Rahul.Arora2

Let us try to understand through an example from procurement: - A multinational company has teams spread across US, Europe & India & these teams procure parts from suppliers. Every region has its own way of working i.e. US team manages purchase orders through excel & email, Europe team leverages an ERP system whereas the team in India use paper-based tracking & performs manual approvals. This results in an inconsistent process & can result in duplicate work as well as posing compliance risk. This is where AI plays a pivotal role in driving standardization. Below are some critical aspects: - 1. Process Mapping & Benchmarking - AI analyzes the procurement data for all the regions (PO cycle time, errors, supplier onboarding time) & identifies that the ERP model pertaining to Europe region is the most efficient one(20% faster & fewer errors) while the processing steps in India region has the most redundancy. 2. Global Standard Design – AI will suggest a standardized workflow by combining all the best practices from all the regions. 3. Automation – AI bots will automate the repetitive work like PO data entry, invoice matching & approval reminders etc. The same automated workflow will be leveraged by every region thus making the process uniform. 4. Compliance Monitoring – AI dashboards can track the adherence of the standard PO process & will raise a flag in case of any deviation from the standard process. Eg: Let’s say India team is doing any manual approval instead of system-based approval, it will flag a risk. 5. Continuous Learning – AI will analyze the supplier performance / cycle times over the course of time & suggests areas where the process can be streamlined. The above approaches will ensure: - 1. All global teams follow the common standardized procurement process. 2. Transparency, speed & improvement in compliance. 3. Local differences like tax rules, languages etc. adjusted automatically.

Special Cause Variation may exist in a process that appears to be in control i.e within upper & lower control limits & it may also be reflected in trends or behaviors that appear non-normal. There are various tests for finding potential special cause variation. Most of these tests divide the control region of the chart into 3 zones which are usually 1 sigma apart from each other. These zones are as shown below:- Now Let us explore some of the most common rules for testing special causes in control charts :- Western Electric rules for control charts : Western Electric rules are the original set of four control chart rules used to conduct stability analysis. These rules are used with all the control charts & are commonly known as WECO rules. These rules are as stated below :- 1 One point above UCL (Upper Control Limit) or below LCL (Lower Control Limit) 2 Two points above or below 2 sigma zone i.e. zone which is usually 2σ above or below mea 3 Four out of five points above or below 1 sigma i.e. zone which is usually 1σ above or below mean 4 Eight points in a row above or below the central mean line Below are the examples demonstrating the WECO rules:- Nelson rules for control charts : Nelson rules were developed in the 1950s & can be used with any control chart & they are the extension of the WECO rules. These rules are as stated below :- 1 One point above UCL (Upper Control Limit) or below LCL (Lower Control Limit) 2 Two points above or below 2 sigma zone i.e. zone which is usually 2σ above or below mean 3 Four out of five points above or below 1 sigma i.e. zone which is usually 1σ above or below mean 4 Eight points in a row above or below the central mean line 5 Six points in a row ascending or descending (trend) 6 Fifteen points in a row between -1σ & 1σ 7 Fourteen points in a row alternating up & down 8 Eight points in a row above 1σ or below -1σ Below are some of the examples showcasing some of the above rules :- Westgard rules for control charts : Westgard rules include the WECO rules-1, 2, 4 & rule-5 of the Nelson rules. These rules are used with levey jenning charts in laboratories. These rules are as stated below :- 1 One point above UCL (Upper Control Limit) or below LCL (Lower Control Limit) 2 Two points above or below 2 sigma zone i.e. zone which is usually 2σ above or below mean 4 Eight points in a row above or below the central mean line 5 A trend of seven points in a row increasing or decreasing 9 Four points above or below 1 sigma or two points in a row with one above & one below 2 sigma Here the rule-9 is unique & is not included in other control chart rule sets. Let us now summarize the differences of different rules :-

