Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
A Study on the Influence of Review Period Interval in Closed
Loop Supply Chains (CLSC) Using System Dynamics
Hassan Fainaze* and Lewlyn L.R. Rodrigues**
Periodic review of capacity plays an important role in CLSC especially in
capacity planning. Managers have to takes decisions regarding the time
interval for the review of capacity expansions and how much they have to
add to their capacity considering the investment cost. In the periodic review
system of capacity, the capacity is reviewed periodically and decision
regarding expansion will be taken accordingly. The focus of this paper is on
the study of the review period interval on the CLSC system using system
dynamics and establishing the relationships between different variables in a
CLSC system and to simulate the influence of the review period interval on
demand, production capacity, production backlog, serviceable inventory,
recycling rate, and the total profit. The results has indicated that even
though our total Investment cost will be more for a shorter review period
interval, the total profit in the system can be very successfully increased
when we are following a shorter review period interval.
Field of Research: Management, Closed Loop Supply Chain and System Dynamics.
1. Introduction
In today‟s competitive market, customers‟ satisfaction has been one of the important
focused areas in every organization. And also because of the restrictions from the
government in bearing responsibility regarding proper disposal, forced the manufacturers
to concentrate on CLSC system and reducing the cost. The systematic review system and
optimum period of reviewing will help in achieving the customer satisfaction in an effective
way. Over the last decade or so, closed loop supply chain management has emerged as a
key area of research among the practitioners of operations research. A lot of research is
being carried out to make the CLSC more efficient and economic. The smooth and
efficient functioning of a business involves the smooth and efficient functioning of the
principal areas of the supply chain, one of these areas is capacity control (Poles and
Cheong, 2011). In this paper the focus is on the study of the review period interval for the
capacity expansions in the CLSC system. In this paper, a system dynamics model is
developed to cope with the dynamics of closed-loop supply chains in capacity planning.
Forrester introduced SD in the early 60‟s as a modeling and simulation methodology for
long-term decision-making in dynamic industrial management problems.
2. Literature Review
The concern about environmental protection and also the economic benefits of using
“used products” has spurred an interest in designing and implementing closed loop supply
chain. In recent decades, many companies focused on reverse logistics activities have
achieved significant successes in achieving their targets. In CLSC system, it is important
to consider the capacity expansion related decisions regarding recollection, recycling and
remanufacturing facilities (Blumberg, 2005).
*Mr. Hassan Fainaze, Department of Mechanical & Manufacturing Engineering, Manipal University,India
Email: hassanfainaze@gmail.com
**Dr. Lewlyn L.R. Rodrigues, Department of Humanities & Management, Manipal University, India
Email: rodrigusr@gmail.com
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
Long term capacity planning in closed loop supply chains involves a huge investment cost.
Managers have to make many decisions regarding the quantity and the time for capacity
expansions related with collection, recycling and remanufacturing capacities. Managers
can adopt either of the following two strategies, strategy of early large-scale investments
or a flexible strategy of low volume but more frequent capacity expansions. The flexible
strategy is the better strategy compared with the large scale capacity expansion strategy
as it involve fewer investment risks. Flexible policies also help to avoid overcapacity in
collection, recycling and remanufacturing capacity (Georgiadis and Athanasiou, 2013).
System dynamics (SD) is a simulation technology that helps to study complex dynamic
systems based on the feedback control theory and the computer imitation technology. SD
method is an effective tool for better understanding complex problems. System dynamics
is proposed to predict the future trends which are difficult to estimate in related with a
complex system (Sterman, 2000).
3. Problem Definition
The focus of this research is on a global steel production which includes the following
distinct operations: supply, production, distribution, use, collection (and inspection),
remanufacturing, recycling and disposal. The forward supply chain includes producer,
distributor and customer. Managers face difficulties in choosing the strategy to be
followed, either a plan of early large-scale investments, or a low-scale plan combined with
more frequent capacity expansions, which is more responsive in capacity adjustments but
less cost effective. In many CLSC systems, overcapacity in the production system has
reported as a reason for decrease in profit margins or losses (Georgiadis and Vlachos,
2004).
