This document presents a mathematical model for determining the optimal collection period for returned products in a reverse supply chain system with stochastic returns. It formulates the problem, defines relevant decision variables and parameters, and develops equations to minimize the total annual cost, which is the sum of the annual inventory cost and annual shipping cost. The model is presented for a general case with discrete random returns and a special case where returns follow a Poisson distribution. An illustrative example is provided to demonstrate the application of the model. The summary aims to capture the key aspects and goal of the mathematical model developed in the document.
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2. DETERMINING THE OPTIMAL COLLECTION
PERIOD FOR RETURNED PRODUCTS IN A
STOCHASTIC ENVIRONMENT
- Shashank Kapadia
- Dr. Emanuel Melachrinoudis
2
3. Introduction
What is Supply Chain?
Components of Reverse Supply Chain
Importance and Impact
Focus of this work
Problem Definition
Mathematical Formulation
Generalized model
Special Case: Poisson Distribution
An Illustrative Example
Conclusion and Future Work
Outline
3
4. What is Supply Chain?
The sequence of processes involved in the production and
distribution of a commodity
Introduction
Supply Chain
Forward Supply Chain Reverse Supply Chain
4
5. What is Supply Chain?
The sequence of processes involved in the production and
distribution of a commodity
Introduction
Supply Chain
Forward Supply Chain Reverse Supply Chain
5
Components/
Raw Materials
Manufacturers
Wholesalers/
Distributors
Retailers
Customers
6. What is Supply Chain?
The sequence of processes involved in the production and
distribution of a commodity
Introduction
6
Components/
Raw Materials
Manufacturers
Wholesalers/
Distributors
Retailers
Customers
Supply Chain
Forward Supply Chain Reverse Supply Chain
7. Components of Reverse Supply Chain
Introduction
7
Reverse Supply Chain
Product
Acquisition
Inspection and
Disposition
Reverse Logistics Reconditioning
Distribution and
Sale
8. Importance
Environmental (regulations, consumer pressure etc.)
Economic (value of used products, cost reduction etc.)
Impact
Macro level
20% of that is sold is returned
According to Reverse Logistics Association, the volume of annual returns is
estimated between $150 billion and $200 billion at cost
~6% of the Census Bureaus figure of $3.5 trillion total of US retail
21% increase in product returns cost in US electronics consumer and manufacturers
market since 2007, by Accenture in 2011
Micro level
Supply chain costs associated with reverse logistics average between 7% - 10% of
costs of goods
Average manufacturer spends 9% - 15% of total revenue on returns
Importance and Impact
8
9. Focus of this Work
9
Supply Chain
Forward Supply
Chain
Reverse Supply
Chain
Product
Acquisition
Reverse Logistics
Distribution
Production
Planning
Inventory
Inspection and
Disposition
Reconditioning
Distribution and
resale
Specifically on the
collection of returned
products and the
economic driving force
that can bring direct
gains to the companies
in terms of cost
reduction
11. Problem Definition
11
ICP CRC
Objective
To determine the finite collection time at an ICP before sending it to the CRC
Prior work
Although, the work has been done on reverse logistics in past, it is diverse and
heterogeneous. Recently, the dynamic interplay between shipping volume and the
collection period was examined
We propose a generalized model for stochastic product returns where the
rate of returns follows a discrete probability distribution
Figure 2: Sub-problem with one ICP and one CRC
12. Problem Definition
12
ICP
Inventory Cost Shipping Cost
$-
$5,000.00
$10,000.00
$15,000.00
$20,000.00
$25,000.00
1 2 3 4 5 6 7 8 9 10
Annualcost($)
Collection period (t days)
Annual Costs at ICP
Inventory Cost Shipping Cost
$18,000.00
$20,000.00
$22,000.00
$24,000.00
1 2 3 4 5 6 7 8 9 10
Collection period (t days)
Total Annual Cost at ICP
Total Annual cost
13. Mathematical
Formulation
Indices
Index for time periods in days
Decision Variables
Length of the collection period in days
Model Parameters
Daily inventory cost per unit, including the penalty of holding a
unit one more day
Annual working days
Discrete random variable representing the number of returned
products on the ≠
day from all the customers; are assumed to
be independent and identically distributed random variables
according to a discrete mass function = Pr =
Standard freight rate
腫
Freight discount rate depending on shipment volume from the
ICP to the CRC, = 1, , and 0 = 1
Preselected shipment volume breakpoints, = 1, , as shown
in Figure 3
The volume of accumulated returned
products over the period of days
as () = =1
.
The objective is to determine the
collection period for returned
products at ICP that minimizes the total
annual cost which is the sum of annual
inventory cost and annual shipping
cost.
Minimize: Total Annual Cost
(Annual Shipping Cost + Annual
Inventory Cost)
Assumptions:
1. Sufficient capacity
2. No transportation cost from
customers to ICP
3. Returned products are of same
kind
13
14. Mathematical
Formulation
Inventory Cost
The cost associated with storing the
returned products at the ICP.
The expected annual inventory
cost 腫駒 can be derived as
腫 1 = 1
腫 2 = 1 + 1 + 2
= 21 + 2
腫 = $1 + 1 2 + +
腫 =
+ 1
2
()
Therefore, accounting for
cycles in a
year, we have
稲 =
+
()
14
$-
$2,000.00
$4,000.00
$6,000.00
$8,000.00
$10,000.00
$12,000.00
1 2 3 4 5 6 7 8 9 10
Annualcost($)
Collection period (t days)
Expected Annual Inventory Cost at ICP
Inventory Cost
15. Mathematical
Formulation
Shipping Cost
The shipping cost is a function of accumulated
returned products over the collection period of
days,(), and the freight discount rate.