A universally accepted fact for a six sigma process is that it attains a process capability of 3.4 defects per million, however statistically a six sigma process translates to 2 defect per billion opportunities. So this 2 defect per billion becoming 3.4 defects per million is attributed to the 1.5 sigma shift. This 1.5 sigma shift is empirically determined by Motorola through years of data collection for processes & it was observed that the processes tend to vary & drift over a period of time, which they called as Long-Term Mean Variation, this variation typically falls between 1.4 & 1.6. Statistically speaking, 2 defects per billion opportunities correspond to six sigma & 3.4 defects per million opportunities correspond to 4.5 sigma, also the overall goal in long term is a near-zero defect process or a 4.5 sigma level. The variation due to changes in environmental conditions causes a shift in the process in the long run & this shift corresponds to 1.5 sigma. No matter how stable a process is, over an extended period of time, the environmental conditions change, which causes variation in the process. Thus at the initial onset, the process capability needs to be balanced by a compensating factor in order to account for the changes in order to ensure that the long term goal is met. Thus Short-Term Sigma Level (6 sigma) = Long-Term Sigma Level (4.5 sigma) + Compensation Factor (1.5 sigma) This overall phenomena of 1.5 sigma shift can be visualized as shown below:- Also after a process has been improved, we calculate the standard deviation & sigma value, however these are considered to be short-term values as the data only contains common cause variation, whereas in the long run a process can have both common cause as well as special cause variation. Since the short-term data does not contain the special cause variation, thus it has higher process capability than long-term & the 1.5 sigma shift attributes to this difference in the short-term & long-term process capabilities. Although the original work was done by Motorola led to the discovery of the 1.5 sigma shift, however lean six practitioners have concluded that the size of the shift depends on the industry & type of process being studied, although the general concept that processes drift over time & the short-term capability needs to be better than the long-term capability remains valid everywhere.

TRIZ is a Russian acronym for the Theory of Inventive Problem Solving. There are certain universal principles of creativity that form the basis for innovation. TRIZ identifies & codifies these principles & uses them to make the creative process more predictable. In simpler words, whatever problem than an individual or a team is solving, somebody, somewhere has already solved it. Thus inventive problem solving involves finding that solution & adapting it to the problem in hand. There are two central concepts that form an integral part of TRIZ i.e. generalizing problems & solutions, and eliminating contradictions. The first concept can be explained as shown below:- Here, a specific problem is taken & is generalized to one of the TRIZ general problems. From the TRIZ general problems, you identify the general TRIZ solution that is required, & then one considers how to apply the same to the specific problem. TRIZ is basically a collection of 40 principles & 76 standard solutions which can be leveraged to solve any kind of problem. The other concept which talks about eliminating contradictions & it explains the fact that there are contradictions at the root of most of the problems, thus it is important to eliminate these contradictions in order to effectively solve the problem. TRIZ has two main categories of contradictions i.e. Technical Contradictions & Physical Contradictions. Let us understand both of these categories:- Technical Contradictions : These are the classical engineering trade-offs, where you can’t reach the desired state because something else in the system prevents it i.e. when something gets better, something else automatically gets worse. Some of the examples can be:- The product gets stronger, but the weight increases. Service is customized to each customer, but the service delivery system gets complicated. Training is comprehensive, but keeps employees away from their BAU. The key technical contradictions are summarized in the TRIZ Contradiction Matrix which is a matrix that is organized in the form of 39 improving parameters & 39 worsening parameters with each cell entry giving the most used inventive principles that may be used to eliminate the contradiction. The contradiction matrix is leveraged using a four step process:- Use the 39 parameters to identify the critical features in the problem. Identify the contradictions between the parameters where one causes problems with other. identify the principles that can be used to resolve the contradictions. Use the numbers from the matrix to look up the resolution principles & use these principles to find solutions to the problem. Below is an excerpt of the contradiction matrix:- Physical Contradictions : These are the situations in which an object or system suffers contradictory, opposite requirements. Some of the examples are:- Software should be complex i.e. have many features, but simple i.e. easy to learn. Coffee should be hot to be enjoyed, but also cool so as to avoid burning the tongue of the drinker. An umbrella should be large to keep the rain off, but small so as to be easily moved in the crowd. Physical contradictions are solved with the TRIZ Separation Principles, these separation principles are as explained below:- Separation in Time : Changing the property, response or behavior vs time. Here the concept is to separate the opposite requirements in time. Here one can try to schedule the system operation in such a way that requirements, functions that contradict each other take effect at different times. One classical example can be traffic lights that are used to sequence the flow of traffic at different points of time. Separation in Space : Changing the property, response or behavior based on special location. Here the objective is to separate requirements in space. Here try to partition the system into sub-systems & then assign each contradictory function or condition to a different sub-system. One common example is bifocal lenses for eyes where you have sections for far vision & near vision at separate locations within the same lens. Separation between Part & Whole : Changing the property so as to make it different in the sub-system/system/super-system. Here the concept is to separate the opposite requirements within a whole object or its parts. Here we try to partition the system & assign one of the contradictory functions to a sub-system or several sub-systems. One common example can be a bicycle chain which has rigid links but is flexible at the system level. Separation between Conditions : Changing the property, response or behavior on condition. Here the concept of separating opposing requirements of a condition can resolve contradictions in which a helpful process takes place when special conditions exist. Consider changing the system or the environment so that only the helpful process can take place. One common example can be Ice Skates where ice which is initially solid but when ice skating, the ice below the skates melts for a fraction of a second, therefor enabling the skaters to slide. When deciding which separation principle to use, 40 inventive principles can be used as guidelines to implement solution. Below is an excerpt of some of these inventive principles:- An interesting thing to note that can never be a situation when you have a physical or a technical contradictions only. Both are two different but interrelated views of the same problem & thus can’t exist separately. Below visual will help us to understand the above statement. Let us now see this conversion through an example:- Let us first define the negative effect of a problem for eg: Long travel time, the cause for this problem is that the car stops at a traffic light, the positive effect of this problem is that it will avoid collision with other cars. Thus the technical contradiction in this will be "Long travel time vs Avoiding collision with other cars”. Thus in this case we have specified both technical contradiction & one side of the physical contradiction (i.e. the cause).

