American Journal of Industrial and Business Management, 2013, 3, 389-394
http://dx.doi.org/10.4236/ajibm.2013.34046 Published Online August 2013 (http://www.scirp.org/journal/ajibm)
389
Impact of Forecast Errors in CPFR Collaboration Strategy
Kamalapur Raj
University of Wisconsin Oshkosh, Wisconsin, USA.
Received May 24th, 2013; revised June 24th, 2013; accepted July 16th, 2013
Copyright © 2013 Kamalapur Raj. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The primary objective of this research is to investigate the impact of random forecast error and bias forecast error in
Collaborative Planning, Forecasting and Replenishment (CPFR) strategy on the cost of inventory management for both
the manufacturer and retailer. Discrete-event simulation is used to develop a CPFR collaboration model where forecast,
sales and inventory level information is shared between a retailer and a manufacturer. Based on the results of this study,
we conclude that the higher random forecast error and negative bias forecast error increases the cost of inventory man-
agement for both the manufacturer and the retailer. When demand variability is high, a bias forecast error has a bigger
impact on inventory management cost compared to a random forecast error for both the manufacturer and retailer. Also,
a positive bias forecast error is more beneficial than a negative bias forecast error to gain maximum benefits of CPFR
strategy.
Keywords: CPFR Collaboration Strategy; Inventory Management; Simulation Modeling
1. Introduction
Inventory is a significant and often one of the largest
assets for most companies. To provide good customer
service, many supply chain members maintain a high
level of safety stock inventory which increases the total
cost in the supply chain. For example, in 1996, approxi-
mately $700 billion of the $2.3 trillion retail supply chain
was in safety stock inventory [1]. Also, in recent Annual
State of Logistics Report, over $1 trillion was annually
spent on logistics, with approximately 33 percent being
attributed to the cost of holding inventory [2]. So in re-
cent years, academic researchers and practitioners have
emphasized that information sharing between supply
chain members can significantly reduce inventory levels
and improve service levels in the supply chain. In order
to encourage retailers to share information with manufac-
turers, collaboration strategies like Vendor Managed In-
ventory (VMI) and Collaborative Planning, Forecasting
and Replenishment (CPFR) have been developed and
implemented in many industries with mixed results [3,4].
Since a supply chain is a complex network, many re-
searchers consider the dyadic structure (two-levels) to
study the benefits of information sharing. Many research
studies have shown the benefits of demand information
sharing in the supply chain [5-7]. Most of these studies
assume that the retailer knows the exact customer de-
mand in the supply chain. However in a variable demand
environment, the retailer may not know the exact cus-
tomer demand and have to develop their demand forecast.
Retailers generally use different forecasting methods and
this can impact the forecast accuracy of the customer
demand. So the benefit of information sharing greatly
depends on the forecast accuracy of the customer de-
mand in a variable demand environment. There are some
studies that consider the impact of forecast errors on the
value of information sharing. One research study [8] uses
simulation modeling to investigate the impact of fore-
casting errors on the value of information sharing in a
supply chain with four retailers and a manufacturer. They
show that forecast errors have an impact on the value of
information sharing. Their study only considered forecast
information sharing between the supply chain partners.
However in a CPFR strategy, along with forecast data,
generally sales data and inventory level information is
shared between the supply chain partners. There are no
studies that consider the impact of forecast errors on the
cost of inventory management in CPFR collaboration
strategy for both the manufacturer and retailer in a vari-
able demand environment. This study uses discrete-event
simulation to develop a CPFR collaboration model where
forecast, sales and inventory level information is shared
between a retailer and a manufacturer. Using this simula-
tion model, we investigate the impact of random forecast

Impact of Forecast Errors in CPFR Collaboration Strategy 390
error and bias forecast error on the cost of inventory
management for both the manufacturer and retailer in a
variable demand environment.
2. Collaboration Model
The CPFR collaboration model used for this research
study is a two echelon production-inventory system with
a make-to-stock manufacturer (plant and warehouse) and
a retailer. Discrete event simulation (Arena software
from Rockwell Automation) is used to develop this
model. The retailer does not know the actual customer
demand and need to develop their demand forecast. Both
random forecast error and bias forecast error are intro-
duced into this forecast to investigate their impact on the
inventory management cost for both the manufacturer
and retailer. Periodic review order-up-to inventory policy
(R, S) is used by both the manufacturer and the retailer,
where the review period considered is one week. All de-
cisions for the manufacturer and the retailer are made
beginning of each period. The order up-to-level “S” for
both the manufacturer and the retailer is calculated so as
to minimize the inventory holding cost and backorder
penalty cost. The customer demand, the order quantity
and the production quantity are non-negative.
