redesign of company
TRANSCRIPT
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ME4042 - PRODUCTION AND OPERATIONS MANAGEMENT
INDUSTRY RELATED PROJECT
FINAL REPORT
PRODUCTION PLANNING AND PROCESS RE-DESIGN
AT PATHMA DISTRIBUTERS – TEXTILE WEAVING
INDEX NO NAME
100219F S.Jeyatharsini
100235B T.Kankeyan
100433F Y.Rameshkanna
100449J R.Ratheesan
100503V S.Seran
GROUP NO 10
DATE OF SUBMISSION 23/09/2013
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PROJECT TITLE
Production Planning and Process Re-design at Pathma Distributers - Textile Weaving at Ja-Ela
BACKGROUND
Company Profile
Pathma Distributers – Textile weaving is situated at B-9, Industrial Estate, Ekala, Ja-Ela, Sri Lanka.
Annual Sales Amount: US$5 – 10 Million
Total No. of Staff: 11 – 50 People
No. of R&D Staff: 5 – 10 People
No. of Engineers: Fewer than 5 People
Business Type: Manufacturer
Main Export Markets: Southeast Asia
No of machines: 28
Production efficiency: 90 %
OBJECTIVES
The objective of the project is to improve the efficiency of the production and optimize the
resources available through production planning and processes re-design.
SCOPE
Our planned and Solutions/Improvement suggested areas;
Material flow from one machine to the other
Process flow
Plant Layout
New processes adoption
Automation of Processes
Quality control
Inventory management
Demand and Sales Forecast
By analysing and studying we have produced solutions for the operations for mentioned areas in
order to achieve the project objectives.
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METHODOLOGY
We had visited the weaving plant and analysed the production flow and the processes. We had got
information regarding the whole process. We had got to know about the available resources for
production. We had collected all the information required produce the required production planning
and redesign.
The following activities have been carried out for this project.
1. Contacted the director and get his approval to visit the place
2. Visited the firm and find out the process and collected the details.
3. Gathered information from director and staffs.
4. Studied about Production Planning in Books and Internet
5. Visited the plant whenever required
6. Checking with the viability of the solutions
7. Communicating the viable solutions with the owner of Pathma Distributers – Textile
weaving
8. Working on the improvements and amendments, he suggests
9. Ensuring the satisfaction of the owner with the final proposal
PROCESSES OF THE COMPANY
1. Introduction
In the textile industry in Sri Lanka is growing continuously so the company need to improve the
quality and quantity of the products. This industry has been developed following both vertical
integration, particularly among spinning and weaving firms, and horizontal integration, promoted
by the idea that a full line of textile products is necessary for effective marketing.
Process Flow Diagram
FIBRES
Natural/
Man-made
YARN
Ginning
Carding
Combing
Spinning
Dyeing
FABRIC
Weaving or
Knitting
Bleaching
Dyeing
Finishing
FINISHED
PRODUCT
Clothing
Homefurnishings
Industry
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The main process that they are using is weaving. It consists of crossing a yarn, called the weft yarn,
with several thousands of yarns composing the warp. Starching is a procedure that comprises
synthesis and special treatment of some warps. Warp making is the arrangement of the warp yarns
in parallel on a roll. Each yarn is taken from a bobbin which is put on a bobbin stand. Production
planning algorithms for each phase are required. For example, the weaving process is characterized
by long planning horizons and relatively slows speed of machines, very long setup times, very large
production batches, and mixed order and stock-based production. On the contrary, the warp making
process is characterized by short planning horizons and high speed of machines, short setup times,
small production batches and only orders-based production. The above phases pose the most
complex production scheduling problems.