Effect size indicates the practical significance of a research outcome. it tells you how meaningful the relationship between two variables or the difference between groups is. A large effect size means that a research finding has practical significance. While statistical significance shows that an effect exists in a study, practical significance shows the effect is large enough to be meaningful in the real world. Statistical significance is denoted by p-value, whereas the practical significance is represented by effect size. Statistical significance alone can be misleading as it is influenced by sample size i.e. increasing the sample size will always make it more likely to find a statistical significant effect, no matter how small the effect truly is in the real world. In contrast to this, effect size is independent of the sample size which makes it relevant to showcase in order to represent the practical significance of a finding. Let us understand the difference in statistical & practical significance through an example:- In a study, we are comparing two weight loss methods with 13000 subjects each in two groups. One group let’s say uses method I of weight loss & the other group uses method II of weight loss. Now basis the results, the mean weight loss in Kg for one group is 10.6 kg with standard deviation of 6.7 kg, which is marginally higher compared to the mean weight loss in Kg for the other group which is 10.5 kg with a standard deviation of 6.8 kg. Statistically these results are significant at p=0.01, however a difference of only 0.1 kg between the groups is negligible & doesn’t really tell you that which of the weight loss method is more effective. Here adding a measure of practical significance can showcase the differences in the two methods. There are various measures of effect size. Let us see some of the common ones:- Cohen’s d : Cohen’s d is designed for comparing two groups, it basically takes the difference between two means & expresses them in standard deviation units. It shows how many standard deviation lie between two means. Cohen’s d is calculated with the below formula:- d = (x̅1 - x̅2) / s where x1-bar is the mean of one group, x2-bar is the mean of the other group & s is the standard deviation. In general, greater the value of cohen’s d, the larger the effect size. Considering the above weight loss example, let us calculate cohen’s d for both the groups:- d = (10.6 - 10.5) / 6.8 = 0.015, now with this value of cohen’s d, there’s limited to no practical significance that one group findings are more effective than the other group’s findings. Pearson’s r : It is also known as the correlation coefficient & it measures the extent of a linear relationship between two variables. The main premise is to compute how much of the variability of one variable is determined by the variability of the other variable. A value of pearson’s r closer to -1 or +1 indicates a larger effect size. Below is the representation of the magnitude of the effect size in terms of both Cohen’d d as well as Pearson’s r methods:- Effect Size : Small, Cohen’s d : 0.2, Pearson’s r : +/- 0.1 to 0.3 Effect Size : Medium, Cohen’s d : 0.5, Pearson’s r : +/- 0.3 to 0.5 Effect Size : Large, Cohen’s d : >=0.8, Pearson’s r : >= 0.5 or <= -0.5 It is always helpful to calculate effect size before commencing any study & post data collection completion. The reason behind this statement is that within an expected effect size, one can figure out the minimum sample size required in order to have enough statistical power to detect an effect of that magnitude. If we don’t ensure enough power in a study, we may not be able to detect a statistically significant result even though it has practical significance, thus it is helpful to perform a power analysis, so that one can use a set effect size & significance level to determine the required sample size.Once data is collected, one can calculate & report the actual effect size.

