7th IFAC Conference on Manufacturing Modelling, Management,
and Control
International Federation of Automatic Control
June 19-21, 2013. Saint Petersburg, Russia
Benefits and limitations of the SCOR model in warehousing
E. LEPORI *. D.DAMAND ** B. BARTH ***
*HUMANIS, Ecole de Management Strasbourg,France (e-mail: elvia.lepori@em-strasbourg.eu)
** HUMANIS, Ecole de Management Strasbourg, France (e-mail: damand@em-strasbourg.eu)
*** HUMANIS, INSA Strasbourg, 67000 France (e-mail: marc.barth@insa-strasbourg.fr)
Abstract: In the Supply Chain, the flows of goods are the result of exchanges between two major parties:
the manufacturer / industrial company / supplier on the one hand and the distributor / customer on the
other. There are many intermediaries between these two parties who, depending on the nature of the
service provided, can be classified as 3PL (Third-Party Logistics provider) or 4PL (4th-Party Logistics
Provider). This paper focuses on the 3PL provider. A 3PL provider is a logistic services provider of the
Supply Chain responsible for implementing all or part of their customers' logistics. This form of
subcontracting concerns warehouse management and transport activities as well as all associated services
such as co-packing. The objective of subcontracting is to improve the performance of the logistical
process. Modelling and evaluating the performance of these processes helps improve the performance of
the Supply Chain. SCOR model (Supply Chain Operations Reference), proposed by the Supply Chain
Council, gives a modelling and an evaluation of the performance in Supply Chain. In the context of 3PL,
the objective of this paper is to identify the benefits and limitations of the SCOR. The study is illustrated
by a distribution warehouse case in an international logistic services provider.
Keywords: Performance evaluation, SCOR, modeling, supply chain
Literature proposes several reference models for performance
evaluation in the Supply Chain. The models of Beamon
[Beamon, 1999], Chan [Chan, 2003], Gunasekaran
[Gunasekaran, 2004] and Kaplan [Kaplan, 1996] recommend
a list of metrics. These models are characterised by: the
classification of metrics into categories, the absence of an
explicit link between metrics and standard processes.
Performance metrics linked to the process respond to the
following question: how to locate measures in the process? In
GSCF (global supply chain forum) and SCOR (Supply Chain
Operations Reference) models, the notion of process is linked
to performance evaluation. According to Huan [Huan, 2004],
SCOR describes all activities relating to the flow of materials
and products and focuses on operational efficiency, GSCF
deals with strategic aspects. The SCOR model was developed
by the Supply Chain Council (SCC). It helps improve the
performance of the Supply Chain [Lockamy A. III,
2004][Bolstorff, 2009] and every link of the Supply Chain
[Danish, 2008]. SCOR is applicable to all links of the Supply
Chain [Huang, 2005][Bolstorff, 2009]. SCOR makes it
possible to model different structures of varying complexity
levels [Jack C.P, 2010]. Several authors use or gain
inspiration from SCOR for their models, in different domains
such as the food processing [Garcia, 2012][Verdouw C.N.,
2010] or construction sector [Jack C.P, 2010]. Applying
SCOR to distribution warehouses can also be envisaged. The
strict definition of processes and performance metrics creates
a common language throughout the Supply Chain [Lambert,
2005][Danish, 2008][Huang, 2005] [Huan, 2004][Verdouw
C.N., 2010]. This common language helps standardise
practices and establish comparisons between Supply Chain
members [Ganga, 2011]. Process standardisation is necessary
to enable internal and external communication between
1. INTRODUCTION
The Supply Chain (SC) is a network of production and
distribution sites [Lee and Billington, 1995]. These sites
provide raw materials which they process into semi-finished
and then finished products. The finished products are
delivered to consumers via distribution networks [Jinxiang
Gu, 2007]. Distribution warehouses are key components of
any Supply Chain [Jinxiang Gu, 2007]. Logistic services
providers, like 3PL, offer their customers the possibility of
outsourcing various activities within their warehouses, such
as storage and co-packing.
