International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
Development of an aggregate planning model for the Assam
Bell Metal Co-Operative Society
Sajjad Hussain#1, Thuleshwar Nath*2, Mafidur Rahman#3
1
M.E. Student, 2Associate Professor, 3Assistant Professor
#Department of Mechanical Engineering, Jorhat Engineering College, Jorhat, Assam, India
Abstract—With the advent of technological
advancements in production, many traditionally
manpower oriented industries like the bell metal
industry strive for survival. Although its
infrastructural pattern, techniques of manufacture
and types of product has undergone extensive
changes, it is still industrially unorganized. For the
traditional bell metal industry in Sarthebari to
survive in the present market it is necessary that the
industry be handled from the industrial engineering
perspective. A planned approach is essential to
fulfill the demand forecasts for the upcoming period.
Demand forecasts form the basis of all supply chain
planning. Obtaining forecasting information
frequently means using sophisticated techniques to
estimate future sales or market conditions.
Aggregate planning transforms forecasts into plans
of activity to satisfy the projected demand. A key
decision managers’ face is how to collaborate on
aggregate planning throughout the entire supply
chain. To accomplish aggregate planning, MSEXCEL 2010 spreadsheet application is used which
helps us to formulate the planning for the upcoming
financial year. The optimized total cost incurred is
determined against decision variables which
cumulatively the total cost for the next financial
year. The aggregate plan becomes a critical piece of
information to be shared across the supply chain
because it affects both the demand on a firm's
suppliers and the supply to its customers. To be
effective, it requires inputs from throughout the
supply chain, and its results have a tremendous
impact on supply chain performance.
Keywords—Bell
metal
industry,
Industrial
engineering, Forecasting, Aggregate planning,
Supply chain, Planning Horizon.
I. INTRODUCTION
Sarthebari is located 27 kms from Barpeta. It is
famous for its centuries old traditional bell metal
industry which dates back to King Bhaskarvarman
in 7th century AD. It has a special place in every
Assamese household. It is the second largest
handicraft of Assam. About 40 percent of the people
in this village are engaged in this cottage industry. It
also has a historical importance in the Assamese
folklore.
ISSN: 2231-5381
Demand forecasts form the basis of all supply
chain planning. All push processes in the supply
chain are performed in anticipation of customer
demand, whereas all pull processes are performed in
response to customer demand. For push processes, a
manager must plan the level of activity, be it
production, transportation, or any other planned
activity. For pull processes, manager must plan the
level of available capacity and inventory but not the
actual amount to be executed. In both instances, the
first step a manager must take is to forecast what
customer demand will be. Companies often use
forecasts both on a tactical level to schedule
production and on a strategic level to determine
whether to build new plants or even whether to enter
a new market [1].
Once a company creates a forecast, the
company needs a plan to act on this forecast.
Aggregate planning transforms forecasts into plans
of activity to satisfy the projected demand. The
aggregate plan becomes a critical piece of
information to be shared across the supply chain
because it affects both the demand on a firm's
suppliers and the supply to its customers [1].
II. LITERATURE REVIEW
The bell metal industry is still industrially
unorganized state. Thus, planning requires an
extensive literature review.
Kalita, B., (2008) [2] discusses the present
condition of the Sarthebari Bell metal industry. The
traditional industry is also discussed from the
historical perspective. The geographical perspective
of Sarthebari town area and nearby villages where
bell metal work is going on is provided. Goswami,
B., (2009) [3] provides an insight to the traditional
bell metal industry in Sarthebari with a special
emphasis on its role in employment generation. Key
issues relating to the industry right from the
procurement of raw materials to the dispatch of the
finished bell metal items have been discussed and
suggestions offered to address them. Problems
relating to marketing have also been discussed.
Gallego, G., (2001) [4] defines aggregate
production planning as being concerned with the
determination of production, inventory, and work
force levels to meet fluctuating demand
requirements over a planning horizon that ranges
from six months to one year. Normally, the physical
resources of the firm are assumed to be fixed during
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
the planning horizon of interest and the planning
effort is oriented toward the best utilization of those
resources, given the external demand requirements.
