Chapter 1
INTRODUCTION
In the most general sense, OEE (Overall Equipment Effectiveness) can be described as a universally accepted set of metrics that bring clear focus to the key success drivers for manufacturing enterprises. The OEE strategy is considered "best practice" and dovetails well with the Lean Manufacturing philosophy. In fact, the OEE set of metrics can provide the key indicators of progress on the Lean journey.
Every Manufacturing Industries are using various manufacturing strategy for their Purposes and their improvements. Now in the world, every industry is going to do "How to Improve" their exits manufacturing work centres and reducing cost. Every Factory Attempts to be an effective, low cost producer. This effort is required in today's challenging environment when customers demand quality product at the best value. Few Industries are doing high level productivity and low costs. Many of these use a correct approach to identify the best improvements to make. Manufacturing industries uses teams to avoid the root problems that otherwise they can not keep their higher levels of effectiveness. They have found Overall Equipment Effectiveness Technology for their improvements.
Aims and Objectives
Aim
To analyse a method an approach to determine the reducing Energy costs through Overall Equipment Effectiveness Technology.
Objectives
Undertake Literature review and survey of Overall Equipment Effectiveness Technology.
Understand about Overall Equipment Effectiveness Technology and Energy costs.
Identify the important factors of Equipment Effectiveness Technology.
Develop a new model of Equipment Effectiveness Technology for reducing Energy costs.
Formation of Overall Equipment Effectiveness
In the competitive world of Industry and Technology, management of improvements through their objectives is vital. Many Key Performance Indicators (KPIs) have been developed across industry. The measurement of the effectiveness of manufacturing equipment, or the Manufacturing Equipment Performance needs to be quantified to determine if the plant is under-achieving. A quantifiable indicator is Overall Equipment Effectiveness (OEE). The measurement of OEE is increasingly common in manufacturing industries as it provides a measure of the machine performance. [w1]
In the manufacturing Industry, the usage of installed capacity is rather low for various reasons.. Consider to the Overall utilization of capacity of 60% in a number of Swedish industry. The completion of total productive maintenance has shown according the results in Japanese Industries. It is increased the level of overall efficiency from 60% to 90% according to Nakajima (1989) which indicates a major increase of product. The implementation of total productive maintenance is a difficult process of corporate change and also never reported of failure of machines. Then Shaffter and Thomson (1992) have also observed that most companies implementing "total quality" or "continuous improvement" fail to achieve results. Total productive maintenance have the foundation for planning and directing activities towards operator maintenance, preventive maintenance etc. In between the 1960s and 70s for the Japanese Industry, Overall Equipment Effectiveness (OEE) is a key metric used by many manufacturing industries today, to improve line and overall plant efficiency. [1]
Through the performance measurement can be quantified as machine hours or capacity utilized. The focus can be put on reducing idle Time and down Time according to Wild (1971).The concept of "down time" is relative to Availability and reliability. In Japan (1970s), Total productive maintenance which is a concept for corporate change, also includes a way of defining Overall Equipment Effectiveness (OEE).The Overall Equipment Effectiveness includes downtime and other production losses which reduce throughput. [1]
Total Productive maintenance
The Overall Equipment Effectiveness was modification of Total Productive maintenance management strategy. If want to continue Equipment Effectiveness, we must do Equipment maintenance in manufacturing industry. TPM is a Japanese idea that can be traced back to 1951 when preventive maintenance was introduced into Japan from the USA. Nippondenso, part of Toyota, was the first company in Japan to introduce plant wide preventive maintenance in 1960. In preventive maintenance operators produced goods using machines and the maintenance group was dedicated to the work of maintaining those machines.
1.3.1 Definition of Total Productive Maintenance
Total Productive Maintenance is a new way of looking at maintenance a reversion to old ways but on a mass scale. In TPM the machine operator performs much, and sometimes all, of the routine maintenance tasks themselves. This auto maintenance ensures appropriate and effective efforts are expended since the machine is wholly the domain of one person or team. TPM is a critical adjunct to lean manufacturing. The maintenance group performed equipment modification that would improve its reliability. These modifications were then made or incorporated into new equipment.
The work of the maintenance group is then to make changes that lead to maintenance prevention. Thus preventive maintenance along with Maintenance prevention and Maintainability Improvement were grouped as Productive maintenance. The aim of productive maintenance was to maximize plant and equipment effectiveness to achieve the optimum life cycle cost of production equipment. [2]
Fig 1. TPM concept
1.3.2 The Aims of Total Productive Maintenance
To maximize the effectiveness of all production equipment.
