Download Framework for discovering supply chain complexity drivers

ARTICLE
Framework for
discovering supply chain
complexity drivers
Said N. Afandi, [email protected], Implement Consulting Group and
Zaza Nadja Lee Herbert-Hansen, [email protected], Technical University of Denmark, DTU
›
Abstract
Supply chain complexity (SCC) arises when supply chains grow large in number
of connection points, and the connectivity between these points is complicated.
To ensure future operation, companies must eliminate non-value-adding
complexities and streamline the business. Based on a literature review and industry
observations, this paper provides a thorough clarification of what is meant by
SCC and interdependencies between complexity drivers. The result, the supply
chain complexity canvas, is a framework that facilitates description and discussion
between supply chain stakeholders. The framework functions as a dynamic
platform that simplifies the identification of SCC drivers and the links between such.
KEYWORDS: Supply chain management, complexity, framework.
Introduction
The network of business entities that
is involved in an organisation’s up- and
downstream activities is what we define
as a company’s supply chain (SC). The
systematic and strategic co-ordination of
this network has gained more attention
during the last decades1.
We refer to SC co-ordination as supply
chain management (SCM). Historically,
organisations found that managing
their supply chain enabled an overall
cost reduction, service level improve­
ments, better quality insurance and
customer satisfaction; hence, providing
the organisation with a competitive
advantage2, 3.
Implement Consulting Group
1
Framework for discovering supply chain complexity drivers
Companies attain wide distribution
ranges and lowest price as they leverage
global manufacturing and distribution
capabilities. However, a side effect of this
is increased complexity1, 3-9.
1. How does literature define supply
chain complexity (SCC)?
2. How can managers identify
main drivers of SCC and their
interdependencies?
Complexity is defined as “a condition of
interconnectedness and interdependency
across a network”10.
Methodology
If this is true, changes in one element
can affect other elements, for better and
worse, in unforeseen ways. As complexity
itself is not bad, managers must ensure
that they distinguish between good
(value-adding) and bad (non-valueadding) complexity11. They must accept
that an SC will always be influenced by
a certain degree of complexity3,7,8 and
thereby the associated uncertainty and
costs9. Christopher8 argues that the
organisation differentiate itself from
competitors through complexity, i.e. if
what they do is straightforward, it would
be too easy for others to replicate.
Complexity management is an
essential part of SCM. If left unchecked,
complexities are potential cause of
inefficient operations and higher costs.
The organisation should manage this
threat by first identifying complexity
drivers and dynamics in order to
understand the interconnectivity and
consequences of these. Then, distinguish
between value-adding and non-valueadding complexity. And lastly, develop
action plans to reduce or remove nonvalue-adding complexity3, 4 , 11. A literature
review revealed that there are several
methods to manage complexity, but
no model or framework that takes into
account the interconnectivities and
consequences of such, even though
several articles acknowledge the
significance of this2, 3 , 10-13.
This paper addresses the research
gap in relation to interdependencies
of complexity drivers. The objective is
to develop a framework that enables
managers and researchers to identify
relevant complexity drivers and their
interdependencies within a company’s
supply chain. Hence, the following
two research questions have been
formulated:
This research seeks to build a normative
standard of how to identify complexity
drivers. Due to the complex nature of
the research aim, a qualitative approach
is used to provide rich and in-depth data.
The explorative nature of the study
allows for in-depth understanding
of the research area. Therefore, the
case study approach is the most
appropriate research methodology14,15.
In total six different companies have
been investigated, and data have been
gathered through observations and
semi-structured interviews. The chosen
companies were selected based on
several criteria, including that a) they
were large organisations with extensive
SCs, b) they had an international reach,
c) they were struggling with both valueadding and non-value-adding complexity
in their supply chain, and they came from
a variety of industries. The interviewees
were selected based on their experience
with complexity management projects.
The research has five stages: (i) an
extensive literature review of research
papers and industry publications,
(ii) semi-structured interviews and
workshops in the case companies, (iii)
structured comparison of the literature
review and the empirical data, (iv)
introduction of the system dynamics
of supply chain complexity and (v)
development of a novel framework
that assists organisational leaders
in identifying drivers of SCCs and
interdependencies between such.