In particular the aim of this paper is to develop a model of production and inventory
system for remanufacturing and recycling using a System Dynamics simulation modeling
approach and to study the influence of capacity review period and to evaluate system
improvement strategies.
4. Methodology & Model Construction
The approach in this case is to develop a system dynamics model as a methodology in
order to analyze the different factors related with production, distribution, remanufacturing
and recycling in a closed loop supply chain system. The steps involved are adapted from
(Sterman, 2000) modelling process:
i)
ii)
iii)
iv)
v)
vi)
vii)
Define the dynamic problem to be solved and its scope;
Identify the dependent and independent variables involved and their relationship;
Select suitable software to model the system;
Construct the stock and flow diagram;
Simulate the model;
Verify the model; and
Validate the model.
The global steel production has taken under consideration for this research. The initial
production capacity of steel is 2.964 MMT per day (OECD, 2011). Initially the crude steel
will be produced based on the production backlog and the product will be taken to the
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
serviceable inventory to meet the orders from the distributors. The serviceable inventory is
a sum of the remanufactured products and the newly manufactured products. From the
distributor inventory, the products are sold to customers as per the demand. After a period
of time, these become used product and the available used product will be collected
depending upon the collection capacity, and most of them will become uncontrollable
disposed products. The collected products will be inspected and the failure percentage will
be recorded and the reusable products will be taken to the reusable product inventory for
remanufacturing and recycling to take place and the unusable products will be disposed.
From the inventory of reusable product, products which can be remanufactured will be
taken for remanufacturing and low quality products will be taken for recycling.
The low quality products will be recycled based on the recycling rate which will be
influenced by the recycling capacity. The Causal diagram and the Stock & Flow diagram of
the model are presented in the Figures 1 (adapted from Wang and Murata, 2011).
5. Simulation and Analysis
The model was simulated for studying the influence of the review period interval on
various endogenous factors in a CLSC as discussed below. Capacity has to be reviewed
periodically and then a decision has to be made whether or not to invest on capacity and
to what extent. The length of the review period generally depends on the construction cost
of facilities as well as the total profit, but it is a decision variable in our model. As it is an
exogenous factor, it is important to study its influence on the entire process. In the industry
under consideration, on an average, the review period interval has varied from 365, 1095,
1825, and 2555 days; results were plotted for 7920 days i.e. for 22 years (year 2001 to
2022). The basis for this time interval of simulation was the past 12 years‟ production data
(Steel Statistical Yearbook archive, BOF route steelmaking costs & EAF steelmaking cost,
2013) that was available and it was intended to forecast for another 10 years.
5.1. Demand
It can be observed from the figure 2 that, till 552nd day demand has no change with
change in the review period interval as remanufacturing and recycling processes requires
an initial set up period. Later on from 553rd day, review period interval will have an
influence on demand as the market share can be increased by focusing on the green
image impact through recycling and remanufacturing processes. After 916th day, if we are
following a review period interval of 365 days, the demand for the product will increase
further due to increase in the remanufacturing and recycling rate mainly because of the
increase in their capacities compared to other review period interval simulations. By the
end of 7920th day, it can be seen that the demands for the product will be 7.42 MMT per
day, 7.37 MMT per day, 7.03 MMT per day, & 6.84 MMT per day for the review period
intervals of 365, 1095, 1825 & 2555 respectively.
