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Monday, July 14, 2008

OVERALL EQUIPMENT EFFECTIVENESS (OEE)


What is OEE?

OEE = Availability X Performance Rate X Quality Rate

Availability - Percent of scheduled production

Note: measures the percent of time that the equipment can be used divided by the equipment uptime (actual production).
Performance Rate - Percent of parts produced per time frame, of maximum rate OEM rated production speed at. If OEM specification is not available, use best known production rate.

Note: Performance efficiency is the percentage of available time that the equipment is producing product at its theoretical speed for individual products. It measures speed losses. (e.g., inefficient batching, machine jams)

Quality Rate - Percent of good sellable parts out of total parts produced per time frame.

Note: Determining the percent of the total output that is good. (i.e. all products including production, engineering, rework and scrap.)

Example: 50% Availability (0.5) X 70% Performance Rate (0.7) X 20% Quality Reject Rate (results in 80%(0.8) acceptable) = 30%OEE



Why use OEE?
Overall Equipment Effectiveness (OEE) can be used to save companies from making inappropriate purchases, and help them focus on improving the performance of machinery and plant equipment they already own. OEE is used to find the greatest areas of improvement so you start with the area that will provide the greatest return on asset. The OEE formula will show how improvements in changeovers, quality, machine reliability improvements, working through breaks and more, will affect your bottom line.

As you strive towards World Class productivity in your facility, this simple formula will make an excellent benchmarking tool. The derived OEE percentage is easy to understand and displaying this single number where all facility personnel can view it, makes for a great motivational technique. By giving your employees an easy way to see how they are doing in overall equipment utilization, production speed, and quality, they will strive for a higher number!

I highly recommend using an automated equipment monitoring system with an LCD display for your OEE in each respective area of your facility so all can monitor. To the employee in each area, it will become as common to glance at, as the speedometer on a car. While showing machine speed with such a display helps, machine speed is only a small percentage of your overall equipment effectiveness - OEE.

How to use OEE?

Implementing the Overall Equipment Effectiveness formula in your facility can take on many different forms. It can be used as an analysis and benchmarking tool for either reliability, equipment utilization, or both. Don't let indecision on how to best use OEE become a barrier that prevents you from using it at all. Start out small if necessary, picking your bottleneck to collect the OEE metrics on.

Once you see first hand what a valuable tool it is, you can gradually take OEE measurements on other equipment in your facility. If you work in manufacturing , there is no substitute for going out to the shop floor and taking some rough measurements of OEE. You will be surprised by what you find!

While monitoring OEE per equipment brings focus on the equipment itself, it may not provide true cause of major costs, unless the cause is obvious. For example OEE can appear improved by actions such as purchasing oversize equipment, providing redundant supporting systems, and increasing the frequency of overhauls.

To improve your OEE percentage, you will need to use other tools and methodologies available to you, like TDC, RCA, FTA etc. TDC is a relatively new methodology that focuses on True Downtime Cost for justification and making better management decisions.





Calculating OEE

The Formulas

Availability
Availability takes into account Down Time Loss, and is calculated as:
Availability = Operating Time / Planned Production Time
Performance
Performance takes into account Speed Loss, and is calculated as:
Performance = Ideal Cycle Time / (Operating Time / Total Pieces)
Ideal Cycle Time is the minimum cycle time that your process can be expected to achieve in optimal circumstances. It is sometimes called Design Cycle Time, Theoretical Cycle Time or Nameplate Capacity.Since Run Rate is the reciprocal of Cycle Time, Performance can also be calculated as:
Performance = (Total Pieces / Operating Time) / Ideal Run Rate
Performance is capped at 100%, to ensure that if an error is made in specifying the Ideal Cycle Time or Ideal Run Rate the effect on OEE will be limited.
Quality
Quality takes into account Quality Loss, and is calculated as:
Quality = Good Pieces / Total Pieces
OEE
OEE takes into account all three OEE Factors, and is calculated as:
OEE = Availability x Performance x Quality
It is very important to recognize that improving OEE is not the only objective. Take a look at the following data for two production shifts.