The different proficiency levels of six sigma explains about who can perform what role & what needs to be dealt with in a project. At each level there is an underlying difference in terms of skills & knowledge (both business & technical) that an individual can have in order to undertake a project. There are primarily six levels of proficiency in Six Sigma parlance. Each of the level is as explained below:- Six Sigma White Belt : Professionals who are considered six sigma white belts have undergone a formal session with an overview of the relevant methods & tools. This extends to all the workforce of an organization. White belts usually work on local problem solving teams that support six sigma projects. For eg: a white belt can participate in a problem solving to identify the potential Xs for the project Y, assist the green belts & yellow belts in data collection, also support in solution deployment in their respective areas. However in some cases this level is not being formalized & many organizations start from yellow belt onwards. Six Sigma Yellow Belt : A yellow belt professional have exposure to six sigma concepts that goes beyond the fundamentals provided for a white belt. They have much deeper knowledge when it comes to leveraging problem various solving tools & they are assigned to a project as fully contributing team members. For eg: A yellow belt can lead smaller incremental improvement projects like lean A3 or Just do-it kind of stuff. They also support the higher belts in their projects, they can lead & support green belts & black belts in various facets of a project like performing fishbone analysis to identify potential Xs, leading the data collection exercise as well as piloting solution i.e. leading pilot experiments etc. Six Sigma Green Belt : A green belt professional either leads low complexity projects(full time) related to improving their business process or support the black belts in high complexity projects (usually 20-50% of their bandwidth). While yellow belts have a good understanding of the various problem solving tools, green belts have a comprehensive understanding of six sigma methodology & its tools which also includes various statistical methods for validating the potential Xs in terms of its impact on project Y. As an example in a black belt level project, green belts can help the project team members (who are generally yellow belts or white belts) collect & organize data for a project. Six Sigma Black Belt : A black belt professional typically works full-time basis in driving high complexity improvement projects. they are the main project & team leaders & guide other employees towards bringing value to the process. They are the first to mentor other belts in their respective projects. They perform training sessions, discussions or other forms of mentorship. In a mature organization you will see, several green belts working under a black belt. As an example for an improvement project that is lead by a green belt, black belt mentors & guides the green belt in executing the project. Black belts possesses knowledge on advanced statistical methods in addition to the problem solving tools & are also actively involved in change management pertaining to the high complexity cross functional projects that they are driving. Six Sigma Master Black Belt : This is the highest level of proficiency in Six Sigma parlance. Master black belts have the most thorough knowledge, comprehensive understanding of the methodology. They work hand in hand with the leadership of an organization & report to them the status of the improvement projects being run. They work at a strategic level & play an important role in identifying potential improvement opportunities that are relevant to the business goals. They are also the evangelist of Six sigma, spreading awareness on six sigma methodology throughout the organization by developing black belts & green belts through training & up-skilling. Also they serve as a mentor to black belts for their projects. Master Black belts have a broader view of strategy throughout a business, coordinating teams across verticals. Six Sigma Champion : Six sigma champion is the individual who translates the mission, vision & values into a six sigma deployment strategy that supports the business goals. As an example, a six sigma champion works closely with master black belt in identifying potential projects & also identifies the resourcing needs & removes roadblocks during the course of the projects. They are generally someone higher up in the organization like Vice President or Director.

The most critical aspect of a high-performing team is the continuous flow of value to its customers which can be in the form of a product or a service. Anything which impedes this flow of value is an impediment, it is crucial as a scrum master to help teams visualize impediments, resolve issues that are within team’s control & advocate on team’s behalf to remove obstacles in their path. An impediment is anything that prevents the team from doing their work, slows progress, or delays delivery of value. If a team’s goal is to optimize for the fast flow of value to its customers (measurable value), an impediment is anything that gets in a way of the team achieving that goal. The three common types of impediments & how as a scrum master one should handle them are as explained below:- Skill or Capability Gap : These are the impediments that arise due to skill gap that exists amongst team members & this can lead to uneven work distribution, which leads leads to burnout of frustration amongst skilled team members. As a scrum master, one can persuade the leadership in order to help secure more resources or support the team in training so as to build new skills. Process or System Issue : These kind of impediments are primarily due to the inefficiencies in the underlying delivery process or systems that are involved in delivery of value. These inefficiencies are the non value added things that are a part of the legacy processes or systems. As a scrum master, it is of paramount importance to reduce the unnecessary bureaucracy or gains approval to change process. Behavioral Issue : This is one of the most common impediments as it is important for a team to exhibit a certain set of behaviors that will ultimately reflect upon their ability to deliver value to its customers, these issues primarily gives rise to conflicts within the team & can impact big time the entire value delivery chain. As a scrum master, it is important to address any conflict proactively & also provide the necessary feedback in order to coach the desired behaviours Impediments must be reviewed on a regular cadence & must be owned at appropriate levels for eg: an impediment that can be resolved within the team, should be owned by the team. Management of impediments requires careful consideration of whether the impediment is resolvable within team’s control, or whether it requires support from leadership or support external to the team. As a scrum master while escalating impediments for leadership support, one must consider four key things:- Try to resolve the impediment at your level i.e. by having a conversation with another team & asking for their support. Make sure that there is a crisp description of the impediment, including its implications. Provide options for resolution, including a recommended option. Indicate priority / severity of the impediment. It is also very important to have an impediment backlog, as by creating a visible, transparent & an actionable impediment backlog, everyone will have a visibility into a list of prioritized problems & issues that the team is experiencing. The team can then be coached & supported as to remove these blockers to value delivery or get support from the leadership as well. Everyone in the team shares responsibility for identifying impediments & most importantly their root cause. It is vital as a team to continually identify new impediments.

