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  3. All, An 'As is' VSM of transaction process of ABC Inc. Also includes - Manpower requirement calculation for 75 calls/ day and 125 calls/day - There are 2 scenarios of different % of escalations to L2 level. Lets build a Future VSM by identifying improvement projects. Regards, Pitchai Trans.Process VSM ABC Inc- Pitchai Aug20.xlsx
  4. Both the published answers have been selected as the best answer to this question. Also review the answer provided by Mr Venugopal R, Benchmark Six Sigma's in-house expert.
  5. Q 285. What is an "Andon"? Describe some varied applications of Andon Systems. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
  6. Queueing Theory Many of us have encountered the frustration of having to await in line. Unfortunately, this experience remains to be popular in crowded, urbanized, “high-tech” environment. We remain in line in our automobiles in traffic jams; we await on-hold for an executive to pick up our phone calls; we wait in line at fast-food joints and we wait in line at outlets to check out. We, as consumers, hardly like these delays, and the organizers of the establishments at which we await also condemn us to wait, since it may yield them business. Why is there awaiting? The claim is straightforward: There is higher need for service than there is a resource for service possible. Why is this so? There may be many reasons; for example, there may be a deficit of servers, it may be infeasible economically for a business to furnish the level of service necessary to limit awaiting, or there may be a space limit to the amount of service that can be provided. We can take these limitations out with the amount of finance, and to know how much service it should then make available, one would need to know claims to such challenges as “How long must a consumer wait? and “How many of us will form within the line?” Queueing theory seeks to clarify these queries through comprehensive analytical analysis. Characteristics of Queueing Systems A quantitative interpretation of a queueing system involves an analytical model of the elemental processes. Most times, six primary characteristics give an acceptable description of the process: Arrival pattern of customers Service pattern of servers Number of servers and service channels System capacity Queue discipline Number of service stages Problems in a Queueing System The ultimate aim of the analysis of queueing systems is to understand the behavior of their underlying processes so we can make informed and intelligent decisions in their management. We can identify three types of problems in this process. Behavioral Problems Statistical Problems Decision Problems Queue discipline / Service disciplines Queue discipline refers to the manner in which it selects customers for service when a queue has formed. (1) First-come, first-served (FCFS) or First in first out (FIFO) Most popular is the first-come, first-served FCFS rule; which is static because no information other than position in line is used to identify the next customer for service. So it serves the customers in the order they arrive. (2) Last come, first served (LCFS) or Last in first out (FIFO) Last come, first served (LCFS), applies too in many inventory systems, because it is less complicated to achieve the closest items which are the last in. The last customers are going to be served first. Goods inside a van usually arranged specified the primary item enter the truck are going to be delivered last. Stacks of pancakes are eaten from the last item on the highest. (3) Processor Sharing (PS) Processor Sharing (PS) within which the server processes all customers (or jobs) simultaneously but works at a slower rate on each job supported the quantity within the system (this is typical in computer systems). Processor sharing discipline is incredibly popular in computers, communication systems and networks. Queueing systems with processor sharing system represent the acceptable models for sharing the resources, e.g., peripherals of a computer or a bandwidth of delivery systems. (4) Priority They will serve particular types of selected consumers early. Business class passengers will join the aircraft early before the economy class. Patients with extreme cases will be served first in the emergency room ahead of ordinary sickness. (5) Shortest job first (SJF) The scheme implements a shortest job first (SJF) in the queue. Shortest Job First (SJF) is also a datum in which we prefer the refining of carrying the smallest execution time for the next execution. This scheduling method can be preemptive or non-preemptive. It significantly reduces the average awaiting time for diverse processes awaiting execution. (6) Preemptive shortest job first (PSJF) In Preemptive SJF Setting, they put activities into the ready queue as they come. A process with shortest burst time begins execution. If a process with a shorter burst time arrives, the current process is taken out or preempted from execution, and it allots the shorter activity to the CPU cycle. (7) Shortest remaining processing time (SRPT) The Shortest remaining time interval discipline (SRPT) is perfect with relevancy minimizing steady-state mean flow time. Under this rule, when employment is to be selected from among those waiting, we choose the one with the bottom remaining interval. An arriving job will preempt the task in process id and providing the interval of the new arrival is a smaller amount than the remaining time interval of the task than in commission. Queue dicipline flow chart:
  7. Benchmark Six Sigma Expert View by Venugopal R The service disciplines as part of the Queuing Theory are applicable to many situations, but very extensively used for CPU scheduling algorithms. 1. FIFO (First In First Out) is a very popular method, also referred as FCFS (First Come First Serve) algorithm in CPU scheduling. FIFO concept is commonly applied on most queues in daily life, say a ticket counter or grocery store billing counter. FIFO is important as part of inventory management, as we would generally like to use or sell materials and products before they become aged, especially when there is a risk of shelf life or obsolescence. For CPU scheduling algorithms, FCFS is preferable when the processes have short burst times. 2. LIFO (Last In First Out) is literally the opposite of FIFO. In day to day life, LIFO is likely to happen when we stack up any material that is expected to be consumed fast with no risk of expiry of obsolescence. For instance, even if a FIFO model is followed by a supermarket or an assembly shop at a batch level to stack their shelves and bins, the consumption of the goods within the batch will happen on a LIFO basis, since the item that has been stacked last has the best reach. LIFO is applied by a business if they want to use their most recent inventory first. If the costs of recent goods may be higher and LIFO will reflect higher inventory costs, meaning less profits and lower tax for that period. LIFO is permitted as an accepted accounting principle in some countries. 3. Processor Sharing - In this approach, all the recipients are served at the same time by sharing of the available resource. It is akin to many households tapping water from a common water tank, with well laid down network of pipes. There is no priority and the available source gets shared by all. Such a scheduling is also referred to as ‘egalitarian processor sharing’ where each client obtains a fraction of the available capacity. The Processor sharing algorithm is considered as a emergence from the ‘Round Robin’ scheduling algorithm. The application of this scheduling discipline making use of internet and other myriad service portals has revolutionized the way world does many activities in the last couple of decades. 4. Priority scheduling- To understand this discipline, let us imagine a queue of patients waiting for seeing a doctor on a FIFO basis. Suddenly, if an emergency case comes in and that patient is given priority, there are two possibilities…. i) the doctor interrupts his session with the current patient and goes to attend to the emergency case – this is pre-emptive ii) the doctor completes the session with the current patient and then attends to the emergency case – this is non-pre-emptive. In the case of CPU scheduling, for multiple processes processed by a CPU, each process will have a priority number assigned. The CPU will start processing the process that arrived first. When another process arrives, the priority numbers will be checked. If it is a non-pre-emptive schedule, the CPU will complete its current process and check the priority numbers of all the available process waiting in the ‘ready queue’. The process with the highest priority will be taken up next. Whereas, if it is a pre-emptive schedule, the CPU will check priority number of new processes as and when they arrive and if a process with higher priority than the current one is available, the CPU will be allocated to that new process and the current process will be moved to the ‘ready queue’ for being resumed later. 5. Shortest Job First - This will be easy to understand if we have understood the ‘Priority’ discipline as explained above. Shortest Job First (SJN) is a non-pre-emptive algorithm where the priority is given based on the execution time, also known as 'burst time'. In this case, the shorter the duration, higher the priority. This finds use for CPU scheduling, where the shorter processes are not made to wait too long, thus reducing the overall waiting time. The SJF algorithm is preferred if many processes come in to the processor simultaneously. 6. Pre-emptive shortest job first - This is a pre-emptive variant of the above discipline, where the current process will be interrupted to accommodate a newly arrived process with shorter duration. The idea is to reduce the overall waiting time and allow faster completion for shorter processes. However, this method is possible only if the processor has knowledge about the burst time for the process. This is not a recommended method if too many short duration processes start coming in between longer duration processes, since it will lead to long waiting time or ‘starvation’ for the longer processes. 7. Shortest remaining processing time - This is a pre-emptive CPU scheduling where; the processing time of new process will be compared with the remaining time of the current process. If the remaining time of current process is lesser than the processing time of the new process, the current process will continue to be executed till completion. On the other hand, if the processing time of the new process happens to be lesser than the remaining time of the current process, the existing process will be pre-empted and the new process will be taken up by the CPU. This discipline can be exercised only if the estimated burst time for the processes are known. This is bit more advantageous than the earlier case of pre-emptive shortest job first, since a current process that has already executed partially and is closer to completion than a new one will be allowed to complete.