OEE Factor
Shift 1
Shift 2
Availability
90.0%
95.0%
Performance
95.0%
95.0%
Quality
99.5%
96.0%
OEE
85.1%
86.6%

Superficially, it may appear that the second shift is performing better than the first, since its OEE is higher. Very few companies, however, would want to trade a 5.0% increase in Availability for a 3.5% decline in Quality!The beauty of 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.



Example OEE Calculation

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

Planned Production Time= [Shift Length - Breaks]= [480 - 60]= 420 minutesOperating Time= [Planned Production Time - Down Time]= [420 - 47]= 373 minutesGood 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%)




GLD- INDUCTION FURNACE

GROUND LEAK DETECTOR

1) GLD is directing the first earth for the system .G stands for electrical component grounding & LD stands for Leak detector
2) It is the voltage sensing circuit it senses voltage across the DC supply. In case of voltage exceeding 200V,GLD will turnoff the power unit automatically.

GLD due to panel problem
After GLD arrives press reset button and release, when the GLD immediately glows GLD shows After GLD arrives press both reset and probe disconnect button then remove the finger from reset button immediately GLD shows the problem in panel.

GLD due to furnace side
· After GLD arrives press probe disconnect button ma value goes to 0 means GLD due to crucible
· Every GLD failure, we need to check GLD & GFL function.

Test button
This button puts an artificial ground on the ground detector to ensure the functioning properly

GLD Voltage
GLD is a 45v DC unit with continuously changing polarity. It is supplied to the power circuit through choke.
GLD OPTO
It converts AC to DC with every 20seconds polarity change
GLD input power detail
Input -415 / 110V Pin no.100 common & Pin no. 111 = 26V DC
Wire no. 144 for GLD connection in control board +15v Healthy & 0v Faulty
GLD Volt trip
This problem is related to converter section or incoming normal 60-volt supply

GLD Current trip
This indication shows there is a problem in crucible side (metal penetration or scrap touching into coil or water leakage from coil and hose)

Probe disconnect
When pressing this button, it disconnects the crucible side GLD.It is helpful to find whether the GLD is due to Furnace or panel

GLD Indication other than furnace
ü Higher water conductivity increases the electrolysis process of Cu tubes and components, etching away gradually. This may also result in false tripping of GLD
ü Good working furnace GLD should be 5 to 6ma only.
ü Increasing of leakage current cause damaging GLD circuit resulting frequent fault GLD trip. (Leakage current increased due to high water conductivity)

GLD Wire & connection
· Wire should be Stainless steel (304 or 316)
· Thickness of the wire should be 3 to 5mm
· Antena wire (3wire &6 terminal) properly welded and in contact with former
· Antena rod and panel GLD wire connection should be tightened with bolt.
· During normal operation GLD sensitivity should be 100% (to give safety to operator and coil) So as to trip at 60ma.leakage current.
· Due to moisture in new lining, GLD value shows high at that time we need to switch off the CT temparely till the GLD value reduces. (Close monitoring required when CT is not running)

COST CALCULATION OF THERMAL RECLAIMED SAND

1. Direct costs involved

PROPANE GAS COST CALCULATION

Density of Propane in liquid phase = 528 kg/m3
Liquid/gas equivalent (1.013 bar and 15°C) = 311 vol / vol
Rfd from the std properties of propane gas
Conversion of Propane from liquid to gas

1 m3 of gas = 1000/311
= 3.2154 litters of Propane
Cost of Propane gas =1.65 Dhs /litters
Average gas consumption per ton of Sand = 6 m3
Gas cost per Ton of sand = 6 x 1.65 x 3.2154 = 31.83 Dhs

Additive Cost calculation:
Additive cost per Ton = Dhs 3080
Additive cost per kg = Dhs. 3.08
Additive consumption per Ton of sand = 12 kgs.
Additive Cost per ton of sand = 12 X 3.08 = Dhs. 36.96