The use of the team approach - small group working.
To involve all departments that plan, use or maintain equipment.
The TOTAL involvement of everyone, from top management to all employees.
The implementation of a comprehensive maintenance program for all equipment throughout its life.
Avoid wastage in a quickly changing economic environment.
Producing goods without reducing product quality.
Reduce cost.
Produce a low batch quantity at the earliest possible time.
Goods send to the customers must be non defective
1.3.3 Typical TPM Benefits
Cleaner, tidier working environment.
Machine breakdowns reduced by 50%.
Defects reduced by up to 30 times.
Accidents reduced to Zero.
Productivity maximized.
Follow pollution control measures.
Increase productivity and OPE (Overall Plant Efficiency) by 1.5 or 2 times.
Rectify customer complaints.
Reduce the manufacturing cost by 30%.
Satisfy the customers' needs by 100 % (Delivering the right quantity at the right time, in the required quality.)
Reduce accidents.
The timescale required to achieve these levels of benefits will vary dramatically between organizations. [W1]
Chapter 2
2. Literature survey of Overall Equipment Effectiveness
Technology
2.0 Introduction
In this chapter introduces definition of Overall Equipment Effectiveness technologies and its functions. Many companies measure the Utilization, Throughput and Quality of a process, but fail to consider the combined effect of these on overall performance.
Overall Equipment Effectiveness is:
A measure which embraces all losses to 'good' output that can occur on any machine or process
The standard machine performance measure adopted by World Class companies
The Overall Equipment Effectiveness measure is based on the premise that all production losses on machines and processes can be quantified.
Fig 2. OEE System
2.1 Definition of Overall Equipment Effectiveness (Technology)
Overall Equipment Effectiveness has several Definitions. It is following below,
Overall Equipment Effectiveness is a system of measure that provides continuous visibility to manufacturing productivity and waste.
Overall Equipment Effectiveness provides clear focus on utilization of facilities and equipment.
Overall Equipment Effectiveness is accepted worldwide as the standard for quantifying manufacturing success.
Overall Equipment Effectiveness is a best practices way to monitor and improve the effectiveness of manufacturing process.
Overall Equipment Effectiveness is frequently used as a key metric in total productive maintenance and lean manufacturing programs and gives a consistent way to measure the effectiveness of total productive maintenance and other initiatives by providing an overall framework for measuring production efficiency.
Overall equipment effectiveness (OEE) is a hierarchy of metrics which focus on how effectively a manufacturing operation is utilized. The results are stated in a generic form which allows comparison between manufacturing units in differing industries.[w4]
Overall equipment effectiveness stands for Overall Equipment Effectiveness and shows the effectiveness of a machine compared to the ideal machine in percentage. The difference is made up of the time loss, speed loss and quality loss.[w3]
Fig 3.OEE score
2.2 Important Factors of Overall Equipment Effectiveness
The Overall Equipment Effectiveness hierarchy consists of two-top level factors and four underlying measures.
2.2.1 The two-top level factors
Overall Equipment Effectiveness (OEE) and Total Effective Equipment Performance (TEEP) are two closely related measurements that report overall utilization of facilities, time and material for manufacturing operations. These top view metrics directly indicate the gap between actual and ideal performance.
Overall Equipment Effectiveness (OEE) quantifies how well a manufacturing industry performs relative to its design capacity, during the periods when it is scheduled to run.
Total Effective Equipment Performance (TEEP) measures OEE effectiveness against calendar hours. In many sitting, management is especially interested in how well a factory's key assets are used relative to total calendar time. Total Effective Equipment Performance is indicates opportunities that might exist between current operations and world class level. It reveals the hidden factory that can and should be leverage to make the company more competitive. Like OEE, TEEP must be used in combination with financial information.
2.2.2 The Four Underlying Factors
In addition to above factors, there are four underlying factors that provide understanding as to why and where OEE and TEEP performance gaps exist. The four underlying measures are,
Loading
Availability
Performance
Quality
Loading: The part of the Total Effective Equipment Performance metric that represent the percentage of total calendar Time that is actually scheduled for operation.