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
Figure 1: Stock & Flow Diagram of CLSC under Consideration
RMI Cover
time
Expected
orders to SI
SI
<Expected
remanufacturing
rate>
Desired RMI
Expected
distributors orders
SI Desired SI
Discrepancy
Production
Production backlog backlogs Production
orders
reduction rate
RMI
Discrepancy
DI
SI cover time
Orders backlog
Orders
backlog
reduction rate
Production SI adj time
<Production
efficiency
capacity>
Expected
retailer's orders
RI
Expected
demand
D
Unit timeDemand slope
Desired RI
RI
cover
time
Total Demand
Desired DI
Market
RI
DI
Demand
<Time
>
DI Cover time
Discrepancy
Discrepancy
Demand
Retailer's
Distributors
orders
order Retailer's Demand backlog backlog
Retailer order backlog
backlog reduction
orders
reduction rate
rate
DI adj time
RI adj time
Demand
market share
Retailer's
Servicable
Distributor's
Customer's in
<Reuse
Inventory Shipment to
production
Inventory Shipment to Inventory Sales
ratio>
hand products
rate
distributor
retailer
Transportation
RR
Delivery time
Shipment time
Remanufacturing
Production
Obsolecence
time
Expected
Shipment time to
efficiency
Recycling
time
rate
remanufacturing rate
Expected
retailer
Efficiency
Expected used
Avg product
<Remanufacturing
recycling rate
Remanufacturing
products
Capacity>
life
Recycling rate
rate Remanufacturing
RCyR
Used
Uncontrollable
Recycling
time
Uncontrollable
product disposal
Products
time
Reuse ratio
disposal rate
<Recycling
Remanufacturable
UP
capacity>
products
Recollection
Probability of getting
<Expected
Customer's
high quality product
Products accepted for recycling
rate> Collection controllable disposal
remanufacturing
Sorting time
time
Product
Re-Usable
Collected
Recyclable
Available
Products taken for
Products
products
Products
keeping time
used
Collection rate
Products accepted
recycling
products
for re-use
<Collection
Controllable Inspection
Failure
Capacity> Collection
RM Order
Efficiency
disposals
Reusable stock
percentage
time
RM reduction backlogs
Rawmaterial
keeping
time
rate
Disposed
Orders
Products
<Expected
Products rejected
RM adj time
orders to SI>
for re-use
Raw
Input rate Materials
% Electricity
Rem Cap Con Rem Cap
<Time> Electricity % Electricity
recycling
Recyc Cap Con
% Electricity
Slope
Con
Cost
BF % Labour BF
Recyc Electricity Cost RemLabour
Other
<Electricity>
EAF
Cap
<Labour>
Electricity
Cap Con Recyc
Rem Cap Costs
Con
Labour
<Electricity>
Reheat
cost
Other
Recycling
Con
Electricity
costs Cap Con Recyc Capital Electricity
Labour Cost % Labour
Electricity Cost % Labour
Capital Cap Con Cost
Cost BF
Electricity
Cost
EAF
recycling
BF
Cost
% Electricity
Cost Labour
<Electricity> remanufacturing
<Labour>
EAF
Residual
Recycling
BOF
Cost
Capital Con.
Rem Cap
Labour Cost
removal
cost
<RC
Labour Cost
Production Cost
% Labour
Expansion Recy Cap
Construction cost Cost BOF
EAF
Remanufacturing
rate> Construction
remanufacturing
Cost per unit <% Iron Ore EAF PRoduction
Electricity cost
<RCyC
<% Iron Ore Labour Con.
Cost <PC
Expansion
<Recycling recycling based steel
based steel Cost BOF
Remanufacturing
Cost
Expansion production>
rate>
rate>
production>
rate>
Labour Cost
Col Cap
Electricity
Cost per unit
Cost
per
unit
tonne
Construction cost
Prod. Con. Con.Cost BOF
<Labour>
remanufacturing Recycling
for
production
<% Scrap
Cost BF
Electricity
Cost
Investment cost
Other costs
Other Const Con. Cost
steel based
<CC
steel
Prodn Cap
Remanufacturing Production <production
rate
remanufacturing
Cost BOF
production> expansion
EAF
cost
rate>
Construction
rate>
cost
Labour Cost
<Remanufacturing
Prod. Con.
Total
Cost
Total
Total
Con.
EAF
rate>
Investment
Variable Cost
Cost EAF
Cost
Total Variable Cost
Cost
Capital Con.
