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Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

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While many supply chain decisions could take advantage of big data, firms struggle with investments into supply chain analytics since they are not able to assess the application areas and benefits of these initiatives. In this paper, we provide a multi-level perspective to assess the value of supply chain data. We develop a framework that highlights the connections between data characteristics and supply chain decisions with different time horizons (i.e. short-or long-term) as well as different supply chain levels (i.e. individual-firm level or supply-chain level). As data gets more complex in one or more of the 4 V dimensions (i.e. volume, variety, velocity, veracity), firms must assess how to best take advantage of the opportunities offered. We use the Dutch floriculture sector as a case study for our framework in which we highlight four data analytics applications to improve logistics processes. In the applications , we demonstrate how the data is used to support the decisions at different time horizons and supply-chain levels. We find that each of the big data's Vs is required differently according to the decisions' characteristics. Based on the findings, applications in other industries and promising directions for future research are discussed.
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International Journal of Production Research
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Value of data in multi-level supply chain decisions:
a case study in the Dutch floriculture sector
Nguyen Quoc Viet , Behzad Behdani , Jacqueline Bloemhof & Kai Hoberg
To cite this article: Nguyen Quoc Viet , Behzad Behdani , Jacqueline Bloemhof & Kai Hoberg
(2020): Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture
sector, International Journal of Production Research
To link to this article: https://doi.org/10.1080/00207543.2020.1821116
Published online: 22 Sep 2020.
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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
https://doi.org/10.1080/00207543.2020.1821116
Value of data in multi-level supply chain decisions: a case study in the Dutch
floriculture sector
Nguyen Quoc Vieta, Behzad Behdania, Jacqueline Bloemhofaand Kai Hobergb
aOperations Research and Logistics Group, Wageningen University & Research Wageningen, the Netherlands; bKühne Logistics University,
Hamburg, Germany
ABSTRACT
While many supply chain decisions could take advantage of big data, firms struggle with investments
into supply chain analytics since they are not able to assess the application areas and benefits of these
initiatives. In this paper, we provide a multi-level perspective to assess the value of supply chain data.
We develop a framework that highlights the connections between data characteristics and supply
chain decisions with different time horizons (i.e. short- or long-term) as well as different supply chain
levels (i.e. individual-firm level or supply-chain level). As data gets more complex in one or more of the
4 V dimensions (i.e. volume, variety, velocity, veracity), firms must assess how to best take advantage
of the opportunities offered. We use the Dutch floriculture sector as a case study for our framework
in which we highlight four data analytics applications to improve logistics processes. In the appli-
cations, we demonstrate how the data is used to support the decisions at different time horizons
and supply-chain levels. We find that each of the big data’s Vs is required differently according to
the decisions’ characteristics. Based on the findings, applications in other industries and promising
directions for future research are discussed.
ARTICLE HISTORY
Received 18 September 2019
Accepted 22 August 2020
KEYWORDS
Value of data; multi-level
decisions; logistics;
coordination; collaboration;
agri-food supply chains
1. Introduction
For many decades, various types of data have been
used for supply chain decision-making. Kuo and Kusiak
(2019) conducted an extensive review on how the usage
and role of data have evolved from 1961 to 2013. The
authors indicate that between 1961 and 1999, data were
mainly used to estimate parameters of analytical mod-
els, whereas from 2000, the role has shifted toward the
discovery of meaningful patterns to support data-driven
decision-making. Especially during the last decade, the
maturing eld of social media, the developments in e-
commerce, and advances in data collection technologies
such as tracking and sensing devices have signicantly
changed the shape and role of data in supply chain prac-
tice and research (Arunachalam, Kumar, and Kawalek
2018; Gawankar, Gunasekaran, and Kamble 2020). Data
used in supply chains has increased sharply not only
in Volume, i.e. amounts of data being collected and
processed, but also in Velocity, i.e. the speed of data
generation and streaming, and Variety, i.e. data types
and sources (Wamba et al. 2020;Chehbi-Gamouraetal.
2020). This phenomenon has resulted in the widely dis-
cussed term big data. Veracity (referring to accuracy) and
Value (referring to realising the benets) are the other
CONTACT Nguyen Quoc Viet viet.nguyen@wur.nl, qvloat@gmail.com Operations Research and Logistics Group, Wageningen University & Research,
Hollandseweg 1, 6706 KN Wageningen, the Netherlands
two Vs mentioned in the literature that link big data to
decision-making in supply chains (Zhong et al. 2016).
Scholars and practitioners seem to agree on the vast
opportunities oered by big data for improvements in
supply chain processes (Gupta, Modgil, and Gunasekaran
2020;Hoberg2020). The eagerness around big data
has encouraged rms to move forward with big data
strategies or risk lagging behind in the competition
with rival rms (Richey et al. 2016; Frizzo-Barker et al.
2016). However, these initiatives often involve substan-
tial investments into infrastructure that captures, stores,
and streams big data and in human resource (analytics)
capacity to manage and make use of big data (Schoen-
herr and Speier-Pero 2015). As a result, it is not uncom-
mon to see rms of all sizes struggle with these invest-
ment decisions because examining the expected bene-
ts, i.e. the value of big data, is complicated (Kamble
and Gunasekaran 2020). In fact, many big data projects
generated disappointing returns on investment accord-
ing to a survey by Gartner in 2016 (Grover et al. 2018).
Therefore, structured methods to identify and assess
the expected value of big data are essential to help
rms understand and rethink their big-data investment
decisions.
© 2020 Informa UK Limited, trading as Taylor & FrancisGroup
2N. Q. VIET ET AL.
The extant literature provides dierent perspectives
toassessthevalueofbigdatainsupplychaindecision-
making. Brinch (2018) conducts a content-analysis-
based literature review and proposes a conceptual big
data supply chain management framework with three
dimensions of value discovery, value creation, and value
capture. The author suggests that the value creation of
big data depends on the ability to utilise the infor-
mation generated from the big data for strategic or
operational decision-making. Kamble and Gunasekaran
(2020) present an extensive review on performance mea-
surement systems for the big data-driven supply chain.
The authors stress the importance to combine perfor-
mance measures from dierent dimensions such as cost
vs non-cost and quantitative vs qualitative. Grover et al.
(2018) reason that the value of big data is created through
the combination of insight generation and its actual use
in specic business decisions. The authors introduce a
framework of strategic value creation by big data ana-
lytics that links big data analytics to six value creation
mechanisms, i.e. transparency and access, discovery and
experimentation, prediction and optimisation, customi-
sation and targeting, learning and crowdsourcing, and
continuous monitoring and proactive adaptation. Also
by connecting big data and decision making, Zhan and
Tan (2020) propose a ve-stage analytic infrastructure
for harvesting big data to enhance supply chain perfor-
mance. The authors particularly emphasise the last stage
in which multiple pieces of information generated from
dierent data sources are put together to drive various
options for decision-making.
From a supply chain decision-making perspective, our
studyalignswiththeabove-mentionedframeworks.To
complement these frameworks, we focus on the primary
elements: the connections from data via information to
supply chain decisions. The objective of our paper is to
highlight how data and big data can be linked to dierent
supply chain decisions and to show how the approach to
utilise big data in supply chain decision-making should
be adapted from that of (small) traditional data. For these
purposes, we present a multi-level framework based on
a data-information-decision perspective. The framework
stresses the underlying connections within data char-
acteristics, information characteristics, and multi-level
decision characteristics. Accordingly, rms in the transi-
tion from data to big data can identify essential elements
so that they can avoid missing business opportunities and
unnecessary investments in inessential elements.