Power cost calculation:
Power consumption per Ton of sand= 8KWH
Power cost per unit = 0.2 Dhr / KWH
Cost of energy = 1.6 Dhr / Ton of sand
Water Cost:
Water cost=0.228 Dhr /Ton @ 100 gallon per day

Depreciation Cost:
Machine depreciation 1734500 Dhr
Depreciation cost per ton=17.345 Dhr estimating life of 10 years and 900 ton monthly production
Man Power Cost
Two additional Man Power for thermal plant = Dhs. 2000 /month
=2.22 Dhr/T
Material Handling:
Bobcat handling cost=0.3 Dhs/T
Compressed air cost:
Cost of compressed air per Ton of sand=0.5 Dhr

Total cost of thermal reclaimed sand =31.83+36.96+1.6+.228+17.34+2.22+0.3+0.5= 90.98 dhs/Ton

Actual cost of New Sand =195 dhs /Ton










2.Indirect benefits involved
a) Forklift usage for sand unloading from container
· Average 3 hrs a trip and 3 trips in every week and 12 trips in every month.
· Total Forklift usage appox. 9 hrs weekly. Therefore appox. 36 hours monthly.
· Total cost involved = 36 X 50 Dhs = Dhs.1800 /month

b) Big shovel usage to clean the k/o sand
· Average of 12 hrs weekly, therefore total of 48 hrs per month.
· Cost per hour of Shovel hire = Dhs. 100
· Total cost involved = 48 X 100 Dhs = Dhs.4800 /month

3. Cleanliness of the plant
· Since the sand was removed regularly (daily) from the k/o area, the plant front appears very clean.
· The sand disposal problem was nullified.
· Eco-friendly clean environment.

SIX SIGMA

What Is Six Sigma?
Six Sigma is a quality discipline that focuses on product and service excellence to create a culture that demands perfection. Its key goal is to achieve excellence by focusing on customer needs and reducing defects in processes, products, and services.
Six Sigma holds that there is a direct relationship between products and customer satisfaction: the fewer the defects, the happier the customer. Six Sigma stops variations in quality at the earliest possible point by attacking variation during design of products and processes.
Sigma is more than just a letter in the Greek alphabet. In this context, Sigma is a statistical measure that tells how much a product, service, or process varies from perfection. Based on defects per million opportunities (Table 1), it holds that the higher the sigma value, the better the quality. To put Table 1 in perspective, a measure of One Sigma would be equivalent to 170 misspelled words on this page; a Six Sigma level would equal one misspelled word in an entire library

The Six Sigma Process
There are four key steps in the Six Sigma process:
• Measure. Identify the key internal processes that influence the “critical to quality”characteristic.
  • This phase ends when you can measure or count the defects that affect quality.
    • Analyze. Understand the root cause driving defects. Statistical tools are used to identify the key variables that are likely to drive process variation the most.
    • Improve. Confirm the key variables and then quantify the effects of these variables on the critical to quality characteristics.
    • Control. Ensure that the modified process now enables the key variable to stay within the maximum acceptable ranges


