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I have 2 Business IT case study assignments, i’m posting one now to understand the work and then I will send the other one and its with less case studies. I will post the questions and upload the case studies and the materials, let me know if anything is unclear or if you have any questions.

Answer the questions below in the context of each of the following five cases (all pdf files) of real-world business information systems.


The information systems described in these articles don’t really fall neatly into a single IS category such as TPS, MIS, DSS, ESS, ERP, SCM, CRM, KMS, collaboration environments, GIS, GDSS, etc. Rather, most seem to possess functionalities from more than one category. Identify and discuss the multiplicity of these categories for each system.

(As a hypothetical example, one particular article may describe a system that primarily appears to be a DSS for mid-to-upper-level managers working in finance and accounting, with other functionalities that resemble an MIS designed for lower-to-mid-level managers in sales and marketing. Your answer will need more elaboration and discussion, of course.)

Each system assists its respective users with decision-making in their work environments. In what stage(s) of their decision-making (Figure 12-2 in the textbook) does it provide them with assistance — intelligence stage, design stage, choice stage, and/or implementation stage? Discuss and justify your answer.

(Address how each completed, implemented system is proving useful,


the process by which it was conceived and acquired/built.)

Each system is probably interconnected/linked to


information systems in its organization. Although the articles themselves do not address this aspect,

from your understanding of organizations, business processes, and systems

, describe some possible/likely examples of such interconnections for


system. Explain your reasoning, while explicitly stating any assumptions.

Inf Technol Manag (2017) 18:241–251
DOI 10.1007/s10799-016-0267-3
A decision support system for improved resource planning
and truck routing at logistic nodes
Alessandro Hill1
Jürgen W. Böse1
Published online: 3 October 2016
Springer Science+Business Media New York 2016
Abstract In this paper, we present an innovative decision
support system that simultaneously provides predictive
analytics to logistic nodes as well as to collaborating truck
companies. Logistic nodes, such as container terminals,
container depots or container loading facilities, face heavy
workloads through a large number of truck arrivals during
peak times. At the same time, truck companies suffer from
augmented waiting times. The proposed system provides
forecasted truck arrival rates to the nodes and predicted
truck gate waiting times at the nodes to the truck companies based on historical data, economic and environmental
impact factors. Based on the expected workloads, the node
personnel and machinery can be planned more efficiently.
Truck companies can adjust their route planning in order to
minimize waiting times. Consequently, both sides benefit
from reduced truck waiting times while reducing traffic
congestion and air pollution. We suggest a flexible cloud
based service that incorporates an advanced forecasting
engine based on artificial intelligence capable of providing
individual predictions for users on all planning levels. In a
case study we report forecasting results obtained for the
truck waiting times at an empty container depot using
artificial neural networks.
Keywords Decision support systems Forecasting
Predictive analytics Truck routing Resource planning
& Alessandro Hill
Jürgen W. Böse
Institute of Maritime Logistics, Hamburg University of
Technology, Am Schwarzenberg-Campus 4 (D),
21073 Hamburg, Germany
1 Motivation
Recent numbers on cargo in industrialized countries show
that road based transport dominates the market. More
importantly, it will have a significant stake in the future
since its market share grows faster than for alternative
modes of transport such as for example railroad. Truck
freight exceeded rail freight by a factor of four with a total
of about 1700 billion ton kilometers in 2012 in the European Union [9]. An increase of 50 % leading to about
600 billion ton kilometers in 2030 is estimated only for
Germany [3]. Accordingly, truck deliveries and pick-ups at
logistic nodes [22] such as warehouses, container terminals, freight stations, empty container depots and logistics
centers will further increase.
Truck arrivals at these nodes are typically followed by a
registration procedure at the gate before the subsequent
assignment of the truck to a loading area. Both, the number
of arrivals and the dispatching time can significantly vary
due to various impact factors. The arrivals depend on
highly stochastic business processes of the truck companies
that are associated with the node. Common causes for the
rise of the dispatching time are peak workloads related to
the truck arrivals, insufficient node resources, node-internal
process issues or external factors such as weather. As a
consequence of such delays, the truck waiting times (e.g.,
at the gate) notably increase. This results in a major drop of
the service level provided by the node. At the same time,
the complexity of operations planning at the node increases
in these periods of strongly fluctuating workload which is
likely to decrease efficiency [5, 19]. Thus, truck companies
as well as node operating companies both experience
notable disadvantages.
In order to mitigate the mentioned issues, this paper
describes a concept for a decision support system that is
based on predictive analytics [8]. The presented iLoads1
system concept essentially aims at supporting operational
planning and control at the truck companies as well as the
nodes by providing forecasted waiting times and truck
arrivals, respectively. This system is innovative since it
extends existing approaches that are currently used in
practice. The most elaborate systems that are used today,
provide either visual real-time gate waiting time information through corresponding web cams or simply list trivial
historical information such as yesterday’s waiting time. In
contrast, we incorporate a forecasting engine based on
artificial intelligence to predict waiting times and truck
arrivals. Decision-makers on both sides benefit from realtime high quality predictions that are tailored to their
individual information needs. The iLoads system is generic
in the sense that it can be implemented at various types of
logistic nodes independent from the precise service it offers
to its customers. The implementation of a system based on
artificial intelligence is motivated by the numerous diverse
dependencies of the highly volatile waiting times in the
described environment. The consideration of additional
external and internal impact factors at the logistic nodes is
crucial for the quality of the forecasts.
The contribution of this paper is twofold. On the one
hand side, we suggest the general concept of providing
forecast information to the actors in this logistic environment. We propose the application of a standard artificial
neural networks approach to incorporate relevant features.
On the other hand side, we describe the implementation of
such a system based on a real world application and provide corresponding results.
The remainder of the paper is organized as follows. In
Sect. 2 we describe the application domain and identify
main user types and associated business processes. The
resulting system requirements are presented in Sect. 3. In
Sect. 4 we give a description of the system architecture and
its interfaces, model components and data components
before concluding this work in Sect. 6.
2 Processes and decision support
The presented iLoads system concept aims at twofold
decision support to simultaneously increase process efficiency at logistic nodes and truck companies. Therefore,
forecasting information is provided to the nodes for
improving internal resource planning and control as well as
to the truck companies and truckers to support their vehicle
routing and scheduling. The requirements regarding future
information are certainly different on both sides. Furthermore, we differentiate between system users according to
Intelligent logistic order arrival decision support.
Inf Technol Manag (2017) 18:241–251
their function, such as management and operations, even if
working in the same company.
In Sect. 2.1, we identify the main user types addressed
by the iLoads system and highlight their information needs
for effective decision making. Subsequently, we explain
the relevant business processes at logistic nodes in
Sect. 2.2.
2.1 Basic truck handling process
The focus of the iLoads system concept is particularly on
logistic nodes as integral part of cargo transport networks.
Logistic nodes are handling and storage locations, as
defined in [10]. The offered logistic services include
transport system changeover, load carrier changeover, repackaging, short and long term storage. In this context, we
consider logistic nodes at which goods are dropped off or
picked up by trucks. Optionally, the nodes interface with
other transportation means such as ship and railroad.
Figure 1 shows the base process that can be identified at
these nodes. Its two main parts are the administrative truck
handling and the physical truck handling. The administrative handling consists of an eventual waiting time that
occurs before the processing of the documents at the document center, also called gate waiting time. This administrative task might require the driver to register at a desk
but may also be done using an electronic terminal which
typically reduces the time needed. The physical truck
handling process can be divided into intermediate waiting
times and loading or unloading operations. Since multiple
container loading or unloading operations are possible,
several intermediate waiting times might occur.
We define the truck waiting time as the sum of the
administrative waiting time plus the aggregated intermediate waiting times before loading and unloading operations during the physical handling as illustrated in the flow
chart in Fig. 1. We note that periods without physical
activity regarding truck, driver or cargo (e.g., document
processing) are frequently experienced as waiting time for
the truck company. Nevertheless, we follow the waiting
time definition in accordance with the legal situation in
Germany as follows. We consider waiting times as periods
in which the node is unproductive with respect to the
corresponding truck. That is, neither the truck is unloaded
or loaded nor is the corresponding order involved in any
administrative process. This matches the formal definition
of waiting times in major countries of the European Union
(see e.g., [6]). The overall dwell time is the sum of the
truck waiting times and the truck handling time which
corresponds to the total time that the truck spends from
queuing at the gate until its departure from the node’s site.
Inf Technol Manag (2017) 18:241–251
Fig. 1 Base process at logistic nodes
2.2 User types and business processes
Regarding the companies which are in the focus of the
iLoads system we can basically distinguish two types of
users according to their information needs. Namely, the
operations planners in charge of the operations management [25] on both sides, the logistic nodes and the truck
companies, respectively. We note that depending on the
company size and its organization the responsible personnel can vary in terms of the number of employees and
the task assignment. Furthermore, executive management
can benefit from forecasted workloads to understand
future trends and trigger strategic initiatives. The predicted data can also be utilized to feed further analytic
models [20].
2.2.1 Node operations planners
On the node side, the presented decision support system is
most useful to operations planners who benefit from truck
arrival forecasts by increased equipment and personnel
planning accuracy. More detailed, this includes assignment
of employees to shifts, deployment of machinery and usage
of policies on a tactical level. The ability to foresee operational events allows a more adaptive planning in general.
