SandhyaKamath

Autonomation commonly termed as Jidoka refers to "automation with human intelligence".It refers to the practice of stopping a manual line or process when something goes amiss. In practice, it means that the process is sufficiently "aware" of itself so that it will: Detect process malfunctions or product defect Stop itself Alert the operator This concept, in which intelligence added to machines, enabled companies to greatly increase the number of machines a single operator could run—with very little extra effort on the worker’s part. With jidoka, production becomes much easier for operators and much more profitable for companies as it is about stopping whenever an abnormal condition is detected, fixing the defect, and then counter measuring to prevent further occurrences. Many jidoka devices are combined with an Andon light, or signalling device, to alert the operator of the abnormal condition. The purpose of jidoka is to separate people from machines, so an operator can do other tasks while the machine is running. Also consider case of two supervisors: one who followed orders and stopped the line immediately when trouble developed and another who was reluctant to stop the line. At first, the line that stopped frequently had lower output. After several months, however, the situation reversed. The line that rarely stopped still had the same problems. These problems stalled productivity improvements and created rework that lowered efficiency. The line that initially saw frequent stoppages found that the stoppages had been reduced and overall efficiency improved. Companies often emulate Toyota and depict their production system as a “Lean house”. Jidoka is frequently depicted as one of its pillars. The other common pillar, JIT or just-in time manufacturing, and jidoka work together to create manufacturing excellence. For Just-in-time to work, all of the components that are made and supplied must meet predetermined quality standards. This is achieved through Jidoka, which helps ensure that workers can highlight, visualise and act on correcting problems in the manufacturing process. Automation on the other hand is basically making a hardware or software that is capable of doing things automatically. Consider the example of fire alarm systems deployed in buildings. As soon as smoke sensor is activated, water start pouring down the pipes. In Automation, you are using a software or hardware that has the ability to do things automatically. They will do exactly what you have intended them to do. There are different types of automation depending on their use. These are Numerical Control, Computer-Aided-Manufacturing, and Flexible Manufacturing Systems & Industrial Robots. On automated transportation, we have; of course, the most prevalent cars, UAVs, and unmanned yachts. Setting up a process which does not involve any THINKING to run without or with little help of human is called automation. Here selecting a thing from many choices is not called thinking. In automation all the environment parameters are well known at the time of design and are not supposed to change during operations. Many automated machines today have jidoka built in. They stop when something goes wrong—a bit breaks, for example. Most of us even have a good example of this in our homes. Washing machines shut themselves off if they get out of balance or if the lid is opened. Human processes have much less of this ‘built-in’. They often have a hard time detecting the abnormality, and frequently attempt to fix it themselves, rather than draw attention to the problem. Imagine if you had to stay near your washing machine to listen for the signs of imbalance. You’d be extremely limited in what you could do and your time would be wasted. Jidoka reduces the need to be near a machine continuously. In short, Jidoka means intelligent automation. When translated to the factory floor, Jidoka is a quality control method, which ensures that all the quality checks and balances are carried out. It aims to prevent the production of defective products, eliminate overproduction and focus attention on understanding the problems and ensuring that they do not reoccur.

In an unlinked production environment, some processes move faster than the average and some operate more slowly. As a result, parts move through the system at varying speeds, only to end up in piles of inventory scattered along the value stream. Even with a takt time in place, there can still be some fluctuation in the actual performance of processes, if they are not somehow linked together. This fluctuation gets even more complicated when scheduling is done at multiple places in a value stream. For this reason, a pacemaker process is established. A pacemaker is the single point is normally towards the end of the line (sometimes final assembly) where a production process is scheduled. The upstream processes don’t produce without a pull signal originating from the pacemaker. It. A key rule for selecting the pacemaker is that all processes after it must “flow” to the customer. The pacemaker simplifies production oversight. Having only one scheduling point greatly reduces the need for coordination. The benefit is amplified when there is mixed-model production in a value stream. The actual demand determines the mix, and the pull signals generated by the pacemaker process ensure that only the types of products that are needed are produced. If there are multiple products, supermarkets are used. Continuous flow is used downstream from the pacemaker to manage production. The following things needs to be considered while setting up a pacemaker process: The pacemaker should be reliable. If it is frequently down for maintenance, it wreaks havoc on the rest of the value stream. It should have minimal setup times to prevent surges. The closer it is to the end of production, the more linked it is to the customer. The downside is that it might drive more inventory into supermarkets on the upstream processes. Branches in production processes need to be upstream of the pacemaker or have a supermarket. The pacemaker process in short simply determines the sequence of production.