Availability takes into account Down Time Loss, which includes any Events that stop planned production for an appreciable length of time. Examples include equipment failures, material shortages, and changeover time. Changeover time is included in OEE analysis, since it is a form of down time. While it may not be possible to eliminate changeover time, in most cases it can be reduced. The remaining available time is called Operating Time
Performance takes into account Speed Loss, which includes any factors that cause the process to operate at less than the maximum possible speed, when running. Examples include machine wear, substandard materials, and operator inefficiency. The remaining available time is called Net Operating Time.
Quality takes into account Quality Loss, which accounts for produced pieces that do not meet quality standards, including pieces that require rework. The remaining time is called Fully Productive Time. The goal is to maximize Fully Productive Time. [w5]
Fig 4. OEE Performance Metrics
2.3 Overall Equipment Effectiveness Losses
OEE LOSSES
Down Time Loss Speed loss Quality Loss
(Availability) (Performance) (Quality)
Availability Losses. (Down Time Losses)
Availability Loss is a general term for any loss which causes a machine to be unavailable to produce good products.
Breakdowns
Changeovers
When a machine has broken down it is unavailable for production. In some OEE systems a machine is only considered to be 'broken down' if a technician is required to restart the machine.
Performance Loss. (Speed losses)
Performance Loss is a general term for a loss occurring during production which reduces the performance of the machine. Performance Loss is sometimes also referred to as Speed Loss.
Short Stops:
When a machine is in production and it stops for a short period of time for a minor fault that the operator can correct in a few seconds, this is called a Short Stop. There are a lot of minor faults happening simultaneously.
Speed Loss (Slow Running):
This loss is not usually apparent by simply looking at a machine. Generally when a machine is running and producing good parts, it may appear that all is well. However there may still be a loss occurring if the machine is operating below its designed speed. That is, if the machine is designed to produce 1000 parts per hour but for some reason is actually only producing 750 units per hour then it is only running at 75% of its capability and there is a 25% loss due to Slow Running.
Quality Losses.
Quality Loss is the general term for the time lost producing bad or rejects parts.
Yield Loss:
When a machine produces a defect not only is the material used in producing the defect lost or in need of rework but the time and other resources used producing the piece are also wasted. A machine with ten defects per hundred is in effect only achieving a yield of ninety per cent of its capability.
Start-up Loss:
If a machine needs to be set up by doing some trial production then the material used is wasted. For example setting up a machine at the start of a shift could involve producing one or two test pieces and then making adjustments until the set up is perfect. The material lost and the time spent producing it is both wasted and again this is a problem. [W6]
fig 5. OEE Losses
2.4 Six Big Losses
One of the major goals OEE programs is to reduce and eliminate what are called the Six Big Losses - the most common causes of efficiency loss in manufacturing. The following table lists the Six Big Losses, and shows how they relate to the OEE Loss categories.
Six Big Loss Category
OEE Loss Category
Event Examples
Comment
Breakdowns
Down Time Loss
Tooling Failures
Unplanned Maintenance
General Breakdowns
Equipment Failure
There is flexibility on where to set the threshold between a Breakdown (Down Time Loss) and a Small Stop (Speed Loss).
Setup and Adjustments
Down Time Loss
Setup/Changeover
Material Shortages
Operator Shortages
Major Adjustments
Warm-Up Time
This loss is often addressed through setup time reduction programs.
Small Stops
Speed Loss
Obstructed Product Flow
Component Jams
Misfeeds
Sensor Blocked
Delivery Blocked
Cleaning/Checking
Typically only includes stops that are under five minutes and that do not require maintenance personnel.
Reduced Speed
Speed Loss
Rough Running
Under Nameplate Capacity
Under Design Capacity
Equipment Wear
Operator Inefficiency
Anything that keeps the process from running at its theoretical maximum speed (a.k.a. Ideal Run Rate or Nameplate Capacity).
Startup Rejects
Quality Loss
Scrap
Rework
In-Process Damage
In-Process Expiration
Incorrect Assembly
Rejects during warm-up, startup or other early production. May be due to improper setup, warm-up period, etc.
Production Rejects
Quality Loss
Scrap
Rework
In-Process Damage
In-Process Expiration
Incorrect Assembly
Rejects during steady-state production.
Now that we know what the Six Big Losses are and some of the events that contribute to these losses, we can focus on ways to monitor and correct them. Categorizing data makes loss analysis much easier, and a key goal should be fast and efficient data collection, with data put to use throughout the day and in real-time.
