INfo Sys Infra-Article Summary in own words

Agile manufacturing practices: the role of big data and business analytics with multiple case studies

Angappa Gunasekarana*, Yahaya Y. Yusufb, Ezekiel O. Adeleyec and Thanos Papadopoulosd

aSchool of Business and Public Administration, California State University, Bakersfield, CA, USA; bSchool of Management, University of Central Lancashire, Preston, UK; cDepartment of Business Administration, Elizade University, Ilara-Mokin, Nigeria;

dKent Business School, University of Kent, Sail and Colour Loft, The Historic Dockyard, Chatham, UK

(Received 4 July 2017; accepted 9 October 2017)

The purpose of this study was to examine the role of big data and business analytics (BDBA) in agile manufacturing practices. Literature has discussed the benefits and challenges related to the deployment of big data within operations and supply chains, but there has not been a study of the facilitating roles of BDBA in achieving an enhanced level of agile manufacturing practices. As a response to this gap, and drawing upon multiple qualitative case studies undertaken among four UK organisations, we present and validate a framework for the role of BDBA within agile manufacturing. The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives. Further, the level of intervention was found to differ across companies depending on the extent of deployment of BDBA, which accounts for variations in outcomes.

Keywords: big data and business analytics; agile manufacturing; enablers; competitive advantage; performance

1. Introduction

Over the last decades, efforts to improve manufacturing were marked by the emergence of several generic strategies such as total quality management; just in time (JIT); and computerised planning systems, including materials requirements planning, manufacturing resource planning and enterprise resource planning (Nandhakumar 2011). How- ever, market turbulence, characterised by big, sudden successes and failures, continues to threaten success, and the deployment of strategies aiming at manufacturing fewer products (Lampel and Mintzberg 1996; Lewis 2000; Power, Sohal, and Rahman 2001; Jadhav, Mantha, and Rane 2014) or process-oriented improvement techniques such as lean production (Sheridan 1998; Bartezzaghi 1999; Nandhakumar 2011) no longer guarantee profits and market share.

To address this issue, several conceptual methods and techniques, including mass customization, supply chain net- working, manufacturing automation, employee empowerment and concurrent engineering, have been proposed and developed (Gunasekaran 1998, 1999; Sharifi and Zhang 2001; Carvalho, Azevedo, and Cruz-Machado 2012). As busi- nesses continued to experience sudden rises and falls, agile manufacturing came into the limelight in the 1990s as a way out of the problem. It was articulated as the enabler of the timely delivery of innovative solutions ahead of the competition through enterprise-wide integration of a wide range of resource capabilities (Bodine 1998; Gunasekaran 1998; Sharifi and Zhang 2001).

Agile manufacturing stresses simultaneous excellence on a wider range of competitive metrics, especially being first to market with leading-edge solutions that surpass customer expectations and derail competitors’ plans, delivered at the cost of mass production (Fitzgerald 1995; Gunasekaran 1998; Adeleye and Yusuf 2006; Nandhakumar 2011). Agile man- ufacturing helps companies be competitive and thrive in environments where change is continuous and unanticipated (Sarkis 2001; Dowlatshahi and Cao 2006). Literature has highlighted the role of information technology as an enabler in agile manufacturing (e.g. Gunasekaran 1999; Yusuf, Sarhadi, and Gunasekaran 1999; Dowlatshahi and Cao 2006; Dubey and Gunasekaran 2015). In a recent study, Dubey and Gunasekaran (2015) suggest that agile manufacturing is inextrica- bly related to the technologies that can share information effectively and efficiently, enabling organisations to improve dynamic sensing and speed (Elkins, Huang, and Alden 2004). Hence, technologies and information-sharing in particular are crucial for the achievement of agile manufacturing. With the advent of digital technologies, big data and business analytics (BDBA) came to the foreground as an important capability that enables companies to create value from an

*Corresponding author. Email: [email protected]