These planning tasks are typically done for a horizon from
1 day to 1 week.
Additionally, future truck arrival information generates
substantial value for better operations control. Ad hoc
decision support is achieved by responsive short term
forecasts which take into account events (e.g., traffic congestions) or notable changes of influencing factors (e.g.,
weather) which were not present during operational
In daily business of logistic nodes, the truck arrival rate,
expressed by the number of trucks that arrive during a
specific time period (e.g., 1 hour), is frequently used as an
indicator for the workload. More accurate resource planning leads to reduced truck waiting times.
Additionally, order specific arrival rates restricted to the
truck type (e.g., light, heavy), the load carrier (e.g., container, pallet) or the customer are of interest since they
yield a more detailed estimation of the corresponding
handling effort. Forecasted truck arrival information is
usually not utilized at the nodes. However, corresponding
historical data is frequently considered by the planners.
2.2.2 Truck company operations planners
The second main user type of the iLoads system is operations planning personnel in truck companies which is
responsible for planning and control of the vehicle fleet.
The forecasted waiting times can be used for truck routing,
truck scheduling and related tasks [4, 11]. Furthermore,
related inter-terminal traffic coordination [13] can benefit
from the information provided by the system. So-called
dispatchers can use forecasted truck waiting times to
improve the operational fleet management. This includes
the daily or weekly order assignment to trucks and drivers
followed by the route planning. In practice, it is of major
importance to plan the tours in accordance with existing
time window restrictions. Therefore, the periods in which a
truck has to visit a logistic node can depend on scheduled
appointment times of preceding and subsequent jobs. For
instance, the pick-up of an empty container has to happen
an appropriate time before the packing date agreed with the
customer, or, a truck might be unavailable during certain
periods due to previously planned trips.
As at logistic nodes, the operational planning at truck
companies is done from 1 day to 1 weak in advance. This
includes the assignment of the orders to the available
trucks, followed by the determination of the individual
tours. Ultimately, this induces the number of resources
(i.e., trucks and drivers) required to manage the workload.
Besides efficient resource planning, tour delays due to
truck waiting times can be reduced which increases punctuality. Furthermore, the logistic nodes benefit from
smoothed peak workloads since the tour planner will try to
schedule truck arrivals within periods of low waiting time
(see Fig. 2).
Currently, only a few logistic nodes provide information
about truck waiting times to their customers [1]. One reason might be the lack of digital information on actual truck
waiting times. In some cases, nodes offer a webcam service
to show the current situation at the gate [12]. Such visual
information can be used by dispatchers to get a rough idea
about current waiting time. Against this backdrop, it is not
surprising that most dispatchers do not anticipate truck
waiting times at all. However, today’s economic and ecologic damage caused by truck waiting times is considerable. Several approaches were undertaken to clarify the
general waiting time situation in major ports [15, 17, 21]).
A survey among more than 550 German logistics services
providers in 2012 revealed that in 50 % of the cases the
waiting times at warehouses exceed 1 h (Bundesministerium für Verkehr und digitale Infrastruktur [7]).
In contrast to truck arrival rates, the calculation of
waiting times requires basic statistical compilation. Typically this is achieved by considering average hourly waiting times which have to be derived from the individual
waiting times. In addition, auxiliary measures such as the
maximal, or minimal, hourly waiting time could be useful
in practice.
We note that the information needs of independent
truckers basically correspond to those of dispatchers.
Nevertheless, differences between both user groups exist
on the soft- and hardware level since the former truckers
Fig. 2 Benefits of forecast
information on truck waiting
times and arrival rates at logistic
Inf Technol Manag (2017) 18:241–251
are permanently on the road in contrast to dispatchers being
located in an office on site at the company. Independent
truckers, organized as one-man companies, basically use
the system as the truck companies. Since they are continuously on the road, they particularly benefit from short
waiting times.
3 System requirements
In this section we describe the iLoads system requirements
in detail. Based on the embedding of the system into the
relevant business processes in Sect. 2.2 we provide the
essential functional requirements and define the necessary
data sources.
3.1 User requirements
The iLoads system has to efficiently provide different
views on the truck arrival data and likewise for the truck
waiting time. In the following we assume that historical
information as well as predicted information is provided by
the system.
3.1.1 Display and forecast horizon
As mentioned in Sect. 2, the use of the iLoads within the
different processes implies individual user needs. To present the corresponding information in a meaningful way,
we suggest the inclusion of minimal and maximal truck
waiting times in addition to the average waiting times. The
estimation of relevant key performance indicators is left to
the user but could represent a practical extension. Another
main feature that has to be addressed is the option of
customizing the forecast horizons as well as the overall
timespan displayed which includes historical data. In this
regard, the user has to be able to clearly distinguish
between past and future data and adjust the corresponding
horizons individually. Naturally, this implies real-time
Inf Technol Manag (2017) 18:241–251
reporting functionality. The forecasting horizon should
range from 30 min for operations control up to multiple
months for resource planning.
Furthermore, the operations manager has to be able to
adjust the forecast granularity. That is, constant time
periods, or buckets, in which the underlying arrivals are
summarized and the waiting times are averaged, respectively. In practice, these buckets should comprise from
10 min to 1 month subject to the chosen forecast horizon.
3.1.6 Forecast accuracy
Regarding helpfulness, reliability and user acceptance of
the system, we require a certain minimum forecast quality.
Depending on the business, an accuracy within 25 % can
be acceptable from a practitioner’s point of view. We note
that one idea of the presented system is to outperform
straightforward approaches such as simple hourly or daily
performance averages through the incorporation of
sophisticated forecasting methods.
3.1.2 Specific data display
3.2 Data and sources
Besides showing the general arrival data for all trucks more
specific views on the arrival rates and waiting times are
required for effective order and customer oriented decision
support. The mentioned information has to be given for
various predefined order types which are commonly categorized by the truck type, cargo type and the customer.
3.1.3 Threshold visualization
A meaningful visual presentation of the relevant information should include clear signals to indicate action required
by the user. This can be accomplished by the use of temperature schemes and traffic light systems which qualify
the recent or future situation. Corresponding thresholds
have to be defined by the planning personnel based on their
experience. Moreover, the forecast information can be
translated directly into best practices such as required
resource quantities or operational plans and strategies. In
both cases such an interpretation should be parameterizable
by the user and, therewith, flexible with respect to operational or strategic changes.
3.1.4 Accessibility
The different user types described in Sect. 2.2 access the
system for different purposes and in particular from their
individual workplaces. Certainly, they are all equipped
with a devices that have access to the information system.
Thus, the user interface has to be accessible to multiple
device types such as desktop computers, tablets and smart
phones using their corresponding operation systems.
3.1.5 Response time
Users with operative duties expect information in real time
within their commonly fast paced environment. In other
words the forecast has to be presented within seconds to
support ad hoc decision making. Even on a tactical level,
the information has to be provided continuously, whereas
the management has rather low requirement regarding the
systems response time.
The presented system relies on sufficient input data to
produce satisfactory forecasts. Corresponding base information is given by historical data which is typically hosted
by the logistic node. The suggested intelligent forecasting
methodology will furthermore utilize external information
to increase the forecasting quality. In both cases the system
has to adapt to the interfaces provided by the host systems
for the sources described below.
3.2.1 Historical data
The historical data which is relevant for the forecasts is
collected by the logistic node and commonly organized in
its Terminal Operating System (TOS). This typically consists of work schedules, customer and order information
whereat we are mainly interested in operational data store
[16]. As a minimum requirement it should comprise actual
waiting times and arrivals. The following check points are
sufficient to collect this data (see Fig. 1). In the following
we describe the necessary time stamps.
Truck arrival time: The point in time when the truck
arrives at the site of the logistic node. Optical character
recognition (OCR) device are widely used to capture
these arrivals.
Administrative wait start and end time: The truck
arrival time followed by the time at which the driver
hands out information to the node’s administration,
either electronically or paper based. The order information is commonly entered in the node’s TOS at the
time of registration and, therefore, available in an
internal database.
Intermediate wait start and end times: The pairs of start
and end times at which the truck arrives at a loading or
unloading area and when the actual physical process
starts. In practice, these waiting time can be negligible but
may also exceed the administrative waiting times notably.
Optionally, truck departure times, i.e., points in time
when the truck leaves the site, are useful to calculate the
overall dwell times. The actual waiting time corresponds to
Inf Technol Manag (2017) 18:241–251
the sum of the administrative waiting time plus the cumulative intermediate waiting times as illustrated in Fig. 1. The
arrival rates are calculated by counting the number of arrivals during a specific period (e.g., each hour).
module and the data components [23]. First, we present the
system architecture given in Fig. 3, followed by the different components.
4.1 User interfaces
3.2.2 Forecasting parameters
A forecasting parameter corresponds to information that
has an impact on the time series that will be predicted.
Such a forecast supporting parameter is given as a time
series and is used to improve the forecast quality by
exploiting the presumed correlation. Therefore, we assume
that historical data for these predictor time series, or predictors, is available as well as some forecasted information.
Typical predictors with a trivial forecast are weekdays,
holidays and shift schedules whereas for example weather
conditions, traffic and economic indices need to be forecasted themselves first in order to be available. We categorize predictor series according to the place of collection
of the corresponding data as follows.