In a dynamic economic context, the search for
competitiveness is a key factor for the sustainability of a
Supply Chain [Jian Cai, 2009]. Sustained and increased
competitiveness is linked to continuous performance
improvement [Jian Cai, 2009]. Competition within the
market requires increasingly high performances for
distribution warehouses [Jinxiang Gu, 2007]. According to
Gunasekaran [Gunasekaran, 2004], controlling the Supply
Chain processes is crucial for improving performance.
Processes are controlled through metrics measurement. This
control is part of the Supply Chain Management. Supply
Chain management can be defined as the coordination of the
Supply Chain stakeholders [Gunasekaran, 2004].
The implementation of a performance evaluation system is
not immediate. This highlights fundamental questions: what
to measure? What measurement frequency? What
measurement update frequency? [Beamon, 1999][Chan,
2003]. And, finally, how to locate measures in the process?
It is not easy to identify and interpret the interactions between
metrics.
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June 19-21, 2013. Saint Petersburg, Russia
Supply Chain partners [Gunasekaran, 2004][Jack C.P, 2010].
Modelling and evaluating processes helps create an audit of
the the Supply Chain ("AS IS"): metric values
deemed "satisfactory" and "not satisfactory". A "To Be" state
describes the guidelines of the processes considered [Supply
Chain Council, 2008]. SCOR helps analyse and develop a
structured performance evaluation [Lockamy A. III,
2004][Bolstorff, 2009] [Persson, 2009]. This framework is
based on the two-dimensional modelling of processes: a
vertical dimension (using levels within the process) and a
horizontal dimension (using the links between the activities
making up the process) [Ganga, 2011][Huang, 2005]. The
SCOR model has been applied to different processes such as
production [Garcia, 2012] and construction [Jack C.P, 2010].
x
x
x
x
x
A literature review failed to identify research on the theme of
this paper within the scope of warehousing. The proposed
contribution is the definition of the benefits, limitations and
shortcomings of the SCOR model within the scope of
distribution warehouses.
x
x
The rest of this paper is structured as follows. Section 2
describes the experimental method. Section 3 describes the
results of the application of the experimental method. Section
4 describes the benefits, limitations and shortcomings of the
SCOR model. Section 5 concludes and proposes research
perspectives.
x
Upside supply chain flexibility: The number of days
required to achieve an unplanned sustainable 20% increase
in quantities delivered.
Upside SC adaptability: The maximum sustainable
percentage increase in quantity delivered that can be
achieved in 30 days.
Downside supply chain adaptability: The reduction in
quantities ordered sustainable 30 days prior to delivery with
no inventory or cost penalties.
SC management costs: The sum of the costs associated with
the SCOR level 2 processes to Plan, Source, Deliver, and
Return.
Cost of goods sold: The cost associated with buying raw
materials and producing finished goods.
Cash-to-cash Cycle time: The time it takes for an
investment made to flow back into a company once it has
been spent on raw materials.
Return on working capital: Return on investment of the
assets of the SC (machines, tools ..).
Return on Supply Chain fixed assets measures the return an
organization receives on its capital invested in supply chain
fixed assets.
The experimental method applied consists of 3 stages:
Stage 1: Selection of a logistic services provider. This
logistic services provider provides its customers with various
services such as transport, storage and co-packing. The
warehouse activities concerned by the study are the storage of
finished goods and raw materials and order picking. The
customer of the logistic services provider works in the food
processing sector.
Stage 2: Application of the SCOR model to the distribution
warehouse.
The second stage is partially derived from the method
proposed by the SCC [Bolstorff, 2009]. The SCOR model
used is SCOR version 9.0 [Supply Chain Council, 2008]. The
4 sub-stages are as follows:
- Stage 2.1: scope determination: the determination of the
practical case which is the most representative (number of
activities, volume of orders and turnover).
- Stage 2.2: implementation of the SCORCARD of level 1
performance metrics,
- Stage 2.3: "AS IS" of the process : "thread diagram" and
"process diagram".