A hypothesis test is a statistical test that is used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. A hypothesis test examines two opposing hypotheses about a population: the null hypothesis and the alternative hypothesis. The null hypothesis is the statement being tested. Usually, the null hypothesis is a statement of "no effect" or "no difference". The alternative hypothesis is the statement you want to be able to conclude is true. Based on the sample data, the test determines whether to reject the null hypothesis. You use a p-value, to make the determination. If the p-value is less than or equal to the level of significance, which is a cut-off point that you define, then you can reject the null hypothesis. A common misconception is that statistical hypothesis tests are designed to select the more likely of two hypotheses. Instead, a test will remain with the null hypothesis until there is enough evidence (data) to support the alternative hypothesis. Hypothesis testing is used in the Six Sigma Analyze Phase for screening potential causes. All hypotheses are tested using a four-step process. The first step is for the analyst to state the two hypotheses so that only one can be right. The next step is to formulate an analysis plan, which outlines how the data will be evaluated. The third step is to carry out the plan and physically analyze the sample data. The fourth and final step is to analyze the results and either accept or reject the null. Try to re-run the test (if practical) to further confirm results. The next step is to take the statistical results and translate it into a practical solution. It is also possible to determine the critical value of the test and use to calculated test statistics to determine the results. Either way, using the p-value approach or critical value should provide the same result. There could be a problem with centering where the process is not centered, it may be precise but not accurate. Processes may be accurate but not precise. Data collection establishes the foundation for appraising the quality of a product or service. But without correct data processing, it becomes challenging to make an objective conclusion. Sometimes, the observation is wrongly interpreted. This is the hypothesis to be tested. Essentially good hypotheses lead decision-makers to new and better ways to achieve your business goals. When you need to make decisions such as how much you should spend on advertising or what effect a price increase will have your customer base, it’s easy to make wild assumptions or get lost in analysis paralysis. A business hypothesis solves this problem, because, at the start, it’s based on some foundational information Theory tells you what you can generally expect from a certain line of inquiry. A hypothesis based on years of business research in a particular area, then, helps you focus, define and appropriately direct your research. You won’t go on a wild goose chase to prove or disprove it. A hypothesis predicts the relationship between two variables. If you want to study pricing and customer loyalty, you won’t waste your time and resources studying tangential areas.