Literature review
Wilson and Perumal11 introduces three
sources of complexity, namely product,
process and organisation, and states
that the associated costs are located
in the interaction between sources, e.g.
product/process11.
Implement Consulting Group
2
Framework for discovering supply chain complexity drivers
This is fully aligned with Ashkenas’2
findings that pinpoint the same three
sources, but adds an extra dimension:
managerial habits. Serdarasan3 further
categorises complexities into static,
dynamic16 and decision-making.
Serdarasan’s “static” complexity covers
three of Ashkenas’ complexity types,
specifically organisation, product/
service and processes. Ashkenas’
latter, the managerial habits, is equal
to Serdarasan’s definition of decisionmaking. Lastly, Serdarasan then adds
a third complexity type, dynamic com­
plexity, as the “operational behaviour
of the system and its [external]
environment”3 introducing the
un­certainty of randomness and time16.
Table 1 below shows the complexity
types derived from literature.
For the remainder of this paper, we refer
to static, dynamic and decision-making
based on the definitions in table 1.
Table 2 (page 4) presents a content
overview of the most influential articles
in the literature review. It illustrates the
environments in scope (internal, external),
whether the articles consider the supply
chain as static or dynamic, and lastly
whether interconnectivity is discussed.
As illustrated, several articles specify the
relevance of interconnectivity and that
the revision of complexity is an iterate
process. Still no model or framework
incorporates the interconnectivity or
associated feedback loops.
The increased complexity found in
the organisation’s supply chain is not
only costly. It also adds to a lower level
of service, inefficient processes and
bad work conditions as employees
of the system need to interact with
an increased amount of internal and
external factors. The potential drivers of
complexity are many and include variety,
connectivity, opacity and dynamics as
well as uncertainty3, 4 , 12, 17-19.
Practitioners cannot easily pinpoint
drivers of complexity as they are located
in every stage of the supply stream
and are influenced by the “parallel
interactions”19. Bozarth et al.12 deliver
an extended overview of possible
complexity drivers at different stages of
the supply chain and few interrelations
across SC stages. Due to dynamic
complexity such as system dynamic
feedback loops, a complexity and its
related cost can have a complexity
relieving or enhancing effect further
down the supply chain. For this reason,
we must define the sum of a complexity
cost as the sum of all interconnected
costs. Full understanding of the entire
supply chain, its interdependencies
and complexities is therefore essential
to minimise total complexity cost and
exploit the SC’s full potential.
Kearney13 argues that managers who
optimise their supply chain design
by reducing inventory, outsourcing,
trimming the number of products and
sourcing globally rarely gain the full
potential. They neglect the dynamic
complexity and optimise based on
the assumption that the supply chain
operates in a stable environment.
Several recent articles present ways for
designing resilient supply chains, but
TABLE 1 – Overview of complexity types and their meaning in this paper
COMPLEXITY TYPES
MEANING
Static
Product, process and organisation complexities
Dynamic
Uncertainty of randomness and time
Decision-making
Managerial habits
Implement Consulting Group
3
Framework for discovering supply chain complexity drivers
TABLE 2 – Review of the most influential articles’ coverage of supply chain complexity
ENVIRONMENT
AUTHOR, YEAR
Internal Interrelated External
COMPLEXITY TYPE
Static
Dynamic
Decision-making
INTERCONNECTIVITY
Ashkenas, 2007
Bozarth et al. 2009
( )
Christopher, 2000
Christopher, 2011
Kearney, 2003
Perona &
Miragliottta, 2004
Serdarasan, 2013
Siling, 1998
Wilson & Perumal,
2009
Winkler, 2006
agree that no design fits all20. Managers
must therefore pick and choose in order
to tailor the network design that fits their
respective organisation. This approach
often results in an iterative process18.