- Stage 2.4: implementation of levels 2 and 3 performance
metrics.
2. EXPERIMENTAL METHOD
SCOR model is composed by two parts: a modelling of
processes of the SC with diagrams and for each process
SCOR proposes metrics.
Processes are modelled with three levels:
- Level 1 corresponds with the top level of the Supply Chain.
It is divided into 5 processes: Plan, Source, Make, Deliver
and Return.
- Level 2 is a breakdown of level 1 according to the major
production categories and the corporate strategy. The
"thread diagram" represents the Supply Chain with the level
2 processes of the model. The choice of level 2 processes of
the SCOR model depends on the production strategy. The
processes concern the "Make-to-stock" category involves
processes of a production linked to sales forecasts. There
are two other categories: the "Make-to-order" category for
production linked to customer orders and the "Engineer-toorder" category where the product is designed for a specific
customer.
- Level 3 describes each process making up level 2 processes
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Stage 3: Identification of the benefits and limitations of the
SCOR model in warehousing.
Metrics are classified with the same levels as processes and
with performance attributes: reliability, responsiveness,
agility, costs and assets. Metrics of the level 1 are:
x Perfect
order fulfilment: The percentage of orders
complying with delivery performance with complete and
accurate documentation and no delivery damage.
x Order fulfilment cycle time: The average actual cycle time
consistently achieved to fulfil customer orders. The cycle
time starts from order receipt and ends with order
acceptance by the customer.
3. APPLICATION TO THE CASE STUDY
The following section describes the results of the application
of the SCOR model to a distribution warehouse.
3.1 Stage 2.1: scope determination
The most representative customer of the platform is
highlighted in stage 1. All activities proposed by the logistic
services provider are performed for this customer. In terms of
turnover and volume, this customer is one of the largest of
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the platform. The study focuses in particular on warehousing
(pallet handling and storage) and order picking activities.
stored in the logistic services provider's warehouses. The
processes selected concern the "Make-to-stock" category.
3.2 Stage 2.2: SCORCARD of level 1 performance metrics
The "thread diagram" for the logistic services provider
studied consists of the following SCOR processes (figure 1):
- Plan P: planning of processes below;
- Source stock product S1: receiving and storage;
- Deliver Stock product D1 PF, Source stock product S1 PF:
procurement of the stock reserved for order picking;
- Deliver Stock product D1 PF 2: order picking;
- Deliver Stock product D1: order picking of raw materials
and packaging for factories.
Stage 2 describes the preparation of a SCORCARD made
up of 10 metrics. Out of these 10 metrics: only one (Perfect
order fulfilment) is already used by the logistic services
provider, and five are pertinent in terms of their application in
a distribution warehouse (Order fulfilment Cycle time, SC
management costs, Cost of goods sold, Return on fixed assets
and Return on working capital).
x
x
x
x
x
x
x
x
The "Perfect order fulfilment" metric is a contractual metric
for the logistic services provider studied.
The "Order fulfilment Cycle time" metric can be calculated.
Its value is equivalent to one day: time between the booking
of the trucking company and the departure of the loaded
truck. This metric can be reduced to the "order fulfilment
process time". This second metric represents the time
between the beginning of the order picking process and the
departure of the loaded truck. The time between these two
metrics is not the responsibility of the logistic services
provider. It depends on the anticipated booking of the
trucking companies.
There are daily and significant volume fluctuations which
exceed 20% from one day to the next. The time frames (30
days) are too long because the orders must be processed
within one day. Therefore the three metrics of the "agility"
performance attribute proposed by the model are not
applicable.
Breaking down costs is possible thanks to the accurate
warehouse's accounting system. 7KH ³6& PDQDJHPHQW
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The "Cost of goods sold" metric is proposed by SCOR to
evaluate the costs of the "Make" process only. This process
is not involved in the practical case as the distribution
warehouse has no production unit. This metric is pertinent
for the warehouse because the payroll is one of the major
costs. The warehouse does not have raw material costs.
