Lecture Notes in Mechanical Engineering
Américo Azevedo Editor
Advances in
Sustainable
and Competitive
Manufacturing Systems
23rd International Conference on
Flexible Automation and Intelligent
Manufacturing
The Power of Analytical Approaches
Towards the Development
of Differentiated Supply Chain Strategies:
Case Study
Alexander A. Kharlamov, Luis Miguel D. F. Ferreira
and Janet Godsell
Abstract Companies are facing challenging circumstances: markets are evolving;
clients are becoming more and more demanding and unpredictable; product variety
is rising; time windows are shrinking; and error tolerance is decreasing. Therefore,
companies must adapt and improve their supply chains, develop a differentiated
supply chain strategy to solve the supply–demand mismatch. So far, the main
differentiation approach has been focused on: (a) product; (b) customer; and
(c) market characteristics. This paper uses a case based research approach in the
context of a business-to-business food company to analyse the use of analytical
tools commonly applied in other fields of research to support the identification of
product and customer characteristics relevant for supply chain strategy differentiation. Using daily recorded sales data over two operational years we apply the
following methods: Principal Component Analysis (PCA) followed by Cluster
Analysis (CA). A new differentiator (order corrections) is introduced, in between
characteristic correlation is spotted and used to generate a more meaningful
attributes (case specific), creating four product segments based on proximate
characteristics. Therefore, by reducing a large product portfolio into manageable
groups of homogeneous SKU’s it is possible to assign a proper set of supply chain
tailored practices.
A. A. Kharlamov J. Godsell
Cranfield School of Management Cranfield, Bedforshire MK43 0AL, UK
L. M. D. F. Ferreira (&)
GOVCOPP, Departamento de Economia, Gestão e Engenharia Industrial, Universidade de
Aveiro, Aveiro 3810-193, Portugal
e-mail: lmferreira@ua.pt
A. Azevedo (ed.), Advances in Sustainable and Competitive Manufacturing Systems,
Lecture Notes in Mechanical Engineering, DOI: 10.1007/978-3-319-00557-7_100,
Springer International Publishing Switzerland 2013
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1 Introduction
Supply chains (SCs) are a source of competitive advantage [1], and management
of SCs is crucial as SCs compete with others SCs, rather than simply between
firms [2, 3]. Over the last decades, it became obvious that one strategy does not fit
all situations [3–6]. Moreover, SCs depend on, for example: product characteristics
[7], product lifecycle [8], and supply and demand patterns [9, 10].
Facing this growing complexity, management of SCs is a broad and overarching function, extending beyond firm’s boundaries [11, 12]. Additionally, the
amount of data regarding daily logistic activity is rocketing due to powerful IT
systems supporting management. The phenomena of big data present both threats
and opportunities [13]. On one hand, without proper approaches business can be
overloaded with meaningless data. On the other hand, mining all that data using
proper tools can provide managers with useful information. This enable increased
SC visibility and valuable knowledge, thus, rational supply chain management
(SCM).
Facing the need for differentiated supply chain strategies and growing amount
of supply chain data, in this paper we describe a single case based research carried
on a business-to-business (B2B) food company. Using a deductive-abductive
approach, we rely on daily captured sales data at order level over two years of
activity to segment stock keeping units (SKUs) based on demand characteristics.
Previous studies have relied on qualitative approaches [5, 7, 8, 14] and quantitative analysis focused on profiling average demand volume against its coefficient
of variation (CV). Additionally, we found none contributions relying on data
mining methods for supply chain differentiation. Consequently, this paper’s
originality is the application of advanced analytical tools to mine supply chain data
using a starting pool of classification variables most commonly found in literature
(enriched with ‘‘Order Corrections Ratio’’ variable). Although the applied data
mining tools are fairly common in other fields such is marketing [15], the value of
this paper is its successful introduction into the field of SCM. Consequently, we
suggest an alternative approach for supply chain differentiation using quantitative
methods.
2 Literature Review
SCM is becoming more and more critical to remain competitive in the market [1].
Consequently, the problematic of the ‘‘right’’ SC has been alive for a while and
after some decades of research there are already many conceptual models and
frameworks [2, 5, 9, 12, 14, 16–18]. One of the first clear references on the
problematic (and one of the most cited articles) is Fisher’s seminal work ‘‘What is
the right SC?’’ [7]. Later, Naylor et al. [19] suggested the integration of the lean/
agile paradigms into the total SC strategy. Then, going to a more conceptual level,
The Power of Analytical Approaches Towards the Development
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Lee [20] published the well-known article ‘‘Triple-A SC’’ stating that the SC
strategy should be developed in spite of adaptability, agility, and alignment. Thus,
these contributions possibly are the best foundation of differentiated (segmented)
SC strategies and as further publications suggest, have inspired many researchers
to go deeper [5, 21–23].
