Equipment Productivity By T.A. Khan

January 2008

Overall Equipment EffectivenessIn an ideal factory, equipment would operate 100 percent of the time at 100 percent capacity, with an output of 100 percent good quality. In real life, however, this situation is rare.The difference between the ideal and the actual situation is due to losses. Calculating the overall equipment effectiveness (OEE) rate is a crucial element of any serious commitment to reduce equipment- and process-related wastes through total productive maintenance (TPM) and other lean manufacturing methods like Operational Excellence, Six Sigma or World Class Manufacturing.

OEE (The six big losses)

Schedule LossesLosses Due to PlanningForecastingInitial Capacity PlanningProduction PlanningExecutionSkillsMachine ConditionMaterial Quality

Forecasting

Principles of ForecastingForecasts are rarely perfect Forecast accuracy is: higher for shorter time horizonsGrouped forecasts are more accurate than individual items

Step-by-Step

Types of Forecasting Methods

Qualitative Methods

Types of Qualitative Models

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TypeCharacteristicsStrengthsWeaknesses

Executive opinionA group of managers meet & come up with a forecastGood for strategic or new-product forecastingOne person's opinion can dominate the forecast

Market researchUses surveys & interviews to identify customer preferencesGood determinant of customer preferencesIt can be difficult to develop a good questionnaire

Delphi methodSeeks to develop a consensus among a group of expertsExcellent for forecasting long-term product demand, technological changes, and scientific advancesTime consuming to develop

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Quantitative Methods

Types of Quantitative MethodsTime Series Models:Assumes the future will follow same patterns as the pastCausal Models:Explores cause-and-effect relationshipsUses leading indicators to predict the future

Capacity PlanningInitial Capacity PlanningHow Much Do we need? (Volume)When do we need? (Time horizon)Where to make? (Location)Capacity Expansion Lead StrategyLag StrategyAverage

Initial Capacity PlanningCapacity decisions are important because:1. They have an impact on the ability of an organisation to meet future demands.2. There is a definite relation between capacity & operating costs.3. Initial investments depends upon capacity decisions.4. It involves long term commitment of resources.

Three BasicQuestionWhat kind of capacity is needed?(Intended product or services)How Much is needed? (The agony of too much & too little CAPACITY.)When it is needed? (Opportunity missed is opportunity lost)Capacity Planning

How much?

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Cost-Volume Analysis

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Variable Cost ($/Unit)Fixed Cost (000$/year)output02468101214161820

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Location Cost-Volume Analysis

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Capacity ExpansionVolume & certainty of anticipated demandStrategic objectives for growthCosts of expansion & operationIncremental or one-step expansion

Capacity Expansion Strategies

Strategies for Meeting Non-Uniform DemandBuild up inventoryBack-orderingSmooth demand through marketingincrease price to reduce demanddecrease price to increase demand

Strategies for Meeting Non-Uniform DemandVary capacity overtimeextra shiftssubcontractinghiring and layoffs

Operational Losses

Loss Categories The Six Big Losses Downtime (lost availability) Equipment failures. Setup and adjustments Speed losses (lost performance) Idling and minor stoppages. Reduced speed operation Defect losses (lost quality) Scrap and rework Start-up losses

Implementing TPM means striving toward a vision of the ideal manufacturing situation, a vision that encompasseszero breakdowns zero abnormalities zero defects zero accidents The path to this ideal situation is a process of continuous improvement that requires the total commitment of everyone in the company, from operators to top management.OEE (TPM)

OEE (TPM)Total productive maintenance (TPM) was first defined in 1971 by the Japan Institute of Plant Maintenance (JIPM). TPM is a company wide strategy to increase the effectiveness of production environments The difference between the ideal and the actual situation, in factory operations, is due to losses. Equipment operators face the results of these losses on a daily basis. TPM gives them the tools to identify the losses and make improvements. A key strategy in TPM is identifying and reducing what we call the six big losses.

Maintenance ManagementThe organisation of maintenance activities within an agreed policyA formalised Framework, accepted by senior mngt.,, within which decisions on maintenance are taken.

THE ELEMENTS OF OVERALL EQUIPMENT EFFECTIVENESS

Overall Equipment Effectiveness(OEE)Overall Equipment EffectivenessInconsistent Times,Insufficient skills.Poor Planning & scheduling,Different methods,Poor tooling,Poor start up controlsMissing parts,Insufficient support,Excess start-up adjustmentLack of maintenance,Low operator interest,not knowing of problems,Poor Training,Design Problems,Inferior MaterialMaterial not available,Change over at start/endJams/misfeeds/ overloads,operator error,operator absenceUnclear design specs.,poor maintenance history,incorrect settings, Poor Training,Speed deliberately reduced,inconsistent Material Poor machine changeover,Inconsistent materials,No start-up check lists,Waiting for temp. - pressures,Minor adjustmentsTemperature & pressure changes,inconsistent materials, Process not followed,poor calibration,Gauges not calibrated properly.