Node-specific forecasting parameters This information
is collected by the logistic node and encompasses
observations that originate from business and operations at the node. Examples are personnel and machinery schedules and operational strategies (e.g.,
dispatching modes, storage policies).
External forecasting parameters Third party data that
describes the economic, environmental and traffic
related situation during the forecasting horizon that
has an influence on the forecasted time series; for
instance weather information (e.g., rain, thunderstorms,
frost, heatwaves) and traffic information (e.g., congestions, travel speed) from urban traffic control systems.
Another way to categorize predictor data is to differentiate between deterministic predictors and stochastic
predictors. These are on the one hand parameters that are
known even for future periods, such as the day of the week
or the holiday schedule. On the other hand information on
the future weather or personnel sick leaves is not known in
advance and could at most be integrated using forecasted
data itself. For more detailed information about forecasting
with multiple predictors we refer to [24].
4 System architecture and technologies
In this section we describe the iLoads system based on the
requirements defined in Sect. 3 such that it can be
embedded seamlessly into the business processes summarized in Sect. 2. As typical for a decision support system
we introduce the corresponding model components consisting of the user interfaces, the intelligent forecasting
The interface of the decision support system to the user is
web based and its functionality is twofold. User information is transferred to the system at the time of the forecast
request. Conversely, the retrieved forecast is presented to
the user. To provide decision support, the latter incorporates statistical evaluation of the forecast data (e.g., average
values) and customizable time series that are derived from
the raw forecasting series (e.g., truck arrivals per time
unit). Furthermore, the user gets access to the complete
historical data. This is accomplished by a dynamic htmlbased interface which can be accessed by every webbrowsing capable device. For this purpose, web application
frameworks are widely available which additionally facilitate database connectivity and provide security features as
well as developing tools. Applications that are tailored to
different operating systems (e.g., Android, iOS, Windows
Mobile) could optionally enhance the usability. Following
the requirements from Sect. 3, we differentiate between the
users at the logistic node and the users at the truck companies, where the latter could either be multi-truck organizations or self-employed truck drivers (see Fig. 3).
4.1.1 Truck company
To support the dispatcher’s truck routing and scheduling,
two forecast horizons are offered. An intra-day setting
supports the dispatcher’s short term decisions that affect the
truck planning and control until the end of the day. Additionally, the planning of weekly operations is supported by a
second scenario. Each outlook optionally uses a specified
order type argument which is supported by the node (e.g.,
container drop-off, pick-up) to increase the forecast accuracy. In Fig. 4 two representations are given for an intra-day
waiting time forecast. The estimated general minimum,
average and maximum waiting times are depicted in a line
plot (left). A more intuitive temperature scheme shows the
expected waiting time evolution for the remaining hours of
the day based on a node dependent threshold setting (right).
As Fig. 4 shows historical data can be considered for displaying and is presented to the user in the same fashion as the
forecasted data. A special case of a truck company with a
single employee is a self-employed truck driver.
4.1.2 Logistic node
The display of the truck arrival rates, which are related to
the expected workload for the node, is done similar to the
Inf Technol Manag (2017) 18:241–251
Fig. 3 The system architecture
with its components and their
Fig. 4 Waiting time forecast
representations. Minimal,
average and maximal waiting
times against actual (left) and
temperature scheme for pointwise prediction (right)
waiting times. At each node, a useful extension could take
care of the calculation of various resource demands based
on the truck arrivals to support the decision maker with
resource planning. For example a proposal for the number
of required container stackers or the needed interchange
staff could be presented to the planner based on the hourly
orders. Moreover, the deduction of key performance indicators such as for instance average wait truck time and
daily order volumes can easily be added for reporting.
information and preparing the raw data for its adequate
presentation to the user. Even though the presented iLoads
architecture is designed for a single node and its collaborating trucking companies, the integration of multiple
nodes neither affects the forecasting module nor the generic structure of the system. Solely the corresponding
databases have to be adequately extended. For more
information on cloud-related literature we refer to [14].
4.2.1 Data handler
4.2 Forecasting module
The forecasting module is the cloud based heart of the
iLoads system and is designed as a hosted service. It
consists of the following components which are responsible
for calculating the forecasts as well as handling the input
data from the different sources, organizing the forecasted
The data handler retrieves the input data that is needed to
perform the forecast. In addition to reading raw data from
external sources (e.g., historical data, forecasting parameters) it writes the information to the forecast input database.
The update of the latter could be done based on the push or
pull principle. Most importantly, the latest forecast inputs
are available just-in-time when a new forecast is calculated
by the forecasting engine. The integration of additional
data sources requires the modification of the retrieval
routines used within this component.
4.2.2 Forecasting engine
For the information system, we suggest a forecasting
engine that implements time series forecasting algorithms
based on artificial intelligence. The relevant time series are
the average waiting times and the number of truck arrivals
with respect to the chosen bucket (e.g., 1 h buckets).
Interval based forecasting information, i.e., claiming
that the future value is within a certain range, is favorable
in terms of user friendliness compared to point forecasts for
larger waiting times. One way to produce an interval based
forecast using a series of pointwise forecasts is the
extrapolation of the time series containing the minimal and
maximal waiting times (see Fig. 4). By adding the pointwise prediction of the average waiting time a meaningful
outlook can be provided to the user.
Numerous successful applications prove that artificial
intelligence based methods are capable of producing high
quality forecasts. For a survey of related techniques for
load forecasting we refer to [2]. However, any time series
forecasting methodology may be suitable. An overview of
relevant alternative methods can be found in [18]. For the
presented decision support system we suggest the implementation of artificial neural networks (ANNs). For an
overview of artificial neural network based methods we
refer to [26] and will not go into detail in this paper.
Several commercial and non-commercial software
packages allow the seamless integration of efficient implementations of ANNs within the system. As a part of the
forecasting module, the forecasting engine is called periodically to generate predictions (e.g., waiting times, arrival
rates). The parameterization of the method used (e.g., ANN
structure and training strategy) have to be tailored to the
node-specific data and to the forecast type (see Sect. 2.1).
As a quality control measure, the forecast information
can be stored along with the actual observations, which
yields the forecast errors. Moreover, this information can
be used to fine-tune the forecasting engine or it could serve
as an alarm system to monitor the analytic performance of
the iLoads system.
4.2.3 Forecasting databases
The forecasting module utilizes two databases to store its
direct input and output (see Fig. 3). A forecast input
database is used to store the consolidated historical data
and the forecasting parameters which are collected from
different sources in different formats. In a preprocessing
Inf Technol Manag (2017) 18:241–251
step this database is updated prior to the computation of the
forecast. Herewith, changes concerning the input data
sources or formats only require an adaptation of this preceding update process. A forecast output database holds the
actual forecasting information which is updated periodically. Both databases can be integrated into a database
management system (DBMS) located in the cloud based
environment. The latter can be accessed by the forecast
engine and the request handler and each request from a
truck company or a logistic node will trigger the transmission of the latest forecast according to the type of user
and the desired forecast information.
4.2.4 Request handler
To meet the requirements of the user groups described in
Sect. 2.1, a dedicated unit handles the forecast requests and
the preparation of the raw forecast data. Besides the calculation of auxiliary data representations used by the user
interface, it is responsible for the extraction and aggregation of specific key figures that correspond to the request of
the individual user. This processing step includes the calculation of derived time series and is performed on request
based on the latest forecast data.
As a minimum requirement, the system provides the
average waiting times to the truck company (see Fig. 4),
although minimal and worst case waiting times are desirable. At the same time, a request from the node is answered
by providing the predicted workloads. Depending on the
forecasting methodology, more advanced interval based
measures such as quantiles could be integrated as well.
4.3 Internal processes
In the following, we describe the workflows foreseen in the
decision support system. Two main interleaved processes
take place to provide proper forecast information to the
users, as illustrated in Fig. 5.
4.3.1 Periodic forecast calculation
The large number of requests from truck companies in a real
time application and the computational forecasting effort
make it practically impossible to run the forecasting engine
on demand. Therefore, we suggest a periodic update of the
base forecasts (waiting times, arrival rates) which are then
submitted to the user at the time of request. Moreover, the
actual forecasting is driven by the availability of new input
data. In theory, the prediction needs to be updated once new
historical data and forecasting parameters are available. In
practice, this dynamic recalculation is unneeded since the
advantages of real time computations are negligible if the
recalculation period is chosen reasonably small.
Inf Technol Manag (2017) 18:241–251
Fig. 5 The workflow of
retrieving forecast information
together with the periodic
forecasting mechanism
The implementation of this static forecasting schedule
requires the computation of multiple forecasts and their
storage in the forecast output database to serve individual
requests from nodes or truck companies. This process is
illustrated in the flow chart in Fig. 5.
4.3.2 Forecast retrieval
Forecast information is retrieved from two different sides,
the logistic node and the truck companies. Each request is
user dependent and requires an individual forecast, as
explained above. However, a shared process, the forecast
retrieval, takes place when a user requests forecast information. After the request is triggered via the web interface,
the corresponding base forecast (waiting times or arrival
rates) is read from the forecast output database by the
request handler. After its preparation the individual answer
is provided to the user via the web interface.
truck arrivals range between 500 and 800 per workday. The
used time series is derived from data based on two given
time stamps for each truck arrival. The time point TIN
specifies the time of arrival of the truck on the site and TOUT
denotes the time when the driver delivered the documents at
the interchange before heading to the assigned unloading or
loading point within the depot. Hence, the truck waiting time
is given by TOUT – TIN. In this scenario we did not make a
difference between loading and unloading and did not take
into account additional order properties such as the number
of empty containers involved or the container type. The time
series of waiting times contains one value for each hour
within the horizon, resulting in 3192 data points. Each of
these values is computed as the average of the waiting times
for trucks with an arrival time TIN in the corresponding hour
interval. Note that the company works in a two-shift mode
(6 a.m.–8 p.m.) resulting in zero values during night time
and weekends.