© 2017 Informa UK Limited, trading as Taylor & Francis Group

International Journal of Production Research, 2018 Vol. 56, Nos. 1–2, 385–397, https://doi.org/10.1080/00207543.2017.1395488

increasingly massive (and unstructured) amount of data, thereby gaining competitive advantage (Chen, Chiang, and Storey 2012). BDBA, according to Wang et al. (2016), comprises two elements: (i) big data and (ii) business analytics. The former term refers to the gathering and processing of data that has the qualities of velocity, variety and volume. The latter term has to do with applying the appropriate methods and techniques to enable decision-making. Within logistics and supply chain management, a number of recent studies have demonstrated the benefits of big data (e.g. Wamba et al. 2015; Wang et al. 2016; Hoffman 2017; Kim and Ahn 2017; Papadopoulos et al. 2017; Zhong et al. 2017) across differ- ent contexts, e.g. manufacturing (Jain, Shao, and Shin 2017), health sector (Wu 2017) and semiconductors (Wang and Zhang 2016). Others have suggested a positive link to firm performance (Ji-fan Ren et al. 2017) and to better decision- making (Tan et al. 2017). This new emergent work suggests that BDBA plays a major role in the agility of an organisa- tion. It does so by providing timely and more accurate information about product demand (demand planning) and by quickly designing and developing an integrated supply chain network, product and process and collaboration among partnering firms. Moreover, BDBA helps in inventory control, transportation, scheduling and quality control at the operational level by having more accurate and timely information for making correct decisions in support of agility.

However, very little has been achieved in understanding the role of BDBA in the establishment of agile practices (Vázquez-Bustelo, Avella, and Fernández 2007; Zhang 2011; Leite and Braz 2016). To this end, the current study addresses the following question: What is the role of BDBA in achieving agile manufacturing? A multiple qualitative case study strategy was followed, which included structured interviews with executives in four organisations. We (i) clarify the definition and relationship between agile manufacturing and BDBA; (ii) propose a conceptual framework that extrapolates particular factors necessary to this relationship; and (iii) discuss this framework through qualitative data.

The organisation of the paper is as follows: the next section discusses the literature on agile manufacturing and the role of big data, and Section 3 discusses the methodology. Section 4 describes our findings and Section 5 compares our findings to the literature. Section 6 concludes the paper and provides future research avenues.

2. The concept of agile manufacturing

Strategic manufacturing approaches such as mass production, lean production, time-based competition and mass cus- tomization have evolved since the 1960s, leading up to agile manufacturing post-2000 as an emergent model for coping with sporadic and turbulent change. The pressures compel an extension of the competitive bases beyond the basic objec- tives of cost and quality to include technology leadership and intense customization. In this regard, the need arises to unfold the manufacturing system in order to marshal new enabling competencies across networks of enterprises, master change and deliver new solutions on a sustainable basis (Bhattacharya 1996; Ward et al. 1998; Mason-Jones, Naylor, and Towill 2000; Oyedijo 2012; Leite and Braz 2016).

Agility, therefore, is the ability to correctly envision change, seamlessly reconfigure operations and offer transparent added value to products and customers (Zhang 2011; Dubey and Gunasekaran 2015). To this end, researchers have dis- cussed several requisite principles, enablers and frameworks, but have highlighted the role of mass customization (Booth and Harmer 1995; Dubey and Gunasekaran 2015).

Mass customization has been articulated as tracking, devising and adding value to current products and customers, offering a wide range of product options in parallel and targeting them to different niche markets and customers (Fitzgerald 1995; Leite and Braz 2016). This is quite unlike mass production, which tenders a few standard products for everyone, and lean production, which offers families of related products in rapid succession (Adeleye, Yusuf, and Sivayoganathan 2000; Power, Sohal, and Rahman 2001). For sustainable mass customization, agility means transparent response to transitions in customer values (Feitzinger and Lee 1997; Thomas 2014).

The intensity of mass customization would differ across product markets, and a continuum – ranging from cosmetic to transparent customization – has been identified, the latter being the goal of agile manufacturing (Lampel and Mintzberg 1996; Fisher 1997; Leite and Braz 2016). Nevertheless, the required resource capabilities are beyond single companies but within the reach of networked companies operating as virtual resource coalitions (Browne, Sackett, and Wortmann 1995). In addition, the knowledge of the intensity of market turbulence and the range and depth of deployable agile manufacturing enablers applicable in different contexts are hard to pin down (Vinodh, Devadasan, and Rajanayagam 2008; Zhang 2011).