The reviewed models provide different
approaches to manage and/or reduce
complexity. The levers must, though,
be chosen wisely to fit the type of
complexity that we seek to manage or
reduce.
Serdarasan3 distinguishes between
external, supply/demand (S/D) interface
and internal complexity drivers. As a
company has little, if any, control over
external complexity drivers, focus
should be directed at internal and S/D
interface drivers, those that can be
managed or removed. Static (structural)
complexity drivers can be reduced,
whereas dynamic drivers may be more
resistant. The aim is then to manage
or adjust in order to cope with the
dynamic complexity3. Decisions directed
towards dynamic complexity drivers
may have positive or negative effects
on various drivers in the system, as
companies risk shifting the complexity
from one driver to another, preferable to
one which can be managed. Decisionmaking complexity drivers should be
managed by centralising and automating
decision-making3.
Datta et. al.20 illustrate that centralising
ensures alignment across the business
units, but can quickly become a burden,
as gathering information can be costly
and the responsiveness declines
drastically. The organisation must
therefore find the appropriate equilibrium
where it decentralises decision-making
on the basis of centralised guidelines
in order to keep flexibility and quick
responsiveness, while still obtaining
alignment and benefits of a centralised
structure. Perona and Miragliotta21 argue
that no model has been able to explain
the relationships between all the relevant
variables that a company must address in
order to reduce or manage complexity in
the supply network. The course of action
will vary dependent on the situation and
driver in question.
Implement Consulting Group
4
Framework for discovering supply chain complexity drivers
Traditional mathematical modelling
approaches have showed ineffective in
dealing with dynamic, complex supply
chains20 and the interaction between
the up- and downstream needs of the
system. Perona and Miragliotta21 give an
example of a conceptual framework.
A framework with several limitations,
but as argued earlier in this paper, the
wide extent of complexity management
and the broad variance in different cases
make it nearly impossible to create a
formula that suits all purposes. The
proposed framework can help as a guide
to understand the origin of SCCs and
how these affect each other.
Winkler et al.17 present the complexity
strategy matrix that proposes one of
four strategies to handle supply chain
complexity based on effort and effect.
The four strategies are: accepting,
controlling, reducing and avoiding17.
Concrete guides on how to control,
reduce or avoid complexities are not
mentioned.
Today’s literature on SCC is limited.
The literature review shows a broad
consensus that no model fits all pur­
poses. The way to tackle complexity in
the supply chain heavily depends on the
type of complexity, and where in the SC
it is located.
Empirical findings
This section seeks to explore which
of the theoretical drivers that is most
common across a selection of companies
from six different industries. Table 3
provides this overview. It illustrates which
of the different drivers that have been
observed at the case companies and in
which industry the companies operate.
Three complexity drivers are found
across all six companies in six different
industries and relate to uncertainty,
organisational processes and IT/ERP
systems. Complexity drivers related to
suppliers and distribution network are
located in four of the six companies.
Product portfolio and product modular­
isation complexities are found in twothirds of the companies, indicating that
most complexities originate across
industries.
A
B2B
Service provider to healthcare
B
B2C
Consumer goods
C
B2B
Energy
D
B2B
Healthcare
E
B2B
Industrial goods and services
F
B2B
Building technology
T
PO
PR
R
M O
TF
O D
O
D U
LI
U C
O
LA T
R
U
I
SA
N
C
TI
ER
O
N
TA
(R
IN
&D
TY
PR
)
(O O
R CE
G
A SS
N -R
IS E
IT
AT L
/E
IO AT
R
N ED
P
A
SY
L)
ST
EM
SU
S
PP
LI
ER
-R
D
EL
N IST
AT
ET R
ED
W IB
O UT
R IO
K
N
C
U
O
D
PR
ST
RY
D
U
IN
/B
B
2B
C
O
M
PA
N
2C
Y
TABLE 3 – The types of complexity that case companies experience
Implement Consulting Group
5
Framework for discovering supply chain complexity drivers
Table 4 shows three concrete examples
of complexity-related difficulties that the
organisations faced.