Since it is usually impossible to consider every finite
details associated with the production process while
maintaining such a long planning horizon, it is
mandatory to aggregate the information being
processed. Johnny, C. H. T., et al. (2015) [8]
describes some unique characteristics of Aggregate
Production Planning, which make the teaching of
this topic in Operations Management courses
somewhat
different
than
other
topics.
Techawiboonwong, A., et al. (2002) [9] presents an
aggregate production planning(APP) model and a
guideline to develop an optimal aggregate
production plan using the spreadsheet solver
approach. Among existing APP approaches, the
spreadsheet solver approach is found to be the most
applicable for industries due to the following
reasons: (1) the solver on spreadsheet software is
readily available on virtually all personal computers,
(2) the APP model is relatively easy to formulate in
a spreadsheet format, and (3) the results are easy to
interpret. Correa, F.A., et al., (2004) [10]
introduces an Excel model for aggregate planning,
characterized by its great flexibility and for the use
of Excel Solver, which in many cases allow us to
find the optimal solution for a given set of
conditions. Spreadsheets are the most common
software tool managers use to analyse data and
model quantitative problems.
III. OBJECTIVE
The objective of the study is to develop an
aggregate planning model for the Assam bell metal
co-operative society(AK in order to fulfil the
forecasted demand for the upcoming financial year.
The demand forecast is made taking into account the
historical sales data.
IV. METHODOLOGY
A step by step approach was undertaken in order
to solve the research problem. An extensive field
study
was
undertaken
for
information
collection.Then the statistical information collected.
The following steps were taken as a part the
research methodology:
Data collection: An extensive field study was
undertaken in order to gather vital information about
the indigenous bell metal industry. Field trips were
made to the manufacturing household units, the
private players, the co-operative society and its
retail outlets throughout Assam, the primary
suppliers i.e. the traders from Guwahati to gather
vital information regarding all stages of the of
supply chain of this industry which is still in the
dormant state in many regards. Statistical data was
collected regarding the production capacity of
manufacturing units, rate of raw material
ISSN: 2231-5381
consumption,
costs
incurred
in
various
aspects.Historical sales data was collected from the
co-operative society and information was gathered
regarding costs incurred in various aspects such as
transportation, making charges paid to the
manufacturing units, cost of raw material, inventory
costs. Information was also collected regarding the
collaboration of the co-operative society with other
players in the supply chain.
Forecasting: Because most supply chain decisions
are based on an estimate of future demand, forecast
of future demand is done based on historical
demand data. Taking historical sales data of the past
three years for the co-operative society, a monthly
sales forecast is made of the upcoming financial
year 2015-2016. The forecasting is accomplished
using the MS- EXCEL 2010 spreadsheet application.
Aggregate planning: After we obtain the demand
data for the financial year 2015-16, planning needs
to be done in order to fulfill the forecasted demand.
Two scenarios are considered.
Case1: The workforce for the co-operative society
is considered variable, where the co-operative has
the authority to hire or layoff household units. The
decision variables are taken into account and the
costs associated with them are also accounted for.
The constraints associated with the decision
variables are also formulated as equations. Thus a
linear programming problem is formulated for the
co-operative society. This LPP is solved using the
MS- EXCEL 2010 spreadsheet application. This
gives us the optimized total cost for the planned
period.
Case 2: Since the co-operative society is bound by
rules and regulations, it cannot hire or lay off its
member units at will. The workforce of the cooperative society is considered to be constant
at117(from field study)(present the strength of the
co-operative society). The total cost for the decision
variables is calculated which cumulatively gives the
total cost for the planned period.
V. ANALYSIS AND DISCUSSION
A. Formation
of
linear
programming
problem for the co-operative society
1.
Inferences from field study
Mean quantity of bell metal produced per
household unit per day = (2896/193) kg =15
kg
Number of charcoal bags consumed in
production of 1 kg of finished bell metal
item = 0.2044 charcoal bags.