A Treemap is a common visualization tool to represent hierarchical data structures. It displays hierarchical data as a set of nested triangles. Each group is represented as a rectangle where area of each rectangle is proportional to its value i.e. higher the value bigger the area of the rectangle. Several dimensions or groups/sub-groups are differentiated through color schemes. Treemaps are used to show two types of information simultaneously:- How the whole is divided i.e. for each level of the hierarchy, it is easy to understand which entity is the most important & how the whole is distributed among entities. How the hierarchy is organized, it make efficient use of space, which makes them useful for representing big amount of data. Let us see a common example of a treemap describing the world population of different countries. Here the world is divided into continents which constitute a group & these continents are further divided into regions which constitute sub-group & these regions are divided into countries. In the tree structure, the countries are considered as leaves i.e. they are at the end of the branches. The treemap for this example is as shown below:- The above treemap represents each node of the hierarchical structure as a rectangle. Each rectangle area is proportional to its value which is the population. We can easily figure out from the treemap that regions of southern & eastern asia are the most densely populated part of the asia continent. In order to showcase the above data more efficiently, we can create an interactive treemap, as it is advised to have not more than 2 or 3 levels of the hierarchy to display in order to avoid cluttering the treemap. Thus the above example of world population can be showcased via an interactive treemap in which the starting level will be the continents i.e. Asia, Americas, Europe, Africa & Oceania, one can then click on any of the continent in order to see further categorization of each continents into regions & then click on any region to see the different countries comprising that region. The same can be visualized as shown below:- Another common example of treemap is to visualize the sales of a company broken down basis the regions i.e. Central, West, South & East, along with the customer segment i.e. Corporate, Consumer, Small Business, Home Office. Here each of the rectangles represents sales for a particular customer segment pertaining to a particular region. The rectangles having a bigger area shows high sales for that particular customer segment for a region. The treemap can be visualized as shown below:- From the above treemap, one can easily decipher that the corporate sales for central region is the highest following by the corporate sales for West region. Thus with the treemap, one can easily drill down into multi level sets of information in order to derive meaningful insights & then drive appropriate actions.

At the simplest level, projects are about a set of activities or tasks that must be completed in some defined sequence. There are a defined set of relationships that exist between the start & end points of the activities. These are Finish to Start, Start to Start, Finish to Finish & Start to Finish. Let us explore these four kinds of relationships along with relevant examples. Finish to Start : Finish to Start is a logical relationship in which a Successor Activity cannot start until a Predecessor Activity is finished i.e. the starting event of a Successor Activity is dependent on the finishing event of a Predecessor Activity. Here the predecessor activity must be fully complete before any successor activity has begun. It is the most common relationship amongst all four relationships. The Finish to Start relationship can be represented on the Gantt Chart & Project Network Diagram as shown below with A as the predecessor activity taking 3 days & B as the successor activity taking 1 day:- Let us take the example of Setting up a router to showcase the Finish to Start relationship. In this case, one must finish deciding where to install the router before one can start the next step i.e. plugging in the router. Another scenario can be configuring a wireless router gateway, before starting to connect it to the router. Start to Start : Finish to Start is a logical relationship in which a Successor Activity cannot start until its Predecessor Activity has started i.e. the starting event of the Successor Activity is dependent on the starting event of the Predecessor Activity. The Start to Start relationship can be represented on the Gantt Chart & Project Network Diagram with Activity A having duration 3 days, Activity B having duration of 1 day as shown below:- Let us again take the same example of router set up with some tweaks. Let’s say that we have multiple, rack-mounted routers & we need to slot them in & then connect them to the internet. Here slotting in the routers is the predecessor activity & connecting them to the internet is a successor activity. Here as soon as we start to slot the routers into the rack, we can also start to make the internet connections. Thus we don’t need to start to make the internet connections immediately, but the first one must have begun ti plug the routers into their rack slot. Finish to Finish : Finish to Finish is a logical relationship in which a Successor Activity cannot finish until its Predecessor Activity has finished i.e. the finishing event of a Successor Activity is dependent on the finishing event of a Predecessor Activity. This relationship exists where two or more activities can only be considered completed when both are completed. The Finish to Finish relationship can be represented on the Gantt Chart & Project Network Diagram with Activity A having duration 3 days, Activity B having duration of 1 day as shown below:- Let us again take the router example, now we are bringing a new server online. You need to load & configure the server operating system & you must also connect the sever to the router. Here configuration of the server operating system cannot be considered complete until the server is connected through the router to the network. Here in this case both activities can begin independently, however to ensure a fully functioning server on the network, both activities need to be completed. Start to Finish : Start to Finish is a logical relationship in which a successor activity cannot finish until its predecessor activity has started i.e. in which the finishing event of the successor activity is dependent on the starting event of a predecessor activity. This kind of relationship is rarely found in real life projects, as soon as the predecessor activity starts, the successor activity will finish. The Start to Finish relationship can be represented on the Gantt Chart & Project Network Diagram with Activity A having duration 3 days, Activity B having duration of 1 day as shown below:- The best example to explain this kind of relationship is let’s say, we are installing a new router to replace the old one. One can’t finish disconnecting the old router until the new router is up & running i.e. starting the go-live activity for the new router is the trigger to finish the disconnect activity for the old one.