  8. Let us discuss the VSM in transactional service example here.
  9. Queuing Theory: 1. Queuing Theory is mathematical study of waiting time or queues in a model. 2. Queuing theory is used to predict the queue length and waiting time in the simulation for the better business decisions such as planning of resources, space allocation, charges for the premium etc. 3. Queuing is not only applicable to the customers, but also to the product, raw materials, Services, or projects which are lined up. How queuing works? An entity will be arriving to the queuing node, where the entity will be treated by service. But it needs to wait before getting treated and then it will take some duration while it been serviced and then leaves the node. Some applications of Queuing theory: 1. Raw materials are stored and then consumed as per requirement. (Queue length – Inventory | Waiting Time - Capital of WIP/ shelf life) 2. Ships waiting for the clearance. (Waiting time and Service time -Demur-rage) 3. Car parking in the mall (Queue/ service length – Number of Parking slots) Scheduling policies: There are different ways to assign or schedule the next entity to the server which are called as scheduling policies. 1. First in, First out: a. This policy states that the first come will be served at first, which means the entity with longest waiting time will be treated first. b. It is considered as an ethical way to schedule the service for the customers. Token system in Bank or Hospital c. In Manufacturing, First in First out have been considered to keep track on the shelf life of the product. Raw material consumption. 2. Last in, First out: a. This policy states that the last come will be served at first, which means the entity with Shortest waiting time will be treated first. b. It is also stated as stack, It is applicable in most of the cases where you have constraint in such a way that you need to treat the last item first c. In Drive in Racking, We need to pick the last item placed as first. 3. Processor sharing: a. In Processor sharing, more entities will be served at the same time. In this case it can be multiple or as many as possible b. In a mall/ theater, it can accommodate more customers. 4. Shortest Job First: a. Shortest job will be scheduled first , which helps to reduce the queue length. b. In super markets, there will be separate queue for less than 5 billing items, they will treat other customers only after treating the shortest job. 5. Longest Job first: a. Longest Job will be scheduled first. b. In case of Project funding (Project funding as server), Longest job can be allocated with funding to start first. 6. Priority: a. Customers with high priority are treated first. b. It can be primitive (Where customer in service will be interrupted) or non- Primitive. c. Priority can be given in the form of premium queue or even emergency d. Patients at life risk will be treated immediately in hospitals. e. High paid premium queue in the amusement parks. 7. Shortest remaining processing time: a. Customers who have been treated already and having shortest remaining processing time. b. In Banks, after filling the forms, priority will be given to the submission of forms. Key Points: 1. Types of Service facility: Single server, Multiple server with single queue, Multiple server with Multiple queue 2. Queue length, waiting time and service time are the key parameters. 3. Customer Behavior: a. Balking: Customer not joining the queue, because of queue length b. Jockeying: Switching between the queue, considering other queue will be treated faster c. Reneging: Leaving the queue ( which is also called as drop outs)
  10. Nowadays a lot of generic modern spying app are available at Google or App store. But I am a little skeptical about these tools. Have you ever used this kind of apps? If yes, please, share your opinion.
  11. Sourabh Nandi has provided the best answer to this question by providing business applications of Venn Diagrams along with an example. Congratulations!