Breakdowns
Eliminating unplanned Down Time is critical to improving OEE. Other OEE Factors cannot be addressed if the process is down. It is not only important to know how much Down Time your process is experiencing but also to be able to attribute the lost time to the specific source or reason for the loss With Down Time and Reason Code data tabulated, Root Cause Analysis is applied starting with the most severe loss categories.
Setup and Adjustments
Setup and Adjustment time is generally measured as the time between the last good parts produced before Setup to the first consistent good parts produced after Setup.
Small Stops and Reduced Speed
Small Stops and Reduced Speed are the most difficult of the Six Big Losses to monitor and record. Cycle Time Analysis should be utilized to pinpoint these loss types. In most processes recording data for Cycle Time Analysis needs to be automated since cycles are quick and repetitive events that do not leave adequate time for manual data-logging.
By comparing all completed cycles to the Ideal Cycle Time and filtering the data through a Small Stop Threshold and Reduced Speed Threshold the errant cycles can be automatically categorized for analysis. The reason for analyzing Small Stops separately from Reduced Speed is that the root causes are typically very different, as can be seen from the Event
Start-up Rejects and Production Rejects
Startup Rejects and Production Rejects are differentiated, since often the root causes are different between startup and steady-state production. Parts that require rework of any kind should be considered rejects. Tracking when rejects occur during a shift and job run can help pinpoint potential causes, and in many cases patterns will be discovered. [w5]
fig 6.Over view of Overall Equipment Effectiveness
2.5 Measurement of Overall Equipment Effectiveness
Why measure Overall Equipment Effectiveness?
Favourable changes in Overall Equipment Effectiveness directly lead to gains in profitability.
Reduced variable manufacturing costs
Reduced inventory as the manufacturing processes become more reliable
Overall Equipment Effectiveness is the universal yardstick allowing benchmarking of manufacturing effectiveness.[w2]
2.6 World Class Overall Equipment Effectiveness
OEE is essentially the ratio of Fully Productive Time to Planned Production Time. In practice, however, OEE is calculated as the product of its three contributing factors:
OEE = Availability x Performance x Quality
This type of calculation makes OEE a severe test. For example, if all three contributing factors are 90.0%, the OEE would be 72.9%. In practice, the generally accepted World-Class goals for each factor are quite different from each other, as is shown in the table below.
OEE Factor
World Class
Availability
90.0%
Performance
95.0%
Quality
99.9%
Overall OEE
85.0%
Table 1
Of course, every manufacturing plant is different. For example, if your plant has an active Six Sigma quality program, you may not be satisfied with a first-run quality rate of 99.9%.
Worldwide studies indicate that the average OEE rate in manufacturing plants is 60%. As you can see from the above table, a World Class OEE is considered to be 85% or better. Clearly, there is room for improvement in most manufacturing plants.
2.7 Relation between Energy cost and OEE
Most of the manufacturing companies have trouble to accurately measuring the costs of the proposed project. Financial statements are scorecards used to communicate and benchmark a company's business. Understanding the link between OEE and financial statements is principal important in ranking reliability and improvement project.
Remember that OEE strategy applied to factory's bottlenecks as well as other key areas that are either higher cost. By focusing on bottlenecks at key stages in the factory, OEE is true measure of factory out put. When measure the performance, availability and quality preciously and also keep good equipment maintenance, we can reduce the energy costs.
Fig 7. Overview of OEE Process
Chapter 3
3. Overall Equipment Effectiveness Calculations
3.1 Overall Equipment Effectiveness (OEE)
Formulas use of OEE calculations
The OEE calculation is based on the three OEE Factors, which are Availability, Performance, and Quality. [3]
OEE(%) = Availability(%) X Performance(%) X Quality(%)
Availability
Availability is calculated by Down Time Loss, and that is expressed as:
Availability = Operating Time / Planned Production Time
Performance
Performance is calculated by Speed Loss, and that is expressed as:
Performance = Ideal Cycle Time / (Operating Time / Total Pieces)
Ideal Cycle Time is the minimum cycle time that process can be expected to achieve in optimal circumstances. It is sometimes called Design Cycle Time or Theoretical Cycle Time. [3]
Performance can also be calculated as:
Performance = (Total Pieces / Operating Time) / Ideal Run Rate
Quality
Quality is calculated by Quality Loss and that is expressed as:
Quality = Good Pieces / Total Pieces
OEE
OEE takes into account all three OEE Factors, and that is expressed as:
OEE (%) = Availability (%) x Performance (%) x Quality (%)
It is very important to identify that improving OEE is not the only objective.