It is clear that one size does not fit all [6], as well as the importance of the link
between the customer value proposition and operations strategy [3] considering the
important detail: similar products can have very different demand patterns leading
towards demand driven SCs discussed further. One of the bottom-lines in the
literature is that matching customer requirement with product characteristics and
ensuring delivery should be one of the greatest concerns for the management
[8, 9, 24]. Therefore, it is important to align SC strategy and products classification
variables accordingly to the target market [9, 14, 25]. SC market orientation is
more and more necessary what requires the identification of the classification
variables [7, 11, 17]. On this idea, Christopher and Towill [14, 26] created models
by considering dominant classification variables using a set of variables such as
duration of life-cycle, lead-time, volume, variety, and CV [27, 28] known as
DWV3 [26]. This set of variables is generalizable, although, its relevance and
applicability is questionable from case to case. Thus, a form of selection and
evaluation is necessary.
SC dependence of product and market characteristics is clear [5, 7, 29]. For
example, one way of distinguishing products is regarding its functional or innovative nature [7] best matched by different SC configurations: physically efficient
SCs for functional products and market-responsive SCs for the innovative ones.
Following the same principle, Lamming [29] expanded the Fisher’s model [7] by
considering the product uniqueness and complexity, while Lee [10] focused the
analysis on supply and demand uncertainty.
Finally, the concept of differentiation concerns the division into different groups
of SKUs/customers sharing similar product/service requirements and demand
patterns. Its main purpose is to enable better understanding so the company can
best satisfy the clients’ needs. Thus, as SCs (and operations strategy in general) are
dependent on customer requirements, market and product characteristics, these
may be considered as the three pillars of a SC and may be used to develop a
differentiated SC strategy.
In summary, this leads towards differentiated SCs that are based on a number of
attributes. Due to the link between SC processes with product life cycles and the
business strategy [30], product life-cycle and SC strategy [8], and finally integration of SC strategy and marketing [12]. This outlines the three bodies of literature that should be used to approach SC differentiation and all three must be
aligned with the business strategy [31, 32]. Finally, this suggests that SC differentiation is based on (a) SCM, (b) SC processes, and finally (c) marketing [5].
From the SC perspective, conceptually the approach on SC differentiation should
start with the context analysis and then, by means of classification variables used
as filters, create distinct segments and profiles that must be matched to different
operations strategies [5].
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3 Research Approach
This is a single instrumental case on the organizational level, focused on the
demand planning processes. The company produces fruit composites (make to
order) on a tailor made basis for the food industry being one of the top European
players. Founded more than two decades ago, it supply’s the major players in the
food industry.
There are several reasons making this case relevant. Food industry, more
specifically food SCM has received little attention in the literature despite the fact
that food sector holds a major relevance. There are several challenges in this field.
For example, SC integration is limited by product and processes specifics [33].
Additionally, maintaining high food quality is demanding, as it depends on
environmental conditions, storing, and transport [34]. One of the main drivers for
SCM in the food industry is the integration of product quality and logistics, named
quality controlled logistics [35]. Consequently, further practical application is
beneficial as it covers one of the actual problems in the food SCM: The matching
of different SC flows with product and customer characteristics. It has been suggested that products with different attributes and different end customers should be
delivered in different distribution channels.
3.1 Data Collection and Methods
The study started with a general meeting for problem identification followed by
the data collection. This was considered a critical stage to understand the situation,
the main problems and opportunities as SCs are dependent on the context and the
needs [36]. The collected data contained demand history logs (two years of
orders), enriched attribute for each SKU, specifically the bill of materials, shipping
requirements, production process details. Finally, we organized the working data
set at a week level for two-year demand volumes for each SKU.
Facing the high variety (more than 1 K different SKUs), it is practically
impossible/inefficient to analyse each portfolio element individually. Thus, we
looked for a way to reduce the complexity. For that purpose, a relatively young
field called data mining and knowledge discovery from data offers many already
recognized methods used in other fields of research [15].
The adopted process was the Cross Industry Standard Process for Data Mining
(CRIPS-DM) [37], being this paper’s focus on the modeling and evaluation phases.