<Input
<% Scrap
<Remanufacturable
changing rate
steel
based
rate>
products>
Cost EAF
Total
Total
steel
Raw material
Profit
Revenue
production> Other Const
<Servicable
Revenue Less
Total Inventory
Inventory>
cost
Iron Ore based steel
Cost EAF
rate
Others
carrying cost Collection
Cost for production cost Revenue
<Raw Materials>
raw
generating
Excise Duty
cost
<Collected
rate
<Shipment
material per
Unit carrying
Products>
to
tonne steel <BF & BOF
Employee R&D
distributor>
production
steel
<Re-Usable <Recyclable cost
Benefits
making>
Products> products> <Collection Cost per unit
Selling price to Depreciation
rate>
tonne for Scrap based steel % Iron Ore based
distributor
collection
production cost
steel production
Interest &
Raw material
Taxes
<EAF
Storage
gathering cost
Ocean Steel
% Scrap steel based SP Slope <Time>
frieght
Administration Interest charges Making>
steel production
Insurance
Handling
Cost
% Electrodes
% Ferro
EAF Electrodes
% Refractories
% Oxygen Alloys Ferro Alloys
<Refractories>
% Flux EAF
EAF
Coal Slope
Oxygen
Electrode Cost Refractories
<Time>
<Time>
<Fluxes>
EAF
Coking
Coal
Scrap Slope
Cost EAF% Oxygen
Oxygen Cost
<Lime
transport Coking Coal
Flux Cost EAF
Steel Scrap BF
stone>
Ferro Alloys
Oxygen Cost EAF
% steel scrap
Limestone Cost
% Coking
Cost BF
% By- Product Steel Scrap
% Limestone EAF
EAF <Oxygen>
Coking Coal Coal
Cost
BF
credits
EAF <Steel
Other Cost
EAF Steel
Cost BF Other costs
By-Product
Scrap>
By Products
<Ferro
EAF
credits
Other Cost BF
Making
Steel Scrap
Cost
BF
BF & BOF steel
Alloys>
% Other costs
<Time>
Ferro Alloys
Cost EAF
Iron Ore Iron Ore Cost making Fluxes Cost Fluxes
Iron
Ore
EAF
% steel Scrap Coking CoalThermal Energy % FerroAlloys Slope
BF
BF
Iron ore
EAF
Refractories % Fluxes
EAF
EAF
Cost EAF
transport
Cost BF % Refractories
Lime stone
<Coking % Coking Coal
% iron ore Cost BF Thermal
Coal>
EAF % Thermal <Thermal
Refractories
Energy
Cost
Energy>
Lime stone
Energy EAF
% lime stone Thermal % Thermal
Energy
Energy
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
Figure 2: Influence of the Review Period Interval on the Demand
Demand
8M
Tonnes/Day
1
4M
2M
2
1 2
6M
1
2 3 4
3 4
1 2
1 2 3 4
1 2 3
4
1 2 4
3
1 2 3 4
1 2 3 4
1 2
1 2
3 4
3
3
4
3 4
0
0
792
1584 2376
Demand : Review Period 1 year
Demand : Review Period 3 year
Demand : Review Period 5 year
Demand : Review Period 7 year
3168 3960 4752
Time (Day )
1
2
1
2
3
1
2
3
1
2
3
4
5544 6336 7128
4
1
2
3
4
1
2
3
4
7920
1
2
3
4
2
3
4
4
5.2. Serviceable Inventory
It can be clearly seen from the figure 3 that the serviceable Inventory for the review period
interval of 365 days will be increasing steadily as our production capacity expansions will
be done at exact pace whenever required. It can also be seen that for the review period
interval of 1095 days, the serviceable inventory will be at a steady level for an average of
about 23MMT between 1800 and 2220th day as we will be in shortage for the production
capacity. Once we start expanding our production capacity the serviceable inventory will
increase suddenly as we have to meet the pending distributor‟s orders. Similar sort of
pattern with little shift can be observed for the review period intervals of 1825 and 2555
days.