These costs are borne by the customer.
The "Cash-to-cash Cycle time" metric is not applicable to
the practical case platform as this platform does not manage
its cash flow. Cash flow is managed by the logistic services
provider's group. The distribution warehouses are informed
of their fixed assets by the group's accounting department.
The "return on fixed assets" metric is pertinent and can be
calculated. It helps highlight the profit generated by the
distribution warehouses, making it possible to maximise the
return on their fixed assets. The distribution warehouses are
in charge of their receivables.
The "return on working capital" metric is pertinent and can
be calculated.
Fig. 1: "Thread diagram" for the logistic services provider
only
The second part of stage is the process diagram. There are 59
level 2 and 3 processes in total. 20 of these processes are used
to map out the case studied. All level 2 processes have been
divided into level 3 sub-processes. Development examples
are set out below.
The "Source stock product S1" process breaks down as
follows (figure 2):
- S1.2 a: Receive Product. In the model, this process
corresponds with the reception activities. In the practical
case, this process corresponds with the actions relating to the
truck's arrival at the platform.
- S1.1: Schedule product deliveries. In the model, this process
corresponds with the planning and management of deliveries.
In the case study, it corresponds with the creation of
assignments for forklift truck operators and controllers via a
software. Once the assignments have been created, they are
sent to the portable terminals of forklift truck operators.
- S1.2 b: Receive Product. In the case studied, this
corresponds with the truck unloading activities of the forklift
truck operator.
- S1.3: Verify product. This process corresponds with product
verification actions. The controller must ensure that the order
matches the delivery slip in terms of quantity and product
references.
- S1.2c: Receive Product. In the practical case, this process
corresponds with the actions relating to the truck's departure
from the platform.
- S1.4: Transfer Product. In the case studied, this process
corresponds with pallet racking activities.
3.3 Stage 2.3: process "AS IS"
Stage 3 describes the modelling of the process ("AS IS"). It
begins with the working-out of the "thread diagram". The
production of the logistic services provider's customer is
based on sales forecasts, not orders. The products are then
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Fig. 2.: stages 2.3 and 2.4: S1 Source stocked product
x
3.4 Stage 2.4: level 2 and 3 performance metrics
The final stage describes the implementation of metrics at
levels 2 and 3 of the process. 52% of combined level 1, 2 and
3 metrics are applicable, and 27% of these applicable metrics
are already used, at least in an equivalent form, by the logistic
services provider studied. The metrics provided by the model
are essentially cost and cycle time metrics.
The "%Product Transferred without transaction errors"
metric corresponds with the number of pallets put into stock
in the wrong location. Erroneous storage results in a waste
of time when the pallet is extracted later.
4. SCOR BENEFITS AND LIMITATIONS IN
WAREHOUSING
The section is divided into two parts. The first part describes
the benefits of the SCOR model applied to warehousing. The
second describes its limitations.
For the S2 process, level 2, two cycle times are measured.
The "order fulfilment cycle time" metric corresponds with
the time spent by the truck at the dock. The time spent at
the dock must be minimised to ensure that the driver spends
most of his time driving.
x The "Source cycle time" metric is the time between the
truck's arrival and the storage of the products. Certain
customers demand that products be rapidly placed in
storage to monitor stock levels in real time.
At level 3, process S1.2a:
x The
"%Orders/Lines Received On-time to demand
requirement" metric corresponds with the percentage of
trucks arrived on time. If transport deadlines are not
complied with, the logistic services provider must manage
and catch up lateness.
Process S1.2b is evaluated by three metrics:
x The "%Orders/Lines Received On-Time To demand
Requirement" metric corresponds with the percentage of
trucks unloaded within the deadline. It helps assess the
unloading efficiency of forklift truck operators.
x The value of the "costs" metric is essentially the working
time of the forklift truck operator.
x The "cycle time" metric indicates the truck unloading time.