2.1 A framework for agile manufacturing

Agile manufacturing hinges on particular drivers, enablers and outcomes (Yusuf, Sarhadi, and Gunasekaran 1999; Vázquez-Bustelo, Avella, and Fernández 2007; Oyedijo 2012; Dubey and Gunasekaran 2015). Figure 1 is based on this literature and presents a theoretical framework of the expected relationship among market turbulence, enablers of agile

386 A. Gunasekaran et al.

manufacturing, competitive objectives and performance outcomes, which are central to agile manufacturing. We propose that BDBA provides the basis for identifying and deploying agile manufacturing enablers (Bartezzaghi 1999; Mason- Jones, Naylor, and Towill 2000) towards the positive attainment of competitive objectives and business performance outcomes (Gunasekaran 1999; Sharifi and Zhang 2001; Zhang 2011), subject to market turbulence.

2.1.1 Big data and business analytics

The role of BDBA within business has been highlighted, especially when considering its elements – that is, volume, velocity and variety (Zhou et al. 2014; Duan and Xiong 2015), or, according to Wamba et al. (2015), volume, velocity, variety, veracity and value. Volume has to do with the amount of generated data, whereas velocity refers to the rate at which data are generated. Variety is a characteristic describing the different formats of data, and veracity refers to the unpredictability with regards to reliable predictions, while value is the benefits to be accrued through the use of big data. Literature has outlined the benefits of BDBA (Wamba et al. 2015; Akter et al. 2016; Dubey et al. 2016; Wang et al. 2016; Amankwah-Amoah 2016; Matthias et al. 2017), stating that the predictive power of big data (through business analytics) is about using the power of statistics to forecast future events based on what has occurred in the past. Tools include, for instance, regression modelling, decision trees, Bayesian statistics, neural networks, Support Vector Machine and nearest neighbour algorithms (Oztekin 2017). Literature has discussed BDBA as a capability that leads to better per- formance and relies on bundling strategic resources (Akter et al. 2016; Wang et al. 2016; Wamba et al. 2017). In a recent study, Dubey et al. (forthcoming) discussed the impact of BDBA on improving social and environmental sustain- ability, while others have explored the impact of BDBA on performance and related contextual factors (Akter et al. 2016; Gupta and George 2016; Gunasekaran et al. 2017; Wamba et al. 2017). However, the impact of BDBA on agile manufacturing has not been explicitly studied in the literature, giving us the impetus for this paper.

2.1.2 Enablers of agile manufacturing

The enablers of agile manufacturing, the tools and resource competencies defend competitive objectives against the per- turbing influence of market turbulence (Almahamid, Awwad, and McAdams 2010; Oyedijo 2012; Zhang et al. 2017). To this end, literature has focused, inter alia, on transparent customization, enterprise-wide supply chain networking, total employee empowerment, intelligent automation and technology integration (Gunasekaran 1999; Yusuf and Gunase- karan 2002) as enablers of agile manufacturing. Oyedijo (2012) identified and justified four enablers: organisation, peo- ple, technology and planning. Likewise, Dubey and Gunasekaran (2015) validated six constructs: technologies, employee empowerment, customer focus, supplier relationships, a flexible manufacturing system and organisational culture.

Transparent customization as an agility enabler strives for significant added value, in contrast to cosmetic customiza- tion, with little or no significant enhancements, or lean product development, which could add more to costs than to rev- enue (Fisher 1997; Thomas 2014). In the pharmaceuticals sector, many manufacturers now supply single-dose packages for midday administration, soluble tablets and blister packs for vulnerable users and nursing homes. In food processing,

Figure 1. Theoretical framework for agile manufacturing.

International Journal of Production Research 387

as well, products now come in a bewildering variety of sizes, packs and variations: diet, low sodium, decaffeinated, kid’s size and so forth. A wider range of options delights customers and creates the potential for higher sales, market share and profits. What is important, however, is the uniqueness or transparency of innovation contained in each product option.