  12. Q 284. Seven service disciplines are shown in Queueing Theory at Wikipedia at https://en.wikipedia.org/wiki/Queueing_theory. Starting with FIFO, and ending with "shortest remaining processing time", each of these disciplines serve a purpose. Provide specific examples where these seven service disciplines could be of use. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
  13. What is a Venn diagram? John Venn who coined the Venn diagram in 1880 was an English mathematician, logician and philosopher. He also called them Euler diagrams after Leonhard Euler, who checked them out a century before. This is an extraordinarily flexible technique of combining circles useful for identify the contrast between overlapping areas of uniqueness. This representation of how groups relate to one another are generally called “sets”. There must be minimum two number of circles, and also the probability of maximum for many uses is 3. However, there can be more shapes in a diagram based on the number of sets and such a diagram can use unique shapes as per the below figures. Once the circles are interlocked, they reveal discrete areas (in which there’s no overlap). These again compared with the qualities of the overlap areas. Where there are three circles, the central area will show multiple overlapping characteristics. The volume of areas revealed should ideally be kept approximately proportional to their percentage of overlap, in order that the extent of the basic is visually representative. When to use a Venn diagram We often see Venn diagrams in mathematical contexts, but businesses and professionals also use these forms. In each case, the person creating the illustration wants to resolve a controversy, make a crucial decision, predict probabilities or visualize or understand how multiple sets or objects relate to at least one another. Instances when a Venn diagram might be useful in Business Market analysis: A Business Analysis Practitioner might use a Venn’s diagram for basic market research. While using two or more sets of data members within the meeting observe overlapping areas, as those areas contain the business’ target market. Competitor Analysis: A firm might use Venn diagrams to match themselves for their products to their competition. Most times, the business of using the Venn’s diagram may only use two sets of data to work out how they differ from the competition and find any similarities. This helps the business discover what advantages they have already got and specialize in areas where they will make improvements. Product Comparison: Alternatively, a business analyst may create an example with overlapping shapes to weigh the advantages of two or more work ideas. Within the same way that the business analyzes the market, a business analyst will weigh any differences and similarities two or more ideas share to work out which features of a product are the foremost desirable, as shown within the overlapping areas. Decision-Making: The same principles for analyzing two or more product ideas apply to a business’ general decision-making process. Advantages of a Venn diagram A Venn diagram provides the following advantages: It allows an analyst to visualize concepts and relationships between two or more data. It defines complex information into terms that an analyst can understand and represent easily. It helps an analyst to better keep information. Venn diagram symbols “∪ ” Union of two sets. An entire Venn diagram represents the union of two sets. “ ∩ “ Intersection of two sets. This type of intersection shows what items it shares between categories. “ Ac “ Complement of a Set. The compliment is that they don’t represent whatever in an exceedingly set. An classic example of Venn Diagrams; In a survey of the fast-food preferences of three people. We assign these three people as A, B, and C, showing which restaurants they enjoy. A three-circle diagram mostly covers every possibility that they’ll choose a restaurant by one, two, three or no respondents. Scores for Restaurant Survey Results: Restaurant A B C McDonald's 1 0 1 Wendy's 1 1 0 Burger King 0 0 0 In-N-Out 0 1 1 Taco Bell 1 0 1 KFC 0 0 0 A&W 0 0 0 Chick-fil-A 1 1 1 While creating the Venn diagram representing the results, we observed that in A∩B, we’ve Wendy’s because respondent A and respondent B both chose it. Few fast-food restaurants like Burger King, KFC & A&W remain outside the diagram but exist within the universe. Since all the three people have chosen Chick-fil-A, the intersection of all three represents A∩B∩C. So the final Venn diagram will represent in the below figure.
  14. Earlier
  15. Venn diagram is a visual method ( usually used in set theory) to enable building perspective about a problem or causes. This enables developing appropriate solution for the problem or causes identified by enabling breaking down of whole situation into smaller disparate components. Example hypothetical scenarios 1. Fatal error in transaction processing : Analyst processing transaction is new, type of transaction picked is complex , team leader is on leave; when these three situations come together, there is a high probability of a fatal error happening. At the intersection of these three circles is the red zone, hence such a situation should be mitigated. Venn diagram provides a simplified way of communication such combination of situations to operations staff for watch out. 2. Not meeting our contractual productivity improvement commitment for a client. When probed further basis experience following components surfaced, a) Delivery not meeting basic SLA's (hence there focus was on improving SLA performance b) Process Excellence not having a plan ( used to engage with delivery on adhoc basis) and c) Client team changing priorities frequently. This resulted in a situation of multiple initiatives none crossing the finishing line 3. Looking at which subset is causing maximum issue, example call quality not meeting client expectation. Similar to pareto looking at sub parts of call quality form where team is not able to meet standards. It appeared form had 5 parts and team was meeting/exceeding expectation on 4 parts and significantly missing on one part (Customer experience) - dead air & hold time. In order to resolve calls accurately team members were validating with SME's /Team leads, hence putting customers on hold frequently resulting in significant dip in customer experience.
  16. Sourabh Nandi has provided the best answer to this question for providing the different ways in which Heijunka can be implemented. Another very common tool used for implementing Heijunka is called a "Heijunka Box". Also review the answer provided by Mr Venugopal R, Benchmark Six Sigma's in-house expert.