A Tornado Diagram is basically a two-sided bar chart where there are two data bars that are opposite to each other. It is a special type of bar chart where data is sorted vertically from highest to lowest & due to this sorting the shape of the chart resembles a tornado, hence it is named accordingly. It is a useful tool for decision making by comparison as one can compare two different items or a single item for the different periods. Let us see an example below where we are comparing the sales made by two different stores for each product category. Tornado chart is commonly leveraged in performing Sensitivity Analysis where it is used to depict the sensitivity of an output as a result of the changes in selected variables. In other words, it shows the effect on the output of varying each input variable at a time while keeping all other input variables at their initial values. Generally a low & a high value for each input is selected & the result is then displayed on a tornado chart, where the bars of each input variable showcases the variation from its nominal or initial value. The bars having the highest variation are placed on the top & rest of the bars are arranged in a descending order of the magnitude of the variation. Let us understand with the help of an example. Below is a tornado chart displaying the impact of variation of different parameters on the reliability of the material. From the chart, it can be clearly seen that the strength of the material & Coil diameter have the highest variation, thus both the parameters greatly influence the reliability of the material. Hence in order to improve the reliability, one has to focus on reducing variation of these parameters. Tornado chart also has its applications in the field of project management where it is leveraged to perform risk analysis i.e. it is used to display the magnitude of each risk in order to identify those risks that can impact the cost, schedule or both of a project. Here the biggest risk is shown on the top of the chart which is having the biggest spread & this risk requires the most attention. Let us see below, how the tornado diagram is leveraged to perform risk analysis:- From the above diagram, it is clear that the Risk 1 will have a significant impact when it comes to the overall project cost, thus appropriate mitigation needs to be put into place.

Inventory Turns / Inventory Turnover Ratio is a financial ratio that represents the number of times an organization turned over its inventory with respect to its cost of goods sold (COGS) in a given time period (typically a fiscal year). It is calculated basis the given formula:- Inventory Turnover Ratio = COGS / Average Inventory Where Average Inventory = (Inventory Balance at the year beginning + Inventory Balance at the year end) / 2 Let us see how this is calculated with the help of an example:- XYZ Company has cost of goods sold amounting to $5M for the current fiscal year. Its inventory balance at the beginning of the fiscal year was $600,000 & at the fiscal year end amounts to $500,000. Here let us first calculate the Average Inventory = ($600,000 + $400,000) / 2 = $500,000 Now let us calculate the Inventory Turnover Ratio = $5,000,000 / $500,000 = 10, thus the value of 10 denotes that the inventory turns are 10 times for the fiscal year i.e. 10 times in a year an inventory has been converted into sales by XYZ Company. Let us now see how does the Inventory Turnover Ratio aids in business growth:- A high inventory turnover ratio is a good sign for an organization as this means that the goods are generally sold at a faster rate & a lower ratio indicates excess inventory in the ecosystem which leads to additional inventory handling & carrying cost burden for an organization. Its is a useful measure for organizations to gauge their operational efficiency by comparison of their ratios with the industry benchmark. A low inventory turns ration is also a sign of weak sales as it can indicate problem with the overall sales & marketing strategy of an organization & will channelize an organization’s effort to improve upon the same. It also signals towards the rate at which the demand is generated, thus aiding in decisions pertaining to regulating the output generation for an organization. We can also leverage the Inventory Turns Ratio to calculate the Days Sales of Inventory (DSI), which gives an idea of how long it takes for an organization to turn its inventory into sales. This is basically calculated as shown below:- Days Sales of Inventory = (Average Inventory / Cost of Goods Sold) x 365 (i.e. no of days in a fiscal year). Let us now calculate the Days Sales of Inventory for the previous example of XYZ Company:- DSI = ($500,000 / $5,000,000) x 365 = 36.5 ~ 37, which means that for XYZ Company approximately 37 days is the number of days for which the inventory is there in their system before being sold.