The modeling phase take into cluster analysis (CA) after principal component
analysis (PCA) using IBM SPSS software, as it is one of the possible ways for
segmentation already applied in customer relationship management for customer
segmentation [15].
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4 Data Analysis
The modeling phase began with the classification variables selection. The data
availability as well as heterogeneity was the very first selection criteria for the
initial set of variables. Constrained by the availability in the historical data-log, we
obtained the raw set for almost one thousand SKU’s at the order level during two
years daily. It was then explored, cleaned, and transformed looking for the maximum number of variables. After obtained the set, we performed some basic
descriptive statistics to explore the obtained variables. Consequently, some of the
extracted variables revealed total homogeneity, thus they were automatically
dropped out, but others remained as described further.
Firstly, variables such as the product life-cycle duration, point of product
configuration, profit margin, or reliability of delivery were impossible to obtain
given the circumstances and data accessibility (confidentiality). However, variables such as: Time windows for delivery; Demand volume; Product variety;
Coefficient of variation; Flexibility; Minimum run size; Change-over times; Frequency of delivery; and Product complexity, were successfully extracted (the
weekly measure for frequency was set up due to the companies weekly expenditures practice.) Additionally, based on several discussions of the problem we
suggested a new variable: Order Corrections Ratio (OCratio), as being the number
of corrected orders divided by the total number of orders for some specific SKU.
The ratio between the number of corrected orders and the total orders for a specific
SKU gives an indicator of how efficient is the client’s inventory management and
planning. This regards to the fact that each SKU is exclusive to only one client
while one client holds multiple SKUs.
Secondly, descriptive statistics revealed that some of the variables presented
equal values for all of the SKU’s. Namely, minimum run size and change-over
time followed one standard protocol, thus, featured equal values for all the SKU’s.
Even though complexity was not equal to all of the SKU’s, its measure was
dubious due to the particularities of different recipes, thus we decided to drop this
variable out to avoid further misinterpretation.
Thirdly, following the descriptive statistics and analysis, we selected five
variables: Time window for delivery; Order corrections ratio, demand volume,
CV, and frequency of delivery (weeks with delivery). Those were simplified by
means of PCA and then clustered using the Ward’s method on the Euclidean’s
distance between observations in order to achieve groups of products sharing
similar characteristics.
Finally, the model’s outputs, namely segments, and its different features were
validated and discussed through several meetings with the supply chain manager,
production manager, and internal control manager.
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4.1 Principal Component Analysis
Principal component analysis (PCA) is a method of variable reduction which
transforms a vector of observations into a new set of vector observation. It assumes
that there are linear correlations among some of the classification variables, thus
only suitable for numerical continuous fields (scale variables). It is a way of
condensing various dimensions into a small, sometimes more meaningful (and
bearable) number of variables when rotation is applied, however, at the expense of
some information loss.
PCA with orthogonal rotation is here used to reduce the number of classification variables (simplification) using the linear correlation among the classification
variables, producing new, standardized, uncorrelated, and often more meaningful
components. This procedure allows better insight on the data structure as well as
its visual representation in most of the cases [15]. An examination of the Kaiser–
Meyer–Olkin measure of sampling adequacy suggested that the sample was factorable (KMO = 0.630).
As shown in Table 1, in this specific case CV is significantly correlated to the
time window for delivery as well as to volume, frequency of delivery (weeks with
delivery) is significantly correlated to the time window for delivery, order corrections, volume, and CV. The strongest correlation is between volume and frequency of delivery (weeks with delivery) while the second strongest correlation is
between CV and time window for delivery.
Regarding how many dimensions should be extracted, the selected method for
this study was the total variance accounted. As those methods are ad-hoc, with no
theoretical justification and rely mostly on judgement we set up the rule as being
greater than 75 %, so we considered the actual 75,993 % acceptable, extracting
three principal components. Orthogonal rotation eases the interpretation of the
components, produces uncorrelated components. The rotation method applied for
this study was the ‘‘varimax’’ with Kaiser Normalization, which converged in four
iterations. After the orthogonal rotation, the total variance is redistributed and each
value accounts for the variance of the original variance contained in each
component.