  17. Q 283. Venn Diagrams represent how groups relate to each other. How can Venn Diagrams be used in problem solving? Explain with example(s) Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
  18. Heijunka is one of the underlying concepts of the Toyota Production System (TPS), shown in below Figure. Levelling any work isn’t easy, but it's the inspiration of Toyota’s celebrated production system. The Japanese coined this idea as Heijunka, extending the concept to include the requirement for ‘standard work’ – the processing of work consistently. The TPS consists of two columns – Jidoka and Just- in-Time both are supported by Heijunka. Heijunka involves production and smoothing processing on using levelling, sequencing and standardising . For a process to run smoothly and consistently with many forms of output, it's to average, not just in volume, but also in kinds. So, we'd like to process the unique customer order, as an example, supporting the date they’re received instead of handling the more straightforward cases first and allowing the harder ones to create up and be delayed. Heijunka provides the foundation and involves the subsequent elements; Levelling involves smoothing the amount of production to cut back variation, the trials and troughs that may make planning difficult. Levelling pursues forestall ‘end-of-period’ peaks, where production is initially slow at the start of the month, then again quickens within the last days of an acquisition or accounting period, as an example. Sequencing involves mixing the styles of work processed. So, as an example, when putting in new loans, the loan being processed is mixed to raise match customer demand and help ensure applications are actioned in date order. Managing this method could also be easier in manufacturing, where a producer may hold a little amount of finished goods to reply to the fluctuation in weekly orders. Keeping a tiny low stock of finished goods at the very end of the worth stream, near shipping, this producer can level demand to its plant, and to its suppliers, making for more efficient utilisation of assets along the complete value stream while still meeting customer requirements. Stability and Standardisation is the third strand of Heijunka. It strives to scale back variation within the way we do our work, which highlights the importance of ‘standard work’, of following a typical process and procedure. This method links well to the concept of process management and also the control plan, where the method owner continuously seeks to search out and consistently deploy best practice. In the spirit of continuous improvement the ‘best way’ of ending this method will keep changing, because the people within the process identify better ways of doing the work. Concepts like Heijunka can’t be implemented overnight – as an example, Toyota has taken a few years to attain the successful application of levelling and spreading the load, but is now a paradigm for the growing awareness of lean-thinking principles within the contemporary world.
  19. Benchmark Six Sigma Expert View by Venugopal R The term Heijunka has emerged from the Toyota Production System and aims to level the irregularity in Production. The Lean Lexicon defines Heijunka as “Levelling the type and quantity of production over a fixed period of time, which enables production to efficiently meet customer demands while avoiding batching and results in minimum inventories, capital costs, manpower and production lead time through the whole value stream”. Heijunka is a pre-requisite for the popular concept of JIT (Just-In-Time). Though Heijunka is referred to as a solution for Mura, it is important to understand how the 3Ms, Mura, Muri and Muda are interrelated. Hence, before we get to discuss Heijunka, let’s take a quick look at the Japanese terms Muda, Muri and Mura. Muda means ‘Waste’ and includes non-value-added activities such as avoidable Transportation, Inventory, Motion, Waiting, Over Production, Over Processing and Defects creation. Muri means ‘Over Burden’ and relates to tasks that are Overbearing, Risky or High stress causing. Mura means ‘Unevenness, Irregularity or Non-Uniform. In Six Sigma terminology, we may refer to it as ‘Variability’. We will understand more about Mura with some example situations. If we have a product whose demand is less during the beginning of the month and very high during the end of the month, we will have unevenness with respect to capacity utilization across the month. This irregularity is Mura. In the beginning of the month, being a low demand period, there will be idle waiting time which is a form of Muda. On the other hand, towards the last few days of the month, the demand becomes higher and is bound to put pressure on the employees to deliver the volumes and this causes Muri or overburden. The three components of Heijunka Leveling – Overall smoothening of the process to reduce the variability Sequencing – Managing the sequencing of work – Mixed production Stability – Reduce Process variation If we have a product with demand levels opposite of that mentioned earlier, i.e. whose demand is higher in the beginning of the month and lower towards the end, then by cross training the employees to work on this process and the earlier mentioned process could help to even out the