• Four companies were struggling
with stock-keeping of essential spare
parts, i.e. procurement and supplier
management, warehouse management
and logistics.
• Four companies struggled with
product portfolio management in
order to serve the increasing customer
demand in a cost-efficient way.
• All companies faced challenges
managing the cost to serve customers.
Five companies had difficulties
balancing service levels
and costs.
From the case observations, it became
evident that, to some extent, all sectors
are affected by complexity.
Discussion
By organising complexity drivers by
the categories of table 2, an overview
is created of the drivers’ proportions
and position in the SC. Table 5 (page 7)
illustrates that the complexity drivers
most often are internal drivers that are
interrelated. In other words, several
internal (or external) drivers affect each
other in positive and/or negative ways.
The table displays that complexity is
found to be both static and dynamic.
Practitioners need ways to manage these
complexities with concern paid to the
interrelatedness of the drivers. The four
dynamic drivers from table 5 should
be carefully addressed as they are all
influenced by uncertainty of randomness
and time. Consequently, the practitioner
will not be in full control of the effects
when addressing the complexity drivers
and must therefore carefully manage the
effects caused by the interrelatedness.
Based on the empirical evidence,
complexity is found in all sectors, and
many drivers impact more than one
sector. The demand for a method or
framework to target complexity is
present in all industries, and because
several drivers affect many industries,
it points towards the possibility of
constructing a generic framework that
enables managers to identify SCC drivers.
B
ST
RY
U
D
IN
Y
N
M
PA
C
O
2B
/B
2C
TABLE 4 – Concrete examples of complexity-related problems in the case organisations
A
B2B
Service provider to healthcare
B
B2C
Consumer goods
C
B2B
Energy
D
B2B
Healthcare
E
B2B
Industrial goods and services
F
B2B
Building technology
STOCK-KEEPING
PRODUCT PORTFOLIO
COST TO SERVE
Implement Consulting Group
6
Framework for discovering supply chain complexity drivers
TABLE 5 – Structured overview of the complexity drivers in the case companies
ENVIRONMENT
COMPLEXITY
DRIVER
Internal Interrelated External
COMPLEXITY TYPE
Static
Dynamic
Decision-making
INTERCONNECTIVITY
Product portfolio
Product
modularisation
(R&D)
Uncertainty
Process-related
(organisational)
IT/ERP systems
Supplier-related
Distribution
network
Interrelation of SCC drivers
Combining the empirical evidence from
the case companies with the results
of the literature review enables us to
structure a system dynamic model
that describes the interrelations and
interconnectivity of the complexity
drivers. The model is built in Vensim PLE
and illustrates how complexity drivers
in different stages of the supply chain
influence each other positively and
negatively.
The model illustration (page 8) clarifies
how the different SCC drivers influence
the three business areas of the supply
chain, i.e. up- and downstream activities
and the internal processes of the
company. The business areas used in the
conceptual model (see figure 3, page 9)
are marked with green text.
The arrows of figure 1 (page 8) illustrate
how the different activities and drivers
are interrelated. These relations create
interdependencies and thereby feedback
loops. This means that the output of one
entity of the system is routed back as an
input for the exact same entity, hereby
creating a course-and-effect loop, known
as a feedback loop. Figure 1 contains a
total of seven feedback loops.
For illustrative purposes, figure 2
(page 8) exemplifies how procurement,
inventory level and production plan
are interrelated in feedback loops.
Procurement/inventory and inventory/
production planning must be balanced
at the same time as the company needs
to ensure that the loop procurement
 inventory  production plan
supports the overall strategy of the
company.
A conceptual framework for
discovering SCC drivers
To accommodate all the areas in which
SCC arises, the organisation should be
addressed as a static entity as this eases
identification of complexity drivers and
in drawing links between the drivers to
create a dynamic system later on.