Takt time is the required pace of production to meet customer demand. It is calculated by dividing the working time available, generally for that shift, by the customer demand during that time period. Takt Time is the pace of production (e.g. manufacturing one piece every 34 seconds) that aligns production with customer demand. In other words, it is how fast you need to manufacture a product in order to fill your customer orders. Takt Time is calculated as: Takt Time = Planned Production Time / Customer Demand The term Takt Time comes from the German word Taktzeit, which loosely translates to “rhythmic time” or “keeping a beat”, similar to the ticking of a metronome or the movement of a conductor’s baton. Takt Time is a key concept in lean manufacturing. It is the heartbeat of a lean organization – matching actual production to customer demand. It is not a goal to be surpassed, but rather a target for which to aim: Producing faster than Takt Time results in overproduction – the most fundamental form of waste. Producing slower than Takt Time results in bottlenecks – and customer orders that may not be filled on time. So Takt time is a function of demand and working time, so can only changes when demand changes, or working time changes. If customer demand rises, takt time drops. If customer demand drops, takt time increases. There are two different yet related ways to use Takt Time. Both are valid and useful – they simply look at customer demand from different perspectives: Planning Perspective: Use Takt Time to set goals for kaizen activities that focus on making improvements to your production process to ensure that it can meet customer demand. Manufacturing Perspective: Use Takt Time to drive a real-time target for production. The actual time it takes you is known as cycle time. So, takt time is what you need to be able to do, and cycle time is what you can do. While you want them to be in alignment, one does not affect the other. Customers changing their purchasing patterns doesn’t change the work content of a product. Making a dramatic improvement during a project doesn’t get customers lining up for your product. Comparing takt and cycle times, once determined, drives actions. It establishes the staffing needed. It drives improvement projects. It provides information to the marketing team about pricing. It can be used to establish lead times based on backlogs. So, takt time serves to give you a real target for improvement, not just an arbitrary, made-up percentage. Goals with a specific purpose are always easier for frontline employees to get behind than annual improvement goals (i.e. improve productivity by 10% per year). When a process is extremely stable, like on an assembly line, it is easy to pace to the takt time. The work advances at a set pace, so the product rolls off the end in very predictable increments. This isn’t the situation in the office. Customers call or e-mail at random intervals. Some requests are small; some are significant. Some orders come in with one line; some are several pages long. Probably most notably, averaging out demand is harder in service operations. If orders come in primarily at the beginning of the week, they can often be allocated throughout the week, or can be built to a small finished goods inventory. Customers on the phone, though, won’t sit on hold very long. In effect, service takt times are relevant in smaller windows because the work does not keep. Furthermore, even with an average demand, the work content can vary substantially. The contribution of operations to performance is not the same in every business. The way operations are designed and run makes the winners and losers. Most business is on a spectrum between these extremes. In luxury goods, for example, operations matter, but they are secondary to brand management. When you make custom-configured or custom-designed products, the customization process itself is part of operations, as support activities like production control, logistics, technical data management, quality management or maintenance. Lean transformation of a company can only be successful as a result of a strategic decision by top management, with personal involvement by the CEO. It cannot be delegated to a “VP of Lean” or to a “Lean department.” It must instead, over time, become part of the way everyone works. It is not easy and requires sustained effort. For despite the enormous time and energy that goes into strategy development at most companies, many have little to show for the effort. Companies on average deliver only 63% of the financial performance their strategies promise. Even worse, the causes of this strategy-to-performance gap are all but invisible to top management. Leaders then pull the wrong levers in their attempts to turn around performance—pressing for better execution when they actually need a better strategy or opting to change direction when they really should focus the organization on execution. The result: wasted energy, lost time, and continued underperformance. Given the poor quality of financial forecasts in most strategic plans, it is probably not surprising that most companies fail to realize their strategies’ potential value. The strategy-to-performance gap can be attributed to a combination of factors, such as poorly formulated plans, misapplied resources, breakdowns in communication, and limited accountability for results. What emerges from our survey results is a sequence of events that goes something like this: Strategies are approved but poorly communicated. This, in turn, makes the translation of strategy into specific actions and resource plans all but impossible. Lower levels in the organization don’t know what they need to do, when they need to do it, or what resources will be required to deliver the performance senior management expects. Consequently, the expected results never materialize. And because no one is held responsible for the shortfall, the cycle of underperformance gets repeated, often for many years. Management can close the strategy-to-performance gap. A number of high-performing companies have found ways to realize more of their strategies’ potential. Rather than focus on improving their planning and execution processes separately to close the gap, these companies work both sides of the equation, raising standards for both planning and execution simultaneously and creating clear links between them. Companies should follow seven rules that apply to planning and execution. Rule 1: Keep it simple, make it concrete. Rule 2: Debate assumptions, not forecasts. Rule 3: Use a rigorous framework, speak a common language. Rule 4: Discuss resource deployments early. Rule 5: Clearly identify priorities. Rule 6: Continuously monitor performance. Rule 7: Reward and develop execution capabilities. Living by these rules enables them to objectively assess any performance shortfall and determine whether it stems from the strategy, the plan, the execution, or employees’ capabilities. And the same rules that allow them to spot problems early also help them prevent performance shortfalls in the first place. These rules may seem simple—even obvious—but when strictly and collectively observed, they can transform both the quality of a company’s strategy and its ability to deliver results.

Two very important theorems in statistics are the Law of Large Numbers and the Central Limit Theorem. The Central limit Theorem (CLT) states that when sample size tends to infinity, the sample mean will be normally distributed. The Law of Large Number (LLN) states that when sample size tends to infinity, the sample mean equals to population mean. CLT establishes the normal distribution as the distribution to which the mean (average) of almost any set of independent and randomly generated variables rapidly converges. It also give precise values for the mean and standard deviation of the normal variable. It is generally an excellent approximation for the mean of a collection of data (often with as few as 10 variables). LLN establishes that as the number of identically distributed, randomly generated variables increases, their sample mean (average) approaches their theoretical mean. For example, if the measuring device is defective or poorly calibrated then the average of many measurements will be a highly accurate estimate of the wrong thing. The CLT says that as the sample size tends to infinity, the distribution of mean approaches the normal distribution. This is a statement about the SHAPE of the distribution. A normal distribution is bell-shaped so the shape of the distribution of sample means begins to look bell-shaped as the sample size increases. The LLN tells us where the centre (maximum point) of the bell is located. Again, as the sample size approaches infinity the centre of the distribution of the sample means becomes very close to the population mean. CLT requires extra assumptions on top of those needed for LLN. So you can have LLN without CLT but not the other way around.