We recommend that the operator collect the daily data about the equipment for use in the OEE calculation. Collecting this data willteach the operator about the equipment focus the operators attention on the losses grow a feeling of ownership of the equipment The shift leader or line manager is often the one who will receive the daily operating data from the operator and process it to develop information about the OEE. Working hands on with the data will;give the leader/manager basic facts and figures on the equipment help the leader/manager give appropriate feedback to the operators and others involved in equipment improvement allow the leader to keep management informed about equipment status and improvement results

*Introduction:Planning is an integral part of a managers job. If uncertainties cloud the planning horizon, managers will find it difficult to plan effectively. Forecasts help managers by reducing some of the uncertainty, thereby enabling them to develop more meaningful plans. A forecast is a statement about the future.There are two uses for forecasts. One is to help managers plan the system, and the other is to help them plan the use of the system. Planning the system generally involves long-range planning about the types of products & services to offer, what equipment and facilities to have, where to locate and so on. Planning the use of the system refers to short-range & intermediate-range planning, which involves tasks such as planning inventory & work force levels, planning purchasing & production, budgeting & scheduling.Business forecasts pertains to more than predicting demand. Forecasts are also used to predict profits, revenues, costs, productivity changes, prices and availability of energy and raw materials, interest rates, movements of key economic indicators (e.g. GNP, inflation, government borrowings), and prices of stocks and bonds. Keep in mind, however, that the concepts and techniques apply equally well to the other variables.In spite of its use of computers & sophisticated mathematical models, forecasting is not an exact science. Instead, successful forecasting often requires a skilful blending of art and science. Experience, judgement and technical expertise all play a role in developing useful forecasts. Along with these a certain amount of luck and dash of humility can be helpful because the worst forecasters occasionally produce a very god forecast and even the best forecasters sometime miss completely. Many forecasting techniques are available, some work better than the others, but no single technique works all the time. Generally speaking, the responsibility for preparing demand forecasts in business organisations lies with marketing or sales rather than operations. Nonetheless, operation people are often called to make certain forecasts & to help others prepare forecasts. In addition, since forecasts are major inputs for many operations decisions, operation managers and staff must be knowledgeable about the kinds of forecasting techniques available, the assumptions that underlie their use and their limitations. In short, forecasting is an integral part of operations management. *FEATURES COMMON TO ALL FORECASTSA wide variety of forecasting techniques are in use. In many respects they are quite different from each other, as you shall soon discover. Nonetheless, certain features are common to all, and it is important to recognise then..Forecasting techniques generally assumed that the same underlying casual system that existed in the past will continue to exist in the future. Comment:A manager cant simply delegate forecasting to models or computers and then forget about it, because unplanned occurrences can wreak havoc with forecasts. For instance, weather-related events, tax increases or decreases, and changes in features or prices of competing products are services can have a major impact on demand for a companys products or services. Consequently, a manager must be alert to such occurrences and be ready to override forecasts, which assume a stable casual system. .Forecasts are rarely perfect; actual results usually differ from predicted values. No one can predict precisely how an often large number of related factors will impinge upon the variable in question; this, and the presence of randomness, precludes a perfect forecast. Allowances should be made for inaccuracies..Forecasts for groups of items tend to be more accurate than forecast for individual items because forecasting errors among items in a group usually have a cancelling effect. Opportunities for grouping may arise it parts or raw materials are used for multiple products or if a product or service is demanded by a number of independent sources. .Forecast accuracy decreases as in time period covered by forecastthe time horizonincreases. Generally speaking, shortrange forecast must contend with the fewer uncertainties than longer-range forecasts, so they tend to be more accurate.An important consequence of the last point is that flexible business organizations---that is, those which can respond quickly to change in demandrequire a shorter forecasting horizon and, hence, benefit from more accurate short line range forecasts than competitors who are less flexible and who must therefore use longer forecast horizons.*STEPS IN THE FORECASTING PROCESSThere are five basic steps in the forecasting process:.Determine the purpose of the forecast. What is its purpose and when will it be needed? This will provide an indication of the level of detail required in the forecast, the amount of resources (men power, computer time, dollars) that can be justified, and the level of accuracy necessary.Establish a time horizon. The forecast must indicate a time limit, keeping in mind that accuracy decreases as the time horizon increases.Select a forecasting technique.Gather and analyse the appropriate data. Before a forecast can be prepared, data must be gathered and analysed. Identify any assumptions that are made in conjunction with preparing and using the forecast.Prepare the forecast.Monitor the forecast. A forecast has to be monitored to determined whether it is performing in a satisfactory manner. If it is not, re-examine the method, assumptions, validity of data, and so on; modify as needed; and prepare a revised forecast.

