the methodology to link improvement and process design directly with management strategy.

Data Dredging is used for selective interference of intuition also known as data fishing or data paralysis. It is highly not recommended practice used by project leaders to prove their already known solution through randomly generating data through different tools in excel and Minitab. It increases the risk of false and misleading conclusion of problem and sometimes do not help in identifying sustainable solutions. There are high chances of incorporating Bias, Predetermined decision making if project leader do data dredging. Generally project leaders use this for proving co-relations between 2 or more variables , Proving a alternative hypothesis as correct one for ease of execution. Below are the ways to avoid data dredging : 1. Look for patterns in data, Compare it with standard distributions as per phenomena i.e Normal, triangle , Log normal, Uniform etc. 2. Devide data in random sub groups and check for distribution patters , it should follow the same distribution patterns as per parent data distributions 3. Do multiple hypothesis test through different statistical tests like Bonferroni corelation

Anova is nothing but Analysis of Variance, We use 1-Sample t-test and 2 sample t-test for comparing difference between 2 means , Comparing means of more than 2 samples ANOVA is used. However, ANOVA does not tell us which mean is better but it tells us that all the sample means are not equal. Based on ANOVA output shorlisted samples can be further tested. Essentially ANOVA tries to find out source of variation, ration of between variation and within variation. Minitab is the simplest method to understand Analysis of Variance based on P-value. There are 2 types of ANOVA one way and two way ANOVA. One way ANOVA is used for one factor benchmark and two way is used for two factor benchmark. ANOVA can be used in many industries like comparison of lead time between outlets, Plant to plant batch release time comparison, Comparison of efficiency across different set up for same factors etc. However certain Limitation restrict free usage of ANOVA for any condition like experimental Units must be homogeneous, it completely neglects local controls and factors, not suitable for field design experiments, Always required higher sample size, Interpretation of intersections are difficult. Kruskal-Wallis test and Median test can be used for comparison of more than 2 means.

Yamazumi Charts are basically stacked bar chart , These charts are very useful in identifying multiple stage wise impact on single category to identify the root cause. These charts help user to identify key pain area within process which require high attention.Generally Yamazumi charts are used to analyze cycle time of each process steps, Takt time calculation or access performance break up of macchine or Operator. It is useful in balancing work flow of the process and prioritize key area of bottleneck. It is also helpful in identifying Value adding activity and non-value adding activity in process flow.Yamazumi charts gives warning signal for to identify key waste to eliminate or reduce. It is generally useful in projects like changeover time reduction, SMED/ECRS activities, Lead time reduction , Station wise waste analysis for Yield related projects etc.