Most textile companies are ageing while the technology changes rapidly. These companies own
machines of different ages and production characteristics, such as processing speed, changeover
possibilities and facilities. In their company they use some traditional machinery, traditional
processes and some conventional materials so; we prefer some new machines and some new
materials to improve their business. The competitive companies in Sri Lanka introduce new
machines and techniques to overcome their
These are the machines that they are using currently in their process
1. V-Bed
2. Power loom
3. Air jet weaving machine4. Water jet weaving machine
5. 4-truck single jersey circular knitting
Some of the Advance technological machine that we are going to suggest
1. Jacquard circular knitting
2. 4- interlock circular
3. Rib circular
4. Collar cuff v-bed auto knit
5. Tricot warp
6. New towel making weaving machine
Types of clothes of the company produce
1. Cotton
2. Polyester
3. Wool
4. Polyester
5. Silk
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New types of clothes
1. Blending fabric
2. Modern types of synthetic materials.
They are producing cotton, polyester, denim materials and some time they make school uniform
material (special contracts not regularly). They didn’t produce much variety of cloths, just doing the
traditional ways. This is in the mass and continuous production range. They have to introduce some
new techniques and some more projects.
PRODUCTION
RESOURCES FOR PRODUCTIONS
1. Human resources
2. Raw material
3. Transport
4. Time
5. Machines
Nowadays the textile industry in Sri Lanka has been facing many problems because they lost their
main market in European countries due to the elimination of the GSP. So they don’t need to
increase their capacity of the production. To control the demand they need to get some outsource
(sub contracting). Increase the labour is not a proper way so they need to make some additional
shifts from their labours. They didn’t have too much of order so the stocking back ordering and the
overtime is not needed in this situation so if they increase their production they have to make the
above things correctly.
Our plan has to be some special consideration
1. Meet the customer due dates comfortably
2. Minimized idle time
3. Must a feasible and cheaper option
So they have a good production planning but if they increase their facilities they have to make their
above planning.
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FACILITIES OF THE COMPANY
Facility Location
Location of the company should be located near to the place where the raw materials are taken and
also near to the stores where the customers purchase the final product. This will reduce the transport
cost and the time consumption to finish the process. Most of the Raw materials which are used for
the process are imported from India as it’s cheaper to them than purchasing here in Sri Lanka. The
Raw Materials are also purchased from the Sri Lankan markets sometimes when there is a delay in
importing. The company is located in Colombo - 01 now which is near to the Colombo Harbour and
in a specific distance from Kattunayaka Airport. Big quantity of the raw materials is taken from the
Colombo Harbour. Small quantity and is delivered from Kattunayaka and only if the Ship transport
is unavailable and if an urgent delivery of Raw materials is required. So the Current Location of the
company is better in this way. Most of the stores are located in Colombo - 01 which is near the
company.
Facility Layout
Facility layout and design is an important component of a business's overall operations, both in
terms of maximizing the effectiveness of the production process and meeting the needs of
employees. The basic objective of layout is to ensure a smooth flow of work, material, and
information through a system. The basic meaning of facility is the space in which a business's
activities take place. The layout and design of that space impact greatly how the work is done — the
flow of work, materials, and information through the system. The key to good facility layout and
design is the integration of the needs of people (personnel and customers), materials (raw, finishes,
and in process), and machinery in such a way that they create a single, well-functioning system.
The company we analysed is a yarning company where the machines over there are Loom
Machines, Drum Winding Machines, and Pirn Winding Machines. There is also a place where themeasurements and cuttings take place for the final product (Clothe).
A1-A18 and B1-B4 – Loom machines (numbers are given according to present machine numbers)
B5, B6, B7 – Pirn-winding machine
B8, B9, B10 – Drum winding machine
EN – Entrance
MA – Measuring area
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The weights were given, considering the importance of the relationship between the particular
regions. Following are some examples in choosing the relationships.
E.g.:-
There are objects deals with the external environment. Those are Drum winding machines (B8, B9,
and B10), measuring area (MA) and Pirn-winding machines (B5, B6, and B7). So, these objectsshould be situated close to the entrance (EN). The interaction between the external environment and
these particular objects are frequent. So the closeness to the entrance is highly required.
There is only one labourer who has knowledge in repairing and maintaining these machines. And
there are some machines which breakdowns frequently (A5, A6, A13, A17, B3, B9) and which
should be under this labourer’s supervision. The company’s layout also has been made out in this
way already.
Departments
D1 : A1-A9
D2 : A10-A18
D3 : B1-B8
D4 : EN
D5 : MA
Shown below is the ―From-To Table‖ by giving the weights to the interactions between the
machines and working areas which is obtained from the details we got from the Company.