5.2 Forecasting implementation
5 Case study
In this section we demonstrate the capability of the system
by conducting experiments within a real world application.
As explained in Sect. 1, we decided to apply a machine
learning approach based on artificial neural networks to be
able to integrate additional features. To this end, we
implemented a forecasting module based on commercial
software. We report results that we achieve through the
latter for a data set that contains historical data from an
empty container depot.
5.1 Real world application and data
In our experiments we use actual truck waiting time data
from a large maritime empty container depot in Northern
Germany. The data set spans 133 days and the number of
It is known that several commercial vendors provide
powerful and flexible frameworks to model and optimize
artificial neural networks. We decided to use the MATLAB
Neural Networks Toolbox2 in version 8.5 (R2015a) which
is widely used in the industry. The net is parametrized as a
two layer architecture with a closed loop for additional
feedback. In an autoregressive fashion we use default lags
of size two, i.e. the two past realizations that contribute to
the value at the next time-step, for our multi-step prediction. A preprocessing procedure was implemented to
eliminate the overnight non-working periods. We add
predictor information in form of the weekday, the daytime
and public holidays. Furthermore, we experiment with the
number of lags.
The MathWorks, Inc. (http://www.mathworks.com/help/nnet).
Inf Technol Manag (2017) 18:241–251
Fig. 6 Forecasted hourly
average truck waiting times
versus historical data for the last
week of the horizon
5.3 Results
We forecast the truck waiting time for the last week within
our historical data horizon. This corresponds to the last 168
data points of the time series. The evaluation is based on
the actual (average) waiting times in this period using the
mean squared error (MSE) and the mean average percentage error (MAPE).
The forecast accuracy could be increased by the integration of the weekday, daytime and holiday predictors.
Additionally, we experimented with different values for the
number of lags. More detailed, we compared the forecast
quality when using 2, 3, 4 and 5 lags. The best results are
achieved when increasing the number of lags to 5. Moreover, we eliminated the night periods (8 p.m.–6 a.m.) in a
preprocessing step which further improved the results. The
obtained MAPE is 29.9 and the MSE is 26.6. For an overall
average waiting time of 30 min this corresponds to a
±5 min accuracy which is sufficient in many instances
considering the requirements for decision support in
operational practice of truck companies. Figure 6 shows
the forecast for the waiting time in minutes during the
evaluation week compared to the actual historical data.
6 Conclusion
In this work we present a concept for an innovative decision support system that provides forecasting based decision support to two types of users. On the one hand it
predicts truck arrivals to logistic nodes such as empty
container depots, packing facilities or terminals. These
facilities can utilize this estimation of future workloads to
improve their resource planning. On the other hand the
estimated waiting times at these nodes are made available
for truck companies that do business at these nodes. These
are able to adjust their route planning in order to reduce the
waiting times at the nodes. The expected result of this
optimization-driven interplay is smoothed peak workloads
at the nodes due to adaptive truck routing and reduced
truck waiting times because of more accurate resource
deployment at the nodes.
Another main strength of the developed system is its
flexibility and, in particular, the implementation at
relatively little effort. Moreover, it can be embedded into
the analytics landscape of the involved companies to
enhance business intelligence.
Acknowledgments The research Project 17694 N, entitled ‘‘Truck
Waiting Time Forecasting at Logistic Nodes’’ (Lkw-Wartezeitprognose für logistische Knoten) at the Institute of Maritime Logistics at
Hamburg University of Technology was funded by the German
Federal Ministry for Economic Affairs and Energy (Vorhaben der
Industriellen Gemeinschaftsförderung, IGF). We thank Sabine Werner for the fruitful discussion.
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Reproduced with permission of copyright owner.
Further reproduction prohibited without permission.
Inf Syst Front (2018) 20:209–222
DOI 10.1007/s10796-016-9720-4
The impact of big data analytics on firms’ high value
business performance
Aleš Popovič 1,2
Ray Hackney 3 & Rana Tassabehji 4 & Mauro Castelli 2
Published online: 28 October 2016
# Springer Science+Business Media New York 2016
Abstract Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage
and enhance high value businesses performance. The purpose
of our study is to investigate the impact of BDA on operations
management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive
qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying
levels of BDA usage (experimental, moderate and heavy). The
information technology (IT) business value literature and a resource based view informed the development of our research
propositions and the conceptual framework that illuminated the
relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in
terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top
management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business
performance. Our results also highlight important managerial
implications related to the impact of BDA on empowerment of
employees, and how BDA can be integrated into organizations
* Aleš Popovič
Faculty of Economics, University of Ljubljana, Kardeljeva ploščad
17, SI 1000 Ljubljana, Slovenia
NOVA IMS, Campus de Campolide, 1070-312 Lisbon, Portugal
Brunel Business School, Brunel University London, Uxbridge UB8
School of Management, University of Bradford, Emm Lane,
Bradford BD9 4JL, UK
to augment rather than replace management capabilities. Our
research will be of benefit to academics and practitioners in
further aiding our understanding of BDA utilization in
transforming operations and production management. It adds
to the body of limited empirically based knowledge by
highlighting the real business value resulting from applying
BDA in manufacturing firms and thus encouraging beneficial
economic societal changes.
Keywords Big data analytics . Business value . Operations
performance . Case analysis
1 Introduction
Within turbulent and highly competitive global environments,
firms are compelled to adapt more rapidly, boldly, and to experiment in order to survive and thrive. They are increasingly
seeking ways to identify the constraints in advancing business
processes which severely hampers their ability to respond to
accelerating competitive demands. Extant studies, thus, advise
firms to focus on the development of organizational agility
(Chakravarty et al. 2013; Tallon and Pinsonneault 2011; Bi
et al. 2013), which, in turn, enables them to respond to a wide
variety of environmental business changes in an appropriate
and timely way. The characteristics of agility are that firms,
while continuously identifying and developing new advantages, orchestrate their business processes in a way to enable
them to explore new opportunities effectively as well as to
exploit those opportunities efficiently, to improve firm performance (Chakravarty et al. 2013).
The potential of information systems (IS) to inform decision
making and improve firm performance has long been emphasized in the information technology (IT) business value literature (Davern and Kauffman 2000; Mithas et al. 2011; Melville
et al. 2004; Bhattacharya et al. 2010). In firm performance
studies, IS have been reported to support timely decisions, provide insights that increase comparative advantage, promote innovation, and offer a means to manage environmental uncertainty (Popovič et al. 2014). Consequently, firms rely on their IS
for the provision of high quality information, i.e. information
that is relevant, reliable, accurate, and timely (Popovič et al.
2012; Wixom and Todd 2005), that facilitates improvements
in decision quality and can, in turn, elevate firm performance
(Mithas et al. 2011). To leverage the benefits of insightful information, firms are thus increasingly investing in various technologies and embedding them into their business processes
(Chen et al. 2012).
The hypercompetitive aspects of modern business environments have drawn firm attention toward agility as a strategic
capability where IT-enabled information is expected to have an
important role in the development of organizational capabilities
(Chakravarty et al. 2013). A form of organizational agility that
is of particular relevance to research is process agility, or the
extent to which firms can easily and quickly retool their processes to adapt to the market environment (Chen et al. 2014). In
particular, data-driven business analytics are regularly emphasized as a foundation for innovation and agility (Chen and Siau
2011; Davenport et al. 2012; Kiron et al. 2012).
Business intelligence and analytics and the related field of
big data analytics (BDA) have become increasingly important
in both the academic and the business communities over the
past years (Chen et al. 2012). From the academic perspective,
BDA research has attracted attention at the level of widely
read scientific outlets such as Proceedings of the National
Academy of Sciences and Science because of the importance
and generic nature of the inquiries (Agarwal and Dhar 2014).
Also, firms are constantly trying to draw insights from the
expanding volume, variety, and velocity of data to make better
sense of the data and to improve decision making (Lavalle
et al. 2011). In addition to interpreting ways to address known
problems, firms are focusing on identifying trends that they
did not know before (Fosso Wamba et al. 2015). The opportunities associated with data and analysis in different organizations have helped generate significant interest in BDA,
which is often referred to as the techniques, technologies,
systems, practices, methodologies, and applications that analyze great variety of critical business data to help a firm better
understand its business and market, and make timely and effective business decisions (Gandomi and Haider 2015;
Mcafee and Brynjolfsson 2012). With an overwhelming
amount of web-based, mobile, and sensor-generated data arriving at huge scale, novel insights can be obtained from the
highly detailed, contextualized, and rich contents of relevance
to any firm (Agarwal and Dhar 2014; Chen et al. 2012).
In operations management, the application of BDA is particularly important in supporting operational and strategic decision-making, and enhancing performance (Kiron et al. 2014).