*APPROACHED TO FORECASTINGThere are two general approaches to forecasting; qualitative and quantitative. Qualitative method consists many of subjective inputs, which often defy precise numerical description. Quantitative methods involve either the extension of historical data or the development of associative models that attempt to utilise casual (explanatory) variables to make a forecast.Qualitative techniques permit inclusion of soft information (e.g., human factors, personal opinion, hunches) in the forecasting process. Those factors are often omitted or downplayed when quantitative techniques are used because they are difficult or impossible to quantify. Quantitative techniques consist mainly of analysing objective, or hand, data. They usually avoid personal biases that some times contaminate qualitative method. In practice, either or both approaches might be used to develop a given forecast. Forecasts Based on Judgement and Opinion Judgmental forecasts rely on analysis of subjective inputs obtained from various sources, such as consumer surveys, the sales staff, manager and executives, and panels of experts. Quite frequently, these sources provide insights that are not otherwise available.Forecasts Based on Time Series (Historical) DataSome forecasting techniques depend on uncovering relationships between variables that can be used to predict future values of one of them; others simply attempt to project past experience into the future. The second approach exemplifies forecasts that use historical, or time series, data with the assumptions that the future will be like the past. Some models merely attempt to smooth out random variations in historical data; others attempt to identify specific patterns in the data. In effect, approaches based on historical data treat the data as a mirror that reflects the combination of all forces influencing the variable in question (e.g., demand) without trying to identify or measure those forces directly.Associative ForecastsAssociative models identify one or more explanatory variables that can be used to predict future demand. For example, demand for paint might be related to variables such as the price per gallon and amount spent on advertising, as will as specific characteristics of the paint (e.g., drying time, ease of cleanup). The analysis in these cases yields a mathematical equation that enables the manager to predict volume of sales, for example, on the basis of given values of the explaining variable(s).*FORECASTS BASED ON JUDGEMENT AND OPINIONIn some situations, forecasts are developed without the use of historical data. When a forecast must be prepared quickly, there is not always enough time to gather and analyse quantitative data. At other times, especially when political and economic conditions are changing, available data may obsolete and more up to data information might not yet be available. Similarly, the introduction of new products and the re-design of existing product or packaging suffer from the absence of historical data that would be useful in forecasting. In such instances, forecasts are based on executive opinions, consumer surveys, opinions of the sales staff, and opinions of the experts.Executive OpinionsA small group of upper-level managers (e.g., in marketing, product, engineering, manufacturing, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long range planning and new product development. It has the advantage of bringing together the considerable knowledge and talents of management people. However, there is the risk that the view of one person will prevail and the possibility that diffusing responsibility that the forecast over the entire group may result in less pressure to produce a good forecast.Sales Force CompositeThe sales staff is often a good source of information because of it direct contact with consumers. Thus, sales peoples are often aware of any planes of the customers may be considering for the future. They are, however, several drawbacks to this approach. One is that sales peoples may be unable to distinguish between what customers would like to do and what they actually will do. Another is that sales peoples are sometimes overly influenced by recent experiences. Thus, after several periods of low sales, their estimates may tend to become pessimistic. After several periods of good sales, they may tend to be too optimistic. In addition, if forecasts are used to establish sales quotas, there will be conflict of interest because it is in the sales persons advantage to provide low sales estimates.