variability in the process and thus reduce Mura and Muda. Another aspect that needs to be considered is to balance the production line with respect to the resource allocated and time taken for each step in the process. By allocating more resources for the process steps that consume more time, we can balance the process and also prevent building up WIP inventory between the steps. When we have products of varying complexities, but handled by the same set of people in the production line, it is bound to cause variation, idle time and overburden if the products of same complexity levels come together. For example, if all the easy products are processed during the beginning of the month and the difficult ones turn up together towards the end of month, we will see Muda (excess time) and Muri (Overburden) alternatively. If we are able to sequence the flow of products in a mixed manner so that the overall complexity levels at any point of time is more or less uniform, this will help in leveling of the variations. It may also be noted that if we want to have all product mix to be available to the customer uniformly throughout the month, the above point of Heijunka becomes very important and the concept of SMED will play an important role. Many of the Lean concepts are essential for successful Heijunka implementation: Takt Time: The time taken to finish a product to satisfy the customer demand Volume Leveling: Understanding the variability in demand, maintaining production at levels comparable to long-term average demand and maintaining a buffer inventory in proportion to the demand variability Type leveling: Maintain product type mix at frequent basis, if possible every day, and reserve capacity for changeover flexibility. Change over time: We already mentioned the importance of SMED concept Implementation of Heijunka is an important element of the Lean implementation for an organization. Successful implementation requires good understanding and data for the 3Ms (Muda, Muri & Muda), building flexibilities in terms of mixed manufacturing, quick changeover and employee allocation.
  20. Heijunka or levelling is defined as balancing work by volume and variety during a period of time, typically a day. The day is further broken down into more manageable units of 2-4 hours The purpose of levelling is to ensure Work is evenly distributed amongst workers by volume and variety No work is waiting in queue No work is released upstream that is not required downstream A pull system of work is established Continuous flow is achieved A visual aid that identifies when and where work is behind schedule Example When a car is scheduled for a tune-up, the customer is given a time slot for the expected work to be done. That time slot may not fit your schedule. The scheduler will then attempt to fit you in at some agreed upon time. The Lean approach would be to increase capacity to meet customer demand by doing one or more of the following: 1.Determine historical demand, by the month, and create capacity for what the historical trend has been (tune-ups, brake problems, electrical problems, oil changes, etc.). 2.Cross-train more employees to handle the higher volume service jobs. 3.Increase daily capacity by reducing cycle times for repairs (this could be by implementing a bonus program based on the performance times of repair jobs, evening hours, additional shifts, etc). 4.Create capacity in bays where usage is not high by purchasing additional testing equipment, using part-time employees, etc. Auto service facilities have well-documented data (i.e., standard work) for servicing cars. They are ahead of many industries in that Lean tool application.
  21. Heijunka is a Japanese term and it can be broadly translated as 'Leveling". The main focus of Heijunka is to eliminate unevenness/ irregularities/ fluctuations (Mura). Lean emphasis on the concept of 1 piece flow, but it's easily said than done. This is where Heijunka comes in picture. Instead of 1 piece flow, heijunka takes average demand in consideration. The average demand should be of a fairly small period of time I.e 3days/ 1 week/ 10 days/ fortnight, etc. (Depending on lead time and other factors) Mostly firms don't work upon a single product but they work with a varied range of products. In this scenerio the Production/ Planning team should take average demand of each product for the specified time period. For example, if the firm get 9 orders per week for Product X, 4 orders for Product Y, 6 orders for Product Z on average, team need to level their capacity to produce a total number of 19 products per week. This way team can establish a stable flow of work and meet the average demand by end of the week, thus keeping your equipment utilised all the time and without overburden at the time of peak demand. Other benefits include reduced inventory, reduced WIP, better cash flow, etc.
  22. Q 282. Heijunka is the solution for Mura (unevenness or irregularities). What is Heijunka and what are some of the ways in which it could be implemented? Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
  23. Meet Maheshwari has provided the best answer to this question by providing an example for divergent and convergent thinking. Also review the answer provided by Mr Venugopal R, Benchmark Six Sigma's in-house expert.