A Waterfall Chart is basically a variation of a bar graph that shows how an initial value changes due to other factors over a period of time. It is also known as a Waterfall Graph or a Bridge Chart particularly in finance parlance. Its purpose is to show a before & after picture of your data, it depicts each step in the journey & shows which factors increases or decreases the progression. Its was made popular by McKinsey & Company, thus many people consider it to be a financial charting tool, however it has its applications in other areas as well. Let us see below a common example of a waterfall chart being leveraged in financial sector in order to study the effect of various revenue streams on the overall profit of an organization. Waterfall chart has its application in business excellence as well, especially while driving improvement projects. Let us see some examples in order to further explore this aspect:- In this example, the waterfall chart is leveraged to showcase the roadmap of achieving the targeted goal broken down in terms of various solution levers. Here we will start off with the initial baseline of the project metric & then showcase each solution lever along with its projected impact on the metric & finally arriving at the targeted value of the project metric. This example showcases the application of waterfall chart at a program level in order to showcase the 5 year projection journey of the program metric by showcasing the projected reduction of the program metric over a period of 5 years. Let us have a look at one more example where we are showcasing the impact of various factor X’s on the output Y of a regression model. Here we are starting with mapping the intercept coefficient value & then also showing the coefficient values of all the relevant X’s & finally arriving at the projected Y.

Below are the key differentiating aspects amongst the three experimental approaches:- Differentiating Aspect Trial & Error One Factor at a Time (OFAAT) Multiple Factors at a time (Factorial Design) Concept Subject matter experts hypothesize the critical independent factors that will create a desired outcome or response. The experiments are done with these factors. If the experiments are not successful, another set of factors is selected & the experiments are repeated on the another set of factors until the hypothesized factors are not validated to impact the outcome The experiments are designed in such a way that one factor is varied throughout its normal range while other factors are kept constant. The factor is set at the optimal setting & the next factor is selected & varied throughout its normal range in order to determine the optimal setting. The process continues until all the factors are tested by varying one factor while keeping all other factors constant This experimental approach consists of two or more factors each with discrete possible values known as levels, the experimentation takes into account all possible combination of these levels of all the factors involved, the experiments are conducted by taking different levels of the factors simultaneously Suitability Appropriate where there is a specific goal or response that is desired from the dependent factors & there are subject matter experts who can confidently select the appropriate independent factors for conducting the experiment Best suited for basic research projects for testing new technologies or inventions. This allows the researchers to define the relationships between the factors & the system performance Often used to create a statistically valid equation for the system performance based upon the input values of the selected factors being studied. It determines the optimal level of performance basis the multiple level factors that are used Advantages This approach is the fastest & lowest cost experimental design approach. By leveraging the expertise of the subject matter experts & focusing the experiments on a specific goal, the number of tests can be held to a minimum The OFAAT methodology is very efficient when it comes to characterizing how the selected factors impact the system performance i.e. product, service or process. By varying each factor at a time through its normal range, one can efficiently study the magnitude of impact of each factor on the output & this will aid in better decision making in terms of factor selection for further optimization This is the most comprehensive approach of experimental design as we can easily perform a comprehensive analysis of the design space for the system being analyzed. The final result of all the experimentation is an equation that can be leveraged to predict performance & this equation camn be used to identify the factor settings that will yield optimal performance, thus making this approach a go-to tool for prescriptive analytics Limitations This approach is highly dependent upon the knowledge & experience of the subject matter experts. Also this is a difficult methodology to estimate, this is because if the estimates of the factors during experiment turns out to be not true, then additional unplanned tests are needed & this can create massive delays & overruns This approach works best if the impact of the factor is linear, however if the effect is non-linear or curvi-linear then the order of factors can impact the final setting & performance. Also this approach only studies the effect of one factor on the outcome however does not take into consideration the intercation effects or two or more factors simultaneously on the overall outcome All the tests basis the possible combination of levels of all the factors must be conducted in order for the statistics to be valid, thus making this approach both time & resource intensive In cases where exploration of the potential factors that can impact the outcome needs to be done, trial & error approach will be more beneficial compared to OFAAT or Multiple Factors at a time. Here we can conduct preliminary experiments to check whether the selected factors are having a significant impact on the outcome or not. If not we can further identify another set of factors in order to validate their impact on the outcome. From a cost, time & resource perspective also, we want to make sure that the factors that are being considered for study have at-least gone through a preliminary validation & now we are sure that the factors being studied through OFAAT or Multiple factors at a time approach are being identified correctly. In such scenarios, trial &b error approach will play a very important role.