From the sales data and production capacity
the mean contribution of each of the
household units to the co-operative society
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
Cost of Broken bell metal + Cost of
Charcoal bags= (∑750* Bt + ∑240* Ct),
t=1,2,3,…….,12
is calculated to be 11.833% of their total
production.
Number of working days = Monthly sales/
(No. of households* Mean contribution of
each of the household* Mean quantity of
bell metal produced per household unit per
day)
Mean contribution to the co-operative
society per household per day = Mean
quantity of bell metal produced per
household unit per day * percentage
contribution of each household to the cooperative society = (0.11833*15) kg = 1.775
kg
2.
Decision variables
Pt= quantity of finished bell metal items in
kg produced in the month t, t=1,2,3,……12
Tt= number of trips for the month t,
t=1,2,3,……12
Ct= number of charcoal bags consumed for
the month t, t=1,2,3,……12
It= inventory for the month t,
t=1,2,3,……12
Ht= number of workers hired for the month
t, t=1,2,3,……12
Lt= number of of workers laid off for the
month t, t=1,2,3,……12
Wt= workforce for the month t,
t=1,2,3,……12
4.
Production costs
Making charge per kg * quantity
produced per month = ∑250* P t,
t=1,2,3,…….,12
Inventory cost
Holding cost per unit * number of units
currently in inventory = ∑2* It,
t=1,2,3,…….,12
Costs incurred by the co-operative society
Broken bell metal: Rs 750/kg
Charcoal bags: Rs 240/bag
Transportation costs: Rs 2500/trip ( One
trip carries 1tonne of broken bell metal and
for charcoal bags the quantity is 100 bags
per trip)
Inventory costs for the co-operative
society: Rs 2/kg
The making charge to artisans is calculated
as Rs 249.60/kg≈ Rs 250 per kg of finished
bell metal items.
3.
Cost of Transportation
Cost per trip * number of trips per month
= ∑2500* Tt, t=1,2,3,…….,12
Therefore, Total cost = Material cost +
Cost of Transportation + Production
costs + Inventorycost= (∑750* Bt + ∑240*
Ct + ∑2500* Tt + ∑250* Pt + ∑2* It),
t=1,2,3,…….,12
5.
Constraints
Inventory constraints
Current inventory= Beginning inventory +
Production for the current month –
Demand for the current month
 It= It-1+ Pt – Dt, t= 1,2,3,……12,
where Dt= Demand for the current
month
Production constraints
Production= number of working days in a
month* share of production to ASKS*
mean quantity of bell metal processed per
day* current workforce
 Pt= 17*0.11833*15*Wt
= 30.174* Wt, t= 1,2,3,……12
Workforce constraints
Current workforce= Initial workforce +
Number of workers hired - Number of
workers laid off
 Wt = Wt-1 + Ht – Lt, t=
1,2,3,……12
Components of cost incurred
Cost of Broken bell metal
Cost per kg * Quantity required per
month = ∑750* Pt, t=1, 2, 3,……., 12
Cost of Charcoal bags
Cost per bag * Number of bags required
per month = ∑240* Ct, t=1,2,3,…….,12
Material cost
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Transportation constraints
Number of trips = (Quantity of broken bell
metal carried in one trip/1000) + (Number
of charcoal bags in carried in one trip/100)
 Tt = (Pt/1000)+ (Ct/100) , t=
1,2,3,……12
Charcoal bag constraints
Number of charcoal bags = Quantity of
charcoal bags required for 1kg of finished
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
bell metal * Production quantity for current
month
 Ct = 0.2044* Pt, t= 1,2,3,……12
Hiring and layoff constraints
The maximum number of household units
that can be hired or laid off per month is
considered at most 40.
Ht ≤ 40, t= 1,2,3,……12
Lt ≤ 40, t= 1,2,3,……12
6.