Next, we apply the framework using an extensive case
study in the Dutch oriculture sector. This case study
focuses on the data-driven multi-level supply chain deci-
sion making to overcome logistics challenges in the sec-
tor. We present four applications to demonstrate how data
are used in very dierent supply chain decisions. The
applications have been chosen in line with the frame-
work to highlight the dierent time horizons of supply
chain decisions (i.e. short- or long-term) as well as dif-
ferent supply chain levels (i.e. individual-rm level or
supply-chain level). Understanding the multi-level value
of data reinforces the commitment of bottom-up timely
and accurate data contributions from rms in the supply
chains.
Therestofthepaperisorganisedasfollows.
Section 2 introduces the multi-level framework. Section 3
describes the case study and highlights how the frame-
work is applied to connect the data to the supply chain
decisions. Section 4 presents in detail the analytics appli-
cations. Section 5 discusses how the framework can be
applied in dierent industry contexts. Section 6 con-
cludes our study and discusses future research directions.
2. The multi-level framework from the
data-information-decision perspective
The framework is constructed from three elements:
data/big data, information, and supply chain decisions.
Data are patterns without meaning and data becomes
information after being interpreted for a decision-
making purpose (Lumsden and Mirzabeiki 2008). When
data increases to big data, Fosso Wamba et al. (2019)
reportsthatwhileevaluatingthebusinessvalueofbig
data, it is essential to analyse how big data can be trans-
lated into better information and enhanced information
characteristics such as information completeness, time-
liness, and accuracy. Through decision-making, action-
able information is turned into decisions to improve
supply chain processes (Grover et al. 2018;Zhanand
Tan 2020). Dierent types of information with dif-
ferent characteristics can become relevant and useful
to dierent supply chain decisions (Janssen, van der
Voort, and Wahyudi 2017; Viet, Behdani, and Bloemhof
2018a). Consequently, assessing the value of data/big data
requires the understanding of the connections within
data/big data, the information derived from the data/big
data, and supply chain decisions.
In the framework shown by Figure 1,wearguethat
the connections within data/big data, information, and
supply chain decisions are determined by the associ-
ations among data characteristics, information charac-
teristics, and decision characteristics. Decision-makers
need to look for data, information and decisions that
match in characteristics because a decision with spe-
cic decision characteristics requires data and informa-
tion with specic characteristics (Jonsson and Myrelid
2016; Viet, Behdani, and Bloemhof 2018a). The following
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 3
Figure 1. The multi-level framework based on the
data-information-decision perspective.
paragraphs elaborate the data characteristics, informa-
tion characteristics, and decision characteristics.
Data. Data is the foundation of all decision-making
and is often characterises by the dierent Vs, i.e. vol-
ume, variety, velocity, and veracity (Govindan et al. 2018).
As descriptive, predictive, and prescriptive models for
supply chain decision-making take advantage of more
and more aspects of the 4Vs, we refer to this as a trans-
formation from data to big data. Decisions are possible
made using data with larger datasets from heterogeneous
sources in a real-time manner (Choi, Wallace, and Wang
2018). Big data’s 4Vs enhance information characteris-
tics, which make the information relevant and useful to
broader supply chain decisions that are not considered
before.
Information. As information is the output of data
processing, information characteristics are linked to data
characteristics. Following Hazen et al. (2014), the essen-
tial characteristics in decision-making are referred to as
timeliness, completeness, and accuracy.
Timeliness indicates how well the information meets
its user’s demands in a particular time and space. Regard-
ing information timeliness, big data are captured from
various supply chain processes in real-time in many cases
(Kim, Kim, and Park 2017;KaurandSingh2017;Singh
et al. 2018). This enables the generation of information
for smaller intervals and at a higher frequency. Recently,
the ‘digital supply chain twin’ concept based on real-time
supply chain data/information has increasingly attracted
research attention (Ivanov and Dolgui 2020).
Completeness refers to the breadth and depth levels
of information granularity. With regard to information
completeness, big data can improve the depth (i.e. more
granular) and broaden the breadth of the information
(i.e. from intra-organisational to inter-organisational)
(Winkelhaus and Grosse 2020). For instance, tracking
and tracing systems can be implemented at a smaller
unit level (e.g. truck level to batch level) and for a wider
scope covering more participating supply chain rms
(Dai, Ge, and Zhou 2015). The same trend applies to
inventory and resources tracking on a manufacturing
shop oor and warehouses and signicantly improves the
level of information detail (Zhong et al. 2017;Fangetal.
2020).
Accuracy denes how the information reects the
underlying reality. Concerning information accuracy,
data capture by tracking and sensing devices can oer
a higher level of data and information accuracy. Many
types of data that were previously inaccessible or inac-
curate due to economic or technical reasons become
available and reliable. Examples include massive data on
trac conditions from sensors (Li et al. 2015), massive
data on speed in urban areas (Ehmke, Campbell, and
Thomas 2016), and networked sensor data (Li and Wang
2017; Coronado Mondragon, Coronado Mondragon, and
Coronado 2020). Readers are referred to Viet, Behdani,
and Bloemhof (2018a)foranextensivereviewonthe
impact of information characteristics on the value of
information in supply chain decision-making.
Supply chain decisions. We distinguish supply chain
decision making into two dimensions: the time horizon
and the supply chain level.
The supply chain literature commonly denes deci-
sions on the strategic, tactical, and operational time
horizon. Concerning the time horizon, for simplicity,
two groups are specied for long-term (i.e. strategic
and tactical) and short-term (i.e. operational and real-
time) following Wang et al. (2016). While long-term
decisions (e.g. supply chain network design) typically
have stronger nancial implications, short-term deci-
sions (e.g. weekly/daily production planning) are typi-
cally done much more frequently. With regard to decision
characteristics, the current literature reports many big-
data enabled decisions that address short-term process
improvements with the aim of long-term values in the
future (Richey et al. 2016;KuoandKusiak2019).
The supply-chain-level horizon includes the
individual-rm level and the supply-chain level. Deci-
sions at the supply-chain level involve multiple rms
and concern the coordination and collaboration in sup-
ply chain processes (Hazen et al. 2016;Kiiletal.2019).
With regard to the supply-chain-level horizon, the cur-
rent literature has focused on exploiting the value of big
data to improve processes only within individual chain
rms (Barbosa et al. 2018). However, with improved
4N. Q. VIET ET AL.
information characteristics, especially information com-
pleteness, decision-makers are able to consider decisions
thatinvolvemultiplermsineitherverticalorhori-
zontal relationships. Based on well-established theories,
such as actor network theory and resource dependence
theory, Hazen et al. (2016) suggest that big data can
upgrade interconnected supply chain processes toward
higher eciency, eectiveness, and sustainability. Conse-
quently, the value of big data at both individual-rm level
and supply-chain level should be investigated.
In the following, we aim to apply the framework in
the Dutch oriculture sector. In particular, we would like
to highlight four decisions with dierent time horizons
(i.e. short- or long-term) as well as dierent supply chain
levels (i.e. individual-rm level or supply-chain level).
3. Exploring the value of data in the Dutch
floriculture sector
This section rst describes the logistics processes and
challenges and the increasing data in the Dutch oricul-
ture sector. Subsequently, the multi-level framework is
applied to assess the potential value of the data.