Fault tree analysis (FTA) is a top down, deductive failure analysis in which an undesired state of a system is analyzed using Boolean logic to combine a series of lower-level events. It is a logical, graphical diagram that organizes the possible element failures and combination of failures that leads to the top level fault being studied. With every product, there are numerous ways it can fail. Some more likely and possible than others. The FTA permits a team to think through and organize the sequences or patterns of faults that have to occur to cause a specific top level fault. The top level fault may be a specific type of failure, say the car will not start. Or it may be focused on a serious safety related failure, such as the starter motor overheats starting a fire. A complex system may have numerous FTA that each explore a different failure mode. The primary benefit of FTA is the analysis provides a unique insight into the operation and potential failure of a system. This allows the development team to explore ways to eliminate or minimize the occurrence of product failure. By exploring the ways a failure mode can occur the changes impact the root cause of the potential failures. FTA is used extensively to analyze the reliability and safety of complex systems such as nuclear power plants and weapon systems. It identifies faults in a system design that may cause potential accidents and helps to eliminate costly design changes and retrofits. Typically it can be used in any field where failure needs to be analysed. Like in a hospital where incorrect prescriptions may be given or in a company where personnel evaluation system is not effective or an airplane parts manufacturer performs FTA as a standard part of the design process to identify critical faults which could cause hazardous failure. When identifying ways in which an item may fail, one should try looking at the problem from different angles. Like Excessive stresses and strains, Potential misuse and abuse, Environmental extremes, Natural variation in the system, Failure of dependent systems, Failure of related processes. In some circumstances, it is desirable to ensure the system continues to operate even if there is an internal failure. An aircraft navigation system should be able to operate even if an internal dc-dc regulator fails, for example. Not everything within some systems benefits by being fault tolerant .For example, a failure of a cabin reading light over a passenger seat is not critical to the safe operation of the aircraft, thus is likely not created to be fault tolerant. One criterion to determine what should be fault tolerant is the criticality of the function the system provides. This also applies to specific subsystems within a system allowing some elements to be created fault tolerant and others within the system not.Evaluating each element may help determine the specific elements that may benefit with fault tolerance. How critical is the component to the essential function or how likely is the component to fail? If an element is very unlikely to fail, the effort to create a fault tolerant system may be wasting resources better used to improve other elements of the system. Cost is another factor. Thus for 911 emergency services phone systems, some banking and commerce system, safety-critical systems of aircraft, public transit, or chemical plants systems fault tolerance is essential but where the decision to add some level of fault tolerance is not so clear it requires careful consideration of the costs, benefits, and criticality.

From a customer's point of view, value-added work is a process that adds value by producing goods or providing a service that a customer is willing to pay for, the step that transforms the product being produced, and if it’s done properly the first time. However, muda (Japanese word meaning "futility; uselessness; wastefulness") is a process that consumes more resources than needed, which causes waste to occur. “Waste” therefore can be defined as any activity that does not add value to a customer. Muda are broken down into: Muda Type I: non-value added activity, necessary for end customer. They are usually hard to eliminate because although it is classified as non-value added activity, it is not necessarily muda. In manufacturing a car, for example, a value-adding step would include attaching the door. The customer is, of course, willing to pay for a door and to have it attached properly. There can be a lot of waste associated with the simple action of attaching a door, though, manufacturers aim to reduce as much of it as possible Another example is the quality inspection process for critical process is needed at car assembly, to ensure the car quality and fulfill safety standards before sending it to the end user; however, from customer point of view, these actions are necessary and do not contribute to the assembly process which add values or to the car assembly. Muda Type II: non-value added activity, unnecessary for end customer. The aim is to eliminate this type of wastage. They are non-value added activities which contributes to waste and incur hidden costs. By planning to reduce manpower, or reduce change-over times, or reduce campaign lengths, or reduce lot sizes the question of waste comes immediately into focus upon those elements that prevent the plan being implemented. Often it is in the operations' area rather than the process area that muda can be eliminated and remove the blockage to the plan. Tools of many types and methodologies can then be employed on these wastes to reduce or eliminate them. The plan is therefore to build a fast, flexible process where the immediate impact is to reduce waste and therefore costs. By directing the process towards this aim with focused muda reduction to achieve each step, the improvements are 'locked in' and become required for the process to function. Without this intent to build a fast, flexible process there is a significant danger that any improvements achieved will not be sustained because they are just desirable and can slip back towards old behaviours without the process stopping. The Seven major categories of office waste with some examples are as follows: Category of Waste Examples 1.Transportation & Handling Movement of paperwork, multiple hand-offs of electronic data, approvals, excessive email attachments and distributing unnecessary cc copies to people who don't really need to know 2. Inventory Purchasing or making things before they are needed (e.g. office supplies, literature...). Things waiting in an in-box, unread email and all forms of batch processing create inventory 3. Human Motion Walking to copier, printer, fax... Walking between offices. Central filing. 4.Waiting Slow computer speed. Downtime (computer, fax, phone...). Waiting for approvals, waiting for customer information or waiting for clarification or correction of work received from upstream process create much waste in office and business systems. 5.Overproduction Printing paperwork or processing an order before it is needed. (things can change) Any processing that is done on a routine schedule - regardless of current demand 6.Overprocessing Relying on inspections, rather than designing the process to eliminate errors. Re-entering data into multiple information systems, making extra copies, generating unused reports, and unnecessarily cumbersome processes. 7.Defects Data entry errors or invoice errors. Engineering change orders, design flaws, employee turnover and miscommunication are all ‘defects’ in office processes Every employee in the organization has the ability to identify and eliminate waste in their work. Empowering staff to be change agents by providing them with the leadership, methodology, and tools they need to improve their work results in a sustainable culture of continuous improvement.