Objective Function
Minimize (∑750* Bt + ∑240* Ct + ∑2500* Tt
+ ∑250* Pt + ∑2* It), t=1,2,3,…….,12, subject
to the following constraints:
It=
It-1+Pt
–
Dt,
t=
1,2,3,……12
………………….(i)
Pt = 17*0.11833*15*Wt
=
30.174*Wt,
t=1,2,3,……12
………………...(ii)
Wt = Wt-1 + Ht – Lt, t= 1,2,3,……12
…….………(iii)
Tt = (Pt/1000) + (Ct/100), t= 1,2,3,……12
...……...(iv)
Ct
=
0.2044*
Pt,
t=
1,2,3,……12
……………..(v)
Ht <= 40, Lt <=40, t= 1,2,3,……12
....…………..(vi)
It, It-1, Pt, Dt, Wt,Wt-1,Ht,Lt,Tt ,Ct >= 0
.….........… (vii)
B. Forecasting of demand for the planned
horizon
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Nov'13
Dec'13
Jan'14
Feb'14
Mar'14
April'14
May'14
Jun'14
Jul'14
Aug'14
Sept'14
Oct'14
Nov'14
Dec'14
Jan'15
Feb'15
Mar'15
Considering monthly sales data of ASKS as the
input Y range and periods as the input X range and
performing regression analysis using MS-EXCEL
2010 data analysis tool pack, we get the following
results
Thus, Level= 3060.12
Trend= -0.84832
So we get the forecast trend for the next financial
year using the formula:
Demand = level + trend * period
The forecast trend for the financial year 2015-16 is
calculated and tabulated in table 2.
TABLE 2: FORECAST TREND WITHOUT
CONSIDERING SEASONALITY
The planned horizon is the financial year 201516. The historical data for the previous three
financial years are arranged as follows:
TABLE 1. HISTORICAL SALES DATA OF
THE CO-OPERATIVE SOCIETY
Serial no
Month
Monthly demand
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
April'12
May'12
Jun'12
Jul'12
Aug'12
Sept'12
Oct'12
Nov'12
Dec'12
Jan'13
Feb'13
Mar'13
April'13
May'13
Jun'13
Jul'13
Aug'13
Sept'13
Oct'13
5302.34
3868.44
2106.35
1698.69
1717.78
1998.25
3716
2756.35
2819.03
4216.52
3059.88
2464.52
5549.6
3925.4
2186.85
1700.12
1728.21
2023.94
3921.19
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2762.09
2838.5
4293.03
3086.92
2477.15
5741.34
3933.48
2211.35
1737.22
1787.66
2178.42
4086.09
2843.72
2900.89
4342.61
3129.64
2489.78
Forecast Trend
3028.73
3027.88
3027.04
3026.19
3025.34
3024.49
3023.64
3022.79
3021.95
3021.10
3020.25
3019.40
Since the forecast done without considering
seasonality factor, the forecast trend in table 2
does not show the seasonal fluctuations in the
forecast demand.
We consider periodicity p=12 i.e. after 12
periods, repetition is noticed in the monthly sales
data of the co-operative society. So, the
deseasonalized demand is calculated using the
formula:
Dt =[Dt-p/2 + Dt+p/2 +∑2Di]/2p
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
Regression analysis is then performed in MSEXCEL considering the deseasonalized demand as
the input Y range and the corresponding periods as
the input X range. The following results are
obtained:
Thus level1=2990.29
Trend1=2.996
The deseasonalized demand after regression
analysis is calculated using the formula:
Demand = level1 + trend1* period
The seasonality indexes are then calculated using
the formula:
Monthly sales for the co-operative society
Deseasonalized demand (reg)
TABLE 3. FINAL FORECAST FOR THE
YEAR 2015-16
April’15
May’15
Forecast with
Seasonality
5662.80
4003.09
June’15
2219.98
July’15
1753.05
Month
August’15
1786.21
September’15
2115.78
October’15
4000.02
November’15
2853.85
December’15
2920.79
January’16
4386.03
February’16
3165.77
March’16
2536.19
C. Aggregate planning using MS-EXCEL 2010
(Variable Workforce)
The approach is chase strategy-using capacity as
the lever, where the production rate is synchronized
with the demand rate by hiring or laying off
employees as the demand rate varies [1]. In this case
the workforce is considered variable i.e. the cooperative society can hire or layoff manufacturing
units as per requirement.