3.1. Case description
TheDutchoriculturesectorisavibrantsectorwith
a long tradition. It consists of approximately 6000
(inter)national small, medium, and large suppliers (i.e.
growers), 2500 customers (i.e. wholesalers, exporter,
retailers), 70 logistics service providers (LSPs), and ve
auction and distribution sites (RoyalFloraHolland 2019).
Among these actors, Royal FloraHolland (RFH) plays a
key role in the auction and logistics processes.
There are two major types of physical ows in the net-
work. Auction ows are for products sold via auction
clocks. Direct ows refer to products sold via direct trans-
actions between suppliers and customers. The volume of
direct ows has increased substantially in the last 5 years
to approximately 57% of sales volume in 2017 (RoyalFlo-
raHolland 2019). The case study in this paper is about
thelogisticsprocessesofdirectows.RFHhasvecross-
docking facilities located in dierent supplier regions and
oers cross-docking distribution for direct ows. Figure 2
depictsthelogisticsprocesses.
3.1.1. Data flows
3.1.1.1. Orders and electronic delivery forms. Cus-
tomers send orders to suppliers via an online trading
platform. Suppliers send ordered products in trolleys
to RFH cross-docking. To facilitate the cross-docking
service, information ows must move ahead of phys-
ical ows (Buijs, Danhof, and Wortmann 2016). Sup-
pliers are required to send an electronic delivery form
(EDF) for each order, in advance to trolley arrivals, stat-
ing supplier information, product quantity, and delivery
destination.
3.1.1.2. Tracking and tracing systems. Asasteptoward
supply chain virtualizstion (Verdouw, Beulens, and van
Figure 2. Physical and information flows in the logistics processes of direct flows.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 5
der Vorst 2013), radiofrequency identication (RFID)-
enabled tracking and tracing systems have been imple-
mented since 2016. Suppliers and customers have access
to the systems to trace product location at the trolley
level.Trolleyscansaremadeatthefollowingpoints:
departing from suppliers (farm departure scan), arriv-
ing at inbound docks (receiving scan), processing at the
buer (processing scan), departing from customer clus-
ters (departure scan), arriving at customer boxes (deliv-
ery scan). The farm departure scan began in November
2017 as a pilot project, and data from that are currently
limited.
3.1.1.3. Data to big data. Thedatainthiscasestudy
concern the data of direct ows including (i) historic and
real-time EDF data of direct ows and (ii) the scan data
captured at all stages in the supply chain network. The
direct-owsdataincreasesrapidlywithregardtovolume,
variety, and velocity dimensions due to the increasing
number of daily transactions among a huge number of
suppliers and customers, and also due to the broader
implementation of tracking and tracing across the sector.
Section3.3assessesthepotentialvalueofthisbigdata.
3.1.2. Physical flows
Suppliers send ordered products by trucks to RFH. The
transport is executed by LSPs or by the suppliers them-
selves as rst-/second-party logistics (1PL/2PL). Around
30% of Dutch suppliers are 1PLs. RFH is responsible for
distributing the trolleys to customers within 1 h from
thereceivingpointatinbounddocks.Trolleysarrivingat
RFH at the weekend or outside 04.00–16.00 h on week-
days are not bound to the time constraint. The case
study concerns only the trolleys with a 1-hour limitation.
Dierent from traditional cross-docking where products
are brought from inbound to outbound trucks, in this
case unloaded trolleys are moved to a processing buer.
From the buer, trolleys are moved to dierent customer
clusters. From the clusters, tractor drivers transport the
trolleys to the customer boxes (5–10 min driving).
3.2. Logistics challenges
The following sections describe the major logistics chal-
lenges for suppliers and RFH where the potential deci-
sions enabled by the big data can be made to create
business value.
3.2.1. Suppliers
Customers in general agri-food supply chains increas-
ingly order small quantities of diverse products and
even with small required lead times (Viet, Behdani, and
Bloemhof 2018b). In the Dutch sector, less-than-full trol-
ley orders and 4-hour order lead times have become a
new normal. To meet the short delivery time, the Dutch
1PL/2PL suppliers have to load and dispatch their trucks
as soon as the products are ready. Therefore, they do
not have enough time to eectively consolidate multiple
less-than-truckload orders. Similarly, time-related di-
culties also arise for the LSPs in scheduling trucks to
pick up multiple less-than-truckload loads. This leads to
lowtruckutilisationandahighnumberoftruckmove-
ments in the areas around the Dutch oricultural green-
houses(deKeizeretal.2015). In-time delivery and higher
truck utilisation (thus lower transportation cost) are the
challenges for suppliers.
3.2.2. Cross-docking operators
To assure 1-hour delivery, cross-docking operators must
schedule an appropriate number of workers at each dis-
tribution stage per time period. Ladier and Alpan (2016)
show that workforce scheduling is actually the most
signicant problem for cross-docking managers. Uncer-
tainty about the volume and timing of inbound arrivals
and a broad range of operating hours complicate work-
force scheduling. Cross-docking terminals often require
suppliers to provide information on estimated product
arrival time to proactively schedule the necessary work-
force (van der Spoel, Amrit, and van Hillegersberg 2017).
However, Van Belle, Valckenaers, and Cattrysse (2012)
indicate that in real-life situations, there are serious devi-
ations between the estimated and actual information.
Thus, predictive models play a crucial role in cross-
docking workforce scheduling. In the Dutch sector, the
estimatedarrivaltimeoftrucksisnotevenavailableinthe
EDFs. This causes high uncertainty about the inbound
ows and makes workforce scheduling a big challenge.
3.3. The potential value of big data to overcome
logistics challenges
Using the framework, the big data are mapped to poten-
tialdecisionsthatcanhelpDutchrmstoovercome
their logistics challenges. The focus is on how these
decisions become possible with the increasing data. The
decisions are organised following the two-dimensional
decision structure in Figure 3. Subsection 3.3.1 and 3.3.2
discuss the decisions at the individual-rm level and
supply-chain level correspondingly. Both short-term and
long-term decisions are addressed for each level.
3.3.1. Individual-firm level
Theincreaseindataatindividualsuppliersislim-
ited compared with the amount of aggregate data at
6N. Q. VIET ET AL.
Figure 3. Case study’s four decisions following the multi-level
framework.
cross-docking. This subsection focuses on the RFH cross-
docking.
Decision 1: Real-time workforce adjustment (short-
term). The current challenge for cross-docking is
workforce scheduling. The literature provides work-
force scheduling models that address strategic/tactical
scheduling (Bard 2004; Defraeye and Van Nieuwenhuyse
2016) and daily scheduling with uctuations in demand
volume and arrival time (Lusa, Corominas, and Muñoz
2008;Steinker,Hoberg,andThonemann2017). For these
levels of demand aggregate, the added values of big
data are not clear because traditional approaches are
still applicable. The major contribution of big data, as
discussed by See-To and Ngai (2018), is the capability
to enable ‘nowcast’ with acceptable accuracy to support
real-time workforce adjustment decisions. According to
two recent literature review articles on cross-docking
logistics, i.e. Ladier and Alpan (2016); Van Belle, Valcke-
naers, and Cattrysse (2012), real-time big data predictive
applications for cross-docking decisions are limited but
highly necessary to support regular real-time decision-
making. Section 4.1 presents a big-data predictive model
using historical time scan data, and historical/real-time
EDF data to forecast the inbound volume at a smaller
time horizon, i.e. hourly, to support real-time workforce
adjustment decisions. In the application, the big data’s
variety, velocity, and veracity (i.e. reliable historical time
scan data, real-time and historical EDF data from all sup-
pliers) help to improve the timeliness and accuracy of
forecast information.