5S is a system for organizing spaces so work can be performed efficiently, effectively, and safely. This system focuses on putting everything where it belongs and keeping the workplace clean, which makes it easier for people to do their jobs without wasting time or risking injury. The term 5S comes from five Japanese words: Seiri (Sort) Seiton(Set in Order) Seiso(Shine) Seiketsu(Standardize) Shitsuke(Sustain) Each S represents one part of a five-step process that can improve the overall function of a business. 5S involves assessing everything present in a space, removing what's unnecessary, organizing things logically, performing housekeeping tasks, and keeping this cycle going. Organize, clean, repeat. This system, often referred to as Lean manufacturing, aims to increase the value of products or services for customers. This is often accomplished by finding and eliminating waste from production processes. 5S is considered an important aspect of manufacturing because until the workplace is in a clean, organized state, achieving consistently good results is difficult. A messy, cluttered space can lead to mistakes, slowdowns in production, and even accidents, all of which interrupt operations and negatively impact a company. By having a systematically organized facility, a company increases the likelihood that production will occur exactly as it should. Over time, the 5S methodology leads to many benefits, including: · Reduced costs · Higher quality · Increased productivity · Greater employee satisfaction · A safer work environment The basic steps of 5S can be applied to any workplace. An office can use 5S to keep supplies organized, as can hospitals and medical clinics. 5S can even be used in a kitchen to keep the fridge from filling up with expired food. Everything we do, use in our day to day life can be brought under 5S. Like for example, security system, parking space, space we eat, space we work, space we meet in conference room, the way we organize meeting, mails, contacts and so on.. 5S can also be used in the IT industry where a lot of things are outsourced for e.g email is outsourced. Using 5S many of the accounts that are stale and had email provisioned, with a corresponding monthly charge can be weeded out resulting in huge Savings for the Company. It's really just a matter of determining what workspaces and work processes will benefit most from improved workplace organization. Business leaders considering using 5S may wonder if 5S is expensive to implement. Generally, it's not. There may be an up-front investment in tools like floor marking tape and labels, and some time does need to be spent on training and on 5S activities, which takes up employees' time. In the long run, though, 5S makes processes run more smoothly and prevents mishaps, and those things usually save businesses money. .