Example of OEE Calculation
Take a look at the following data for two production shifts.
OEE Factor
Shift 1
Shift 2
Availability
91.0%
94.0%
Performance
97.0%
96.0%
Quality
99.6%
98.0%
OEE
87.9%
88.5%
Table 2
From the output, it may show that the second shift is performing better than the first, since its OEE is higher. The OEE is not that it gives you one magic number; it's that it gives you three numbers, which are all useful individually as your situation changes from day to day. And it helps you visualize performance in simple terms - a very practical simplification. [w5]
Example OEE Calculation in manufacturing factory
The table below shows theoretical shift data, to be used for a complete OEE calculation, starting with the calculation of the OEE Factors of Availability, Performance, and Quality. Note that the same units of measurement (in this case minutes and pieces) are again and again used during the calculations. [w5]
Item
Data
Shift Length
8 hours = 480 min.
Short Breaks
2 @ 15 min. = 30 min.
Meal Break
1 @ 30 min. = 30 min.
Down Time
47 minutes
Ideal Run Rate
60 pieces per minute
Total Pieces
19,271 pieces
Reject Pieces
423 pieces
Table 3
Planned Production Time
= [Shift Length - Breaks]
= [480 - 60]
= 420 minutes
Operating Time
= [Planned Production Time - Down Time]
= [420 - 47]
= 373 minutes
Good Pieces
= [Total Pieces - Reject Pieces]
= [19,271 - 423]
= 18,848 pieces
Availability
=
Operating Time / Planned Production Time
=
373 minutes / 420 minutes
=
0.8881 (88.81%)
Performance
=
(Total Pieces / Operating Time) / Ideal Run Rate
=
(19,271 pieces / 373 minutes) / 60 pieces per minute
=
0.8611 (86.11%)
Quality
=
Good Pieces / Total Pieces
=
18,848 / 19,271 pieces
=
0.9780 (97.80%)
OEE
=
Availability x Performance x Quality
=
0.8881 x 0.8611 x 0.9780
=
0.7479 (74.79%)
3.2 Total Effective Equipment Effectiveness (TEEP)
The Loading part of the TEEP factor represents the percentage of time that an operation is scheduled to operate compared to the Calendar Time That is available. The Loading factor is a pure measurement of schedule effectiveness and is designed to exclude the effects how well that operation may perform.
Loading
Loading is calculated as:
Loading = Schedule Time / Calendar Time
Sample Calculation of loading:
A given Work centre is scheduled to run 6 Days per week, 18 hours per Day,
For a given week, the total Calendar Time is 7 Days at 24 hours.
Loading = (6 days x 18 hours) / (7days x 24 hours) = 64.28 %
TEEP
TEEP takes into account all three OEE Factors, and loading that is expressed as:
TEEP (%) = OEE (%) x Loading (%)
According to above calculation, The TEEP is calculated as:
TEEP (%) = OEE (%) x Loading (%)
= 74.79 % x 64.28 %
= 48.07 %
Visualizing OEE/TEEP by use of a Cascade Chart
To more clearly understand the metrics and their roles, a Cascade Chart is useful. An example of comprehensive OEE/TEEP presentation is shown below in a sample report
The left three bars indicate what is presented to the production team in terms of scheduled time and/or booked business volume. This combination is also referred to as Loading. The three bars on the right side display how effectively that scheduled capacity is utilized. This is the OEE performance. Note that the blue OEE Goal Line depicts plant OEE targets based on the actual Loading. In total, the six bars signify the progressive erosion of ideal capacity and the categories of loss that are responsible. The remaining utilized capacity, after losses, is reflected by the TEEP metric. It can now be seen that TEEP = Loading x OEE. Study of the Cascade Chart reveals that each category of loss in the Capacity Stream can now be identified. Ownership of each loss will naturally flow to specific functional areas. [w5]
Chapter 4
4. Analysis of Overall Equipment Effectiveness
4.0 Introduction
In the manufacturing factories, data collection is very difficult. We can take measurement by two methods. That is Automatic measurement using computers and manual measurement by using supervisor or foreman. The quality of data collected determines the accuracy of OEE estimate. After data collections, that should be analyse for OEE purpose.