Sequence of Events
During each period, the retailer shares forecast, sales and
inventory level information with the manufacturer as
shown in Figure 1. The manufacturer does not forecast
and uses this information to determine their production
quantity during each period. All decisions for the manu-
facturer and retailer are made at the beginning of each
period. The sequence of events is as follows. Beginning
of each period, the retailer receives shipments (if any)
from manufacturer, and the customer demand (plus any
backorder) is fulfilled from the available inventory.
Similarly, the manufacturer’s warehouse receives ship-
ments from the plant, and the retailer order (plus any
backorder) is fulfilled from the available inventory. Any
unfulfilled demand for both the retailer and manufacturer
is backordered with a backorder penalty cost. Next, both
the retailer and manufacturer use the forecast and their
inventory level information to calculate their target order
up-to inventory level to determine the order quantity (by
retailer) and production quantity (by manufacturer). The
manufacturer follows an echelon-based inventory policy
in their production planning and inventory replenishment
decisions. Under echelon-based inventory policy, the
manufacturer considers their own inventory level plus
inventory level of retailer and any backorder quantity to
determine their production quantity [9]. The manufac-
turer has unlimited production capacity and uses a
lot-for-lot production policy with a lead time of one pe-
riod. The delivery lead time from the manufacturer to the
retailer is assumed as one period.
Retailer cost and manufacturer cost are used as the
performance measures and they are calculated based on
the inventory level and backorder quantity at the end of
each period. The inventory holding cost is assumed $1.5
per period for the retailer and $1.0 per period for the
manufacturer. Similarly, backorder penalty cost for re-
tailer is assumed 1.5 times the backorder penalty cost for
the manufacturer. The customer demand, forecast de-
mand, order-up-to inventory level, the order quantity and
production quantity is updated during each period of the
simulation run. To facilitate valid comparison and deter-
mine the impact of the control variables on the perform-
ance measures, the inventory policy and production pol-
icy remain the same for all factor combinations.
The output data (i.e. performance measures) from the
simulation model is used to determine the impact of
forecast errors in the CPFR collaboration strategy. To
reduce the impact of random variations of input data (i.e.
customer demand), the same random number sequence is
utilized to generate the same customer demand for all
factor combinations. A sample size of 30 (number of
replications) is selected for the simulation run. The
simulation model is run for a total of 1144 periods, with
the first 104 periods considered as warm-up to initialize
the system and the remaining 1040 periods is used for the
analysis. The statistical software “Minitab 16” is used for
the analysis.
3. Experimental Design
The purpose of an experimental design is to develop a
methodology to track changes in performance measures
by varying factors under study during the experimental
Figure 1. Information sharing in CPFR collaboration strategy.

Impact of Forecast Errors in CPFR Collaboration Strategy 391
runs. According to Law and Kelton [10], “One of the
principal goals of experimental design is to estimate how
changes in input factors affect the results or responses of
the experiment”. Generally, a variety of experimental
designs can be used in the simulation experiments when
the objective is to explore the reactions of a system (re-
sponse variables) to changes in factors (control variables)
affecting the system. Some of the relevant experimental
designs include the full factorial, fractional factorial and
response surface designs. A factorial experiment is one in
which the effects of all factors and factor combinations in
the design are investigated simultaneously. Each combi-
nation of factor levels are used the same number of times.
This study employs a full factorial design to gain insight
on the impact of the control variables on the performance
measures.
Four control variables as shown in Table 1 and two
performance measures as shown in Table 2 are consid-
ered for this study. Demand variability plays an impor-
tant role in the supply chain collaboration. This study
considers the auto-correlated demand pattern with three
levels of demand variability. Auto-correlated demand is
generated using the following formula
tt1
Dd Dt
ρ
ε
−
=++ (1)
where, d = initial mean, ρ = correlation factor and εt =
i.i.d. normally distributed with mean zero and standard
deviation σ. The correlation factor is 0.5 and three levels
of demand variability are generated by varying σ in the
above equation. The average customer demand for the
retailer is 100 units per period. The demand forecast is
generated according to the following formula.
tt
FDBFERFEsnorml(a)=++ × (2)
where, Ft and Dt are forecast and demand during period t
(t = 1, 2, 3 …), BFE is the bias forecast error, and RFE is
the random forecast error, and snormal() is the standard
normal random number generator.