*Opinions of Managers and StaffA manager may use staff to generate a forecast or to provide several forecasting alternatives from which to choose. At other times, a manager may solicit opinions from a number of other managers and/staff peoples. The Delphi method is sometimes useful in this regard. This method works by circulating a series of questionnaires among individuals who posses the knowledge and ability to contribute meaningfully. Responses are kept anonymous, which tends to encourage honest responses. Each new questionnaire is developed using the information extracted from the previous one, thus enlarging the scope of information on which participants can base their judgements. The goal is to achieve a consensus forecast. The Delphi Method originated in the Rand Corporation in 1948, where it was used to assess the potential impact of an atomic bomb attack on the United States. Since that time, it has been applied to a variety of situations, not all of which involve forecasting. The discussion here is limited to its use as a forecasting tool. As a forecasting tool, the Delphi Method is useful for technological forecasting; that is, the technique is a method for assessing changes in technology and their impact on an organization. Often the goal is to predict when a certain event will occur. For instance, the goal of a Delphi forecast might be to predict when Video Telephones might be installed in at least fifty percent of residential homes or when a vaccine for a disease might be developed and ready for mass distribution. For the most part, there are long term, single time forecasts, which usually have very little hand information to go by or data are costly to obtain, so the problem does not lend itself to analytical technique. Rather, judgements of experts or others who posses sufficient knowledge to make prediction are used. The main reasons for using a Delphi approach are the following:.The group of experts can provide needed judgmental inputs..More individuals may be needed than can interact effectively in a face to face situation, and/or the individuals cannot be conveniently assembled in one place. Time and cost can also be factors..It is important to avoid a band wagon effect..It is desirable to preserve the anonymity of participants. The Delphi method also has a number of weaknesses, some of which are fairly serious:.The Questions may contain ambiguous phrasing so that the panel members reach a false consensus. .Panel membership may change, especially if the process requires a long time (some may take a year or more)..The experts may not be experts..Studies have failed to prove that Delphi forecasts generally achieve a high degree of accuracy.Preserving anonymity removes accountability and responsibility.

*FORCASTS BASED ON TIME SERIES DATAA time series is time-ordered sequence of observations taken at regular intervals over a period of time (e.g., hourly, daily, weekly, quarterly, annually). The data may be measurements of demand, earnings, profits, shipments, accidents, output, precipitation, productivity, and the consumer price index. Forecasting techniques based on time series data are made on the assumption that future values of the series can be estimated from past values. Although no attempt is made to identify variables that influence the series, these methods are widely used, often with quite satisfactory results.Analysis of time series data requires the analyst to identify the underlying behavior of the series. This can often be accomplished by merely plotting the data and visually examining the plot. One or more patterns might appear: trends, seasonal variations, cycles, and constant variations (variations around an average). In addition, there can be random or irregular variations. These behaviours can be described as follows:Trend refers to a gradual, long-term movement in the data. population shifts, changing incomes, and cultural changes often for such movements.Seasonality refers to short-term, fairly regular variations generally related to factors such as weather, holiday, and vacations. Restaurants, supermarkets, and theaters experience weekly and even daily seasonal variations.Cycles are wavelike variations of more than one years duration. These are often related to a variety of economic political, and even agricultural conditions.Irregular variations are due to unusual circumstances such as severe weather conditions, strikes, or a major change in a product or service. They do not reflect typical behaviour, and inclusion in the series can distort the overall picture. Whenever possible, these should be identified and removed from the data.Random Variations are residual variations that remain after all other behaviour have been accounted for.These behaviours are illustrated in Figure 10-1. The small bumps in the plots represent random variability.The remainder of this section has description of the various approaches to the analysis of time series data. Before turning to those discussions, one point should be emphasised: A demand forecast should be based on a time series of pas demand rather than sales. Sales would not truly reflect demand unless demand was less than the amount of a good or service available for sale. Similarly, shipments would not truly reflect demand if there are backlogs of orders; the timing of shipment would not correspond to the timing of demand.

*Techniques for AveragingHistorical data typically contain a certain amount of random variation, or noise, that tends to obscure systematic movements in the data. this randomness arises from the combined influences of manyperhaps a great manyrelatively unimportant factors, and it cannot be reliable predicted. Ideally, it would be desirable to completely remove any randomness from the data and leave only real variations, such as changes in the level of demand (e.g., a step change). As a practical matter, however, it is usually impossible to distinguish between these two kinds of variations, so the best one can hope for is that the small variations are random and the large variations are the real thing.Averaging techniques smooth out some of the fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average. A forecast based on an average thus tends to exhibits less variability than the original data (see Figure 10-2). This can be advantageous because many of these movements merely reflect random variability rather than a true change in level, or trend, in the series. Moreover, because responding to changes in expected demand often entails considerable cost (e.g., changes in production rate, changes in the size of a work force, inventory changes), it is desirable to avoid reacting to minor variations. Thus, minor variations are treated as random variations, whereas larger variations are viewed as more likely to reflect real changes, although these too, are smoothed to a certain degree.Averaging techniques generate a forecast that reflects recent values of a time series (e.g., the average value over the last several periods). These techniques work best when a series tends to vary round an average, although they can also handle step changes or gradual changes in the level of the series. Three techniques are described in this section:.Naive Forecasts.Moving Averages.Exponential smoothing

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