4.1 Purpose of Overall Equipment Effectiveness
The overall Equipment Effectiveness measure can be applied at several different levels within a manufacturing environment. Overall Equipment Effectiveness can be used as a benchmark for measuring the initial performance of a manufacturing plant in its total. In this manner the initial overall Equipment Effectiveness (OEE) measure can be compared with future overall Equipment Effectiveness values, thus quantifying the level of improvement. An overall Equipment Effectiveness (OEE) value Calculated for one manufacturing line can be used to compare line performance across the factory, thereby highlighting any poor line performance.
If the machine's processes work individually, an overall Equipment Effectiveness measure can identify which machine performance is worst, and therefore indicate where to focus TPM resources. [3]
The overall Equipment effectiveness measure could provide topical information for daily decision making that is preventive maintenance, material utilisation, accidents, absenteeism, labour recovery, conformance to schedule, set - up and changeover data, etc. However, the overall Equipment effectiveness (OEE) goes far beyond the task of monitoring and controlling. It takes into account process improvement initiatives, prevents the sub - optimisation of individual machines or product lines, provides a systematic method for establishing production targets, and incorporates practical management tools and techniques in order to achieve a balanced view of process availability, performance rate and quality. [3]
4.2 Data collection Process
Overall Equipment Effectiveness (OEE) continues to gain acceptance as an effective method to measure production performance. Capturing and recording accurate production performance information is critical for producing reliable OEE Reports. There are two type of Overall Equipment Effectiveness data collection process in manufacturing industry. [w11]
Manual Data Collection
Remote or Automatic Data collection
4.2.1 Manual Data Collection
Manual Data collection means, Collect records (data) information manually. For example, if the machine shuts down for more than 10 minutes, the machine operator manually records this new status on the record sheet. Once production restarts, the operator records the carrying on time. This type of method is mostly suitable where there is at least one operator at the machine and where the machine can run without the direct involvement of the operator. [w12]
Manual data Collect creates ownership for OEE measurements with the operator. He is responsible for recording data. The outcomes of analyses and their resulting improvements will be easily supported. With Manual data Collect, data is entered twice: once on paper and then processed in the software. This is less of a disadvantage if the team leader processes the data rather than back office as it gives him valuable information about what has occurred in the previous shift. [w11]
4.2.2 Automatic Data collection
Remote Collect is an automatic data recording system whereby the operator enters the reason for the standstill manually. Remote Collect works via remote I/O with sensors which record the status of the machine. Entering the idle reason can be done using a touch screen or a PC. Remote Collect also records the number of good and rejected products. Remote Collect can be deployed when it is possible to position a computer in the surrounding area of the machine. In this data collection process is very accurate for OEE measurement. [w12]
4.2.3 Cost Justification for Data collection Methods
The implementation of an automated data collection system with an integrated database provides immediate financial returns. The labor cost associated with manual data collection on production lines by production personnel and the manual compilation of the data to calculate OEE are eliminated with an automated system. The accuracy and integrity of the source data is significant improved. With more accurate OEE reports you will make better financially feasible decisions that will result in even greater savings. The timeliness of the OEE reports themselves are also significantly improved with automated data collections. In most cases, the OEE Reports are available for review the same date as the final element of information is captured.
Data Analyse
The Data analyse is difficult to identify the precious OEE figure. Some authors have tried to do it though;
Example: Nakajima (1988) has indicated that under ideal conditions firms should have Availability > 0.90, Performance >0.95, Quality > 0.99. These figures would in an OEE >0.84 for world class firms, and Nakajima considered to be good benchmark for typical manufacturing capability.
Kotze (1993), on the other hand, argues that an OEE less than 0.50 are more realistic. This figure corresponds to the summary of different OEE measurements presented by Ericsson (1977), where OEE varies between 0.30 and 0.80.These figures indicate the difficulties of comparing OEE between processes.
Case Study of Overall Equipment Effectiveness Technology.