Table 1. Control variables for the experimental design.
Control
Variables Details for Variables Other Details
Demand
Variability
(DVR)
Low Demand Variability, σ = 05
Med Demand Variability, σ = 15
High Demand Variability, σ = 25
Average Demand
is 100 units per
period
Random
Forecast Error
(RFE)
Low Random Error, ε = 05
Med Random Error, ε = 10
High Random Error, ε = 15
Random Forecast
Error for
Demand Forecast
Bias Forecast
Error
(BFE)
Negative Bias Error = −10
Neutral Bias Error = 0
Positive Bias Error = +10
Bias Forecast
Error for
Demand Forecast
Back Order
Penalty
(BOP)
Low Backorder Penalty, 09
Med Backorder Penalty, 19
High Backorder Penalty, 32
Backorder
Penalty is factor
of Holding Cost
Table 2. Performance measures for the experimental de-
sign.
Performance Measures Performance Measure Details
Retailer Cost
per Period
Inventory Holding Cost for Retailer
plus Backorder Cost for the Retailer
Manufacturer Cost
per Period
Inventory Holding Cost for
Manufacturer plus Backorder Cost for
Manufacturer
Periodic order-up-to inventory policy is used to deter-
mine the target inventory levels for both the manufac-
turer and retailer. Generally, inventory holding cost and
backorder penalty cost are important parameters in de-
termining order-up-to inventory level. Instead of chang-
ing both the inventory holding cost and backorder pen-
alty cost at the same time, the inventory holding cost is
held steady and backorder penalty cost is changed as
shown in Table 1.
4. Results and Discussions
The output data from the simulation model is analyzed to
determine the impact of forecast errors in the CPFR col-
laboration strategy. In a supply chain, the consequences
of forecast error can either lead to increased inventory
holding cost or increased stockout/backorder penalty cost.
Some of the main results of this research study are shown
below.
4.1. Impact of Random Forecast Error and
Demand Variability
The impact of random forecast error and demand vari-
ability on the inventory management cost for both the
retailer and manufacturer in CPFR collaboration strategy
are shown in Figure 2. When demand variability is low,
the cost for both the manufacturer and retailer increases
as random forecast error increases. It is interesting to
note that when demand variability is high, the impact of
random forecast error is generally lower. This may be
due to the fact that when demand variability is high, gen-
erally higher level of inventory is carried which can off-
set any forecast error. Higher inventory levels can help in
reducing backorder penalty costs when demand variabil-
ity is higher. However, when demand variability is high,
the overall cost of inventory management is higher for
both the manufacturer and the retailer.
4.2. Impact of Bias Forecast Error and Demand
Variability
The impact of bias forecast error and demand variability
on the inventory management cost for both the retailer
and the manufacturer in CPFR collaboration strategy are
shown in Figure 3. It is interesting to note that positive

Impact of Forecast Errors in CPFR Collaboration Strategy 392
15105
140
120
100
80
60
40
Random Forecast Error
Manufacturer Cost
5
15
25
Variability
Demand
Interaction Plot for Manufacturer Cost
(a)
15105
80
70
60
50
40
30
Random Forecast Error
Retailer Cost
5
15
25
Var ia b ility
Demand
Interaction Plot for Retailer Cost
(b)
Figure 2. Impact of random forecast error and demand
variability.
bias forecast error is more beneficial for both the manu-
facturer and retailer in reducing cost compared to nega-
tive bias forecast error. Generally, the positive bias fore-
cast error helps both the manufacturer and retailer to
carry enough inventories to reduce backorder penalty
cost. However when bias forecast error is negative, the
cost goes up for both the manufacturer and the retailer
due to increased backorder penalty costs. So a positive
bias forecast error is preferable to negative bias forecast
error. The impact is more significant for both the manu-
facturer and retailer when demand variability is high and
bias forecast error is negative. At lower demand variabil-
ity, no bias forecast error has lowest cost for the retailer.