Flow diagram is given below,
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Value Closeness Rating
A Absolutely Necessary 6
E Especially Important 5
I Important 4
O Ordinary Closeness 3
U Unimportant 2
X Undesirable 1
Activity Diagram
The current Layout is already a better layout which only needs some modifications on it to be a
better and efficient Layout to make the process is easy and efficient.
INVENTORY
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A
company
should be
nibble
and fast
enough to
fulfil
customers
demands
to sustain
in the
market.
For an effective business it is important to maintain the inventory small as possible to use the
money tied up in inventory in an effective way. If we could maintain the inventory in a reasonable
level we can invest the unnecessary capitals which are struck in the inventory for new product
development, expanded marketing and sales, acquisitions, modernization, re-engineering,
expansion, debt reduction, and many others. So it is highly recommended to design the inventory.
Our inventory includes
Raw material: natural and synthetic fibers in rolled form, tools for different weaving
techniques, dies, indirect materials used to process raw materials.
Work-In-Process: Wrapped strings on shuttle, Wrapped threads on a wheel
Finished Goods: garments
Inventory control is important in whole operation. Inventory must be well managed to maximize
profits. Uncontrolled inventories are inefficient and costly. Old goods and damaged goods often
have to be discarded to keep the inventory producible, recover the holding costs and save the space
too. Inventories that have too many products in one category often result in a surplus of hard-to-sell
items that result in deep discounting. Such discounts can often eliminate profit on the item. It is
necessary to maintain enough inventories to meet demand prevent product aging and avoid under-
performing products in the industry.
The reasons for inventory control
Helps balance the stock as to value, size, colour, style, and price line in proportion to
demand or sales trends.
Help plan the winners as well as move slow sellers
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Helps secure the best rate of stock turnover for each item
Helps reduce expenses and markdowns.
Helps maintain a business reputation for always having new, fresh merchandise in wanted
sizes and colours.
In some cases, inventory is so bloated that a high percentage of it will become obsolete before it is
sold. These may include poor forecasting, inadequate order/product specifications, ineffective
production scheduling, poor quality, bottlenecks, long cycle times, product and process problems,
and/or inappropriate performance metrics, to name a few. And these problems can compound
themselves.
Long lead times lead to a requirement to forecast, and long-range forecasts are by nature inaccurate.
When actual customer demand is not what was forecasted, unsold inventory quickly accumulates in
expensive piles, while expensive expediting is used to produce the needed products that are in shortsupply. Salable throughput decreases while customer service goes down.
Every decision affects the inventory size. When designing inventory theses expenses should be
considered to make it profitable.
Holding costs
Setup costs
Ordering costs
Shortage costs
Holding costs includes the costs for storage facilities, handling, insurance, taxes, pilferage, breakage
and opportunity costs for capital. It is needed to keep the holding costs in a moderate level to
maintain the inventory level and to avoid frequent replenishments.
Data
76m width fabric
Holding cost = 1.50Rs/meter
Ordering cost = 35Rs/meter
Cost of a unit fabric = 100 Rs/m
68m width fabric
Holding cost = 1.50Rs/meter
Ordering cost = 33Rs/meter
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Cost of a unit fabric = 90Rs/meter
49m width fabric
Holding cost = 1.00Rs/meter
Ordering cost = 30Rs/meter
Cost of a unit fabric = 85Rs/meter
Lead time = 7days
All productions are supplied as rolls. But depending on transportation and economical factors the
quantity of a specific roll varies from other roll. So we used the least unit to standardize all
calculations.
EOQ model
It is a mathematical tool for determining the order quantity that minimizes the costs of ordering and
holding inventory. This model assumes that the demand equation faced by the firm is linear. In
other words, the rate of demand is constant or at least nearly constant.
