Avik chatterjee

A run chart is a line graph of data plotted over time. By collecting and charting data over time one can find trends or patterns in the process. They can show how the process is running however as they do not use control limits hence they cannot tell if a process is stable. There are four basic patterns of no randomness that a run chart can detect—mixture, cluster, oscillating, and trend patterns. A mixture is characterized by an absence of points near the center line: Clusters are groups of points in one area of the chart: Oscillation occurs when the data fluctuates up and down: A trend is a sustained drift in the data, either up or down: Advantage of run chart over control chart: · Easy to draft analyze and interpret. · Does not require much technical skill. · Straightforward representation of data.

RPA: Robotic process automation is intended to automate repetitive, low-level tasks by mimicking human actions and behavior. It may be suitable for mundane tasks that require little or no human interventions such as entering purchase invoices in an ERP system, setting up a new customer account and others. RPA is a preconfigured software that works on a predefined program to complete autonomous execution of a combination of processes, activities and tasks. AI: AI on the other hand is used for automating tasks in a much efficient manner. It is capable of delivering better results as it has the capability of rewriting themselves in response to their environment. It can make decisions and predictions either based on rules (as RPA) or based on numeric parameters achieved via machine learning. A key difference between AI & RPA is that the latter is used for running rule-based processes and requires structured inputs and AI is leveraged for unstructured inputs. Another key difference is that RPA software is trained exactly for the task, for example SAP’s Process Automation software. Key Differences: Training Method RPA: RPA is rule-based. It works on a set of instructions, or a set of rules and performing tasks accordingly. It uses a set of statements or steps to define a repetitive activity which it does by using algorithms to automate it. It is the systematic and organised inputs that RPA depends on to deliver desired results. As it is rule-based, it has no inbuilt intelligence and is prone to errors given its limited scope of intelligence. For instance, in a bill management system, if it can automatically record printed bills or automatically communicate customer issues, then RPA may be playing a key role. AI: AI is much more than just rules. It is data-hungry and requires a large amount of data to train. The data could be anything from customer information to images that helps machine understand the underlying concept. AI systems have the capability of learning over and over again using mathematical and statistical methods. Apart from ML, NLP is also used to deliver AI capabilities, which allows a machine to acquire an understanding of the human language. Process-Driven Vs Data-Driven RPA: It is highly process driven, meaning, it is all about automating repetitive and rule-based processes which typically requires interaction with multiple, disparate IT systems. Every activity needs to explicitly programmed, exposing it to the risk of delivering effective performance in many cases. Moreover, RPA cannot work on unstructured information. AI: AI, on the other hand, is all about good quality data. It would be unfair to say that AI is all about ‘thinking’ as opposed to RPA which is all about ‘doing’ a task. AI comes in handy when there is loads of unstructured data to deal with, and can easily manage variability in data to get better with time, based on its own experiences. For example, image recognition, text recognition or search are some of the most mature AI applications seen in businesses today. Learning and Thinking Capabilities RPA: Robots or machineries activated with RPA will do exactly what you ask them do and in the same way all over again, every time. AI: AI on the other hand is expected to perform a judgement based processing. This means that AI trained programs will act based on their learning from past data and trends. It can manage and understand patterns and trend over time. The bottom line is AI is an excellent self-learner and is good in capturing information such as vision recognition, sound recognition, search, data analysis and others. In short AI is where machines are trained to think like humans and possess the ability of rationalizing and take actions accordingly. The Human Involvement RPA: RPA is practically a software that reduces human efforts and compliments their work. As RPA mimics the steps followed by humans, it is often programmed to relieve human workforce from mundane activities, so they could focus on other important activities to accelerate business growth. Having said that, it often requires human intervention to keep the robotic processes constantly updated. AI: AI on the other hand has the capability of eliminating human effort to a significant extent. AI comes to a rescue when RPA fails and may not demand constant human intervention after the initial process of setting it up. Conclusion If we are looking to opt for either of the two processes, it is always wise to first analyse the nature of your process—decreasing turnaround time, saving cost, accelerating process, among others—and then decide on opting RPA or AI or a combination of both to achieve an extremely powerful result. Examples of difference between RPA and AI: A supplier sends the electronic invoices by email, you download the invoices into a folder, extract the relevant information from the invoices, and finally create the bills in your accounting software. In this scenario, RPA is suitable for automating the grunt work of retrieving emails (for simplicity, retrieval is based on the email’s subject), downloading the attachments (i.e. invoices) into a defined folder, and create the bills in the accounting software (mainly through copy and paste actions). On the other hand, AI is required to intelligently “read” the invoices, and extract the pertinent information such as invoice number, supplier name, invoice due date, product description, amounts due, and many more

