2 Three questions

The impact of big data analytics on firms’ high value

business performance

Aleš Popovič1,2 & 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 phenom-

enon 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 acknowl-

edged infrequently researched context. Using an interpretive

qualitative approach, this empirical study leverages a compara-

tive 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 re-

source based view informed the development of our research

propositions and the conceptual framework that illuminated the

relationships between BDA capability and organizational read-

iness and design. Our findings indicate that BDA capability (in

terms of data sourcing, access, integration, and delivery, analyt-

ical capabilities, and people’s expertise) along with organiza-

tional readiness and design factors (such as BDA strategy, top

management support, financial resources, and employee en-

gagement) facilitated better utilization of BDA in manufactur-

ing 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

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 ex-

periment 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 advan-

tages, 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 perfor-

mance (Chakravarty et al. 2013).

The potential of information systems (IS) to inform decision

making and improve firm performance has long been empha-

sized in the information technology (IT) business value litera-

ture (Davern and Kauffman 2000; Mithas et al. 2011; Melville

* Aleš Popovič

[email protected]

1 Faculty of Economics, University of Ljubljana, Kardeljeva ploščad

17, SI 1000 Ljubljana, Slovenia

2 NOVA IMS, Campus de Campolide, 1070-312 Lisbon, Portugal

3 Brunel Business School, Brunel University London, Uxbridge UB8

3PH, UK

4 School of Management, University of Bradford, Emm Lane,

Bradford BD9 4JL, UK

Inf Syst Front (2018) 20:209–222

DOI 10.1007/s10796-016-9720-4

et al. 2004; Bhattacharya et al. 2010). In firm performance

studies, IS have been reported to support timely decisions, pro-

vide insights that increase comparative advantage, promote in-

novation, and offer a means to manage environmental uncer-

tainty (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 in-

formation, firms are thus increasingly investing in various tech-

nologies and embedding them into their business processes

(Chen et al. 2012).

The hypercompetitive aspects of modern business environ-

ments 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 pro-

cesses to adapt to the market environment (Chen et al. 2014). In

particular, data-driven business analytics are regularly empha-

sized 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 oppor-

tunities associated with data and analysis in different organi-

zations have helped generate significant interest in BDA,

which is often referred to as the techniques, technologies,

systems, practices, methodologies, and applications that ana-

lyze great variety of critical business data to help a firm better

understand its business and market, and make timely and ef-

fective business decisions (Gandomi and Haider 2015;

Mcafee and Brynjolfsson 2012). With an overwhelming

amount of web-based, mobile, and sensor-generated data ar-

riving 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 par-

ticularly important in supporting operational and strategic deci-

sion-making, and enhancing performance (Kiron et al. 2014).

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 organization-

al 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 in-

frastructure flexibility are two important sources for an organi-

zation’s agility (Chen and Siau 2011), our understanding of the

processes and factors enabling, facilitating, or impeding suc-

cessful utilization of BDA in operations, remains limited.

Emphasis is, therefore, increasingly placed on the underlying

mechanisms that link BDA to operations’ agility and perfor-

mance. To address this gap, we conducted a comparative case

study of three manufacturing firms that utilize big data analyt-

ical capabilities in their operations. We explored what a firm

must do right in order to utilize its big data analytical capabil-

ities 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 manufactur-

ing operations can enhance agility and manufacturing perfor-

mance. The shift toward BDA-supported performance indica-

tors enables decision makers to utilize additional data in con-

sidering 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 ef-

fectiveness 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 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 capabili-

ties, and people) and organizational factors (such as BDA strat-

egy, top management support, financial resources, and engag-

ing 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).

210 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 proce-

dure. This is followed by our findings on how the utilization of

BDA affects organizational agility and the underlying mecha-

nisms that link BDA to improvements in operations perfor-

mance. In the discussion section, we explore the contributions

and practical implications of our findings. Finally, some inher-

ent limitations and avenues for future research are given.

2 Theoretical background

Much consideration is currently being paid in both the aca-

demic 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 com-

parison with business (non-IT) resources. Second, since the

resource-based theory establishes a clear link between re-

sources 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, al-

though they can act as key enablers of higher-order organiza-

tional capabilities or interact with other business resources to

increase firm performance.

A significant number of IS scholars support so called medi-

ation 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 organiza-

tional 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 pro-

cesses (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 gener-

ation 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 perfor-

mance is reinforced by the presence of other resources and

capabilities. This rationale incorporated into the stream of re-

search is termed as the contingency approach (Powell and

Dent-Micallef 1997; Ray et al. 2005).