The goal is to minimize total inventory cost. Inventory costs are made up of Holding and ordering
cost. Holding cost include the cost of financing the inventory along with the cost of physically
maintaining the inventory. These costs are usually expressed as a percentage of the value of the
inventory. Ordering cost include the cost associated with actually placing the order. These include a
labour cost as well as a material and overhead cost. The equation for total inventory cost is
developed as follows:
Assumptions of Basic EOQ Model (Assumed these for companies):
Demand is known with certainty
Demand is relatively constant over time
No shortages are allowed
Lead time for the receipt of orders is constant
The order quantity is received all at once
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Total annual cost = Annual purchase cost + annual ordering cost + Annual holding cost
TC – Total cost
D – Demand
C – Cost/Unit
Q – Quantity
S – Setup cost
R – Reorder point
L – Lead time
H – Annual holding cost
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76m width 68m width 49m width
2011 2012 2011 2012 2011 2012
Annual Demand 66175 62441.67 63316.67 52364.28 37383.33 34658.33
Total cost 6620136 6246727 5711394 4715062 3179389 2947722
Re-order level 1269.11 1197.511 1214.292 1004.246 716.9406 664.6803
Q (opt) 1745 1695.6 1663.2 1497.4 1204.5 1155.7
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Fixed order quantity model needs a constant known demand. In actual case demand varies with
time. In our case also seasonal changes affects majorly the demand. Consumer needs for the
garments changes with seasonal changes. So demand for weaved materials is not actually constant.
So it is must to maintain safety stocks to overcome stock outs in peak demand times. With the
previous time data it is possible to forecast the demand for seasons and it is advised to maintain
safety stocks. For fixed quantity model orders are replenished when the inventory level drops to a
certain level, danger of stock out in this model ours only during lead time. In this case we planned
to maintain a 90% probability for not stocking out.
Fixed time period models
It is an inventory control method where orders are periodically placed, but the order quantity is
different every time, and is also called Fixed Period Deficit Ordering System. The method has the
following features:
An order is periodically placed.
The order quantity is different every time.
Even relatively large fluctuations in demand can be handled properly.
Even seasonal variation can be handled modestly.
The inventory volume can be reduced compared with ordering point system.
A-group items are usually best for this method.
Longer lead time is acceptable.
Longer time for paperwork is needed.
In fixed time period system inventory is counted only at particular time. This system needs higher
level of stock than fixed order quantity model. In a fixed time period system reorders are placed at
the time of review T and the safety stock
̅( )
The weaving mill we went is a supplier of the fabric wanted for garments. So the seasonal changes
and other factors won’t affect much of their business. But still there are some impacts due to
weather changes and other environmental facts. For example in the rainy season demand for cotton
fabric is lower than normal level. So they need to think about these factors to make the business
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much profitable. But from the past data we could find that they reduce the production of the fabrics
which are not suitable for the ongoing season. But we recommend that from past data it is better to
produce more fabrics which are suitable for the specific seasons. As it is a fabric factory they need
to consider fashions changes and all aesthetics. And here again they need to think about the holding
costs. When planning the inventory again we have to think about both the two cases mentioned
above. Fixed order method and fixed period methods. Both have their own advantages and
disadvantages. For the fixed period method we need to maintain the inventory with the large
quantity. Thus that increases the holding costs and effects in profit too. And in this model it is
possible to run out of stock as replenishment done in certain intervals. So there is a possibility to
face critical situation for supply without stocks. And Pathma traders have some prefixed supplies.
So their orders are fixed somehow. So we prefer fixed order method to control the inventory.
SOLUTION FOR IMPROPER SALES FORECASTING
The company faces many problems, but the main reason for the profit loss which the company face
is Improper Sales Forecast. We are planning to give a detailed solution for the above problem.
Simple methods of Sales Forecasting are not enough to forecast their Sales. Detail calculations are
shown below. So some extra effect they have to put forecast their sales. In this case we try some
new techniques to forecast and try to match the demand approximately.