The aggregate planning problem formulated for the
co-operative society is set up in MS-EXCEL 2010
spreadsheet application. The decision variables are
The seasonality indexes are arranged in a monthly
order and their averages are calculated. The
averages are unadjusted and they sum up to k=
0.99970904.
The unadjusted averages are adjusted using the
formula:
Unadjusted average
k
The final forecast is calculated using the formula:
Demand = (level1+ trend1* period) *adjusted
average
set up periodically for the planned period in a
tabular form corresponding to the demand forecast
for the planned period. The constraints and the cost
components are also set up periodically for the
planned period in a tabular form. The equations for
the constraints and the cost components are written
in the formula bar for the respective constraint and
cost component.
Then we select the solver tool in MS-EXCEL 2010.
The Linear Programming Problem is set up in the
solver tool MS-EXCEL tool.
The objective is set the cell containing the total cost
which is to be minimised.
The variable cells to be changed are the cells
containing the decision variables over the planned
period.
The constraints are set up as in the formulation of
the LPP
The solving method is chosen Simplex LP because
the problem is a linear solver problem.
The solver tool returns the optimized quantity of
decision variables to be utilised in order to fulfil the
demand forecast for the planned period.
The total cost corresponding to the decision
variables across the planned period are calculated
which cumulatively returns the total cost to be
incurred in the planned period. The total cost is
found out to be Rs. 39523091.26.
Table 4 shows the quantity of decision variables to
be utilized by the co-operative society for the
financial
year
2015-16.
TABLE 4. AGGREGATE PLANNING DECISION VARIABLES
AGGREGATE PLAN DECISION VARIABLES
PERIOD
It
(in kgs)
0
1000.00
1
74.54
2
0.00
3
4
Ht
Pt
(in kgs)
Tt
0
0
157
4737.34
14
968
5662.80
40
130
3928.55
12
803
4003.09
0
40
90
2721.58
8
556
2220.00
0
40
50
1514.62
5
310
1753.05
Lt
Wt
0
0
117
40
0
13
501.58
263.15
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Ct
(in bags)
DEMAND
{in kgs)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
5
0.00
0
0
50
1523.06
5
311
1786.21
6
338.64
40
9
81
2454.42
7
502
2115.78
7
0.00
40
0
121
3661.38
11
748
4000.02
8
0.00
13
40
95
2853.85
9
583
2853.85
9
129.14
40
34
101
3049.93
9
623
2920.79
10
0.00
40
0
141
4256.89
13
870
4386.03
11
0.00
4
40
105
3165.77
10
647
3165.77
12
1000.00
40
28
117
3536.19
11
723
2536.19
TOTAL
3307.05
271
270
1357
37403.58
114
7645
37403.58
TABLE 5. AGGREGATE PLANNING COST COMPONENTS
AGGREGATE PLAN COSTS (IN RUPEES)
Period
Inventory
Production
Transportation
Material
1
2000
1184335.39
36051.17
3785401.19
2
149.08
982137.11
29896.25
3139130.21
3
0
680395.61
20711.24
2174696.79
4
1003.16
378654.11
11526.23
1210263.36
5
526.30
380765.28
11590.49
1217011.11
6
2.35431E-13
613604.25
18678.11
1961216.63
7
677.27
915345.75
27863.12
2925650.05
8
2.27374E-13
713462.50
21717.80
2280385.97
9
0
762481.75
23209.94
2437062.47
10
258.27
1064223.25
32394.96
3401495.89
11
0
791442.50
24091.51
2529627.51
12
0
884047.50
26910.41
2825613.84
Total
4614.09
9350895
284641.24
29887555.02
Total Cost (in Rs)
39523091.26
D. Aggregate planning using MS-EXCEL 2010 (Constant Workforce)
In practice, achieving synchronization of production
of varying machine or labour capacity over time is
rate with the demand rate with the chase strategy
high.
using capacity as the lever approach can be very
Next we will consider the number of household
problematic because of the difficulty of varying
units as constant and devise an aggregate plan for
capacity and workforce on short notice. This
the
financial
year
2015-16.
strategy can be expensive to implement if the cost
.