Innovation is the successful exploitation of new ideas. The concept of innovation is quite diverse, depending mainly on its application. People often confuse innovation and innovation processes with continuous improvement and processes. For an innovation to be characterized as such, it must cause a significant impact on the pricing structure, in the market share, in the company’s revenue, etc. Those who innovate are at an advantage over the others. Continuous improvements are not usually able to create competitive advantages of medium to long term, but they are able to maintain the competitiveness of the products in terms of cost. Then, companies must understand what innovation is and what its dynamic. From there, they can define a strategy aligned with the objectives of the organization and its vision. Thus, it is possible to identify other essential concepts for companies to become innovative: attention to the future is a requirement for the company to innovate. The next step is to develop and internalize management tools of the innovation process. These solutions must be tailored to each situation. The size of the company, its sector of activity, culture and organizational structure, the agent system in which it is inserted, its future vision and ambitions should be taken into consideration. Lean Six Sigma, a relatively well-known approach for obtaining some excellent and efficient results, can help the executives of multinationals to discover new innovation opportunities beyond carrying out simple operations, to reach a high degree of productivity, a solid financial performance, as well as an inherent disposition towards innovation. "Lean Six Sigma" is a methodology which is a combination of the good things taken from Lean with the advantages of Six Sigma, thus it can be defined as an integrated management approach. It focuses on obtaining a constant improvement of the processes. In order to do this, it absorbs both elements of the Lean methodology with the purpose of reducing the time of their progress, by eliminating the loss factors, as well as elements of the Six Sigma methodology, represented by a decrease of the variability degree of the process. This method can be used in any company or enterprise which registers losses on the cycle of processes with input and output parameters that are fluctuant in time. Lean Six Sigma is a natural evolution of the subjects of improving quality and processes, which have their origin in the 1950s, in view of making the production mechanism more efficient. It all started with defining the focus on quality improvement, in order to reduce the cost of the flawed material production. This evolved in the implementation of similar principles in order to improve the efficiency of the process at factory level, by eliminating the waste of effort only to manufacture the necessary quantity, not to over-agglomerate the storehouses. In time, other parts of the organization have felt the need to approach this process optimization perspective, a relevant example in this respect being also the commercial departments within an enterprise. Lean Six Sigma measures the performance of a product or process on a statistic level (or the degree of satisfying the customers" claims). At the same time, this methodology provides organizations with all the tools necessary in view of optimizing the capability of its processes, increasing the performance of the business environment and reducing the variation of the results → higher quality of the products and services.

For Measurement, I will talk about software measurement here. Software measurement is a challenging but essential component of a healthy and highly capable software engineering culture. Software projects are notorious for running over schedule and budget, yet still having quality problems. Tracking project status is meaningful only when the actual effort and time spent on each task is known and compared against plans. Product stability cannot be gauged unless the rates at which finding and fixing defects are measured. Efficacy of new development processes can be gauged only when measures of current performance is available as a baseline to compare against. Metrics help you better control your software projects and learn more about the way your organization works. Software measurement enables quantification of the current performance of an organization, like schedule, work effort, product size, project status, and quality performance so as to improve future work estimates based on empirical data rather than guesses. Estimates based on such collected data tend to be more realistic and achievable. Measurement also helps an organization to benchmark current performance with Industry averages and forms the basis for software process improvement plans. Successful organizations link measurement program to organizational goals and process improvement program. They use the process improvement initiative to choose improvements, use the metrics program to track progress towards goals, and use recognition program to motivate and reward desired behaviors. Measurement activities are to be planned carefully because they can take significant effort to implement and the payoff will come over time. What to Measure: Successful organizations are driven by goals. Goals are set as a means to improve operational efficiency by performing operations in better ways. It is important to select a small and balanced set of metrics that will help to track progress toward defined goals that includes different aspects of software products, projects, and processes. Goal-Question-Metric (GQM)” is a good technique for selecting appropriate metrics to match the goals set. If the goal was “reduce maintenance costs by 50% within one year”, some appropriate questions to answer are: - How much is spent on maintenance each month? What fraction of our maintenance costs is spent on each application that is supported? How much money is spent on adaptive (adapting to a changed environment), perfective (adding enhancements), and corrective (fixing defects) maintenance? Now identify metrics that will let each question to be answered. Some of these could be data items that can be counted directly, such as the total budget spent on maintenance. Other metrics could be computed from two or more data items measured directly. In the above example collect direct measures like hours spent on each of the three maintenance activity types and the total maintenance cost over a period of time. A typical balanced metrics set should include items relating to product size, product quality, process quality, work effort, project status, and customer satisfaction.

In any organization numerous problems exist in all facets of its activities. The efficiency and survival of the organization depends on how promptly these problems are recognized and their root causes are isolated and eliminated. A systematic analysis of each potential problem area should be carried out to recognize the root causes that are responsible for creating the problem. Such analysis is called Root Cause Analysis. A cause may be sufficient but may not be necessary for a problem to occur. If the cause is rectified then the problem will be solved.

A management approach of an organisation, centered on quality, based on participation of all its members and aiming at long term success through customer satisfaction and the benefits to the members of the organisation and the society at large.