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Demand and Forecast for 49m width yarn
Year MonthDemand for
49m
Simple Moving Average Weighted Moving Average Exponential Smoothing
Three
months
Seven
months
Three
months
Five
monthsα = 0.3 α = 0.5 α = 0.7
2011
January 47300
February 28900 47300 47300 47300
March 34400 41780 38100 34420
April 50300 36867 34685 39566 36250 78301
May 39800 37867 42320 42786 43275 58700
June 48200 41500 42140 40460 41890 41538 78302
July 51400 46100 45995 42520 43783 44869 57231
August 50700 46467 42900 48700 46415 46068 48134 78303
September 16900 50100 43386 50535 48700 47458 49417 58981
October 35600 39667 41671 32215 39235 38290 33159 78304
November 17800 34400 41843 32255 37575 37483 34379 48411
December 27300 23433 37200 23005 30365 31578 26090 26983
2012
January 28600 23433 37200 23005 30365 30295 26695 27205
February 32700 27325 34563 26410 27755 29786 27647 28182
March 16800 26600 32625 29235 27550 30660 30174 78306
April 19300 26033 28300 23340 25165 26502 23487 35252
May 21500 22933 24375 20560 22600 24342 21393 78307
June 49400 19200 24950 20135 22400 23489 21447 38542
July 36500 30067 26675 36515 29845 31262 35423 78308
August 42800 35800 29257 38120 32175 32834 35962 49042September 46700 42900 31286 41900 37000 35824 39381 78309
October 51200 42000 33286 44000 41570 39087 43040 56183
November 50900 46900 38200 48590 46010 42721 47120 78310
December 19500 49600 42714 50360 47480 45174 49010 59123
P.S: Weight for three months weighted moving average 1st
– 0.15, 2nd
– 0.3 & 3rd
– 0.55 and weights for five months weighted moving average 1st
–
0.1, 2nd
– 0.15, 3rd
– 0.2, 4th
– 0.25 & 5th
– 0.3
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Demand and forecast for 68m yarn
Year MonthDemand for
68m
Simple Moving Average Weighted Moving Average Exponential Smoothing
Three
months
Seven
months
Three
months
Five
monthsα = 0.3 α = 0.5 α = 0.7
2011
January 65400
February 71300 65400 65400 65400
March 69400 67170 68350 69530
April 65300 68700 69370 67839 68875 78301
May 70800 68667 67430 67077 67088 69200
June 47400 68500 68940 68680 68194 68944 78302
July 38500 61167 57105 62520 61956 58172 56671August 70400 52233 61157 46015 54295 54919 48336 78303
September 68900 52100 61871 57380 57375 59563 59368 72771
October 64300 59267 61529 64790 60160 62364 64134 78304
November 58800 67867 60800 66595 61110 62945 64217 68501
December 69300 64000 59871 61965 61905 61702 61508 61710
2012 January 69400 64000 59871 61965 61905 63981 65404 67023
February 36000 65450 60875 65455 65755 65607 67402 68687
March 45800 58375 59450 49440 56445 56725 51701 78306
April 76000 50400 60363 46400 51870 53447 48751 55552
May 56700 52600 61063 60940 57740 60213 62375 78307
June 78600 59500 59538 60855 57510 59159 59538 63182
July 67900 70433 61325 71640 63425 64991 69069 78308
August 75400 67733 61486 69430 67340 65864 68484 71022
September 65600 73967 62343 73630 71420 68725 71942 78309
October 78400 69633 66571 68885 69570 67787 68771 69413
November 46800 73133 71229 74110 73045 70971 73586 78310
December 52700 63600 67057 59100 64860 63720 60193 56253
P.S: Weight for three months weighted moving average 1st
– 0.15, 2nd
– 0.3 & 3rd
– 0.55 and weights for five months weighted moving average 1st
–
0.1, 2nd
– 0.15, 3rd
– 0.2, 4th
– 0.25 & 5th
– 0.3
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Demand and forecast for 76m yarn
Year MonthDemand for
76m
Simple Moving Average Weighted Moving Average Exponential Smoothing
Three
months
Seven
months
Three
monthsFive months α = 0.3 α = 0.5 α = 0.7
2011
January 78300
February 56000 78300 78300 78300
March 36800 71610 67150 62690
April 78900 57033 48785 61167 51975 78301
May 6780057233 62835 66487 65438 78720June 56400 61167 66480 63655 66881 66619 78302