TABLE 6. AGGREGATE PLANNING WITH CONSTANT WORKFORCE
AGGREGATE PLANNING WITH CONSTANT WORKFORCE
Month
Demand
Cumulative
net demand
No. of
working
days
No. of
units
produced
per
household
(in kgs)
Cumulative
no. of units
produced
per
household
(in kgs)
No. of
workers
Monthly
production
(cumulative)
(in kgs)
Ending
inventor
y
(in kgs)
Charcoal
bags
(cumulati
ve)
(in bags)
Number
of trips
(cumula
tive)
April
5662.8
5662.8
28
49.49
49.49
117
5790.83
128.03
1184
18
May
4003.09
9665.89
19
33.91
83.40
117
9758.22
92.33
1995
30
June
2220
11885.89
11
19.06
102.47
117
11988.64
102.75
2450
36
July
1753.05
13638.94
8
14.98
117.44
117
13740.83
101.89
2809
42
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
August
1786.21
15425.15
9
15.41
132.85
117
15543.90
118.75
3177
47
September
2115.78
17540.93
11
18.78
151.63
117
17741.10
200.17
3626
54
October
4000.02
21540.95
20
35.22
186.86
117
21862.42
321.47
4469
67
November
2853.85
24394.8
14
24.51
211.37
117
24730.65
335.85
5055
75
December
2920.79
27315.59
14
25.01
236.38
117
27656.55
340.96
5653
84
January
4386.03
31701.62
21
37.44
273.82
117
32036.60
334.98
6548
98
February
3165.77
34867.39
15
26.98
300.80
117
35193.22
325.83
7193
107
March
2536.19
37403.58
12
21.46
322.26
117
37704.46
300.88
7707
115
Total
322.26
The above table shows the aggregate planning
using constant workforce for the financial year
2015-16. The approach to the aggregate planning
is discussed step by step below.
Now, we assume that the goal is to eliminate
completely the need for hiring and firing during
the planning horizon.
Workforce available in hand= 117
We tabulate the forecasted demand for the planned
horizon and calculate the cumulative demands as
well. We also tabulate the number of working days
each month.
The quantity of finished bell metal products
produced per household for ASKS each month is
calculated as:
Mean contribution to ASKS per household per
day * number of working days in a month.
Example for the month of May, the quantity of
finished bell metal products produced per
household for ASKS is given by 1.775*19 = 33.91
kg.
The cumulative quantity of finished bell metal
products produced per household for ASKS is also
calculated.
The monthly production for ASKS is calculated
using the formula:
Number of workers * cumulative quantity of
finished bell metal products produced per
household for ASKS
Example, the production for the Month of May
shows the total production for both the month of
April and May. Thus, production for the month of
May is given by the formula:
Number of workers * cumulative quantity of
finished bell metal products produced per
household for ASKS for the month of May
= 117* 83.40 = 9758.22 kg
The results for each month are tabulated. Then the
monthly ending inventory is calculated using the
formula:
Cumulative monthly production – Cumulative
net demand
The results are tabulated. The number of charcoal
bags required for production is given by:
Number of charcoal bags consumed in
production of 1 kg of finished bell metal item *
quantity of finished bell metal items
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3703.90
Example, number of charcoal bags consumed in
April = 0.2044 * 5790.83 ≈ 1184
The results are tabulated. The number of trips
required for transportation of broken bell metal
from Guwahati to Sarthebari and charcoal from
different sources to Sarthebari is given by the
formula: (Quantity of broken bell metal carried
in one trip/1000) + (Number of charcoal bags in
carried in one trip/100)
Example, the number of trips for the month of
April = (5790.83/1000) + (1184/100) ≈ 18
The results are tabulated. The aggregate plan for
the financial year 2015-16 is thus accomplished
which is shown in table 6.