Process S1.3 corresponds with the control process. Two
metrics are highlighted for this process:
x The "%Orders/Lines received defect free" metric defines
the number of pallets unloaded without any damage.
x The "%Orders/Lines Received with Correct content" metric
corresponds with the number of pallets with the correct
references and quantity.
Process S1.4 is evaluated using two metrics:
x The
"%Product Transferred On-time to demand
requirement" metric corresponds with the transfer of the
product into stock on time.
4.1 Benefits of SCOR in distribution warehouses
x
The accuracy of the SCOR model's definitions enables
platforms to use a common language. This common language
makes it possible to standardise the vocabulary. This
language enables a benchmark between the customers of the
platform, then between platforms of the logistic services
provider.
The provision of metrics and performance attributes helps
decide which measurement to opt for. Performance attributes
are essential characteristics of the performance for the
logistic services provider. Performance attributes help
classify the metrics which do not feature in the SCOR model
but are used by the service provider studied. This
classification differs from a classification by function or
service. Classification by function does not provide a
complete picture of the performance. Each service improves
independently without considering the effect of its action on
the performance of the overall system. The SCOR model
provides metrics which evaluate the performance of the
logistic services provider studied but also its customers.
These metrics help highlight the sources of non-performance
and the stakeholders responsible within the Supply Chain.
Metrics are classified by level according to their location
within the process. The location of these metrics within the
modelled process provides an answer to the question of
measurement location. The visual mapping of the SCOR
model makes it possible to identify measurement points and,
subsequently, the processes deemed ineffective.
The SCOR model helps model the processes of the Supply
Chain. The thread diagram helps identify the stakeholders of
the Supply Chain and helps position the logistic services
provider within its Supply Chain. Knowledge of these
stakeholders helps determine the company's boundaries and
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customer only. If the study is extended to the entire platform,
the complete representation is difficult to achieve because of
its size, making navigation through the entire system long
and arduous. This navigation generates time cost overruns.
the links between the different companies within the Supply
Chain. Process modelling helps understand how warehouses
operate and describe the actual state. This modelling helps
identify the inputs and outputs of each process. It also helps
observe the flows of materials and information through the
processes.
50% of the metrics are not applicable. The logistic services
provider provides companies with a service but does not own
or manage the stock in the warehouse. Some "performance
attributes" such as agility do not have a metric applicable to a
distribution warehouse. Some processes are not evaluated.
Certain processes only partly correspond with their
constituent activities, in which case the metrics cannot
evaluate their performance. Furthermore, during the
successive use of the same process, the redundancy of the
applicable metrics has been observed (for example, process
S1.2 in the Source stocked product S1).
The breakdown of the SCOR model into processes,
associated metrics and levels provides a framework for
performance evaluation. Classification by level and
performance attributes makes it possible to follow the
propagation of the metrics through the process. For example,
costs are broken down level by level. This breakdown helps
search for cost overrun causes in the process. The
propagation gives a complete picture of the performance
evaluation of the entire process. Once the SCOR model is in
place, it is possible to simulate new scenarios. Performance
evaluation distributed across the process helps highlight
ineffective processes. Once these points have been detected,
the changes to be implemented to achieve the determined
performance objectives have still to be defined.
The measurements of the "cycle time" metric often
correspond with a change of unit compared with the
estimated process cost. The most important cost is the
payroll. The conversion of working time into monetary value
corresponds with the cost metric. This results in metrics
redundancy. Time measurements often depend on the volume
processed. The volumes processed vary significantly.
Therefore time measurements will vary. It is sometimes more
pertinent to measure productivity so as to compare volume
with time. Some metrics cannot be calculated as sources of
information are lacking. To make the recommended
calculations, the model must have the right sources of
information. This calculation must not increase the model
usage time. It must be automated. The incomplete evaluation
of process performance means that the application of the
model can no longer continue with the implementation of
the "TO BE" state. Metrics redundancy generates cost
overruns in terms of timeframe and monitoring of the
metrics.