  24. Benchmark Six Sigma Expert View by Venugopal R Divergent & Convergent Thinking - Definition During divergent thinking, we look for several potential ideas for a problem or solution and during convergent thinking we tend to focus on a specific idea or solution. For example, if we want to think about increasing the sales of a product, we would start with a divergent thinking and explore various potential opportunities such as expanding market, adding more product types, improving features, optimizing price, placing more promotional programs and so on. However, once we assess and evaluate all these ideas, we will have to narrow down to one or very few number of ideas based on various facts and factors. This is where convergent thinking happens. Design Thinking – brief overview ‘Design Thinking’ goes through five phases viz. Empathize, Define, Ideate, Prototype and Test. A quick explanation of each of these phases is as below: Empathize: During this phase, the customer requirements and expectations are gathered, not just through the voice of customer, but also from what they think, feel, see, hear, say and do. The pains and gains as perceived by customer are also captured. This is a very important aspect of Design Thinking. Define: The requirements are processed and defined in a structured manner which can be incorporated into the design of the product, process or service, as the case may be. Ideate: A wide variety of potential solutions are explored, and multiple options are generated. Out of these, the best options for the given situation are narrowed down. Prototype: Based on chosen solution, a working model of the product, service or process is developed and subjected to actual customer use. This will help in refining the Define and Ideate phases. Test: By using the prototypes, test out whether the design intent for satisfying customer requirements and expectations is being achieved; use the feedback to improve the prototype. The above phases do not happen in a linear fashion, but will loop back to previous phases to get refined. Double Diamond: There will be very high amount of divergent thinking during the Empathize phase and some Convergent thinking during the Define phase. Once again when we move into Ideate phase, there would be a divergent thinking to come up with alternate design solutions, and by Convergent Thinking, we narrow down on the option for which we create prototype. Thus, the Divergent and Convergent thinking happen in two cycles, often referred to as ‘Double Diamond’ Supply Chain example: Let’s consider an example – A company wants to reduce their supply chain related costs. Applying just Convergent Thinking might limit yourself to reduce transportation costs and material handling costs. Whereas if we apply the Design Thinking process, the Empathize phase will pave the way for divergent thinking. Some of the likely aspects that would come out with Divergent Thinking may include: 1. Better space utilization 2. Automation of material handling 3. Streamlining ordering process 4. Monitoring customer demand 5. Leaning out the supply chain process 6. Inventory management 7. Outsourcing 8. Relocation of sites 9. Alternate methods of transportation Convergent Thinking has to be applied now to narrow down on the priority areas to work upon. Let’s assume that the chosen areas are point nos. 1, 6 and 7. For each of the chosen points, we will have to apply Divergent Thinking to identify the potential factors that need to be addressed. After this, we move to Convergent Thinking to shortlist the solutions and finalize the set of actions. Apart from Design thinking, Divergent and Convergent Thinking are used in many situations, for instance during the DMAIC cycle for a Six Sigma project. Both these types of thinking are important and often go hand-in-hand.
  25. I'll take the example of designing a new tyre Divergent- Now the team has to be creative and add maximum features that they can add into a tyre(Leave constraints of physics, material science, economics etc aside) For our tyre team can have ideas such as following 1. Colorful tyre for aesthetics 2. Tyre made of fibre for less weight 3. Tyre with spikes for better grip 4. Tyre that can inflate itself etc. Convergent - once the team is done with divergent thinking start thinking rationally, put constraints such as follows. 1. Will the customers show positive response with color change (can use results of some survey) 2. Will fibre glass infused tyres be safe? 3. Does the terrain requires tyres with spikes? 4. Is the self inflating technology reliable and affordable? Etc. Thus using divergent followed by convergent technique for designing a product can utilise the maximum creativity of the team as well as hell the team reach to laser point solutions for various customer needs.
  26. Design thinking mainly uses of divergent thinking and convergent thinking for problem solution and idea generation. Divergent thinking refers to a strategy to generate proposal for multiples possible solutions in attempt to identify that one can work.It is possible only when multiple ideas are evaluated. Brainstorming and free flow writing are two processes that involves divergent thinking. Making list of questions, mapping out a subject, artwork creation are some of examples of divergent thinking. Convergent thinking refers to technique for problem solving in which different ideas from different peoples bringing together to identify a single best solution against a defined problem. Multiple choice questions, pattern identification, Quizzes are some of examples of convergent thinking.
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