The MAGIC criteria was put forth by Robert Abelson in his book “Statistics as Principled Argument” & is leveraged for making persuasive statistical argument. The five letters in the MAGIC criteria are as explained below with analogy to DMAIC :- The M in the acronym stands for Magnitude i.e. How big is the effect? - here we can tell how big an effect is through various measures of the effect size. It tells that big effects are impressive, small effects are not. Let’s take the scenario in the Improve phase of two DMAIC projects working on reducing the Vendor Payment Cycle Time of processes spread across two different locations of a company, now the first project has yielded a 40% cycle time reduction while the second one has yielded 10% reduction, thus clearly the magnitude of effect of the first project on the cycle time is much more substantial than the second project. The A in the acronym stands for Articulation i.e. How precise stated it is? - it is measured in form of ticks & buts. A tick is a statement while but is an exception. The more precise the statement is i.e. more ticks the precise the statement is. From a DMAIC parlance this is analogous to the Define phase, where we are stating the problem statement. Here we are leveraging 4W 1H in order to have a precise problem statement framed in terms of What is the problem, Where it has occurred, When it has occurred, Who is impacted by this problem & How much is the magnitude of the problem. All these aspects helps us in creating a precise problem statement which becomes the basis for creating the goal statement so that everyone in the project team is calibrated. The G in the acronym stands for Generality i.e. How widely does it apply? - it states that how broadly the empirical conclusion can be generalized, in other words it refers to how general an effect is?, this can be very general or can be very specific. Usually more general effects are of greater value than more specific ones. It we strike an analogy with DMAIC, this would refer to working out the scope of an improvement project. Too broad of a scope makes the project more impactful, although it adds to the complexity of the project as well & too narrow a scope makes the project less impactful. The I in the acronym stands for Interesting i.e. How interesting it is? - it basically identifies the potential of an empirical finding to change people’s beliefs. This is analogous to the Analyze phase of a DMAIC project where we are leveraging hypothesis to identify which of the potential causes are significantly impacting the effect or the problem being focussed upon. The significant causes are then only considered for further root cause analysis & rest are ignored as they fail to prove that they have a significant impact on the problem. The C in the acronym stands for Credibility i.e. How believable it is? - this means that the research method should be sound & disciplined, in other words, the more hard a result is to believe, the more stringent you have to be about the evidence supporting it. From a DMAIC perspective, this makes sense while planning for hypothesis tests, where it is of paramount importance that you precisely define your hypothesis & also calculate a statistically significant sample size considered both the type-1, type-2 errors & power of the test. This will ensure that the test undertaken will be effective in delivering accurate results.

Let us understand the difference between RPA & IPA based on certain criteria along with relevant examples :- Execution Approach : Robotic Process Automation (RPA), refers to a set of automated tools that help businesses to automate processes by mimicking human actions on computers, with little or no assistance. RPA tools are designed specifically for automating mundane, repetitive tasks that follows defined business rules & requires mimimal decision making. Eg : Bank reconciliation where the transactions recorded in general ledger are matched with bank statements. In this case the RPA bot logs into bank portal as well as the oracle in order to fetch the general ledger as well as bank statements, performs reconciliation of each transaction based on a business rule. Intelligent Process Automation (IPA), on the other hand augments the traditional rule-based automation with decision-making capabilities in order to deliver results with utmost efficiency. It is combination of RPA & AI technologies, which handle more complex processes rather than just routine rule-based tasks. Eg : In an accounts payable process where we are getting scanned invoices in different formats from different vendors, here we will not be able to get the information in a standardized format, here RPA will not be able to decipher information from those invoices & requires OCR technology to extract required information from invoices & then using RPA will copy paste that extracted information into the ERP. Automation Development Complexity : RPA helps automate high volume rule-based processes & does not require any prior coding knowledge in order to build the automation. Most platforms such as UiPath & Blue-Prism provide flowchart based designs which makes it easy to automate business processes. Nowadays platforms like Uiath provide StudioX where a business owner can build automation logics himself/herself in order to automate its simple routine tasks. IPA requires coding knowledge & require expert IT professionals to build the automation logic as it includes scenarios like API automation in order to integrate applications or classification of invoices involves a complex logic, which requires knowledge of model building & these tasks only be performed by expert developers having expertise in such areas. Scalability : RPA bots can easily be monitored & scaled up or down quickly basis the changes in work volume since we are here dealing with repetitive, rule-based tasks. Thus with changing work demand, RPA bots can be easily & quickly deployed at minimal costs without any compromise in terms of quality of work. Eg: RPA bots can be easily monitored & scaled by leveraging Uipath Orchestrator which will ensure their efficient utilization with minimal to no human intervention. IPA on the other hand, requires considerable effort when it comes to scaling the technology as the work involved is of higher complexity & this results in higher deployment costs as well. However coupled with RPA can be scaled up or down to a lesser degree of effort as it will be implemented to augment the effort of RPA bots. Underlying Technology : RPA thrives on common technologies like Screen Scrapping, Workflow Automation which supports in automating back end business processes with more accuracy compared to when they were manually executed. IPA brings & supplements RPA with newer technologies such as Natural Language Processing (NLP), Machine Learning (ML), Data Extraction (OCR) in order to supplement the existing capabilities of RPA so as to perform more complex tasks.