Decision 2: Strategic design of storage and
fullment (Long-term). Big data improve the insights
into business activities in the sector. Accumulated EDF
data allow RFH to rethink their role in the sector and
provide new services. A new service concerning the tem-
porary storage and fullment by cross-docking is intro-
duced as follows.
Short order lead times and frequent small orders
lead to low truck utilisation, limited delivery time win-
dows, and high daily truck movements. In addition, buy-
ers often order the same products every day or even
at two dierent periods in the same day. A potential
strategy for suppliers is to pre-ship their products to
temporary storage facilities at RFH cross-docking, from
where near-future orders can be fullled directly. The
pre-ship strategy is also known as anticipatory ship-
ping in the literature (Viet, Behdani, and Bloemhof
2020).
RFH needs to decide on storage and process redesigns
to facilitate the pre-ship strategy. The historical data can
support this strategic decision. Having detailed EDF data
at the product level from thousands of suppliers, RFH
can use the data to estimate the storage and fullment
demands for each supplier and the aggregate demand for
the whole supply chain network. In this decision, the vol-
ume and variety of the data improve the completeness of
the information on potential suppliers for the new ser-
vice. Section 4.2 presents the analytics to support this
strategic decision.
3.3.2. Supply-chain level
At the supply-chain level, the value of the big data are
linked to decisions that facilitates the coordination and
collaboration among the Dutch rms.
Decision 3: Delivery postponement (short term).
At the operational/real-time decision-making level, the
valueofthedataarecreatedthroughbettersupplychain
visibility, which enables eective and ecient process
coordination. Two specic coordination issues are iden-
tied.
First is the coordination between suppliers and the
cross-docking with regard to inbound scheduling (i.e.
truck scheduling/sequencing and truck-to-door assign-
ment). When the farm departure scan is implemented
network-wide, dynamic inbound scheduling becomes
more ecient with real-time updated truck departure
times and real-time estimated truck arrival times. The
important big data characteristics in this coordination
arevelocity,i.e.real-timetimescandata.Thedataveloc-
ity can enhance the timeliness of information on the
inbound ows. This type of operational decision has been
studied in the literature, see for examples studies by Larbi
et al. (2011), Maknoon, Soumis, and Baptiste (2017), and
Ladier and Alpan (2018).
Second is the coordination between cross-docking
and customers. Cross-docking aims to distribute all
inbound trolleys within 1 h. However, in many circum-
stances, customers do not have enough capacity (e.g.
conditioned storage, workers) to hold and process a
high volume of products arriving in a short time, which
resultsinlongtrolleyqueueinfrontoftheboxesand
then quality decay occurs. Cross-docking can postpone
the delivery when the customer boxes are fully occu-
pied. This not only helps to avoid quality decay of the
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 7
product but also provides more time to distribute other
trolleys, which can improve the service level of the dis-
tribution processes. This type of coordination can be
facilitated by timely communication between numerous
customers and cross-docking operators, and real-time
trolley scan data at inbound docks. The key charac-
teristics here are data velocity, information timeliness,
and information completeness. Section 4.3 describes a
coordination system based on tracking and tracing and
a simulation to demonstrate the benet resulting from
coordination.
Decision 4: Strategic partner selection (long-term).
At the strategic decision-making level, the data can
support the horizontal collaboration in transportation
amongtheDutchsuppliers.
Horizontal collaboration in transportation is a
promising method to improve truck utilisation, reduce
transportation costs, and increase service level (Crui-
jssen, Cools, and Dullaert 2007). At the network level,
collaboration reduces the workload at the consecutive
supply chain stage (i.e. truck docking at cross-docking),
reduces truck movements and road congestion, and
improves the sustainability of the sector. Many unsuc-
cessful attempts by the Dutch 1PL/2PL suppliers were
made to collaborate horizontally on sharing truck capac-
ities, i.e. on the way to RFH, supplier A picks up the
products of supplier B if the truck is not full. In 2017, RFH
conducted interviews with the suppliers to gain insights
into the factors that hindered the collaboration. Time
limitations and limited information on potential parties
to collaborate were identied as the two determinant
factors.
The missing information on potential parties with
strategic t for collaboration and the time limitation can
be tempered using historical tracking data. To answer
the question ‘with whom to collaborate’, several dimen-
sions can be used to measure the tness, including
company characteristics, companies’ internal processes,
external parameters, geographic and cultural similari-
ties (Naesens, Gelders, and Pintelon 2009). In this case
study, the time dimension is crucial. Due to the short
delivery time window, suppliers need to know who the
specic suppliers are whose products frequently share
thesamedeliverytimewindowsastheirproductsto
establish an eective and ecient collaborating proto-
col. Section 4.4 introduces descriptive analytics using
historical tracking data to discover sets of strategic-t
suppliers who can potentially collaborate with each other.
It is important to have data on numerous suppliers in
the analytics. In this application, the volume and vari-
etyofthebigdatahelptoimprovetheinformation
completeness.
Tab le 1. Characteristics of the decisions and their connections
with big data characteristics and information characteristics.
Decision 1.
Real-time
workforce
adjustment
at cross-
docking
Decision 2.
Strategic
design of
storage and
fulfilment
service at
cross-
docking
Decision 3.
Delivery
postpone-
ment in
real-time
process
coordination
Decision 4.
Strategic
partner
selection in
horizontal
collaboration
Decision
characteristics
Strategic/tactical X X
Operational/real-
time
XX
Individual-firm
level
XX
Supply-chain
level
XX
Information
characteristics
Timeliness X X
Completeness X X X
Accuracy X
Big data
characteristics
Volume X X
Variety X X X X
Velocity X X
Veracity X
3.3.3. Summary of the decisions
Table 1summarises the four decisions across all dierent
dimensions. In all the decisions investigated, the vari-
ety (i.e. data sources, data types) is required to improve
the information characteristics. In the decisions involv-
ing multiple supply chain actors, information complete-
ness, i.e. the coverage of the information, becomes crit-
ical. Another observation is that not all big data’s 4Vs
are necessarily required in every decision. Specic deci-
sions appreciate specic big data characteristics. The next
section presents the analytics models that support the
four decisions.
4. Four analytics applications to support
logistics decisions in the Dutch floriculture
sector
This section includes four parts that describe the ana-
lytics models to support the four decisions summarised
in Table 1. All the analytics work was implemented
in Python using CORE i5 computer. Through each
part, readers can see how the data are transformed
and used in the descriptive, predictive, and prescrip-
tive models, and especially how and which data and
information characteristics are linked to the decision
characteristics.
8N. Q. VIET ET AL.
4.1. Real-time workforce adjustment at
cross-docking
The major challenge in daily workforce planning is to
schedule the right number of employees at the peak
hours.ThehistoricaldatainFigure4(units: trolleys)
reveals four frequent peaks at 07.00, 10.00, 11.00, and
14.00 h. However, the inbound volumes at these peaks
vary widely, which causes diculty in determining the
required workforce. Taking the peak at 14.00 h for exam-
ple, it is usually the highest and the most uctuating
with a mean of 158 trolleys and standard deviation of
55 trolleys. This deviation can result in the dierence of
±3 workers. The operator needs a timely forecast with
acceptable errors, i.e. information timeliness and accu-
racy, on how many trolleys will arrive between 14.00 and
15.00 h. For demonstration purpose, the modelling aim
is formulated specically for the peak at 14.00–15.00 h as
‘to forecast the arrival volume between 14.00 and 15.00
h given the forecasting moment at 13.00 h, assumed that
1 h is enough to request additional employees (e.g. from
the auction department)’.