Inf Syst Front (2018) 20:209–222
However, scholars argue that leveraging performance benefits
depends less on having the technology and more on being able
to make the best use of new insights in advancing organizational agility (Kretzer et al. 2014). Insights from BDA have the
potential to enable real-time business process monitoring and
measurement, enhancing quality management (Waller and
Fawcett 2013; Davenport et al. 2012), reinforcing customer
relationships, managing operations risks, improving operational
efficiency and effectiveness, or to improve product or service
delivery (Kiron 2013; Zelbst et al. 2011).
While prior research has suggested BDA usage and IT infrastructure flexibility are two important sources for an organization’s agility (Chen and Siau 2011), our understanding of the
processes and factors enabling, facilitating, or impeding successful utilization of BDA in operations, remains limited.
Emphasis is, therefore, increasingly placed on the underlying
mechanisms that link BDA to operations’ agility and performance. To address this gap, we conducted a comparative case
study of three manufacturing firms that utilize big data analytical capabilities in their operations. We explored what a firm
must do right in order to utilize its big data analytical capabilities so as to fully leverage the value of BDA in enabling better
agility and improvements of its operations.
Our contribution to the business value of IT literature is
twofold. First, we show that utilization of BDA in manufacturing operations can enhance agility and manufacturing performance. The shift toward BDA-supported performance indicators enables decision makers to utilize additional data in considering different courses of action when pursuing set goals.
Echoing extant studies in operations literature, we find that
when firms utilize more BDA, they better forecast previously
unpredictable outcomes, and improve process performance. As
a result, firms realize operational process benefits in the form of
cost reductions, better operations planning, lower inventory
levels, better organization of the labor force and elimination
of waste, while they leverage improvements in operations effectiveness and customer service. Second, drawing on resourcebased logic (Ray et al. 2005), we argue that such improvements
in manufacturing operations, driven by increased utilization of
BDA, can foster differential agility and performance impacts
(Hvolby and Steger-Jensen 2010). However, we warn scholars
and practitioners that a firm’s BDA capabilities (in terms of data
sourcing, access, integration, and delivery, analytical capabilities, and people) and organizational factors (such as BDA strategy, top management support, financial resources, and engaging people) can facilitate (or inhibit) effective utilization of
BDA in operations, and thus moderate differential performance
benefits of BDA utilization. As such, we extend the IT business
value literature, which argues that seeking strategic advantage
merely by developing IT capability may not necessarily realize
enhanced performance; organizational design/ readiness factors
are critical for effective IT utilization (Hong and Kim 2002;
Dezdar and Sulaiman 2010).
Inf Syst Front (2018) 20:209–222
The remainder of this paper is organized as follows. We first
set out the theoretical background of our research. We then
outline the research approach and introduce the three case firms,
outline the sources of data and explain our data analysis procedure. This is followed by our findings on how the utilization of
BDA affects organizational agility and the underlying mechanisms that link BDA to improvements in operations performance. In the discussion section, we explore the contributions
and practical implications of our findings. Finally, some inherent limitations and avenues for future research are given.
2 Theoretical background
Much consideration is currently being paid in both the academic and practitioner literatures to the value that firms could
create through the use of BDA (Mithas et al. 2013; Wixom
et al. 2013; Chen et al. 2012). Sharma et al. (2014) argue here
that while there is some evidence that investments in business
analytics can create value, the claim that ‘business analytics
leads to value’ needs deeper analysis. In particular, the roles of
organizational decision-making processes, including resource
allocation processes and resource orchestration processes
(Teece et al. 1997), need to be better understood in order to
understand how firms can create value from the use of BDA.
This study is consistent with the resource-based theory
(Barney 1991). The resource-based theory argues that the
competitive advantage of a firm is determined by its resources,
and that, under specific circumstances, these resources can
generate superior long-term performance (Mata et al. 1995;
Ray et al. 2005). The resource-based theory has been used
extensively by IS scholars. According to (Wade and Hulland
2004), this theory is useful for business value of IT research
for two reasons. First, through resource attributes, this theory
facilitates both the specification of IT resources and their comparison with business (non-IT) resources. Second, since the
resource-based theory establishes a clear link between resources and sustained advantage, it provides a useful way to
measure the value of IT resources.
The link between IT resources and firm performance has
been investigated by a number of researchers, and their results
have been mixed (Mata et al. 1995; Ray et al. 2005). It is
generally accepted in the IS community that IT resources
and capabilities per se do not enhance firm performance, although they can act as key enablers of higher-order organizational capabilities or interact with other business resources to
increase firm performance.
A significant number of IS scholars support so called mediation view, through which IT resources and capabilities do not
seem to help the firm directly to improve its position, but can do
so indirectly through the mediation of higher-order organizational capabilities (Benitez-Amado and Walczuch 2012). Prior
research has found that several types of these capabilities (e.g.
agility (Sambamurthy et al. 2003), knowledge management
(Tanriverdi 2005), innovation-supportive organizational culture
(Benitez‐Amado et al. 2010)) act as intermediate variables on
the relationship between IT capabilities and firm performance.
IT capability is defined as the firm’s ability to mobilize, deploy
and use IT-based resources to improve the firm’s business processes (Santhanam and Hartono 2003). Agility is the ability to
adapt and alter businesses and business processes to effectively
manage unpredictable external and internal changes quickly
and easily (VAN Oosterhout et al. 2006). This research stream
constitutes what has been termed the IT-enabled organizational
capabilities perspective. Consistent with the mediation view,
our study analyzes the role of IT implementation in the generation of business value of IT.
A complementary body of IS research is consistent with so
called moderation view, which holds that IT resources and
capabilities impact firm performance only when they interact
with other resources (IT and non-IT/business resources). This
means that the link between IT resources and firm performance is reinforced by the presence of other resources and
capabilities. This rationale incorporated into the stream of research is termed as the contingency approach (Powell and
Dent-Micallef 1997; Ray et al. 2005).
Building on the above theoretical background, our understanding of how BDA implementation affects agility and further performance in manufacturing industry remains limited.
Moreover, against the equivocal findings on the relationship
between investments in IT and financial performance (Davis
and Golicic 2010), our knowledge of the underlying mechanisms that link BDA to improvements in firm performance is
also scarce. These gaps have motivated our research questions: what a firm must do right in order to utilize its big data
analytical capabilities so as to fully leverage the value of BDA
in enabling better agility and improvements of its manufacturing operations? Our research explores these questions through
a comparative case study of three manufacturing firms that
utilize big data analytical capabilities in their operations. We
now detail our research approach.
3 Methodology
3.1 Research sites and data collection
Due to the early stages of research on how BDA may transform operations and improve performance and the significant
lack of empirical analysis within the context of manufacturing,
we adopted an exploratory case study method (Benbasat et al.
1987). Case studies provide a source of well-grounded, rich
descriptions and explanations of developments that are relatively weakly understood (Miles et al. 2014). In our study, we
employed a multi-case design that supports a replication logic,
through which a set of cases are treated as a series of
Inf Syst Front (2018) 20:209–222
experiments, each serving to confirm or disconfirm a set of
observations (Yin 2014).
We carried out our research in large manufacturing firms,
as the manufacturing sector has proven well suited to study the
benefits of BDA implementation (Lee et al. 2013; Auschitzky
et al. 2014) as the use of analytics for product development,
operations and logistics is increasing (Dutta and Bose 2015).
The BDA revolution has set the stage for the use of large data
sets to predict future events and actions (e.g. resource failure,
adaptation of manufacturing operations) by taking into account the real-time outcomes of complex and unexpected
events (Babiceanu and Seker 2015). We theoretically sampled
firms to fit our research focus (Eisenhardt 1989). The three
case firms have all implemented BDA within a year apart. In
their respective markets, each firm is ranked among top performers in terms of annual revenues and number of employees. While we sought firms with similarities that would
aid comparisons and replication, we also looked for sufficient
heterogeneity to help assess potential generalizability. Table 1
provides relevant details about the three firms in our study.
We conducted our research using semi-structured interviews with a total of 13 employees who were directly (e.g.,
head of operations, warehouse supervisors) and indirectly
(sales managers) involved in the manufacturing process. The
experience of participating respondents related to their years
working in the industry and the time working for the firm
presented in Table 2. Interviews were conducted from
September to November 2014 and lasted around 1 to 2 h.
Interviews were audio recorded and transcribed with permission of the respondents. The study was longitudinal in respect
that the individuals interviewed had insights of the organization before and after the adoption of BDA and were able to
make comparisons and provide information about their
BDA supports operations and examined the underlying mechanisms that linked BDA to improvements in operations’ agility. To assess the reliability of the generated open codes, we
then involved a second coder, with substantial qualitative research experience.
Next, we linked related concepts within each case. During
this phase, we examined all conclusions derived from the initial coding and established links between and among previously stated categories. We allowed concepts and patterns to
emerge based on the primary data collected, while new categories were added and others were regrouped with further
analysis (Cassell and Symon 1994). To improve generalizability (Firestone and Herriott 1983), as well as to deepen understanding and explanation (Miles et al. 2014), we then compared each category and its properties across cases. Our main
objective was to compare and contrast changes in the operations among the three case firms. To evaluate the reliability of
each dimension, we first involved the second coder. All disagreements were resolved through discussion. Second, we
shared the results of the initial analysis with key informants
at the three case firms and with an independent professional in
the field to assess plausibility of the reached conclusions.