Table 1 Correlation matrix (a = 0.01)
Time
Order
window
correction
for delivery
Correlation (sig. 1tailed)
Order correction -0.030
(0,184)
Volume
-0.086
(0.004)
CV
0.239
(0.000)
Weeks with
-0.107
delivery
(0.001)
-0.022
(0.255)
0.028
(0.200)
-0.185
(0.000)
Volume
CV
-0.150
(0.000)
0.527
(0.000)
-0.159
(0.000)
The Power of Analytical Approaches Towards the Development
Table 2 Rotated component matrix
Classification variables
Component
1
Volume
Weeks with delivery
Time window for delivery
CV
Order corrections
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Communalities
2
3
Initial
Extraction
0.984
1.000
1.000
1.000
1.000
1.000
0.656
0.972
0.802
0.602
0.767
0.888
0.846
0.806
0.761
As listed in Table 2, the focus is on significant loadings, as it eases component
interpretation by improving the readability of the rotated component matrix, hiding
loadings lower than 30 %.
• Component 1 (‘‘Volume ? Frequency’’) represents variances of 89 % for
volume and 85 % of weeks with delivery (frequency of delivery) both positively
correlated, with 0.527 significant correlation from Table 1 meaning that greater
volume represents greater frequency of delivery.
• Component 2 (‘‘Time window ? CV’’) accounts for 90 % of time window for
delivery variance as well as COV variance, both positively correlated (0.237 in
Table 1), meaning that longer time window for delivery tends to have greater
CV.
• Component 3 (‘‘Order Corrections’’) concerns only one classification variable
at 98 %, the new proposed order corrections ratio reflecting client’s inventory
management and planning performance.
Finally, the question is if the derived solution accounts for all the original
dimensions. Table 2 lists communalities for each original dimension after the
extraction, ranging from 0.602 (the worse) to 0.972 (best). Typically, extractions
above 0.500 are considered acceptable [15], suggesting a satisfactory solution,
allowing the next step: clustering.
4.2 Cluster Analysis: Segmentation
Cluster analysis is a method used to identify patterns and identify groups (clusters)
of objects (individuals, entities, etc.) sharing similarities in an n-dimensional
space. Usually those groups are not pre-defined. The advantage of clustering
techniques is that it can handle efficiently large number of attributes, which
dimensions are many times beyond human capabilities. They are not based on a
priori personal concepts, intuitions, and perceptions of business people. Instead,
segments are induced by data providing real business meaning and value results.
The preferred method for this study, based on the bearable number of entities
and few variables, was hierarchical clustering, namely Ward’s method based on
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the squared Euclidean distance between elements. Additionally, hierarchical
clustering methods are slow and old-fashioned, yet, very robust being easy to
understand performing well for under 10 K entities. We used the dendrogram as
the criteria for the number of clusters to extract.
Figure 1 illustrates all SKU’s plotted in space using the three new components
(after PCA extraction), where each SKU is marked with its respective segment
domain. We characterized each segment (cluster) based on its centroid Table 3.
Consequently, the following key characteristics arose for the four segments:
• Cluster 1: This cluster is characterized by the fewest order corrections as well
as low average monthly volume per SKU.
• Cluster 2: The most significant characteristic of this group is the shortest time
window for delivery and high frequency of deliveries what is associated with
high volume per month and the lowest average CV.
• Cluster 3: SKU’s delivered rarely and low average monthly demand with the
highest order correction ratio.
• Cluster 4: It contains SKU’s with the longest average time window for delivery
as well as highest CV.
Fig. 1 SKU’s relative position to the extracted components
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Table 3 Segment characteristic
Classification variables
Segments
Revenue (segment sum)
Variety (number of SKUs)
Volume (mean tons)
Weeks with delivery
(mean)
Time window delivery
(average num. of days)
CV (mean)
Order corrections ratio
(mean)
Cluster 1:
sure and lowvolume SKU’s
Cluster 2:
quick and
frequent
SKU’s
Cluster 3:
rare and
undecided
SKU’s
Cluster 4:
Flexible and
unstable SKU’s
26 %
498
1,702
9
63 %
188
13,235
37
8%
168
1,517
4
3%
63
1,449
10
33
24
36
196
0.61
0.29
0.37
0.45
0.59
1.72
1.28
0.36
5 Discussion and Implications
The need for differentiated SC strategies is eminent. However, most of the differentiation is so far based on pure intuition and managers experience. It should
move from this emotional and subjective ‘‘system one’’ thinking toward a more
objective and systematized ‘‘system two’’ thinking [38], enabled by data mining
methods combined with managers expertise gets the best of both worlds [15].
Moreover, machines are most of the times able to undercover data patterns which
even the most talented and experienced human can miss and vice versa: human
reasoning and critical pattern recognition is so far unmatched by any machine
concerning some cases.