4.1.1. kNN-based forecasting model
The knearest neighbours (kNN) algorithm is selected
because it is intuitive and easy to communicate model
parameters and output. The algorithm searches in histor-
ical databases for kdates that share the highest similarity
withthetargetdateandusethosedatesasreferencesfor
calculating the forecast (Wu et al. 2008). The following
explains the most important features of the kNN model.
4.1.1.1. Similarity function. Thehistoricaltimescan
data help to trace backward the EDF registration time
of trolleys arriving between 14.00 and 15.00 h. It is
observed that most of the 14.00-15.00 trolleys have been
registered in the EDF database within 7 h before their
arrivals. As the decision-making moment is at 13.00 h,
thetotalregisteredvolumeperhourintheEDFdatabase
from 06.00 to 13.00 h are selected to model the similar-
ity function, denoted as x1,x2,...,x7.Anon-weighted
Euclidian-distance-based similarity function S(Da,Db)
between two dates Daand Dbis dened as S(Da,Db)=
7
i=1(xa
ixb
i)2. The smaller S(Da,Db)is, the more sim-
ilar two dates Daand Dbare. The model selects the
kmostsimilardatestothetargetdateD,denotedas
D1,D2,...,Dk.
4.1.1.2. Model training. The value of kimpacts the fore-
cast accuracy. Moreover, similarly to overtting issue in
regression models, using a xed kisnoteectivedueto
the seasonality and trends in oriculture supply chains.
Therefore, for each specic target date D,aspecicvalue
of kis used. The process of looking for the best parame-
ter kis called ‘model training’. A training set is required
for this purpose. The arrival volume per hour (Figure 4)
depends on the week day, especially Monday and Friday.
As a result, the selection of ddays for the training set
follows the approach below:
If Dis a Monday or a Friday, dprevious Mondays or
dprevious Fridays are selected.
Otherwise, dprevious days among Tuesdays, Wednes-
days, and Thursdays are selected.
Given a training set, possible values of kare tested. The
value that generates the highest forecast accuracy, i.e. the
lowest mean of absolute errors (MAE), is selected.
Figure 4. Inbound volume per hour in high season.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 9
4.1.1.3. Target dimension calculation. The target
dimension of the target date Disestimatedasaratio
rof the total volume registered via EDF from 06.00 to
13.00 h as T(D)=r×(
7
i=1
x
i).The ratio ris esti-
mated as the average of the kratios of the kselected dates
D1,D2,...,Dk,asr=1
k
k
n=1
T(Dn)
7
i=1xn
i
.Detailedstepsof
the forecast model is described in Algorithm 1.
Algorithm 1. kNN forecasting model
Input
AtargetdateD
Historical EDF data of the previous 8 weeks from D
Real-time EDF data from 06.00 to 13.00 hours on D
AsetKof values for k
Step 1. Model training
Make training set of ddays: trainset =(t1,t2,...,td)
for each kiin K:
for each day tjin trainset:
Find kimost similar dates to tj
Calculate the target dimension T(tj)
Calculate the absolute error between the actual value and
T(tj)
end for
Calculate the MAE resulting from using ki
end for Select the kwith the smallest MAE
Step 2. Generate the forecast using k
Find kmost similar dates to D
Calculate the target dimension T(D)
Output
T(D)as the forecast for D
4.1.2. Results and discussion
The model is tested for two months of May and June. This
period includes weeks of both high, shoulder, and low
season. Regarding computational time, less than 2 s was
required to generate the forecasts for the whole period
with dierent sizes of training sets. Table 2presents the
MAE measured in number of trolleys, ranging from 16.1
to 20.1 trolleys. Because the employee productivity is
around 25–30 trolleys per hour, the MAEs are translated
to the exact number or only ±1 dierent from the actual
required workers. These small forecast errors helps to
reduce the labour costs and maintain the service level.
The best model (in italic) uses K3.Thissetincludes
both small and high values of k,whichimprovesthe
exibility of the model in choosing the parameter for cap-
turing trends and seasonality. Additionally, it is observed
Tab le 2. Forecasting results by MAE (number of trolleys).
Size of training set (days)
Set Kof potential values for kd=2d=3d=4
K1={1, 2, 3, 4}18.7 19.1 19.4
K2={5, 6, 7, 8}19.8 20.1 19.9
K3={1, 2, 3, 4, 5, 6, 7, 8}16.1 16.717.4
that the most immediate dates, i.e. d=2, represent bet-
ter the target date. Other heuristic approaches can also
be used to calculate the similarity function and target
dimension. Data mining algorithms such as support vec-
tor machines or neural networks may improve forecast
accuracy. However, these algorithms are constrained by
interpretability of the model output. Given the intuitive-
ness, the forecast accuracy, and the fast computation, the
kNN model is able to support the real-time workforce
adjustment decision.
As seen in the modelling process, the timeliness and
accuracy of the forecast information rely on the real-
time and historic EDF data from all the suppliers, i.e.
data velocity and variety, and the reliable historic time
scan data, i.e. data veracity. These data requirements are
usually satised in most of today’s cross-docking ware-
houses.
4.2. Strategic design of storage and fullment
services at cross-docking
Using the historical EDF data, RFH can identify poten-
tial suppliers for the pre-ship strategy and accordingly
estimate the aggregate demand for storage and fullment
services. In detail, RFH needs to perform a descriptive
analysis for each supplier. The aim is to identify a list of
products that are frequently ordered (i.e. every day) by
customers located from the same cross-docking destina-
tion. This can be done using the time-base association
rule analysis.
4.2.1. Time-based association rule analysis
The EDF does not include the detailed timing but the
product, customer, and date of the order. A EDF data
point can then represent a customer order. The associ-
ationruleamongtheordersisdenedas‘ifanorderof
productXtoacross-dockingYisreceivedondateD,the
consecutiveorderofXtoYisreceivedbydate(D+τ)’.
By using τ, the rule imposes a limitation on the inter-
arrival time of orders, which is required to reduce the
potential spoilage in perishable supply chains. Depend-
ing on the quality decay characteristics of products, dif-
ferent values of τcanbeused,e.g.1or2days.
TheaimoftheanalysisistolocatetheproductXthat
satisestherulewithapredeterminedthresholdofcon-
dence. Details of the analysis for a supplier is shown in
Algorithm 2.
Arelevantquestionforthepre-shipstrategyis‘what
to do if the pre-shipped products are not ordered after,
for example, one day?’. In the setting of the Dutch sector,
pre-shipped products can be registered for auction chan-
nel after a predened time period. Nevertheless, selecting
a high condence level helps to temper this undesirable
10 N. Q. VIET ET AL.
Algorithm 2. Time-based association rule mining (for data of a supplier)
Input
mproducts Xi(i=1, 2, ...,m)and ncross-docking destination Yj(j=1, 2, ...,n)
m×nsets of historical EDF data Eij of product Xisupplied to cross-docking Yj
τis the predetermined limitation on inter-arrival time of orders
ωis the predetermined threshold on order size to filter products that are not frequently ordered
Analysis
for each Eij:
sis the size of Eij
if sthen break for
Sort Eij by time ascending
count =0
for kfrom 1 to (s1):
if (inter-arrival time of orders kand k+1) τthen count+=1
end for
cij =count
s1as the confidence of product Xito be considered for pre-shipping to cross-docking Yj
end for
Output
Alistof(Xi,Yj)pairs with cij satisfying the predetermined confidence threshold
outcome. Especially for potted ornamental plants with
slow quality decay rate, τcanbeextendedto2or3days
instead of 1 day and the corresponding condence levels
can be set higher.