Building on the above theoretical background, our under-

standing of how BDA implementation affects agility and fur-

ther 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 mecha-

nisms that link BDA to improvements in firm performance is

also scarce. These gaps have motivated our research ques-

tions: 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 manufactur-

ing 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 trans-

form 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 rela-

tively 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) 20:209–222 211

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 ac-

count 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 per-

formers in terms of annual revenues and number of em-

ployees. 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 inter-

views 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 permis-

sion of the respondents. The study was longitudinal in respect

that the individuals interviewed had insights of the organiza-

tion before and after the adoption of BDA and were able to

make comparisons and provide information about their

experiences.

3.2 Data analysis

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

BDA supports operations and examined the underlying mech-

anisms that linked BDA to improvements in operations’ agil-

ity. To assess the reliability of the generated open codes, we

then involved a second coder, with substantial qualitative re-

search experience.

Next, we linked related concepts within each case. During

this phase, we examined all conclusions derived from the ini-

tial coding and established links between and among previ-

ously stated categories. We allowed concepts and patterns to

emerge based on the primary data collected, while new cate-

gories were added and others were regrouped with further

analysis (Cassell and Symon 1994). To improve generalizabil-

ity (Firestone and Herriott 1983), as well as to deepen under-

standing and explanation (Miles et al. 2014), we then com-

pared each category and its properties across cases. Our main

objective was to compare and contrast changes in the opera-

tions among the three case firms. To evaluate the reliability of

each dimension, we first involved the second coder. All dis-

agreements 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 trans-

formed operations in the three case firms. Second, we uncover

the underlying mechanisms that link BDA to improvements in

operations.

4 Findings

4.1 Changes in operations with the utilization of BDA

In response to our research question, we examined how the

introduction and utilization of BDA has transformed opera-

tions in the three case firms. We found that utilization of BDA

Table 1 Overview of the case firms

Firm Year founded Manufactured goods (primary products) Number

of employees

Annual Revenue Year when BDA was

implemented

Firm A 1958 Buildings materials and construction systems 422 105.6 million € Partially in 2012,

finalized in 2013

Firm B 1954 Prescription pharmaceuticals, non-prescription

products and animal health products

4607 664.6 million € Early 2014

Firm C 1950 Home appliance 4112 1116.3 million € 2014

Source: Agency for Public Legal Records and Related Services; data obtained from 2013 Audited Annual Report database

212 Inf Syst Front (2018) 20:209–222

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. sched-

ule 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 ad-

ditional 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 availabil-

ity 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 reduc-

ing 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, fur-

ther 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 irreg-

ular 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 holi-

days, product launches, local/national incentives and the like.

Another (Firm B’s Supervisor of Process Automation) elabo-

rated: 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 ad-

dress 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 2 Respondents’

characteristics Firm Respondents Years in the

industry

Years working

for the firm

Firm A Sales Manager 8 6

Head of Research Operations 11 8

Lead Operator for the Packaging Operations 7 5

Warehouse Supervisor 6 3

Firm B Market Sales Leader 10 7

Manufacturing Specialist 8 4

Head of Research and Development 15 14

Supervisor of Process Automation 13 13

Diagnostic Laboratory Specialist 5 5

Firm C Regional Sales Manager 12 7

Technical Production Manager 16 16

Chief Project Leader 7 3

Warehouse Supervisor 9 9

Inf Syst Front (2018) 20:209–222 213

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

Manager).

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 sen-

sors 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 sta-

tion that then analyses them in real-time and detects any prob-

lems 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 sen-

sors 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 pre-

vent machines from suffering frequent breakdowns. In con-

trast, Firm B’s manufacturing phase focus was less on improv-

ing availability and equipment efficiency (direct process as-

pects) 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

Table 3 Assessing firms’ operations performance and the support from BDA

KPIs for assessing

operations’ performance

Explanation

of the indicator

Value from

utilizing BDA

Potential

performance benefits

Firm A Firm B Firm C

Planning

Schedule and cost

variance

Extent to which a firm is

capable of delivering on

schedule and within budget.

Better planning due more

comprehensive information;

providing accurate estimates

of order-to-delivery times

Customer satisfaction;

Operating expenses

✓

Capacity utilization Extent to which a firm is

using its production potential.

Improved prediction of daily

demand fluctuation;

Better prediction of Bblack

swans^

Operating expenses ✓ ✓ ✓

Manufacturing

Process downtime Extent to which the production

process is available and running.

Predicting potential interruptions

in process execution

Production time;

Operating expenses

✓ ✓

Machine efficiency Extent to which a particular type

of equipment was used during

the production time.

Maximized equipment uptime

by minimizing maintenance and

preventing breakdowns

Production time ✓ ✓

Waste reduction Level to which a firm is able to

reduce the waste it is generating

as part of its operations.