A dynamic model is ultimately required
as it stresses how complexities affect
each other across the organisation
and the supply chain. The model is
structured in such a way that it facilitates
description and discussion between
Implement Consulting Group
7
Framework for discovering supply chain complexity drivers
FIGURE 1 – Vensim PLE model that illustrates the interrelations of the SCC drivers
stakeholders of the supply chain. It is
highly recommended to include external
partners of the supply chain to create a
shared language and set the direction
across the supply chain. The framework
functions as a dynamic platform that
practitioners can use to model their SCC
drivers and the links between such.
The model is illustrated in figure 3
(page 9) and consists of 10 building
blocks that cover three business areas:
downstream, internal and upstream
activities. In addition, it is possible
to integrate partnerships with both
down- and upstream partners.
The building blocks are:
• Supplier capabilities: The processes
or features that make the supplier
involvement necessary to serve the
customers.
• Upstream logistics: Represents the
logistics network from suppliers to the
company.
• Organisational structure and
governance: Denotes the
administrative and work-related
processes and governance models the
organisations follow.
• Purchasing: The purchasing
governance process.
• Sales: The sales governance process.
FIGURE 2 – Illustration of the
feedback loops
• IT systems: IT systems’ ability to
promote or obstruct the company in
fulfilling its value proposition.
• Operation: The operation process’
ability to promote efficient delivery of
the value proposition.
• R&D: The innovative efforts that drive
higher complexities.
• Customer demands: The demands
from the customers that the
company seeks to fulfil via their value
proposition.
• Downstream logistics: Represents the
logistics network from company to
customer and all its challenges.
• Partnerships: Partnerships made with
external associates, e.g. suppliers,
NGOs, 3PL, innovation labs and
outsourcing partners.
Implement Consulting Group
8
Framework for discovering supply chain complexity drivers
FIGURE 3 – The supply chain complexity canvas
VALUE PROPOSITION
SUPPLIER
CAPABILITIES
ORGANISATIONAL STRUCTURE AND GOVERNANCE
PURCHASING
UPSTREAM
LOGISTICS
PARTNERSHIPS
CUSTOMER
DEMANDS
SALES
IT SYSTEM
PARTNERSHIPS
DOWNSTREAM
LOGISTICS
OPERATION
R&D
Practitioners need to start by formulating
their value proposition and writing
it at the top of the model. The value
proposition represents the value factor
that the customers want to buy. This
step is essential in order to distinguish
between value-adding and non-valueadding complexities throughout the
session.
Discussion
The model reaches its appearance and
capabilities based on the empirical
research, i.e. literature, case studies and
input from complexity management
experts in the industry. The framework
intends to build the foundation of a
company-wide complexity management
process. A process that brings together
qualified managers from all business
units into one or more workshops
facilitated around the canvas. The
managers should have extensive
knowledge of their own business area
and its connectivity to internal and
external business units. The extensive
stakeholder involvement produces
valuable insight and data input, but
it also demands time and effort from
the stakeholders and their team.
Without knowing the possible gain
from a complexity relieving project, it is
difficult to motivate stakeholders to fully
prioritise the project, possibly on the
expense of daily operational tasks. The
issue is partly managed as the model
enables the organisation to focus on
either a part of the canvas or the full SC.
An organisation might find more value in
focusing on the internal and downstream
activities at first, then upstream and
internal activities before targeting
the whole supply chain. This allows
an organisation to focus their efforts
according to the resources they are
able to invest in the complexity relieving
project.
Implement Consulting Group
9
Framework for discovering supply chain complexity drivers
Conclusion
Based on present literature, this
paper highlights the contemporary
understanding of the term SCC. This is
found to cover three types of complexity:
static, dynamic and decision-making. All
of which are found in the internal and
external environment. More importantly,
research point to the importance of
the interconnectivity of the complexity
drivers. This means that choosing to
relieve or manage one complexity driver
within a company’s SC may have either
positive or negative consequences on
other areas of the SC. Contemporary
literature is tested on six companies
representing six different industries in
order to acquire further insights into the
complexity drivers that companies face.
B
We learnt that all industries are affected
by complexities and that several drivers
are found across more than one industry.
Understanding the theory and practice
enables us to design a framework
that facilitates practitioners’ effort in
understanding SCC.