Case study 1
Paper Production Company - Reduce carbon footprint with OEE
Increasing your OEE score not only means enhanced production capacity, but greater energy efficiency too, resulting in visible benefits to the bottom-line. A greater general awareness of environmental issues together with rising energy costs has caused manufacturing energy consumption to rise to a board-room level concern. It is no longer acceptable for companies to treat energy simply as a fixed cost of production, there is an urgent requirement to monitor and improve energy-efficiency. Most manufacturers have already implemented a wide-range of energy related programs with varying degrees of success including: switch-off campaigns; intelligent production scheduling at a lower time-tariff; installing new energy-efficient equipment or enhancing maintenance to reduce leaks. All of these programs are necessary for an ongoing improvement in energy consumption but they are not sufficient on their own. There is a bigger prize to be gained in operating the factory as effectively as possible to maximize energy-efficiency. Most manufacturing companies using OEE strategy to reduce energy costs.
The results of one or more of these factors on a normal production cycle could have a dramatic impact on both the productivity and the energy-efficiency of a production line, as illustrated in the following diagram. As you will see slow running, breakdowns and material shortages push energy efficiency 'into the red'. The wrong data code not only results in the scrapping of material but also the waste of energy associated with that particular production cycle.
Implementing an OEE system provides the monitoring and indicators to enhance the overall efficiency of a factory in terms of productivity, quality and energy consumption.
OEE systems provide a platform for greater efficiency
Based on OEE concepts, OEE systems provide rich functionality to quickly calculate, clearly report and deliver intelligent drill-down capabilities to uncover previously hidden issues as a platform for continuous improvement. A number of forward-thinking manufacturers have already embraced OEE systems with remarkable success, achieving 15 - 25% improvement in the OEE score in less than 3 months. The score in itself is not important; however the corresponding improvement in profitability and subsequent competitive advantage provides clear motivation for these companies.
The following table is a simple illustration o f the potential a 1% improvement in OEE could make to the bottom-line profitability of a company.
With a 1% improvement in OEE we can save £750 in materials, require 3 hours less production, and reduce energy costs by £1,168. The illustration highlights the important link between increased OEE scores and enhanced energy consumption. Energy reductions also generally lead to some of the greatest manufacturing cost savings. Taking our illustration further, improving the OEE score to around 60% or 70% would allow us to fulfill the orders in 5 days and safely reduce operations to a 2-shift pattern. This would obviously result in far greater energy savings than any of those shown here. With OEE system users quickly gaining and holding 20% improvements, it is easy to see why OEE, as a key driver for change, generates so much interest. That's an annual reduction of 2,080 hours of energy usage, whilst achieving the same output.
Chapter 5
Development of Overall Equipment Effectiveness Technology
5.0 Introduction
Many systems in use today are not performing as intended, nor are they cost effective in terms of their operation and support. Manufacturing systems, in particular, often operate at less than full capacity, productivity is low, and the costs of producing products are high. In dealing with the aspect of cost, experience has indicated that a large percentage of the total cost of doing business is due to maintenance- related activities in the factory; i.e. the costs associated with maintenance labour and materials and the cost due to production losses. Further, these costs are likely to increase even more in the future with the added complexities of factory equipment through the introduction of new technologies, automation, the use of robots, and so on. In response to maintenance and support problems in the typical factory environment, the Japanese introduced the concept of total productive maintenance (TPM), an integrated life cycle approach to factory maintenance and support. TPM methods and techniques have been successfully implemented in Japan through the past decade, and more recently in the USA. Inherent within the TPM concept are the aspects of enhancing the overall effectiveness (efficiency) of factory equipment, and providing an optimal group organizational approach in the accomplishment of system maintenance activities. Both the equipment and the organizational sides of the spectrum need to be addressed in fulfilling the objectives of TPM. It is believed that while many successes have been realized in structuring organizations to respond better to the maintenance challenge, very little progress has been made relative to the influence of equipment design for minimal maintenance and support (i.e. the incorporation of reliability, maintainability, and supportability characteristics in design). Briefly addresses this aspect of the problem, identifies some design analysis/evaluation tools that can be used, and recommends an approach for the continuous improvement of manufacturing systems in terms of operation and support.
Presently development of OEE is automatic software data collection and measurement. In manufacturing industries, they are using lots of new software to measure the Overall equipment effectiveness. Maintenance of Equipments is most important for Overall Equipment effectiveness.[2]
5.1 Cross-functional team working with OEE
Cross- functional team work is necessary to improve the OEE. Cross- functional teams have the combined necessary skills and knowledge of the entire system of manufacture to identify correctly the practices and activities that relate to the six big losses. Furthermore, the fact that a Cross- functional approach is taken gives the opportunity to address immediately identify improvements or to ensure that plans could be developed during the team meeting. This ensures the best utilisation of operational and other resources because of authority and responsibility of team members, who represent various departments and functions within the organisation.[4]
5.2 Benefits of OEE and TEEP
OEE Management means that much more than posting a monthly scorecard. Unfortunately, many Companies attempt to perform top-level OEE calculations as a stand-alone key success metric, but without the ability to say much more about it. An optimal implementation of OEE Management will essentially provide the structure and tools to drive the key results in favorable directions. The result will be improvements to the overall business and manufacturing processes as the organization strives to push the OEE metrics up.