Reduce manufacturing waste

to optimize production - lean

Operating expenses ✓ ✓ ✓

Quality Assurance

Defective units Number of units produced by the

firm that had defects compared

to the total units produced.

Insights into factors leading to

faulty products

Operating expenses ✓ ✓

Rejected units Number of units produced by the

firm that were returned by the

customer.

Preventing returns and rework;

keeping firm image high

Operating expenses;

Customer

satisfaction

✓ ✓

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

214 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

Specialist).

Nevertheless, all three case firms gave merit to quality as-

surance 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, op-

erators, 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 custom-

er. 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 rejec-

tion 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 adop-

tion. 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, particu-

larly additional information from other sources about the

event in question was regularly requested. Through the avail-

ability 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 mainte-

nance, changes in execution etc.) on their own. A Warehouse

Supervisor from Firm C reinforced this point: If anything un-

planned happens in the process, we immediately take correc-

tive 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 catego-

ries 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 in-

clude 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 re-

sponses 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 con-

sequences (both financial as process-related) can be con-

trolled. 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 im-

proved 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

Inf Syst Front (2018) 20:209–222 215

tasks, exception analysis, as well as what-if analysis. The in-

formative 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 produc-

tion 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 per-

formance, but also functional area excellence and value prop-

osition 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 high-

quality product, delivered on time, and with all agreed char-

acteristics. 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 empha-

sized: 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

operations.

Overall, findings illustrated that introduction and utiliza-

tion 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 in-

sights to key performance areas of manufacturing

operations.

4.2 Exploring the underlying mechanisms that link BDA

to improvements in operations

Drawing on critics, who claim that firms only enjoy differen-

tial performance when IT is combined with capabilities that

drive comparative advantage (Mithas et al. 2011) and is en-

dorsed by other organizational factors (McLaren et al. 2011;

Oh and Pinsonneault 2007), we delved deeper within our

Table 4 Manufacturing operations before and after BDA implementation

Firm A – moderate user Firm B – experimental user Firm C – heavy user

Before BDA

implementation

After BDA

implementation

Before BDA

implementation

After BDA

implementation

Before BDA

implementation

After BDA

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

decisions)

✓ ✓ ✓ ✓ ✓ ✓

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

activities)

✓ ✓ ✓

Organizational capabilities

Improved performance management

(financial reporting, performance

measurement, dashboards for management

reporting)

✓ ✓ ✓ ✓

Functional area excellence ✓ ✓ ✓ ✓

Value proposition enhancement ✓

Estimates provided by the case informants during the interviews

216 Inf Syst Front (2018) 20:209–222

cases to gain richer explanations of factors that may have

influenced differential performance from BDA implementa-

tion in our sample. Our investigation surfaced some interest-

ing insights. The three case firms differed in their BDA capa-

bility (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 op-

erations 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 pro-

duction 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 en-

gagement 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 manage-

ment 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 or-

ganizational design/readiness factors moderate the rela-

tionship between BDA implementation and operations

performance.

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).

Table 5 Benefits from the

utilization of BDA in

manufacturing operations

Firm

A – moderate user

Firm

B – experimental user

Firm

C – heavy user

More accurate estimation of product

delivery times and budget

✓ ✓

Improved prediction of unplanned events ✓ ✓

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 dash-

boards

✓ ✓

Inf Syst Front (2018) 20:209–222 217

Consistent with our theoretical stance in decision making

and resource-based perspectives, our study makes two theo-

retical contributions. First, we show that utilization of BDA in

manufacturing operations can enhance manufacturing perfor-

mance. The shift toward BDA-supported performance indica-

tors enables decision makers to utilize additional data in con-

sidering 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 in-

ventory levels, better organization of the labor force and elim-

ination of waste, while they leverage improvements in opera-

tions effectiveness and customer service.

Second, drawing on resource-based logic (Ray et al. 2005),

we argue that such improvements in manufacturing opera-

tions, driven by increased utilization of BDA, can foster dif-

ferential 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 man-

agement support, financial resources, and engaging people)

can facilitate (or inhibit) effective utilization of BDA in oper-

ations, and thus moderate differential performance benefits of

BDA utilization. As such, we extend IT business value litera-

ture, which argues that seeking strategic advantage merely by

developing IT capability may not necessarily realize enhanced

performance; organizational design/ readiness factors are crit-

ical 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 perfor-

mance 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 recom-

mend that researchers replicate and extend this study to wider

contexts. For instance, we should underline that the change

Table 6 Firm differences in BDA capabilities

Firm A – moderate user Firm B – experimental user Firm C – heavy user

Data sourcing, access, integration,

and delivery

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.

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.

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.

Analytical capabilities 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.

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.

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.

People’s expertise 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.

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.

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.

Source: Interview transcripts

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