The cost components are tabulated in the following
table.
TABLE7.
PLANNING
COST
COMPONENTS
OF
AGGREGATE
COST COMPONENTS (in Rs)
Inventory
Material
Production
Transportation
7407.80
30127975.27
9426115.09
286930.94
Total Cost
(in Rs)
39848429.10
VI. CONCLUSION
Thus the development of an aggregate planning
model for the Assam Bell Metal Co-operative
society is accomplished using MS-EXCEL 2010
spreadsheet application. Two cases are considered:
Case1: The approach is chase strategy-using
capacity as the lever, where the production rate is
synchronized with the demand rate by laying off
employees as the demand rate varies. In practice,
achieving this synchronization can be very
problematic because of the difficulty of varying
workforce on short notice. The aggregate planning
model is developed using the solver tool in MSEXCEL 2010 spreadsheet application. The total
cost incurred in order to fulfil the forecasted
demand for the financial year 2015-16 is estimated
to be Rs. 39523091.26
Case 2:The workforce for the co-operative society
is considered constant at 117, which is the present
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 5 - September 2015
strength of the co-operative society. Using this
approach, the total cost incurred in order to fulfil
the forecasted demand for the financial year 201516 is estimated to be Rs. 39848429.10.
Because costs of the two plans are close, it is likely
that the company would prefer the constant
workforce plan in order to avoid any unaccounted
for costs of making frequent changes in the
workforce.
ACKNOWLEDGMENT
We would like to thank Dr. Parimal Bakul Barua,
Professor and H.O.D, Department of Mechanical
Engineering, Jorhat Engineering College, for his
constant support and valuable advice throughout
this research work. We would also like to thank
Mr. Pranjit Deka, Secretary, Assam bell metal
utensils co-operative society for his valuable
inputs and information and all the respondents for
their valuable feedback.
REFERENCES
[1] Chopra, S., Meindl, P., Supply chain management: strategy,
planning, and operation 3rd edition, ISBN: 0-13-208608-5,
2007;.pp. 01-121.
[2] Kalita, B., ‘The Bell Metal industry of Sarthebari’, XVIII no
edition, 2008, Assam State Museum bulletin board, pp. 37-47.
[3] Goswami, B., ‘Traditional crafts of Assam and their role in
employment generation- A study in lower Assam (with focus on
some special crafts.)’, 398-GOS, 2007, pp. 93-111.
[4] Guillermo, G., ‘IEOR 4000: Production Management’,
Lecture 5, October 2000.
[5] Kleiner, B. H and Pan Lin., ‘Aggregate planning today’,
Vol. 44 No. 3, 1995, pp. 4-7, © MCB University Press, 00438022, May/ June 1995.
[6] Sankaranarayanan, T, Khound P.K.., Sellathurai, G,
‘Productivity studies
in Bell
metal industry at
Sarthebari(Assam)’, Prepared by National productivity
Council,Guwahati-24;sponsored by IDBI in the library of AIDC
office, Guwahati.
[7] Sultana, N., Shohan, S., Sufian. F., ‘Aggregate planning
using transportation method: a case study in cable industry’,
International Journal of Managing Value and Supply Chains
(IJMVSC), Vol.5, No. 3, September 2014.
[8] Johnny, C. Ho., Francisco J. L, David Ang,, ‘Teaching
aggregate planning in operations management’, ISSN: 21639280; Spring, 2015; Volume 14, Number 1.
[9] Techawiboonwong, A. and Yenradee, P., ‘Aggregate
Production Planning Using Spreadsheet Solver: Model and
Case Study’, Industrial Engineering Program, Science Asia,
pp. 291-300, 2002.
[10] Correa, F.A., Garrido N. P., ‘Excel Model for Aggregate
Planning’; Second World Conference on POM and 15th Annual
POM Conference; Cancun, Mexico; April 30-May 3, 2004.
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