Supply chain strategy is part of a more general, company’s competitive strategy
composed by a set of practices that must fit together [1, 39]. The current company’s strategy intends to fulfil all requests of his industrial customer (B2B),
allowing total customization on a pure make-to-order basis. The actual extra
manufacturing capacity is able to buffer all the demand volatility; however, with
the expected business growth this strategy will soon become obsolete.
Segmentation is a compromise between ‘‘one size fits all’’ strategy and the
individual, in most cases topic, least complex approach when each entity of the
system is managed separately with ‘‘the unique best’’ strategy. Thus, looking for
SC specific classification variables to finding similarities in large number of
entities, allow complexity reduction and better demand understanding.
Therefore, the advantage of reducing the product portfolio into few groups is
that it enables the differentiated strategy matching. As each group features a
distinct profile, it can be matched to a proper set of tailored practices [39] as well
as KPI’s target adjustment. For example, a simple principle of collaborative
forecasting and planning is best match for segments with high order corrections
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Table 4 Matching segments with SC practices and principles
Segment
Key features
Managerial recommendations
Sure and lowvolume
SKU’s
Quick and
frequent
SKU’s
Rare and
undecided
SKU’s
Flexible and
unstable
SKU’s
High variety; low volume
S&OP; minimize wasted resources; reduce
variety; and postponement
Predictable; frequent deliveries; MTF; forecast base demand statistically;
stable demand; and high
forecast surge demand manually (if any)
volume
Rare deliveries; unreliable orders; Collaborative forecasting and planning;
and short time windows
improve visibility and SC transparency;
VMI
Unpredictable; long time
MTO; reduce variety by allowing less
windows
customization; postponement
ratio, as it accounts for client’s inventory management and planning. As a result,
Table 4 shows each segment key features and the best matched SC practices and
principles.
6 Conclusion
Organizations must be adaptable [1, 20], namely its SCs which works like a vessel
system providing what is necessary to keep it alive (and growing). SC differentiation might well be one of the essentials for adaptability and its advantages are
various. For example, on one hand it might provide ground for SC risk management, revealing high risk segments, enabling the development of mitigation
strategies and on the other hand, highly predictable and easily managed segments
allowing more efficient and effortless management. In addition, facing the growing
complexity, segmentation is one of the best countermeasures, allowing a differentiated view on the SC entities. Conceptually, this notion naturally grew out of
several decades of research and practically SC differentiation possibilities are
limitless because despite the fact that segmentation methods might be common, its
outcomes will be unique for each company.
Firstly, there are no doubts on the need for differentiation and adaptability to the
context and needs. Seeking for that adaptability and differentiation, practitioners
have been relying mostly on their ‘‘intuition’’ and domain expertise to develop SC
strategies. Data mining models cannot substitute or replace the significant role of
domain experts and their business knowledge being useless without active support
of business experts. However, such techniques can spot patterns that even the most
experienced expert may have missed. Thus, these techniques complemented with
human business expertise constitute a very powerful mean of developing a more
successful and robust SC strategies. To be clear: human expertise is critical, no
matter how sophisticated a tool can be.
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Secondly, feeding SC segmentation models provided with data-mining methods
is no longer a problem because modern ERPs generate numerous records every
day. The data is there, the Big Data [13], what turns the old problem of ‘‘no data’’
into ‘‘too much data’’. Therefore, managers need the right frameworks and proper
tools to extract useful knowledge for SCM, namely SC segmentation must be fed
with actual data and not mere beliefs or personal assumptions, enhancing model’s
adaptability.
Thirdly, an objective and data driven approach enable the fine-tuning of the
existing strategies, as well as to automate, enrich, and standardize manager’s work,
which so far, is mostly based on personal perceptions and views. This lowers the
decision subjectivity, simplifying time-consuming processes. By learning from
real data, practitioners can spot natural groups sharing similar characteristics,
grouping entities into clusters (segments) lowers the complexity (variety) which
allows better match between supply and demand.
Additionally, following the need for adaptability to the constant change of
customer behavior and SC requirements, re-segmentation (real-time monitoring)
keeps managers aware of the market dynamics, allowing the organization to react
having solid arguments about what is going on, which ultimately might be automatized into an intelligent system defining the best matching SC strategy ‘‘on the
fly’’.
In conclusion, the contributions of this research are not only the successful
application of data mining techniques on SC differentiation, as well as the suggestion of a new classification variable, the order changeability ratio. This new
classification variable is based on standard measures (order change, date, type of
change, and other related properties) usually present in most of order log systems
and is used for quality management.
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