    How We Applied Six Sigma To Membrane Manufacturing
    Membrane quality is determined by two criteria:
    • Flux (how much water the membrane lets through during a given amount of time) and
    • How much salt (or other impurities) is removed from the water.
    If a membrane has a high flux variation (i.e., it either lets too much or too little water through), then the membrane modules that contain it has a high flux variation too. If a membrane module does not meet customer specifications for flux variation, it is not shipped to the customer. As the products are rejected internally it becomes harder to satisfy demand in a timely manner. This affects customer loyalty and ultimately, our bottom line.
    Membrane material is manufactured in large flat sheet rolls. Samples from each are tested for performance parameters. Rolls that do not perform to standard are discarded, creating both a waste problem and delays in delivery to customers.
    To respond to the quality issue, a Six Sigma “black belt” and his team applied Six Sigma methodologies to reduce the variation in flux in membranes and increase the yield of membranes to meet customer demand.
    As we began the Six Sigma project, we knew that:
    • A number of variables affect the quality of the finished membrane products.
    • Reducing large deviations from the desired recipe value would reduce scrap cost and improve yield.
    • Controlling less severe process variation will produce better performing products for the customer with the additional aim of improving the correlation between membrane properties and final spiral wound element properties. So our project focused on reducing the flux variation and the scrap that it generated due to large deviations from product specifications.
    We used Principal Component Analysis (PCA) to determine the most important process variables that affect flux.
    There are two ways to find out how variables affect operations: designed experiments and undesigned experiments. Designed experiments tend to test variables at extreme ranges of operation and are not always a realistic way to assess process variables. Knowing the constraints of designed experiments, the team chose to analyze data from the operation as it occurred.
    The team constructed a detailed process map of the membrane manufacturing process, including the preparation and introduction of all raw materials and the testing/grading/dispositioning of each finished roll of membrane material. The exercise of writing an as-is process map created a forum for good communication and the chance to more critically analyze individual operations. One of the results of the process map was that the team discovered that plant operators were not necessarily performing process operations the way the jobs were designed.
    Once the study was completed, members of the team used this as an opportunity for behavior-based changes in operating discipline as well as process changes. Starting with a process flowchart, team members identified steps in the process most likely to contribute to variable flux based on their experiences with the process. Those areas of concern were then compared to a Cause and Effect matrix. As stated before, the customer has two requirements: flux and salt rejection; both properties were included in the cause and effect matrix.
    Three factors were ultimately identified as negatively affecting flux and salt rejection. Only one, however, was severe enough to warrant process operating changes.






    Principal Component Analysis
    Using Microsoft Access™, data were pulled together from a variety of sources:
    • The computer log that recorded machine parameters and operating conditions;
    • Manual run sheets used to record hand collected samples (which had to be entered into a spreadsheet for the purpose of this study); and
    • The VAX database, which contained several tables recording the flux and salt passage of the tested product rolls.
    Operational computers gathered data from the operation such as temperature and other external conditions for three weeks. They then analyzed the data using Principal Component Analysis. The Six Sigma team performed a statistical analysis of data logs over several weeks of production to identify the variables that affected membrane flux most significantly.
    The team compared variables that changed and how performance changed when compared to those variables. They concluded that the presence or absence of a certain process chemical, and even the solution integrity, were the prime factor(s) in membrane performance.


    Drawing Conclusions
    Using Six Sigma reasoning tools, the team found that the concentration of a raw material varied because of interruptions in the process. The interruptions occurred because sometimes the container would be empty and would have to be replaced with a full one. Occasionally the empty container would go unnoticed for some time. To reduce the variation, a small, inexpensive reservoir was added to feed the chemical while the containers were changed. A level transmitter with an alarm was installed on the additional reservoir to alert the operators of a nearly empty container.
    Other times, a second chemical was improperly mixed,which also led to problems with flux. The Six Sigma team explained to plant operators the importance of proper ly mixing the solution.


    Sustaining Productivity Gains
    To sustain the gains from the project, the Six Sigma team made some changes to help manufacturing operators identify problems before they occur. Before the Six Sigma project, the measurements had been displayed in tabular form, in rows of numbers that were difficult to read. After the project, the measurements were displayed in the form of a Microsoft Excel™ charts that illustrate trends in the numbers and therefore processes. The improvements were significant. For standard surface water membranes with a target flux of 45 gal/ft2/day (gfd), the standard deviation of the process for the 8-month period preceding the improvements was 4.46 gfd and 14.5% of the product was out of specification, a Sigma level of 2.56. After implementing the Six Sigma improvements, the standard deviation was reduced to 1.83 gallons per day, with only 2.2% of the product out of specification, which is a sigma level of 3.51, almost one Sigma better than the initial performance