4.3. Impact of Random Forecast Error and
Backorder Penalty Cost
The impact of random forecast error and backorder pen-
alty cost on the inventory management cost for both the
retailer and manufacturer in CPFR collaboration strategy
are shown in Figure 4. It can be seen that as random
forecast error increases, the inventory cost increases for
all backorder penalty costs for both the manufacturer and
100-10
175
150
125
100
75
50
Bias Forecast Error
Manufacturer Cost
5
15
25
Variability
Demand
Interaction Plot for Manufacturer Cost
(a)
100-10
110
100
90
80
70
60
50
40
30
Bias Forecast Error
Retailer Cost
5
15
25
Variability
Demand
Interaction Plot for Retailer Cost
(b)
Figure 3. Impact of bias forecast error and demand vari-
ability.
the retailer. The cost of inventory management becomes
higher for both the manufacturer and the retailer with
increase in random forecast error. We can see that when
the random forecast error increases, the benefit of infor-
mation sharing in CPFR strategy decreases under all
backorder penalty costs.
4.4. Impact of Bias Forecast Error and
Backorder Penalty Cost
The impact of bias forecast error and backorder penalty
cost on the inventory management cost for both the re-
tailer and the manufacturer in CPFR collaboration strat-
egy are shown in Figure 5. It can be seen that for all
backorder penalty costs, negative bias forecast error has a
significantly higher cost for both the retailer and the
manufacturer. For the manufacturer, positive bias fore-
cast error is beneficial under all backorder penalty costs.
When backorder penalty costs are higher, negative bias-
forecast error can significantly increase the cost for both
the manufacturer and the retailer. However, it is interest-
ing to see that, the lowest cost for the retailer is achieved
when the forecast has zero bias error and the lowest cost

Impact of Forecast Errors in CPFR Collaboration Strategy 393
15105
140
130
120
110
100
90
80
70
60
50
Random Forecast Error
Manufacturer Cost
9
19
32
Penalty
Order
Back
Interaction Plot for Manufacturer Cost
(a)
15105
70
65
60
55
50
45
40
Random Forecast Error
Retailer Cost
9
19
32
Penalty
Order
Back
Interaction Plot for Retailer Cost
(b)
Figure 4. Impact of random forecast error and backorder
penalty cost.
for the manufacturer is achieved when the forecast has
positive bias error. So to gain maximum benefits of
CPFR strategy, it is important to minimize the negative
bias error to help in reducing the cost of inventory man-
agement for both the manufacturer and the retailer.
5. Conclusion
This research study investigated the impact of a random
forecast error and a bias forecast error on the cost of in-
ventory management in CPFR collaboration strategy for
both the manufacturer and the retailer. In the real world,
the consequences of a forecast error (i.e. positive or
negative) are not the same. Positive forecast error (i.e.
forecast higher than demand) leads to increased inven-
tory holding cost and negative forecast error leads to in-
creased stockout/backorder penalty costs. Based on the
results of this study, it is fair to conclude that higher
random forecast error and negative bias forecast error
significantly increases the cost of inventory management
for both the retailer and the manufacturer. Also, positive
bias error is generally preferable (than negative bias error)
to reduce inventory cost for both the manufacturer and
retailer. Generally, when demand variability is high, bias
100-10
175
150
125
100
75
50
Bias Forecast Error
Manufacturer Cost
9
19
32
Penalty
Order
Back
Interaction Plot for Manufacturer Cost
(a)
100-10
90
80
70
60
50
40
Bias Forecast Error
Retailer Cost
9
19
32
Penalty
Order
Back
Interaction Plot for Retailer Cost
(b)
Figure 5. Impact of bias forecast error and backorder pen-
alty cost.
forecast error has a bigger impact on the inventory cost
compared to random forecast error for both the manu-
facturer and the retailer. Also when backorder penalty
cost is high, the impact of negative bias forecast error is
significantly higher for both the manufacturer and the
retailer. So in conclusion, negative bias forecast error and
higher random forecast error increases the overall cost of
inventory in a CPFR strategy for both the manufacturer
and the retailer. So to gain maximum benefits of CPFR
collaboration strategy for both the manufacturer and the
retailer, it is important to minimize the random forecast
error for all demand variability’s and to avoid negative
bias forecast error in the demand forecast.
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