4.2.2. Numerical example
For demonstration to RFH, the analysis of the histori-
cal EDF data are performed for a large-size supplier A.
The same analysis is applicable to other suppliers. Sup-
plier A supplies 194 dierent types of products to two
RFH cross-docking centres, CD1 and CD2, from January
to June 2017. The time limitation τwas set at 1 day. The
condence level for all six 1-month periods was set at
80%. Instead of one 6-month period, six 1-month peri-
odsareadoptedtoprovidefurtherinsightsintotherobust
suitability over time of products for pre-shipping strategy
andalsotheseasonaleects.
Table 3shows the results. 12 product types meet the
rule and condence conditions for CD1 and their volume
accounts for 39% of the total ordered volume (received
by supplier A) for CD1. 12 product types do so for CD2
and their volume accounts for 49% of the total ordered
volume. Moreover, 9 types satisfy the rule and the pre-
determined condence for both cross-docking destina-
tions. The high volumes of these products indicate a high
potential for these products to be included in the pre-
shipping strategy. These numerical results suggest that
supplier A is a promising client for the new storage and
fullment service by RFH.
4.2.3. Discussion
The information obtained from the association rule anal-
ysis can serve as hard proof in communication with sup-
pliers about the strategic new service of storage and ful-
lment. This information allows the suppliers to realise
if the pre-shipping strategy is suitable to their products.
Because RFH needs to perform the analysis to extract the
information for each supplier, i.e. information complete-
ness, RFH is required to use the historical EDF data of all
thesuppliersforalongtimeperiod,i.e.datavolumeand
variety.
Tab le 3. Confidences in percentage for selected products supplied to CD1 and CD2.
CD1 CD2
Product Jan Feb Mar Apr May Jun Jan Feb Mar Apr May Jun
Bromelia cc 94 94 93 90 91 93 95 92 93 92 90 92
Bromelia gemend 95 93 89 91 93 93 95 93 91 91 92 93
Guzmania cc 92 89 89 87 86 88 92 90 90 86 87 89
Guzmania tempo 91 92 92 88 87 85 91 91 91 87 84 88
Multiflower astrid 93 86 89 87 85 89 93 89 81 86 83 89
Multiflower shannon 93 89 88 86 88 87 92 89 81 84 86 85
Tillandisa anita 93 92 83 92 88 91 93 90 82 88 90 93
Vrieseacc 9293 91 91 89 90 9491 93 88 90 93
Coupe quito 84 88 81 84 84 86 88 84 81 81 88 82
Guzmania deseo 92 91 83 82 84 86
Guzmania hope 84 86 82 90 85 88
Vriesea era 89 89 83 81 82 83
Bromelia mix 91 89 89 85 86 89
Tillandisa josee 92 81 84 83 83 83
Bromelia op hout 91 90 91 88 92 91
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 11
4.3. Delivery postponement in real-time processes
coordination
An eective coordination requires eective real-time
dataandinformationsharingbetweentheinvolvedactors
(Thomas et al. 2015). In this case study, a coordina-
tion system for data streaming and information sharing
between customers and cross-docking is presented and
the coordination’s benet is assessed using a discrete-
event simulation model.
4.3.1. A coordination system based on tracking and
tracing
A real-time coordination system based on tracking and
tracing systems is used to coordinate the delivery pro-
cesses from cross-docking to the customer boxes.
The tracking and tracing systems, based on real-time
receiving and processing scan data, provide customers
with real-time information on the arrival timing and
quantity of trolleys in their orders. Using this timely
information, customers can estimate the time and vol-
ume of trolleys arriving at their boxes. In case of expected
insucient capacity, customers can directly send signals
to postpone the delivery to the cross-docking operator.
Accordingly, the corresponding trolleys can be marked as
postponed delivery at the corresponding customer clus-
ters (Figure. 4) and (if necessary) moved to the postponed
delivery buer. As soon as signals to resume delivery
are received from customers, cross-docking workers can
resume the delivery of these trolleys.
4.3.2. A discrete-event simulation to assess the
benefit of coordination
Using the coordination system, customers can actively
smooth out the trolley arrival process at their boxes.
Consequently, it is easy to see that product quality
can be maintained and congestion at boxes can be
avoided. Moreover, coordination can potentially improve
the internal distribution processes within cross-docking.
Postponing a delivery means more time is available
to expedite another delivery. A discrete-event sim-
ulation model was built using Enterprise Dynamics
9(EnterpriseDynamics2019)toexaminethepoten-
tial improvement. The model is described in the
following.
4.3.2.1. Conceptual model. The entire distribution sys-
temismodelledattheaggregatelevel.Theinboundvol-
ume is divided into two streams. The rst stream goes
to Customer Cluster 1, whose box has sucient capac-
ity. The second stream goes to Customer Cluster 2, whose
box has limited capacity. Customer Cluster 2 is the actor
involved in the coordination system. The delivery to Cus-
tomer Cluster 2 is postponed when the box’s status is
marked full, and the delivery is resumed when the box
becomeslessthanfull.Thus,trolleysdonothavetoqueue
outside the box of Customer Cluster 2.
4.3.2.2. Key performance indicator. Because the simu-
lation aims to examine the eect of the coordination
system on the internal distribution process, the key per-
formance indicate (KPI) is dened as the percentage of
inbound trolleys for Customer Cluster 1 that are deliv-
ered within 1 h.
4.3.2.3. Input data and experimental design. The actual
trolley arrival times of a selected day in the peak season
May-June 2017 was used as input data. On this day, the
KPI is only 86%. Among 216 customer boxes observed in
2017 with capacity of 120 trolleys on average and ranging
from 10 to 2000 trolleys, 59% of the boxes were often over
full. Therefore, to model the current situation, the volume
for Customer Cluster 2 of the total inbound volume was
set at 59% and the average box size was set at 120. In the
experimental design, the volume of Customer Cluster 2
and the average capacity of customer boxes were varied.
For each parametric setting, 200 separate runs were exe-
cutedtoachievenarrowintervalsofthelowerandupper
bounds of the 95% condence level.
4.3.3. Results and discussion
The simulation results are displayed in Figure 5.Withthe
current situation (the solid line at a box size of 120), the
improvement in the KPI is 4% (from 86% to 90%). This
improvement is positively correlated with the volume
for Customer Cluster 2 and negatively correlated with
the average box size. These are intuitive because either
increasing the volume or decreasing the average box size
of Customer Cluster 2 causes more frequent postpone-
ment signals, which saves more time for processing the
trolleys of Customer Cluster 1.
As the volume for Customer Cluster 2 falls under
55%, the improvement in the KPI is no longer signif-
icant. Therefore, timely postponement signals from a
large number of customers to the cross-docking operator,
i.e. information timeliness and completeness, is crucial
to gain more benet from the coordination. Moreover,
improvement in the KPI enabled by the real-time coordi-
nation system can lead to a reduction in the workforce. It
is then promising to integrate the real-time coordination
system with the real-time predictive model for workforce
adjustment to leverage the use of the big data. This is
possible by combining real-time big data analytics and
tracking and tracing systems.
12 N. Q. VIET ET AL.
Figure 5. The improvement in KPI resulting from real-time coordination.