Improved Profitability
1. Reduced Manufacturing Cost - Driving variable costs down through waste reduction with a properly implemented OEE/TEEP measurement strategy, all levels of the organization have clear visibility to a universal scorecard as well as extensive cause detail for each performance gap. This ability allows leadership to put all players on the same team, systematically addressing the top causes of waste, and then proceeding down to the next item. The result will be visible in improved variable costs and higher margins.
2. Take full advantage of Capital - Driving overhead costs down by utilization improvement.
The TEEP metric directly relates the utilization of capital equipment and facilities. When an operation fails to perform at planned or designed capacity, the result is that fixed cost allocation to each part produced must increase. The ideal target for TEEP should reflect an operation that runs 24 hours per day 365 days per year, and runs at world class OEE, say 85%. Although ideal TEEP may not always be attainable, it must persistently remain on the radar screen. Generally operations management is challenged to deliver excellent OEE results. Given the OEE results, it is now up to senior leadership to take the steps necessary to achieve ideal TEEP. Strategies for improving TEEP will target the Loading Metric and are based on a combination of increased sales volumes and/or consolidation of manufacturing capacity.
3. Reduction of Working Capital - Driving inventories down as processes improves. It is commonly accepted that the primary purpose of work in process and finished goods Inventories are to provide layers of protection against failures in the manufacturing processes. Successful Lean Manufacturing initiatives directly improve the reliability and responsiveness of these processes. Stated more directly, drive OEE improvements, then go back and reduce inventory targets accordingly. These reductions lead to immediate favorable cash flow as well as the reduction of associated carrying cost and property tax.
Increased Customer Satisfaction
Successful realization of OEE Management techniques will lead to improvements of manufacturing processes on many fronts. The process characteristics that are most transparent to customers are throughput and quality. Throughput capability is readily visible via a supplier's ability to deliver on time - every time. External quality, the quality issues seen by the customer, is generally accepted to be proportional to internal quality. Therefore, any improvement in internal reject rates will result in similar improvement as viewed by the customer. This relationship is valid regardless of attempts to protect the customer through extra inspection.
Chapter 6
6. Conclusion/ Recommendations
Successful Manufacturing improvement and control costs can only happen if proper production measurement system is used. Overall Equipment Effectiveness is considered within this report as a correct measurement for manufacturing organisations as it aims to identify the six big losses according to the failure, and present within the system of manufacture. These six big losses affect, to varying degrees, the three factors of performance, Availability, and Quality percentage.
This report discussed the necessary skills and knowledge of the entire system of Overall Equipment Effectiveness technology. Furthermore, getting a good OEE score should not be about running a machine faster or harder - rather, the machine has to run at the speed that you need it to, be available (only) when you need it, and be capable of producing parts to specification. Creating an aggregate of the performance, availability, and quality rates can only serve to mask the unwanted variability in any of sub-components.
More over, all manufacturing industries should be started OEE strategy to reduce costs and also have good maintenance of equipments. Recommendation of every industry should use automated data collection method is best for Overall Equipment Effectiveness calculations. One difficulty could arise in OEE, that is, collection and preservation of data related to availability, quality etc, then only, we can monitor OEE of a given machine or equipment.
If OEE is used in energy conversion or energy generation from renewable energy source such as bio fuel, solar power, wind power etc, Cost reduction can be achieved. For an example, some cost reduction has been achieved, from the literature, in Lancaster design in converting wave energy to electrical energy, by using OEE and lateral thinking.
Similarly, OEE has resulted in reduction of number of failures in Bio gas production in china and India. So, conclusively, what I could respectfully state is OEE in industry will end with less cost, higher productivity and increased efficiency.
Finally, The Companies who have benefited from OEE systems all share certain characteristics; an interest with performance measurement, detailed failure analysis and a focus on improvement. They also share corresponding improvements in profitability.