July 76400 67700 63195 60770 63737 61509 62971
August 68300 66867 64371 69110 66095 67536 68955 78303
September 42600 67033 62943 68945 68930 67765 68627 71301
October 75300 62433 61029 55380 60375 60215 55614 78304
November 78300 62067 66529 64440 64000 64741 65457 76201
December 79000 65400 66443 72045 68720 68809 71878 77670
2012
January 69400 65400 66443 72045 68720 71866 75439 78601
February 36000 75500 68213 72955 68675 71126 72420 72160
March 45800 65675 65663 52365 59365 60588 54210 78306
April 76000 50400 61838 46400 55895 56152 50005 55552
May 56700 52600 62800 60940 59690 62106 63002 78307
June 78600 59500 64563 60855 57510 60484 59851 63182
July 67900 70433 64975 71640 63425 65919 69226 78308August 75400 67733 61486 69430 67340 66513 68563 71022
September 65600 73967 62343 73630 71420 69179 71981 78309
October 78400 69633 66571 68885 69570 68106 68791 69413
November 46800 73133 71229 74110 73045 71194 73595 78310
December 52700 63600 67057 59100 64860 63876 60198 56253
P.S: Weight for three months weighted moving average 1st
– 0.15, 2nd
– 0.3 & 3rd
– 0.55 and weights for five months weighted moving average 1st
–
0.1, 2nd
– 0.15, 3rd
– 0.2, 4th
– 0.25 & 5th
– 0.3
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10000
20000
30000
40000
50000
60000
0 2 4 6 8 10 12
Demand for 49m
2011
2012
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30000
40000
50000
60000
70000
80000
0 2 4 6 8 10 12
Demand for 68m
2011
2012
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30000
40000
50000
60000
70000
80000
0 2 4 6 8 10 12
Demand for 76 m
2011
2012
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10000
20000
30000
40000
50000
60000
0 5 10 15 20 25
Demand and Forecast for 49m
Demand for 49m
Linear (Demand for 49m)
Expon. (Demand for 49m)
5 per. Mov. Avg. (Demand for 49m)
3 per. Mov. Avg. (Demand for 49m)
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30000
40000
50000
60000
70000
80000
0 5 10 15 20 25
Demand and Forecast for 68m
Demand for 68m
Linear (Demand for 68m)
Expon. (Demand for 68m)
3 per. Mov. Avg. (Demand for 68m)
5 per. Mov. Avg. (Demand for 68m)
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30000
40000
50000
60000
70000
80000
0 5 10 15 20 25
Demand and Forecast for 76m
Demand for 76m
Linear (Demand for 76m)
Expon. (Demand for 76m)
3 per. Mov. Avg. (Demand for 76m)
5 per. Mov. Avg. (Demand for 76m)
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Acceptable forecast using Adjusted Exponential Smoothing. Fewer variations with actual Demand
Year MonthDemand for
49m
Exponential Smoothing T T r r eennd d Adjusted Exponential Smoothing
α = 0.3 ββ == 00..66
2011
January 47300 78300 0
February 28900 63480 -8892 54588
March 34400 54756 -8791 45965
April 50300 53419 -4319 49101
May 39800 49333 -4179 45155
June 48200 48993 -1876 47118
July 51400 49715 -317 49398
August 50700 50011 50 50061
September 16900 40078 -5940 34138
October 35600 38734 -3182 35552
November 17800 32454 -5041 27413
December 27300 30908 -2944 27964
2012
January 28600 30215 -1593 28622
February 32700 30961 -190 30771
March 16800 26713 -2625 24088
April 19300 24489 -2384 22105
May 21500 23592 -1492 22100
June 49400 31335 4049 35383
July 36500 32884 2549 35433
August 42800 35859 2805 38663
September 46700 39111 3073 42184October 51200 42738 3405 46143
November 50900 45187 2831 48018
December 19500 37481 -3491 33989
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15000
25000
35000
45000
55000
0 5 10 15 20 25
Demand and Forecast 49m
Demand for 49m
Adjusted Exponential Smoothing
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Year MonthDemand for
68m
Exponential Smoothing TTrreenndd Adjusted Exponential Smoothing
α = 0.3 ββ == 00..66
2011
January 65400 78300 0