4.4. Strategic partner selection in horizontal
collaboration
The historical time scan data provide details on arrival
times at cross-docking for products from all the suppliers,
i.e. data volume and variety. Using the data, the follow-
ing descriptive model aims to answer the question ‘who
should collaborate with whom’ by revealing sets of sup-
pliers whose products frequently arrived at RFH in the
same time window. The aim is translated to frequent pat-
terns mining. Frequent patterns are patterns (e.g. set of
items in an order, set of sequences in logistics trajecto-
ries) that appear frequently in a dataset (Han et al. 2004).
Apriori is a well-established and well-developed patterns
mining algorithm. Details of the algorithm are referred
to Wu et al. (2008). The following presents important
concepts of the Apriori algorithm.
4.4.1. Pattern and frequency
A pattern in this case is a set of suppliers whose product
arrives at RFH inbound docks in the same time win-
dow. The patterns are created from a historical time scan
database by dissecting the data into 24 time windows of
60 min, i.e. 00.00–01.00, 01.00–02.00, ... , 23.00–24.00.
The minimum support (i.e. the minimum frequency
of a pattern appearing in the dataset to be considered as
frequent) aects the computational time. A smaller min-
imum support results in a larger set of frequent patterns,
thus a longer computational time. In this case, the min-
imum support is set at 100 times (approximately 38% of
260 working days in 2017), which resulted in about 20
min (implemented in Python, core i5 CPU) given the
dataset of around 1,200,000 data points.
4.4.2. Results of frequent patterns mining
The model is run for the 2017 data from two RFH cross-
dockingwarehouses.RFHcross-docking1hasgreater
inbound volumes than RFH cross-docking 2. The results
are shown in Table 4. In general, the high numbers of sets
indicate a huge potential for reducing truck movements
and increasing truck utilisation in the sector.
Manysuppliersbelongtomultipletwoorthreedier-
ent supplier sets. These suppliers can have more options
to collaborate with one or more suppliers. An example
is displayed in Figure 6.Threesuppliers1,2,and3are
located less than a 3-minute drive from each other. Their
production sizes are quite similar. From the geographic
dimension, supplier 1 can possibly collaborate with either
supplier2or3.However,accordingtothemodelout-
put, supplier 1 shares signicantly more frequent product
arrival time windows with supplier 2 (i.e. 371 times to
CD1and415timestoCD2)thanwiththesupplier3(i.e.
164 times to CD1 and 115 times to CD2). This indicates
that it is more benecial for supplier 1 to collaborate with
supplier 2.
4.4.3. Discussion
The descriptive model output provides the essential
information to initiate communication regarding the
Tab le 4. Number of frequent supplier sets.
RFH cross-docking 1 RFH cross-docking 2
Frequency
100–200
Frequency
200–300
Frequency
>300
Frequency
100–200
Frequency
200–300
Frequency
>300
Two-supplier sets 453 53 21 120 11 2
Three-suppliersets841 0111 0
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 13
Figure 6. Example on selecting potential partners.
collaboration in transportation among the Dutch sup-
pliers. The 1PL/2PL suppliers that belong to the same
frequent sets can immediately discuss how the collabo-
rationcouldbearranged.Forexample,astaticoptimal
routing among the suppliers locations is needed. More-
over, information-sharing aspects, such as which infor-
mation (e.g. truck capacity requests or oers) and when
to share information, are important issues that need to
be addressed (Yuan, Viet, and Behdani 2019). For suppli-
ers who use LSPs, a collective arrangement (i.e. pooling
the transportation orders from the suppliers) with LSPs
is promising (Yilmaz and Savasaneril 2012).
5. Applying the framework in other industries
While we have presented four applications for the pro-
posed multi-level framework in the oriculture sector,
the framework is generic and can be applied to other
industrial contexts for studying the value of data at the
dierentlevelsofasupplychain.InTable5,wepro-
vide examples of the potential decisions in the food and
pharmaceutical industries.
The food industry is a straightforward application
area given the similar challenges. Advanced Internet-of-
Things (IoT) technologies enable the collection of prod-
uct location and condition data along the entire supply
chain. A well-developed technology for cool/fresh supply
chains is intelligent containers (Jedermann et al. 2014).
An intelligent container equipped with sensors and con-
trol units allows to monitor and adjust the atmospheric
conditions such as cargo’s temperature or humidity (Lüt-
jen, Dittmer, and Veigt 2013). On an operational level,
this real-time data makes it possible for shippers to obtain
information on product quality and remaining shelf-life
in a timely manner. Using this information and consid-
ering the dierent requirements on product quality, the
shippers can allocate stocks to appropriate supply chain
partners. This information also further allows supply
chain partners to collaboratively optimise their short-
term transportation/storage/issuing decisions to reduce
food spoilage and food waste throughout the supply
chain. On a tactical/strategic level,shipperscanusethe
data to identify recurring issues along with the sup-
ply chain processes, e.g. locations where food frequently
remained in non-chilled areas for too long. This informa-
tion can be used by the shippers to leverage supply chain
partners with higher process compliance. At the supply-
chain level, the information is used to initiate necessary
supply chain process redesigns toward quality-controlled
logistics or quality-driven distribution, which is about
integrating the quality of fresh products in managing
inventories as well as distribution planning for perishable
products (van der Vorst, van Kooten, and Luning 2011).
In the pharmaceutical industry, it is critical to match
supply and demand for potentially live-savings drugs on a
day-to-day basis. While hospitals and pharmacies require
a huge portfolio of drugs, inventory holding cost and
spoilage must be minimised. A new opportunity to obtain
data in this context is being introduced by serialisation in
the pharmaceutical industry (Cordon et al. 2016). In this
approach, each individual drug package is assigned with a
unique serial number that can be traced through the sup-
ply chain. A huge amount of data on inventories, trans-
portationandsalesisgatheredasbillionsofindividual
drug packages are scanned at the dierent points of the
supply chain in a short time. While originally designed
to increase compliance, serialisation enables and sup-
portsmanysupplychaindecisionsinthetwodimensions
Tab le 5. Examples for decisions in food and pharmaceutical industries.
Time horizon
Supply-chain
level horizon
Food industry: product
location/condition data
Pharmaceutical industry:
data from serialisation
Operational/real-time Individual-firm level Stock allocating according to cus-
tomer requirements on product
quality
Production scheduling in packag-
ing for different markets
Supply-chain level Collaborative planning to reduce
food spoilage and food waste
Improving on-shelf availability
across supply chain
Strategic/tactical Individual-firm level Leveraging supply chain partners Introducing demand shaping pro-
grammes for new and end-of-life
products
Supply-chain level Redesigningsupply chain processes
towards quality-controlled logistics
Product design and distribution
across countries
14 N. Q. VIET ET AL.
of our framework. On an operational level,real-time
data about sales and inventories enable companies to
improve customer demand sensing (demand informa-
tion). This timely information allows individual manu-
facturing companies to improve their decision making
withminimallatency,e.g.toenablemanufacturerstoship
out goods to the right markets or to schedule the pack-
aging of drugs for dierent counties. Serialisation data
can also help dierent supply chain actors to improve
short-term on-shelf availability (OSA) in hospitals and
pharmacies. Given the mutual interest of all parties to
avoidstock-outs,betterprocessestosteerOSAcanbe
introduced and the true available inventory (e.g. reserved
or quarantined inventories) can be better located. On a
tactical/strategic level, real-time information about sales
and consumption will enable pharmaceutical manufac-
turers to improve their demand forecasting and sensing
capabilities, which result in better forecasts for decisions
on new demand shaping programmes. Until now point-
of-sale (POS) and point-of-consumption (POC) infor-
mation have rarely been available to the manufacturers.