This paper introduces the supply
chain complexity canvas, a model
that provides practitioners with a new
structure to retrieve an overview of the
complexity drivers within their SC and
the connectivity between such. As not
all complexities are bad, i.e. non-valueadding, managers are encouraged
to encounter the canvas with the
company’s value proposition as the
guiding parameter.
Bibliography
Christopher, M. (2000). The Agile Supply Chain.
Industrial Marketing Management, 29(1), pp 37 – 44.
1
Ashkenas, R. (2007). Simplicity-Minded Management.
Harvard Business Review, (December 2007), pp 1 – 10.
2
Serdarasan, S. (2013). A review of supply chain complexity drivers.
Computers & Industrial Engineering, 66(3), pp 533 – 540.
3
Scheiter, S., Scheel, O. & Klink, G. (2007).
How Much Does Complexity Really Cost? ATKearney.
4
5
Frizelle, G.D.M. (2004). Complexity in the supply chain. 2004 IEEE International
Engineering Management Conference (IEEE Cat. No.04CH37574), 3, pp 1181 – 1184.
Gottfredson, M. & Aspinall, K. (2005). Innovation Versus Complexity:
What Is Too Much of a Good Thing? Innovation Versus Complexity.
Harvard Business Review, 83(11), pp 62 – 71.
6
7
Blecker, T. & Kersten, W. (2006). Complexity Management in Supply Chains:
Concepts, Tools and Methods, W. Torsten, ed., Erich Schmidt Verlag GmbH &
Co KG.
Christopher, M. (2015). Keeping a Lid on Supply Chain Complexity, p. 1. Available
at: http://www.martin-christopher.info/news/keeping-lid-supply-chain-complexity
[Accessed November 4, 2015].
8
Implement Consulting Group 10
Framework for discovering supply chain complexity drivers
Serdarasan, S. & Tanyas, M. (2012). Dealing with Complexity in the Supply Chain:
The Effect of Supply Chain Management Initiatives.
Journal of Enterprise Information Management, Forthcoming, pp 1 – 25.
9
Christopher, M. (2011). Logistics & Supply Chain Management, FT Press.
10
Wilson, S.A. & Perumal, A. (2009). Waging War on Complexity Costs: Reshape
Your Cost Structure, Free Up Cash Flows and Boost Productivity by Attacking
Process, Product and Organizational Complexity. McGraw-Hill Education.
11
Bozarth, C.C. et al. (2009). The Impact of Supply Chain Complexity on
Manufacturing Plant Performance. Journal of Operations Management, 27(1),
pp 78 – 93.
12
Kearney, A.T. (2003). Supply chain in a vulnerable, volatile world.
Executive Agenda, VI(3), pp 5 – 16.
13
Yin (1989)
14
Oakley (1999)
15
Casti, J. (1979). Connectivity, Complexity and Catastrophe in Large-scale
Systems. International Series on Applied Systems Analysis, 7, p. 203.
16
17
Winkler, H., Kalu, za, B. & Bliem, H. (2006). Strategies and Metrics for Complexity
Management in Supply Chains. In T. Kersten, Wolfgang; Blecker, ed. Complexity
Management in Supply Chains. Erich Schmidt Verlag, pp 3 – 19.
Wharton, K. (2006). Creating the Optimal Supply Chain. Available at: https://
www.bcg.com/documents/file14863.pdf [accessed December 12, 2015].
18
19
Wilding, R.D. (1998). The Supply Chain Complexity Triangle: Uncertainty
generation in the supply chain. International Journal of Physical Distribution &
Logistics Management, 28(8), pp 599 – 616.
B
Datta, P.P., Christopher, M. & Allen, P. (2007). Agent-based modelling of complex production/distribution systems to improve resilience. International Journal of
Logistics Research and Applications, 10(3), pp 187 – 203.
20
Perona, M. & Miragliotta, G. (2004). Complexity management and supply chain
performance assessment. A field study and a conceptual framework.
International Journal of Production Economics, 90(1), pp 103 – 115.
21
Implement Consulting Group
11