February 71300 76200 -1260 74940
March 69400 74160 -1728 72432
April 65300 71502 -2286 69216
May 70800 71291 -1041 70251
June 47400 64124 -4717 59407
July 38500 56437 -6499 49938
August 70400 60626 -86 60540
September 68900 63108 1455 64563October 64300 63466 797 64262
November 58800 62066 -521 61545
December 69300 64236 1094 65330
2012
January 69400 65785 1367 67152
February 36000 56850 -4815 52035
March 45800 53535 -3915 49620
April 76000 60274 2478 62752
May 56700 59202 348 59550
June 78600 65021 3631 68652
July 67900 65885 1970 67855
August 75400 68740 2501 71240
September 65600 67798 435 68233
October 78400 70978 2083 73061
November 46800 63725 -3519 60206
December 52700 60417 -3392 57025
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30000
40000
50000
60000
70000
80000
0 5 10 15 20 25
Demand and Forecast 68m
Demand for 68m
Adjusted Exponential Smoothing
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Year MonthDemand for
76m
Exponential Smoothing T T r r eennd d Adjusted Exponential Smoothing
α = 0.3 ββ == 00..66
2011
January 78300 78300 0
February 56000 71610 -4014 67596
March 36800 61167 -7871 53296
April 78900 66487 43 66530
May 67800 66881 254 67135
June 56400 63737 -1785 61952
July 76400 67536 1565 69101
August 68300 67765 764 68529September 42600 60215 -4224 55991
October 75300 64741 1026 65766
November 78300 68809 2851 71659
December 79000 71866 2975 74841
2012
January 69400 71126 746 71872
February 36000 60588 -6024 54564
March 45800 56152 -5072 51080
April 76000 62106 1544 63650
May 56700 60484 -356 60129
June 78600 65919 3119 69038
July 67900 66513 1604 68117
August 75400 69179 2241 71421
September 65600 68106 252 68358
October 78400 71194 1954 73148
November 46800 63876 -3609 60266
December 52700 60523 -3455 57068
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30000
40000
50000
60000
70000
80000
0 2 4 6 8 10 12 14 16 18 20 22 24
Demand and Forecast 76m
Demand for 76m
Adjusted Exponential Smoothing
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Annex 1
Machine Details
Machine
Index Imported from
Area required
for the machine
Average
Production Rate
1 A1
China 1.9 m x 1.5 m 160m / day
2 A2
3 A3
4 A4
5 A5
6 A6
7 A7
8 A8
9 A9
10 A10
11 A11
12 A12
13 A13
14 A14
15 A15
16 A16
17 A17
18 A18
19 B1
India 2.1 m x 1.8 m 130m / day
20 B2
21 B3
22 B4
23 B5
24 B6
25 B7
26 B8
27 B9
28 B10
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Annex 2
Maximum Production Rate
Sales of 2011 & 2012
Demand for 76m width,
Width Day or 15 hrs Month
76m 3192m 79800m
68m 2856m 71400m
49m 2058m 51450m
Months of 2011 Demand (m)
January 78300
February 56000
March 36800
April 78900
May 67800
June 56400
July 76400
August 68300
September 42600
October 75300
November 78300
December 79000
Months of 2012 Demand (m)
January 69400
February 36000
March 45800
April 76000
May 56700
June 78600
July 67900
August 75400
September 65600
October 78400
November 46800
December 52700
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Demand for 68m width,
Demand for 49m width,
Months of 2011 Demand (m)
January 65400
February 71300
March 69400
April 65300
May 70800
June 47400
July 38500
August 70400
September 68900October 64300
November 58800
December 69300
Months of 2012 Demand (m)
January 71000
February 39500
March 48500
April 64300
May 62700
June 63800
July 36800
August 26800
September 69300October 64900
November 58700
December 56400
Months of 2011 Demand (m)
January 47300
February 28900
March 34400
April 50300
May 39800
June 48200
July 51400
August 50700
September 16900
October 35600
November 17800
December 27300
Months of 2012 Demand (m)
January 28600
February 32700
March 16800
April 19300
May 21500
June 49400
July 36500
August 42800
September 46700
October 51200
November 50900
December 19500