The shipping cost can be expressed as:
=
告腫 ()
告 ()
1 < , = 1, ,
By defining another breakpoint at infinity,
i.e. +1 = , we can express above equation as:
= 告腫 , 1 < , =
1, , + 1, and its expected value can be
expressed as
=
=1
+1
腫1
= 1
1
Therefore, accounting for
cycles in a year, we
have
咋
=
=
+
駒
=倹
倹
15
$9,000.00
$11,000.00
$13,000.00
$15,000.00
$17,000.00
$19,000.00
$21,000.00
1 2 3 4 5 6 7 8 9 10
Annualcost($)
Collection period (t days)
Expected Annual Shipping Cost at ICP
Shipping Cost
告 告1 告2 告3
0 = 0 1 2 3
Figure 3: Preselected shipment volume breakpoints
16. Mathematical
Formulation
駒
=
告
=1
+1
腫1
= 1
1
Above equation can be simplified
using approximation as:
咋 金
where,
金
=
=
+
駒
=倹
倹
金
can be considered as the
effective discount rate.
The approximation was extensively
tested and was found to be quite good.
16
$-
$5,000.00
$10,000.00
$15,000.00
$20,000.00
$25,000.00
1 2 3 4 5 6 7 8 9 10
Annualcost($)
Collection period (t days)
Shipping Cost Approximation
Comparison
Shipping Cost Theoretical(Approx) Shipping Cost Theoretical (Exact)
17. Let us now assume that , = 1, , are independent and identically distributed random
variables following the Poisson distribution with mean = . Then ~ .
The expected annual inventory cost:
稲 =
+
The expected annual shipping cost:
咋 金
=
+
駒
=
The total cost which is the sum of inventory cost and the shipping cost can be expressed as
諮
+
+ 金
Special Case: Poisson Distribution
17
18. Special Case:
Poisson
Distribution
Proposition 1
Based on the approximation and
extensive simulation, there is drop
in expected annual total cost
wherever,
金
金
>
Reduction in the number of
possibilities for optimal collection
period.
18
Collection
Period t (days)
Total Cost 留*(t) 留*(t-1)-留*(t)
1 $ 22,000.00 1.000
2 $ 22,993.54 1.000 0.000
3 $ 20,011.88 0.801 0.199
4 $ 21,000.00 0.800 0.001
5 $ 21,965.98 0.799 0.001
6 $ 19,296.21 0.618 0.181
7 $ 19,893.86 0.596 0.022
8 $ 19,098.54 0.506 0.090
9 $ 20,000.00 0.500 0.006
10 $ 21,000.00 0.500 0.000
Table 1: Results that illustrate Proposition
19. An Illustrative
Example
We consider a cluster of
customers where the total daily
returns volume follows a Poisson
distribution with = , the
daily inventory cost = . , the
unit standard freight rate is =
and the annual working days
are = . All the customers
return products to a single
designated ICP and subsequently
the products are collected during a
period before they are shipped
to a single designated CRC. The
shipment volume breakpoints
are = , =
and = , beyond which the
freight discount rate decreases
to = . , = . and =
. , respectively.
19
Time Period Inventory Cost Shipping Cost Total Cost
1 $ 2,000.00 $ 20,000.00 $ 22,000.00
2 $ 3,000.00 $ 19,993.54 $ 22,993.54
3 $ 4,000.00 $ 16,011.88 $ 20,011.88
4 $ 5,000.00 $ 16,000.00 $ 21,000.00
5 $ 6,000.00 $ 15,965.98 $ 21,965.98
6 $ 7,000.00 $ 12,296.21 $ 19,296.21
7 $ 8,000.00 $ 11,893.86 $ 19,893.86
8 $ 9,000.00 $ 10,098.54 $ 19,098.54
9 $ 10,000.00 $ 10,000.00 $ 20,000.00
10 $ 11,000.00 $ 10,000.00 $ 21,000.00
$18,000.00
$19,000.00
$20,000.00
$21,000.00
$22,000.00
$23,000.00
$24,000.00
1 2 3 4 5 6 7 8 9 10
Annualcost($)
Collection period (t days)
Expected Annual Total Cost
Table 2: Expected annual costs
20. Conclusion and
Future Work
One of the first paper to tackle the reverse
logistics network problem involving random
returned products
The proposed model can aid in coping up
with a new challenge of uncertainty in
product returns
From the theoretical standpoint, the proposed
model was proven to be efficient in
determining a functional relationship between
the expected total inventory cost and the
shipping cost, and with the returns collection
period.
Future work
The model can be extended to reflect the
continuous time collection period and
freight rates fluctuations
Future research should be able to tackle
different types of product returns with
multiple ICPs and CRCs
Model could be validated for large-sized
real-world problems with actual data
20
Time Period
Theoretical Total
Cost
Simulated Total
Cost
Error (%)
1 $ 22,000.00 $ 21,967.99 0.146%
2 $ 22,993.54 $ 23,040.60 -0.204%
3 $ 20,011.88 $ 20,022.07 -0.051%
4 $ 21,000.00 $ 21,007.55 -0.036%
5 $ 21,965.98 $ 22,009.25 -0.197%
6 $ 19,296.21 $ 19,295.07 0.006%
7 $ 19,893.86 $ 19,939.87 -0.231%
8 $ 19,098.54 $ 19,139.38 -0.213%
9 $ 20,000.00 $ 20,052.77 -0.263%
10 $ 21,000.00 $ 21,058.91 -0.280%