With serialisation, rather than information about ship-
ments to the POS/POC, information about drugs dis-
pensed to patients become available. This information
enables rms with new strategic decisions for driving or
slowing down demand, e.g. for new products or prod-
uctsattheendofthelifecycle.Onasupply-chainlevel,
strategic decisions about product designs can be made
that relate to pack-sizes and multi-country packaging.
Currently,alargeportionofdrugsarehandledinagrey
marketforexportandreliableinformationaboutthenal
usage of products is not available. With more data avail-
able from the dierent parties, the entire design of the
distribution can be improved.
6. Conclusion
This paper discusses a multi-level framework to show
howdata(andbigdata)canbelinkedtomulti-levelsup-
ply chain decisions for process improvement. While dis-
cussing the framework, we explicitly address the under-
lying connections within data (i.e. data characteristics),
information (i.e. information characteristics), and supply
chain decisions (i.e. decision characteristics). The frame-
work is demonstrated by an extensive case study in which
four logistics decisions at dierent levels are investigated.
For each decision, we address the required information
and information characteristics and identify which char-
acteristics of data (or big data’s Vs) can enhance the infor-
mation characteristics and accordingly, improve the deci-
sion making process. Through the framework and the
case study, the main message is that the value of data (or
big data) lies in how the data characteristics can enhance
the characteristics of the information generated from the
data. In the transition from data to big data, rms are
suggested to identify their desired information charac-
teristics and examine how the big data’s 4Vs contribute
to improving these characteristics because they deter-
mine the relevance and usefulness of the information to
dierent supply chain decisions.
The case study explores the potential value of the
increasing data to support multi-level decisions on the
logistics processes in the Dutch oriculture sector. Fol-
lowing a two-dimensional matrix of decision character-
istics, the values of the data are connected to four dif-
ferent logistics decisions. At the individual-rm level,
the data are used in a kNN forecasting for real-time
workforce adjustment and a time-based association rule
mining for the strategic design of a new storage and
fullment service. At the supply-chain level, the data
enable real-time coordination in the distribution process
from cross-docking to customers and an Apriori appli-
cation to support the suppliers in selecting their strate-
gic partners in horizontal collaboration. The analytics
work reveals that decisions with dierent characteris-
tics benet from dierent big data’s Vs and information
characteristics. Due to the limited data provided for this
research, only parts of the available EDF and time scan
data were used for demonstration in the applications
for long-term decisions, i.e. association rule mining and
Apriori. However, the proposed approaches are applica-
bletolargerdatasetsasthecomputationaltimeremains
polynomial.
Fromthedataanalyticsofthefourapplicationsin
the case study, we nd that data variety is required by
allthefourdecisions,yetnotthesamefortheother
Vs. Not every decision necessarily requires all the 4Vs.
Also,weobservedthatinvestingindata/information
ows requires accompanying investments in other logis-
tics resources at rms and at supply chains. Taking the
real-time workforce adjustment decisions as an example,
real-time and accurate forecast information enables the
decision, yet the implementation of the decision depends
largely on the exibility of the workforce to meet the real-
timeadjustment.Likewise,arelevantquestionishow
toassurethescansaremadebyworkersinatimely
and accurate manner (information timeliness and accu-
racy), and if it is necessary to further expand the track-
ing systems from trolley level to item level (information
completeness).
The following directions are suggested for future stud-
ies. First, recent literature reviews on big data analytics
by Wang et al. (2016) and Nguyen et al. (2018)indicate
that the value of big data has been linked mainly to pre-
dictive power (e.g. forecasting). The descriptive models
in the case study have shown that big data descriptive
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 15
analytics can help rms to uncover hidden patterns, cor-
relations, and insights that allow them to incrementally
or radically change their processes. The value of big data
from big data descriptive analytics should receive fur-
ther research attention. Second, the big data in the case
study supports the co-creation of values among rms
through supply chain coordination and collaboration.
This points to the necessity of investigating big data appli-
cations from the perspective of the supply network and
sector. Potentially with greater scope, big data applica-
tions for cross-industry supply chain processes in the
circular economy are worth exploring (Tseng et al. 2018).
Third, the role of human dealing with big data in supply
chains need to be better understood (Hoberg, Thorn-
ton, and Wieland 2020). While many decisions have the
potential for automation, humans will continue to per-
form many tasks in interaction with the systems and
will be ultimately responsible for the decision-making
process.
The presented framework does not address
organisational aspects such as data-driven culture in
upstream and downstream supply chain rms and data
governance (Arunachalam, Kumar, and Kawalek 2018;
Kamble and Gunasekaran 2020; Bansal, Gualandris, and
Kim 2020). Dynamic behaviours in the multi-actor set-
ting of supply chains can result in a highly dynamic
environment for data capture and sharing, which directly
aect the data characteristics and thus the value of the
data. Readers are recommended to combine the multi-
level framework with the aforementioned frameworks in
the literature for a multi-aspect understanding.
Disclosure statement
No potential conict of interest was reported by the author(s).
Notes on contributors
Nguyen Quoc Viet is a postdoctoral
researcher at Operations Research and
Logistics Group, Wageningen University
& Research. His current research topics
include sustainability in logistics sector,
consumer behaviour and retailing, supply
chain optimiation and simulation.
Behzad Behdani is Assistant Professor
in Operations Research and Logistics
at Wageningen University and Research
(WUR). His research focuses on, among
others, the value of information in agro-
food supply chains, cold chain design,
intermodal transport, and reefer logistics.
Behzad’s research also deals with the ques-
tion of how the Sustainable Development Goals can be embed-
dedinthedesignandoperationsoffoodsupplychains.
Jacqueline Bloemhof wasEmeritusPro-
fessor of Sustainable Logistics Manage-
ment at the Operations Research and
Logistics Group, Wageningen University
& Research. She passed away in June 2020
attheageof51.Shewasanactivemember
of the Closed-Loop Supply Chain network
and a founding member and coordinator
of the EURO Working Group on Sustainable Supply Chains.
Kai Hoberg is Head of Operations and
Technology Department and Professor of
Supply Chain and Operations Strategy at
Kühne Logistics University. His current
research topics include supply chain ana-
lytics, role of technology in supply chains,
inventory modelling, and the link between
operations and nance. In particular, he
explores the fundamental drivers of supply chain performance
and strategies applying real-world data. His research ndings
have been published in academic journals like Journal of Oper-
ations Management, Production and Operations Management,
and European Journal of Operational Research.
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... These discrepancies make sense, however, multiple different algorithms were used for these two applications. For the former, decision trees [76,77], random forests [78], neural nets [79], or k-nearest-neighbor [68] algorithms are applied. For the latter, simple linear regressions [76], ARIMA [70,80], or more complex machine learning models, such as support vector machines, neural nets, and many more [81,82], are used. ...
... Finally, prescriptive analytics is the least discussed category (30% WL, 32% GL), and these papers use optimization or simulation to assess the consequences of decisions or test alternative scenarios, such as for determining optimal order quantities [83]; they also might analyze supply chain networks' performance, depending on different market behavior scenarios [84]. For that, mostly optimization methods analytics (23% WL, 39% GL) mainly revolve around supply chain transparency and visibility, such as by identifying often-ordered products [68] or implementing a tool that provides real-time visibility into the operations of a pharmaceutical company [69]. Most works used different statistical analyses such as vector autoregression [70] or correlation analyses to investigate the dependence of multiple variables [71]. ...
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