In the last stage we connected emergent themes and ideas
with the theoretical concepts from the literature. Our data
analysis moved back and forth between the emerging themes
and extant literature to explore broadly possible explanations
for our findings and enable focus on the justification that best
fit with the data (i.e. explanation building) (Yin 2012).
In the following section we discuss our findings. We first
reveal how the introduction and utilization of BDA has transformed operations in the three case firms. Second, we uncover
the underlying mechanisms that link BDA to improvements in
3.2 Data analysis
4 Findings
The data analysis process, following Miles et al. (2014), was
systematic and iterative, where comparisons of data, emerging
categories and existing literature aided the process. We first
compiled separate case studies of each firm. We identified
patterns and variance in descriptions of how utilization of
4.1 Changes in operations with the utilization of BDA
Table 1
In response to our research question, we examined how the
introduction and utilization of BDA has transformed operations in the three case firms. We found that utilization of BDA
Overview of the case firms
Year founded
Manufactured goods (primary products)
of employees
Annual Revenue
Year when BDA was
Firm A
Buildings materials and construction systems
105.6 million €
Partially in 2012,
finalized in 2013
Firm B
664.6 million €
Early 2014
Firm C
Prescription pharmaceuticals, non-prescription
products and animal health products
Home appliance
1116.3 million €
Source: Agency for Public Legal Records and Related Services; data obtained from 2013 Audited Annual Report database
Inf Syst Front (2018) 20:209–222
Table 2 Respondents’
Years in the
Years working
for the firm
Firm A
Sales Manager
Head of Research Operations
Lead Operator for the Packaging Operations
Warehouse Supervisor
Market Sales Leader
Manufacturing Specialist
Firm B
Firm C
Head of Research and Development
Supervisor of Process Automation
Diagnostic Laboratory Specialist
Regional Sales Manager
Technical Production Manager
Chief Project Leader
Warehouse Supervisor
mobilized enhancements in insights across the case firms.
Moreover, manufacturing operations’ performance has now
improved. Below we discuss these findings in more detail.
4.1.1 Value from utilizing BDA in manufacturing operations
The three case firms utilized BDA to support a wide range of
performance aspects in relation to their planning (e.g. schedule and cost variance, capacity utilization), manufacturing
process (e.g. process downtime, machine efficiency, waste
reduction), and quality assurance (e.g. defective units, rejected
units) (see Table 3 for a detailed description). Informants
across cases argued that the utilization of BDA provided additional performance insights into various manufacturing
phases and, therefore, improved their performance indicators
across these areas. Specifically, informants emphasized four
improvements the utilization of BDA brings to operations
management. First, they argued that the utilization of BDA
improved the prediction of potentially unfavorable events.
BDA-enabled information provides more comprehensive
and accurate insights (Waller and Fawcett 2013; Babiceanu
and Seker 2015). Second, they noted that equipment availability for the manufacturing process had also improved as a result
of exploiting BDA (Munirathinam and Ramadoss 2014).
Third, informants discussed the benefit of BDA use in reducing manufacturing waste, which aided the move toward lean
manufacturing (Lee et al. 2013). Lastly, the utilization of BDA
improved insights into identification of faulty products, further preventing returns and rework (Lavalle et al. 2011).
However, our findings also revealed that the value of BDA
utilization in different phases of manufacturing operations was
wider in Firm C than in Firms A and B (see Table 3). Based
upon the utilization of BDA across different phases of
manufacturing operations, we can classify our case firms as:
1) experimental user (Firm B), where BDA use is mainly at
the planning phase, seldom during the manufacturing and
quality assurance phases; 2) moderate user (Firm A), where
the firm uses BDA within manufacturing phase, occasionally
also in planning and quality assurance; 3) heavy user (Firm C),
where BDA is employed regularly across all phases, from
planning to quality assurance.
Within the planning phase, all three firms utilize BDA for
improving their capacity utilization. Firm A’s Lead Operator
for the Packaging Operations, for instance, explained:
Production volumes fluctuate daily – one day there is a lot
to make, the next day there is merely anything. Due to irregular demand, we can’t predict it very well, and as a result we
end up with unused capacity. Through utilization of BDA we
learnt that these fluctuations in demand are not random. They
depend on a large number of external factors, such as holidays, product launches, local/national incentives and the like.
Another (Firm B’s Supervisor of Process Automation) elaborated: As we have warehousing limitations, we use a very
detailed short-term forecasting (2–4 weeks) where we Bgrasp^
any available information from the markets (e.g. competitors’
pricing deals, delays in material delivery, political signals
from distant markets, production-relevant information for
parts directly provided by our suppliers) to have a better
chance of predicting rather rare, but yet high impact event
that might seriously influence our production/warehousing
operations. Yet, Firm C expanded their utilization of BDA
in planning phase to further predict whether they are capable
of delivering on schedule and within budget: Schedule and
cost planning are always two important issues we try to address with highest priority when starting a production of a
particular product. On one hand, accurate planning provides
us an effective way to estimate the economic value. On the
other hand, particularly concerning the delivery of goods to
Table 3
Inf Syst Front (2018) 20:209–222
Assessing firms’ operations performance and the support from BDA
KPIs for assessing
operations’ performance of the indicator
Value from
utilizing BDA
performance benefits
Better planning due more
comprehensive information;
providing accurate estimates
of order-to-delivery times
Improved prediction of daily
demand fluctuation;
Better prediction of Bblack
Customer satisfaction;
Operating expenses
Firm A Firm B Firm C
Schedule and cost
Extent to which a firm is
capable of delivering on
schedule and within budget.
Capacity utilization
Extent to which a firm is
using its production potential.
Operating expenses
Process downtime
Machine efficiency
Waste reduction
Quality Assurance
Defective units
Rejected units
Extent to which the production
Predicting potential interruptions
Production time;
process is available and running.
in process execution
Operating expenses
Extent to which a particular type
Maximized equipment uptime
Production time
of equipment was used during
by minimizing maintenance and
the production time.
preventing breakdowns
Level to which a firm is able to
Reduce manufacturing waste
Operating expenses
reduce the waste it is generating
to optimize production – lean
as part of its operations.
Number of units produced by the
firm that had defects compared
to the total units produced.
Number of units produced by the
firm that were returned by the
Insights into factors leading to
faulty products
Operating expenses
Preventing returns and rework;
keeping firm image high
Operating expenses;
Performance benefits explained
Production time: The actual time taken to manufacture
Operating expenses: Determines the effectiveness of the firm in keeping operating cost in control
Customer satisfaction: Customers’ overall satisfaction regarding the firm’s product, quality of the product, and level of customer service
the customer, it increases the satisfaction of our customers.
Through a more comprehensive information BDA enable us
to include previously unconsidered events (e.g. cross-demand)
that put a burden on our production line and resulted in not
being able to meet set deadlines and costs (Regional Sales
Within the planning phase both Firms A and C utilized
BDA to minimize process downtime, maximize equipment
efficiency, and reduce production waste. Firm’s A Head of
Research Operations noted: Our manufacturing line has sensors attached to production assets (e.g. assembly machines,
transport bells etc.) that send continuous streams of data
about the assets’ operational conditions to a monitoring station that then analyses them in real-time and detects any problems in the behavior or state of the asset. Once a problem is
detected, a preconfigured action is taken to notify the operator
or take corrective action. Thus, the potential unavailability of
the production process is brought to its minimum. Another
(Firm C’s Technical Production Manager) added: With our
new solution we are monitoring and predicting potential
equipment faults, to avoid or curtail process downtime or to
help prevent faults reoccurring. Specifically implemented sensors are preventing process downtime by detecting changes in
inputs and equipment functioning that could be caused by
unobservable conditions. If left undetected, these changes
cannot only affect individual equipment utilization but bring
whole process down. His colleague (Chief Project Leader)
further emphasized: Besides aiming at having our capacities
fully utilized, our goal was to have as many machines as
possible operating 24/7. To achieve this, the machines had
to be closely monitored and undertake proactive maintenance.
With the ability to closely monitor machines’ technical data in
real-time (e.g. temperature, pressure, power, and other sensor
readings) enabled us to better plan for maintenance and prevent machines from suffering frequent breakdowns. In contrast, Firm B’s manufacturing phase focus was less on improving availability and equipment efficiency (direct process aspects) but more on reducing waste (direct cost aspects): Our
company has long discovered that production resource waste
is a significant factor in operations costs. In fact, with the
implementation of BDA solutions we gradually became able
to reduce the utilization of materials (10–15 %), reduce
Inf Syst Front (2018) 20:209–222
energy (about 5 %), reduce scrap and rework (about 15 %), as
well as reduce manual labor (about 20 %) (Manufacturing
Nevertheless, all three case firms gave merit to quality assurance phase as important predictors of customer satisfaction
and firms’ operating expenses. As such, Firm A was able to
gain better insights into factors leading to faulty products
while firm B was able to further reduce returns and rework,
keeping firm image high. A Warehouse Supervisor in Firm A
noted: It is inherent to the production process to face defects.
With the implementation of BDA we gained an additional
layer of filtering during the inspection process which enabled
us to improve confidence in identifying defective products.