4.2 SCOR limitations in distribution warehouses
Not all processes and sub-processes proposed by SCOR at
level 3 are used for the practical case. The method
recommends making choices according to the activities
involved. The choice is made thanks to the definition of the
model which describes the content of the processes. Certain
activities involved may belong to different processes. Certain
actions in distribution warehouses correspond with none of
the processes proposed by SCOR. The incomplete modelling
of the processes results in the unsatisfactory measurement of
their performance. This means that users will not make full
use of the model and will incur further delays in defining the
modelling process.
In the processes selected, 159 metrics are proposed.
Monitoring all these metrics can be long and tedious. Each
metric involves measurement, monitoring and the definition
of action plans. Assistance in the identification of the most
important metrics is required. This assistance will help reduce
cost overruns in terms of time for the use of the model.
SCOR proposes standard process names. Certain warehouse
processes do not or only partly correspond with the SCOR
process; therefore the names do not match the content of the
process. Process names at level 3 are changed into terms
which correspond with the company's culture. However, the
following question can be asked: should the model adapt to
the company or vice-versa? It is difficult to change the
company's culture. Using a standardised language within the
same company is essential. A poorly modelled activity or an
activity with the wrong name results in a poor standard for
the company and can lead to communication problems. The
use of the SCOR standard makes it possible to communicate
with other companies. This communication is however no
longer possible if the vocabulary differs from that of the
Supply Chain and the use of benchmarking is also no longer
possible. Certain processes are not applicable to the case
study. The stock in the warehouse does not belong to the
logistic services provider.
Thorough understanding of the model definitions is required
to optimise the use of this model. Modelling via SCOR
requires an in-depth understanding of how activities actually
work and the completion of a field study. Acquiring and
making use of the company's model is a long and tedious
process. It generates additional costs and time.
Computerisation is a key future development to make full use
of the model acquired.
5. CONCLUSION AND PERSPECTIVES
Business competitiveness is closely linked to business
performance. The performance evaluation system is difficult
to implement and various questions are raised concerning the
choice or location of measurement, for example. The SCOR
reference model provides answers to questions on
performance evaluation in a Supply Chain. These questions
have been addressed for distribution warehouses. This study
relies on the practical application of the SCOR model to a
The SCOR model provides no template for mapping out level
4. The method stipulates however that this is an important
stage. This stage generates a cost overrun in terms of time for
users who they must create their own templates. The
development of the SCOR model requires its complete
representation on paper, in our case in A0 format for one
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Ganga G.M.D., L.C.R. Carpinetti. (2011). A fuzzy logic
approach to supply chain performance management.
Int.J. Production Economics 134 p177±187.
Garcia F.A. et al. (2012). A framework for measuring
logistics performance in the wine industry. Int. J.
Production Economics 135 p284±298.
Gunasekaran A., C. Patel , Ronald E. McGaughey. (2004). A
framework for supply chain performance measurement.
Int. J. Production Economics 87 p333±347.
Gunasekaran A., C. Patel, E. Tirtiroglu. (2001). Performance
measures and metrics in a supply chain environment, Int.
J. Oper. Product. Manage. 21 (1) p71±87.
Huan, S.H., Sheoran, S.K., Wang, G., (2004). A review and
analysis of supply chain operations reference (SCOR)
model. Supply Chain Management: An International.
Journal 9 (1), 23±29.
Huang S.H. et al. (2005). Computer-assisted supply chain
configuration based on supply chain operations reference
(SCOR) model. Computers & Industrial Engineering 48
p377±394.
Jack C.P. Cheng, Kincho H. Law, Hans Bjornsson, Albert
Jones, Ram D. Sriram. (2010). Modeling and monitoring
of construction supply chains. Advanced Engineering
Informatics 24 p435±455.
Jian Cai, Xiangdong Liu, Zhihui Xiao, Jin Liu. (2009).
Improving supply chain performance management: A
systematic approach to analyzing iterative KPI
accomplishment. Decision Support Systems 46 p512±
521.