Figure 3: Influence of the Review Period Interval on Serviceable Inventory
Servicable Inventory
60 M
Tonnes
45 M
30 M
15 M
1 2 3
2 3 4
4 1
1 2 3
1 2 3 4
1 2
3
4
1 2
4
1 2 3
4
1 2
3 4
1 2 3 4
1 2 3 1
4
2
3
3
4
0
0
792
1584 2376 3168 3960 4752 5544 6336 7128 7920
Time (Day )
Servicable Inventory
Servicable Inventory
Servicable Inventory
Servicable Inventory
: Review Period 1 y ear 1
: Review Period 3 y ear
: Review Period 5 y ear
: Review Period 7 y ear
1
2
1
2
3
3
4
1
2
3
4
1
2
3
4
1
2
2
3
4
4
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
5.3. Production Backlog
The production backlog has a great impact by the review period interval which we are
choosing (Figure 4). For the capacity review period interval of 365 days, the production
backlog will be very low compared to the 2555 day review period interval. In case of 2555
day review period plan, the production backlog will start piling up from the 1707th day and
it will reach a peak point of 1288 MMT by 2684th day and after that the production backlog
will start declining once we start expanding the production capacity. Similarly the
production backlog will reach 503.1 MMT on 2324th day for the review period interval of
1095 days and 1019 MMT on 3775th day for a review period interval of 1825 days.
Figure 4: Influence of the Review Period Interval on Production Backlog
Production backlogs
2B
Tonnes
1.500 B
999.9 M
3
499.85 M
4
-200,000
4
1 2 3 4 1 2 3 4 1 2 3
0
1188
Product ion backlogs : Review
Product ion backlogs : Review
Product ion backlogs : Review
Product ion backlogs : Review
1 2 3
2376
4
1 2
4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3
3564
4752
Time (Day )
Period 1 year
Period 3 year
Period 5 year
Period 7 year
1
5940
1
2
1
2
3
4
1
2
3
1
2
3
4
7128
3
4
1
2
2
3
4
4
5.4. Recycling Rate
It can be observed that till 552nd day recycling rate will be zero since it requires some time
for the recycling to start and later on the recycling rate increases depending upon the
availability of reusable products during the review period (Figure 5).
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
Figure 5: Influence of the Review Period Interval on the Recycling Rate
Recycling rate
2M
Tonnes/Day
1
1
1.5 M
2
2
1
1
3
1M
1
1
4
4
4
3
1
2
1
0
2 3
2
500,000
2
3
1
1 2 3 4 1 2 3 4
0
Recycling rate
Recycling rate
Recycling rate
Recycling rate
792
:
:
:
:
2
1584
Review
Review
Review
Review
2
3 4
3
3
4
4
4
4
2376
Period
Period
Period
Period
3
1
3
5
7
3168 3960 4752
T ime (Day )
year1
year
year
year
1
2
1
2
3
1
2
3
4
7920
1
2
3
4
7128
1
2
3
4
6336
1
2
3
4
5544
2
3
4
4
It can also be seen that the recycling rate for the 365day review period interval will be
increasing in a steady rate and will be able to recycle 1.71 MMT per day by the end of
7920th day. By the end of 7920th day, if we are following 365 day review period we will be
able to recycle (1.71 MMT per day) almost double the products which we may be able to
recycle if we are following 2555 days review period interval (0.9 MMT per day).
5.5. Production Capacity
In our consideration, production will be reviewed initially on the 30th day and reviewed
regularly depending on the review period interval that we are deciding. During the initial
stage of production we were having a production capacity of 2.964 MMT per day which
later on will be expanded to 3.6 MMT per day after few days. Production capacity of 3.6
MMT per day will be maintained till 1125th day and later on the production capacity will be
expanded depending on the capacity expansions required. It is clear from the figure that
for the review period interval of 2555 days, the production capacity will be maintained at
3.6 MMT per day till 2585th day and which will later on expanded to 7.17 MMT per day for
the next 2555 days and by the end of the 7920th day we will be able to produce at the rate
of 8.5 MMT per day (Figure 6).