Data, such us production line environmental conditions, operators, task where failures occurred, time/season of failures,
material suppliers, lot numbers, helped us better understand
the reasons behind defects and make more educated guesses
about faulty items before they were dispatched to the customer. A Market Sales Leader from Firm B added: While defective
units identified during the production typically result in sunk
costs or rework costs, an even greater problem is when these
units pass our control mechanisms unnoticed and make it to
the customer. Thus, dismissing potentially problematic items
through the utilization of predictive analytics improves rejection rate by 8–10 %, saving us from additional costs and
worsening firm reputation. On the other hand, Firm C was
able to tackle both issues through BDA utilization.
Moreover, wide use of BDA endorsed informed decision
making and transformed extant organizational capabilities.
Our findings suggested that the more widely the case firms
utilized BDA, the more they improved decision making in
manufacturing operations, resulting in added benefits for all
involved partners (customers, firms themselves). Across the
cases, informants stressed that BDA was pivotal in promoting
employee empowerment, fact-based and real-time decision
making, as well as promoted proactive actions that enabled
improvements in performance management, functional area
excellence, and value proposition enhancements.
Before BDA adoption, in all three firms, the ability to
transform decision making and organizational capabilities
was limited. On the contrary, this flourished after BDA adoption. Detailed descriptions are available in Table 4.
Informants from Firms A and C mainly emphasized how
shifts in employees’ power was transformed in relation to
managing the production phases. A Head of Operations from
Firm A explained the situation before BDA implementation:
People had rather limited powers regarding reconfiguring the
production process as a result of changes in the environment.
Everything had to be approved by their supervisors, particularly additional information from other sources about the
event in question was regularly requested. Through the availability of more detailed, up-to-date, and new insights these
approvals were not needed as much as employees were given
the power to make several decisions (e.g. requesting maintenance, changes in execution etc.) on their own. A Warehouse
Supervisor from Firm C reinforced this point: If anything unplanned happens in the process, we immediately take corrective actions to limit the potential future negative outcomes. We
have the power to do so as well as to decide – since now we
have a more comprehensive view of the reasons leading to the
event – how to reconfigure our operations in the next few
hours after the event that are the most crucial as they bring
the greatest variability in our established procedures.
Power shifts are were also found consistently related to
increased fact-based decision making (across all case firms),
a shift toward more real-time decision making (Firm A and C),
as well as a shift from prevailing reactive actions to unplanned
events to more proactively following the activities (all case
firms). A Market Sales Leader from Firm B elaborated: Our
previous pricing models included some estimated cost categories that could not be fully given a value to. With BDA, this has
changed in a sense that now we have better, more reliable
information about the potential costs that we can readily include in our price estimates. As the business environment is
getting more and more competitive, cost-effectiveness – both
planned and achieved – is very important in our field. A
Regional Sales Manager from Firm C added: We owe it to
our customers and ourselves. To the former, we are obliged
as good partners to provide an honest value for their money,
to ourselves, we are required to know how much can we
Bstretch^ in price competitiveness. Regarding real-time and
proactive decision-making the Supervisor of Process
Automation from Firm B noted: In our process, timely responses to production events are crucial. I believe every major
manufacturing firm agrees. If we see a problem coming, and
now we can frequently even spot it before it occurs, the consequences (both financial as process-related) can be controlled. For example, when a specific machine is about to give
up, several events are there that once carefully analyzed can
help us pinpoint the breakdown with a time window with 70–
80 % probability. This is a huge help for us to immediately
steer the activities as to solve the issues before they become
serious problems. A Chief Project Leader from Firm C added:
We always wished we had a crystal ball – many our problems
resulted from being unable to adequately address what the
data has been saying time before the problem happened. In
fact, with the investment into this new technology [referring to
BDA] we reduced our maintenance and waste costs for about
12,5 % on a year-to-year basis.
Moreover, various organizational capabilities were also improved in regards to BDA implementation and use. A Lead
Operator for the Packaging Operations and a Warehouse
Supervisor from Firm A jointly noted: The new tools we have
significantly added to the way we manage our manufacturing
performance. We now have real-time updating reports, with
possibility to dig deeper into root causes of lower-performing
Table 4
Inf Syst Front (2018) 20:209–222
Manufacturing operations before and after BDA implementation
Firm A – moderate user
Firm B – experimental user
Firm C – heavy user
Before BDA
After BDA
Before BDA
After BDA
Before BDA
After BDA
implementation implementation implementation implementation implementation implementation
Decision making
Power shifts
(empowering employees so that they can
take initiative and make decisions to
solve problems and improve performance)
Fact-based decision making
(relying on a consideration of
operations-related facts when making
Real-time decision making
(making changes in the execution of the
process based on real-time events)
Proactive vs. reactive actions
(actions are not only made as corrective
response to events but also as preventive
Organizational capabilities
Improved performance management
(financial reporting, performance
measurement, dashboards for management
Functional area excellence
Value proposition enhancement
Estimates provided by the case informants during the interviews
tasks, exception analysis, as well as what-if analysis. The informative dashboards, fuelled with huge variety of data, help
us focus on important performance indicator more quickly. A
Chief Project Leader from Firm C added: Now I have a better
overall picture about the process/activity times, maintenance
periods, waste and quality control throughout every production phase. The link between performance indicators across
these phases is conveniently implemented for those of us who
are responsible to make decisions. Yet, not only internal performance, but also functional area excellence and value proposition were enhanced. A Regional Sales Manager from Firm
C noted: Throughout constantly monitoring and correcting
the process we are able to provide our customers a highquality product, delivered on time, and with all agreed characteristics. While our customers don’t really know what is
happening in the Bproduction black box^ of our firm, they
perceive our efforts as being the acceptable reason for price
premiums we charge. A Sales Manager from Firm A emphasized: Each functional area within our firm has a role to play
both in the implementation of the strategy but also in the
design and selection of the strategy. Each functional area,
also manufacturing, has its own strategy which ‘feeds into’
the corporate strategy. This strategy sets out the plan for how
manufacturing is going to do its part to make the corporate
strategy a success. With BDA we are able to make a cleared
contribution in terms of feasibility of achieving a high product
quality levels as emphasized in our corporate strategy.
Table 5 provides a summary of the benefits for the three
case firms from the utilization of BDA in their manufacturing
Overall, findings illustrated that introduction and utilization of BDA leveraged better insights in fundamental aspects
of manufacturing operations, resulting in added benefits for all
case firms. Therefore, we argue that:
Proposition 1: Implementation of BDA added novel insights to key performance areas of manufacturing
4.2 Exploring the underlying mechanisms that link BDA
to improvements in operations
Drawing on critics, who claim that firms only enjoy differential performance when IT is combined with capabilities that
drive comparative advantage (Mithas et al. 2011) and is endorsed by other organizational factors (McLaren et al. 2011;
Oh and Pinsonneault 2007), we delved deeper within our
Inf Syst Front (2018) 20:209–222
Table 5 Benefits from the
utilization of BDA in
manufacturing operations
A – moderate user
More accurate estimation of product
delivery times and budget
Improved prediction of unplanned events
B – experimental user
C – heavy user
Maximization of equipment uptime
through minimization of maintenance
times and breakdowns
Reduction of production waste
Minimization of returned products due
to poor quality
Accurate, comprehensive and real-time
information through informative dashboards
cases to gain richer explanations of factors that may have
influenced differential performance from BDA implementation in our sample. Our investigation surfaced some interesting insights. The three case firms differed in their BDA capability (see Table 6 for details), but also in organizational
design/readiness factors (presented in Table 7).
To begin with, Firm C appeared to have the most advanced
BDA capabilities in place to mobilize best use of BDA and
enjoy the performance benefits. In particular, compared with
Firms A and B, Firm C worked on the full access to data from
various sources, offering an integrative view of the operations,
and timely delivery of mission critical information to the right
people. Both Firms B and C implemented adequate tools for
historical view of business performance (e.g. standard
reporting, ad-hoc reporting, query and drill down), descriptive
analytics (e.g. statistical analysis, sensitivity analysis), and
dynamic, predictive insights (e.g. optimization, simulation,
predictive modelling) and visualization. Firm C also leveraged
employee expertise to identify and prioritize the problems
worth solving.
Moreover, organizational factors seem to have facilitated
better utilization of BDA or subdued its benefits among the
case firms. In Firm C, for instance, they developed a BDA
strategy as a blueprint for BDA implementation. A Chief
Project Leader from Firm C recalled: BDA strategy preceded
our BDA implementation in manufacturing process. We
invested considerable effort into to establish our operations
business vision and identify the supporting BDA capabilities
required to achieve this. Moreover, while top management
only partially supported BDA initiatives in Firm A and B,
within Firm C BDA implementation was fully supported by
top management. A Warehouse Supervisor noted: As our operations are closely tied to costs and customer satisfaction,
our executive level firmly believes we need good information
from each of the production phases as to better estimate production times and costs, capacity utilization, prevent potential
downtimes, reduce waste and secure appropriate quality.
In addition, in Firm C, effective BDA utilization was also
linked to financial resources and the level of employee engagement in the project. A Chief Project Leader in Firm C
argued: The budget to fully introduce BDA was carefully
planned for and secured in yearly financial planning. We
managed to keep the project within the budget. During the
implementation project regular meetings were organized
where employees (managers, specialists etc.) were informed
about the new capabilities as well as actively participated in
the adjustments that needed to be carried out to fine-tune the
operations. On the contrary, a Head of Research Operations
from Firm A recalled: A specific budget was not allotted and
the firm had limited financial resources. We had reserved the
funds for this investment, yet, these were limited as the firm
was restricting new IT investment funding.