Jinxiang Gu, Marc Goetschalckx, Leon F. McGinnis. (2007).
Research on warehouse operation : a comprehensive
review. European Journal of operational research 177
p1-21
Kaplan R.S., D.P. Norton. (1996). Using the balanced
scorecard as a strategic management system, Harvard
Business Review p75±85.
Lambert, D.M., Garcia-Dastugue, S.J., Croxton, K.L., 2005.
An evaluation of process-oriented Supply Chain
Management frameworks. Journal of Business Logistics
26 (1), 25±51.
Lee H.L, Billington C. (1995). The evolution of supply chain
management models and practice at Hewlett-packard.
Interfaces 25 (5) p42-63
Lockamy A. III, K. McCormack. (2004). Linking SCOR
planning practices to supply chain performance: An
exploratory study, International Journal of Operations
&Production Management 24 (11/12) p1192±1218.
Persson Fredrik, Mirko Araldi. (2009). The development of a
dynamic supply chain analysis tool Integration of SCOR
and discrete event simulation. Int. J. Production
Economics 121 p574 ± 583.
Robert S. Kaplan, Steven R. Anderson. Time-driven activitybased costing : a simpler and more powerful path to
higher profits. ISBN-10: 1422101711
Supply Chain Council, Supply Chain Operations Reference
Model, Version 9.0, (2008). ISBN 0-615-20259-4
Verdouw C.N. et al. (2010). Process modelling in demanddriven supply chains : A reference model for the fruit
industry. Computers and Electronics in Agriculture 73
p174±187.
distribution warehouse. The distribution warehouse is
modelled and the possibility of measuring its performance is
examined.
This study has highlighted the benefits of the SCOR model
for evaluating the performance of distribution warehouses.
SCOR provides a performance evaluation framework. This
framework and the model's definitions provide a common
language. Performance evaluation is based on a
representation of the process which highlights measurement
locations. This representation helps understand how the
warehouse and related flows function. SCOR provides a vast
number of metrics, classified by level and performance
attribute. Metrics provide a choice of measurements for
evaluating the performance of a warehouse and its customers.
Performance attributes provide the key points of the
warehouse's performance. This classification helps reveal
how the metrics of the same performance attribute propagate.
SCOR is recognised as a best practice for evaluating
performance in a Supply Chain. The modelling provided by
SCOR makes it possible to model out a current "AS IS" state.
The "AS IS" state emphasises ineffective processes. This
must help develop a future "TO BE" state. "TO BE" is a state
of the process in which its operation, performance and
control are improved [Supply Chain Council, 2008]. The
development of the "AS IS" state for distribution warehouses
is incomplete and does not make it possible to optimise the
use of the model. The case study reveals the limitations of the
model's application to distribution warehouses. The
evaluation of the performance of certain processes and
performance attributes is lacking. Monitoring a vast number
of metrics is tedious; assistance in the selection of metrics or
the definition of composite metrics is a research theme. A
study on the computerisation of this model could be
envisaged as its manual application is time consuming.
Persson's research [Persson, 2009] addresses this issue. Other
research themes would make it possible to improve the
SCOR model applied distribution warehouses: developing a
template for the representation of level 4, completing the
evaluation of the performance of certain processes
and performance attributes, and examining the correlation
between the metrics of different performance attributes.
REFERENCES
Beamon B.M. (1999). Measuring supply chain performance,
International Journal of Operations & Production
Management 19 (3), p275±292.
Bolstorff Peter, Robert Rosenbaum. Supply Chain
Excellence. A handbook for dramatic improvement using
the SCOR model. Edition Amacon. (2007).
Chan F.T.S., H.J. Qi. (2003). An innovative performance
measurement method for supply chain management,
Supply Chain Management: An International Journal 8
(3±4) 209±223.
Danish Irfan, Xu Xiaofei, and Deng Sheng Chun. July
(2008). A SCOR Reference Model of the Supply Chain
Management System in an Enterprise. The International
Arab Journal of Information Technology, Vol. 5, No. 3.
429