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
Figure 6: Influence of the Review Period Interval on Production Capacity
Production capacity
Tonnes/Day
10 M
3
7.5 M
4
4
2
5M
2.5 M
1
1
2
3
4
1
3
4 1
1 2
2
4
1
2
1 2
3
1
3
4
2
4
2
1
3
3
1 2 3 4
2 3 4 1 2 3 4
0
0
792
Product ion capacity
Product ion capacity
Product ion capacity
Product ion capacity
1584
2376
: Review
: Review
: Review
: Review
3168 3960 4752
Time (Day )
Period 1 year
Period 3 year
Period 5 year
Period 7 year
1
5544
1
2
1
2
3
1
2
3
4
7920
1
2
3
4
7128
1
2
3
4
6336
2
3
4
4
5.6. Total Profit
It can be observed from the figure 7 that the total profit will be same till 730th day. In 731st
day the total profit for the review period interval of 365 days will be less as we have to
invest extra amount on increasing the capacity for recycling and remanufacturing. Similarly
we have to make an extra investment on 1856th day and 2584th day for the review period
intervals of 1825 days and 2555 days respectively. Till 2538th day the total profit for the
review period interval of 2555 days will be more due to fewer inventories and later on the
total profit in the case of the review period interval of 2555 days start reducing because of
the low contribution margin and it can be clearly seen from the figure that the industry will
have to face a loss if they are following a review period interval of 2555 days. By the end
of 7920th day, the industry can achieve a profit of about $624343 Cr if they are following
365 day review period plan, almost 3 times the profit we can achieve through 1095 days
review period plan.
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
Figure 7: Influence of the Review Period Interval on Total Profit
Total Profit
8e+012
5.85e+012
$
1
3.7e+012
1
-600 B
2
1
1 2 3 4
2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1
0
Total Profit
Total Profit
Total Profit
Total Profit
2
1
1.55e+012
1188
: Review
: Review
: Review
: Review
Period
Period
Period
Period
2376
1
3
5
7
y ear
y ear
y ear
y ear
2
2 3 4
2
3 4
3564
4752
Time (Day )
1
1
2
3
4
1
2
3
4
4
1
2
3
4
4
7128
1
2
3
3
3 4
5940
1
2
3
4
1
3
4
1
2
2
3
4
4
6. Model Validation
The most commonly used and reliable method for validating a system dynamics model is
to check the simulated values against the actual values. In this model, we have taken the
market demand for comparison and it is observed that the model follows the trend to a
given degree of accuracy. The gaps which are identified between the two may be because
of the external factors which are affecting the demand in the market.
Figure 8: Actual vs. Simulated market demands
7. Conclusion
In this paper, a System Dynamics simulation model of a closed loop supply chain system
for was developed. The main objective of this research was to study the influence of time
interval for capacity expansions on the entire system. Through simulation analysis, we
Proceedings of European Business Research Conference
Sheraton Roma, Rome, Italy, 5 - 6 September 2013, ISBN: 978-1-922069-29-0
concluded that the total system cost increases more rapidly if higher capacity is allocated
for production, recycling, and remanufacturing processes, i.e., the total cost will be more if
we are following a shorter review period interval. Even though our total cost will be more
for a shorter review period interval, the research based on modelling and simulation has
very successfully proved that the total profit in the system can be very successfully
increased when we are following a shorter review period interval as we can reduce the
production backlogs and meet customer needs effectively. In the long run for a firm
producing an average of about 2.964 MMT per day, can achieve a profit of almost about 3
times the profit we can achieve if we are following a higher review period interval. Also,
reducing the review period interval will help to analyze the system properly and manage
the capacity requirements and in turn can increase the total profit for the company in the
long term.
References
Bean, JC., Higle, JL and Smith, RL 1992, „Capacity expansion under stochastic demand‟,
Operations Research, Vol. 40, No. 2, pp. 210–216.
Blumberg, DF 2005, „Introduction to management of reverse logistics and closed loop
supply chain processes‟, Boca Raton: CRC Press, London.
Georgiadis, P and Athanasiou, E 2013, „Flexible long-term capacity planning in closedloop supply chains with Remanufacturing‟, European Journal of Operational Research,
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