Web analytics is the process of analyzing the behavior of visitors to a website. The use of web analytics helps in growth and CSAT enhancement for a business in more than one way : ü Determining the likelihood that a given customer will repurchase a product after having purchased it in the past ü Personalizing the site to customers who visit it repeatedly ü Monitoring the dollar volume of purchases made by individual customers or by specific groups of customers ü Observing the geographic regions from which the most and the least customers visit the site and purchase specific products ü Predicting which products customers are most and least likely to buy in the future In a nut shell web analytics enables a deep dive study of customers buying patterns and accordingly promoting specific products to those customers. This in turn can help in enhancing the ratio of revenue to marketing costs. Industries where web analytics can be used in a better manner in achieving the objectives of growth and CSAT Sports: Web analytics can be used to understand the patterns of viewership of different sport events in specific regions which enables TV channels to design program and attract revenue to advertisement. In addition the performance of individual players and teams by analysis. Major sporting events like Cricket world cup, FIFA world cup and Wimbledon can use of web analytics in a big way. Healthcare: Web analytics can be used to collect public health data for faster responses to individual health problems and identify the global spread of any new virus strains such as Ebola/ Zika. Health Ministries of different countries incorporate big data analytic tools to make proper use of data collected after Census and surveys. Transportation: Web analytics enables better route planning, traffic monitoring and management, and logistics. This is mainly incorporated by governments to avoid congestion of traffic in a single place. Insurance: Web analytics can be used from developing new products to handling claims through predictive analytics. Insurance companies use business big data to keep a track of the scheme of policy which is the most in demand and is generating the most revenue. Education: Web analytics can help in updating and upgrading prescribed literature for a variety of fields which are witnessing rapid changes. Universities across the world are using it to monitor and track the performance of their students and faculties and map the interest of students in different subjects via attendance.

Cp =1.33 and Cpk = 1.11 are the value which we derived from the above mentioned data. We can draw the following inferences about the process from this data: · Process is capable of producing with specification limits but the process average is off target · The process complies to a 4 sigma process capability · 99.73% of the products produced comply with the set customer standards · Cpk needs improvement to bring it at par with Cp · Cpk can be improved by examining the current process flowchart, removing areas of duplication / unnecessary steps, prioritize improvement and draw new control charts to evaluate how the effect of these changes

I will be going with the stance that innovation needs to be based on customer needs and exceptions. This has been further elaborated with a brief summary and few examples : Customer needs are highly dynamic in nature therefore for retaining customers it becomes imperative for every organization to keep coming up with new products and services. This also defines the future growth prospects in their respective area of business. Modern customers through their inherent trait of comparing , going by word of mouth and being connected 24/ 7 through various internet devices are in the best position for driving innovations and product development indirectly by sharing their real time quality experiences with the ultimate intent of getting more value for their spending. This in turn gives a good insight to organizations for making product decisions, investing in R&D , making new innovation and drive sales more prudently by making customers co-creators in new product innovation. Examples Of Customer Driven Innovation : A fast food organization through customer feedback (by appropriate means like interviews/ surveys ) can always improve their existing lines of offerings and introduce new products in the market as per customer preference. A toy manufacturing company can involve customers (children of various age groups) by asking them to come up with new toys design suggestion as per their imagination . This can be shared online on the company website with an option for voting on for them . The design with maximum votes and feasibility will be created and sold in the respective market. The creator can be rewarded handsomely in accordance with the company policy. This concept can certainly help in promoting innovation, creativity and entrepreneurship.