Overall, our findings indicate that BDA capability (in terms
of data sourcing, access, integration, and delivery, analytical
capabilities, and people’s expertise) along with organizational
design/readiness factors (such as BDA strategy, top management support, financial resources, and employee engagement)
facilitated better utilization of BDA in manufacturing decision
making, and thus enhanced operations performance. We,
therefore, argue that:
Proposition 2: Distinct BDA-enabled capabilities and organizational design/readiness factors moderate the relationship between BDA implementation and operations
5 Discussion
We contribute to the business value of IT literature by
unpacking how the utilization of BDA changes manufacturing
operations and enables them to perform better (see Fig. 1).
The access to data from various sources is fully
available, offering an integrative view of the
operations, and delivery of mission critical
information to the right people is also timely.
Adequate tools for historical view of business
performance (e.g. standard reporting, ad-hoc
reporting, query and drill down), descriptive
analytics (e.g. statistical analysis, sensitivity
analysis), and dynamic, predictive insights
(e.g. optimization, simulation, predictive
modelling) and visualization are available.
To provide expertise in statistics the firm has 2
data scientists on board, to identify and
prioritize the problems worth solving and the
business relevance of data anomalies and
patterns identified by the data scientists
business analysts are in charge, whereas for
managing IT solutions needed to collect,
clean and process data the firm relies on
technical specialists’ expertise.
The firm has access to all its operations-relevant
data sources, yet, integration of such data only
occurs at certain parts of the process, with
delivery not always being consistent.
Adequate tools for historical view of business
performance (e.g. standard reporting, ad-hoc
reporting) and descriptive analytics (statistical
analysis) are available. Among more advanced
capabilities optimization and simulation tools
are also available.
Source: Interview transcripts
Since BDA implementation considerable attention
was paid to secure a team of experts that provide
statistics expertise, business perspective and
technical expertise to the analysis of data and
identified patterns. When appropriate skills
were missing, the firm readily consulted field
experts to fill the gap.
People’s expertise
Analytical capabilities
The access to data from various sources is fully
available, offering an integrative view of the
operations, and delivery of mission critical
information to the right people is also timely.
Adequate tools for historical view of business
performance (e.g. standard reporting, ad-hoc
reporting), descriptive analytics (e.g. statistical
analysis, sensitivity analysis), and dynamic,
predictive insights (e.g. optimization,
simulation, predictive modelling) are available.
There are not many people with appropriate skills
and expertise. While the firm has enough
technical specialists, it lacks data and business
analysts to bring sense to the data and provide
relevance to identified patterns.
Firm C – heavy user
Firm A – moderate user
Firm B – experimental user
Inf Syst Front (2018) 20:209–222
Data sourcing, access, integration,
and delivery
Table 6
Firm differences in BDA capabilities
Consistent with our theoretical stance in decision making
and resource-based perspectives, our study makes two theoretical contributions. First, we show that utilization of BDA in
manufacturing operations can enhance manufacturing performance. The shift toward BDA-supported performance indicators enables decision makers to utilize additional data in considering different courses of action when pursuing set goals.
Echoing extant studies in operations literature, we find that
when firms utilize more BDA, they better forecast previously
unpredictable outcomes, and improve process performance.
As a result, firms realize operational process benefits in the
form of cost reductions, better operations planning, lower inventory levels, better organization of the labor force and elimination of waste, while they leverage improvements in operations effectiveness and customer service.
Second, drawing on resource-based logic (Ray et al. 2005),
we argue that such improvements in manufacturing operations, driven by increased utilization of BDA, can foster differential performance impacts (Hvolby and Steger-Jensen
2010). However, we warn scholars and practitioners that a
firm’s BDA capabilities (in terms of data sourcing, access,
integration, and delivery, analytical capabilities, and people)
and organizational factors (such as BDA strategy, top management support, financial resources, and engaging people)
can facilitate (or inhibit) effective utilization of BDA in operations, and thus moderate differential performance benefits of
BDA utilization. As such, we extend IT business value literature, which argues that seeking strategic advantage merely by
developing IT capability may not necessarily realize enhanced
performance; organizational design/ readiness factors are critical for effective IT utilization (Hong and Kim 2002; Dezdar
and Sulaiman 2010).
Our results should be interpreted with caution, as it is not
possible to completely rule out alternative explanations. An
alternative explanation for the performance differences across
the three case firms could be differences in firm size. One
could suggest that Firm C (a heavy BDA user) had a larger
system scope for implementation, and hence that size drove
the enhanced use of BDA. Yet, on the flipside, we could also
argue that the larger system size could have made it more
challenging to implement BDA and leverage the operational
benefits of systems integration. In either case, firm size did not
emerge as an alternative explanation through our qualitative
findings. One could also claim that firm age, the industry
sector and location of the firms could have influenced our
results. We, therefore, recommend that future studies control
for firm size, age, and industry sector to account for performance differences attributable to organizational resources,
inter-industry or country differences.
Our case study design also limits our ability to generalize
our results to a wider population of firms. Hence, we recommend that researchers replicate and extend this study to wider
contexts. For instance, we should underline that the change
Employees (managers, specialists etc.) were
brought to the project from its beginnings
and were actively involved throughout the
Engaging employees
Source: Interview transcripts
A specific budget was not allotted and the firm
had limited financial resources.
We had reserved the funds for this investment,
yet, these were limited as the parent company
was restricting new IT investment funding.
Financial resources
Top management support
While there was no explicitly formed strategy,
there was a common understanding about the
need to at least carefully think what the firm
wanted to achieve with BDA and how.
We were eager to try the new technology, yet,
several project implementation group
members tried to Bslow down^ things a bit
in order to give some deeper thought about
what we want and where we want to be in
the next 3–5 years with our BDA.
BDA implementation was partially supported
by top management.
The prevailing opinion was that we needed
better insights into our operations, particularly
for predicting unfavorable future outcomes
within our production process, yet, I still
believe that we could also go by reasonably
well without them.
Firm A – moderate user
Firm differences in organizational design/readiness
BDA strategy
Table 7
A minor budget within IT budget was assigned
for exploring new technological solutions,
yet, as there were many requests from
throughout the firm the part allocated to
BDA was rather small.
We included this technology investment in our
regular yearly plan, but had to limit its
amounts to 50.000 € as other competing
requests for IT investments in other areas
of the firm were heavily influencing the
BDA were implemented and employees were
informed about the upgrade of the system.
Only a short introductory seminar with
handed out minutes was delivered, leaving
many employees not really Bbuying-in^.
BDA implementation was partially supported
by top management.
The current operation functioned well, we did
not have much downtime, complaints, or
frequently under-utilized capacities. In this
sense we see this initiative more as an
exploration test.
There is no BDA strategy. BDA strategy was
seen as an unnecessary waste of resources.
We have no BDA strategy in place, nor have
we thought of needing one.
Firm B – experimental user
During the implementation project regular meetings
were organized where employees (managers,
specialists etc.) were informed about the new
capabilities as well as actively participated in the
adjustments that needed to be carried out to
fine-tune the operations.
BDA implementation was fully supported by
top management.
As our operations are closely tied to costs and
customer satisfaction, our executive level firmly
believes we need good information from each
of the production phases as to better estimate
production times and costs, capacity utilization,
prevent potential downtimes, reduce waste and
secure appropriate quality.
The budget allocated was included in the yearly
budget and was large enough to support the
The budget to fully introduce BDA was carefully
planned for and secured in yearly financial
planning. We managed to keep the project
within the budget.
The firm developed a BDA strategy as a blueprint
for BDA implementation.
BDA strategy preceded our BDA implementation
in manufacturing process. We invested
considerable effort into to establish our
operations business vision and identify the
supporting BDA capabilities required to
achieve this.
Firm C – heavy user
Inf Syst Front (2018) 20:209–222
Fig. 1 Conceptual framework
Inf Syst Front (2018) 20:209–222
BDA capabilities
Organizational Design /
BDA strategy
People skills
Engaging people
associated with implementation and utilization of BDA can be
a costly and risky. In our study, all three firms were, in general,
good performers across various indicators. Further research
should study how BDA utilization influences manufacturing
operations in lower-performing firms. Furthermore, studying
failure cases will add valuable insights. For example, investments in process and IT were shown they can lead, ironically,
to unintended technology traps over time (Grover and
Malhotra 1999), resulting in not only in process agility
(Goodhue et al. 2009) but also rigidity (Galliers and Whitley
In addition, a longitudinal design would be desirable to
further examine the causal dynamics of the relationships
outlined in our conceptual framework. Moreover, further research should delve deeper on the mechanisms that foster
BDA capability building. Future research should extend our
work and examine how other elements, such as firm structure
and people, act together with BDA capabilities in enabling
differential operations performance.
Our results also highlight important managerial implications. To begin with, we recommend increased utilization of
BDA in manufacturing operations, as this can facilitate cost
reductions, improve planning, foster better prediction of
events and enhance customer satisfaction. As more and more
big data technologies are readily available and, thus, not rare
or hard to imitate, investing in these technologies per se is
unlikely to yield differential performance returns against
competitors. Instead, the performance impact is conditional
upon the firm’s …
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