In support of this strategy, manufacturers seek programmable, intelligent automation to enable a wider range of machining and assembly operations without the changeover costs of flexible automation. This is in addition to efforts to leverage knowledge and compress cycle times by building stakes in agile supply chains for opportunistic response while also investing in the total empowerment of employees. Apart from a few studies on the partial impacts of some specific agile manufacturing enablers, their joint or interaction effects when deployed, relative to the extent of market turbulence, were yet to be fully investigated (Zhang 2011; Oyedijo 2012). Yet quantification and total agile design are crucial to sustainable deployment in practice (Vinodh, Devadasan, and Rajanayagam 2008; Vinodh, Sundararaj, and Devadasan 2009; Jadhav, Mantha, and Rane 2014; Routroy, Potdar, and Shankar 2015).

2.1.3 Competitive objectives

Competitive objectives consist of the set of values delivered to customers, seven of which are widely discussed (Vokurka and Fliedner 1997; Ward et al. 1998; Yusuf, Sarhadi, and Gunasekaran 1999; Ling 2000; Islam and Karim 2011). They are low-cost, quality, speed, dependability, product customization, volume flexibility and leadership in new technology products. If the agility enablers are correctly identified and deployed, it is possible to minimise trade-offs, score simultaneously on them and compete on all fronts (Ward et al. 1998). This ability is invaluable in shielding the business from the perturbing influence of market turbulence (Gunasekaran 1998; Ling 2000; Sharifi and Zhang 2001; Almahamid, Awwad, and McAdams 2010).

2.1.4 Business performance

Business performance is the barometer for measuring business success – both financial and non-financial. The most popular measures are sales turnover, net profit, market share, proportion of sales turnover from new products, customer loyalty based on repeat orders and performance relative to competitors (Ling 2000; Islam and Karim 2011).

Competitive objectives impact business performance (Bodine 1998; Ling 2000). Put simply, any cost savings in space or material utilisation benefits the end customer through a lower price and translates into higher sales and profit. Likewise, enhanced quality (competitive objective) motivates customer confidence, hence, higher sales, market share and profit.

Business performance is connected back to BDBA. This may occur, for instance, when financial and non-financial outcomes as well as sustainability in terms of consistent innovation and growth that respects resource use and environ- mental stewardship need to be used for BDBA (Ocampo and Ocampo 2015). Therefore, we propose that BDBA is the originating point for our theoretical framework.

2.1.5 Market turbulence

Market turbulence is generally perceived as the driving force for agile manufacturing. Four sources of market turbulence have been widely discussed – global competition, new technology, customised solutions and new product introduction (Browne, Sackett, and Wortmann 1995; Booth and Harmer 1995; Vokurka and Fliedner 1998; Dubey and Gunasekaran 2015). However, the intensity of market turbulence differs across companies, hence, the level of required agility will be context-specific (Zhang 2011).

2.1.6 Research objectives

There is a paucity of literature discussing the role of BDBA in achieving agile manufacturing. In particular, the literature has not discussed the effect of BDBA on the enablers of agile manufacturing that lead to competitiveness and perfor- mance. Therefore, our suggested framework lacks prior empirical validation, while the link between BDBA and various agility enablers remain poorly understood (Sarkis 2001; Vinodh, Sundararaj, and Devadasan 2009; Zhang 2011). We address this gap in the literature using a qualitative multiple case study strategy, which is presented in the following sections.

388 A. Gunasekaran et al.

3. Methodology

In-depth data were collected from four manufacturing companies in the UK The aim was to conduct more in-depth and context-specific study of BDBA, agile manufacturing practices and the associated competitive and business performance outcomes (Eisenhardt 1989; Collins and Cordon 1997).

The case study as a research strategy was selected because of its ability to study a phenomenon in real conditions, and especially as the phenomenon and its context (turbulent environment) was not clearly evident (Yin 1981; Ketokivi and Choi 2014). Over the years, there has been a significant increase in the use of case study, especially for theory building (Ketokivi and Choi 2014). Pagell and Wu (2009) have illustrated the importance of the case study method in their seminal paper where they discussed how to build a more complete theory of sustainable supply chain management. To avoid any bias, this research paid attention to the context and selection of participants, data collection and analysis (Rymaszewska, Helo, and Gunasekaran 2017). All the selected cases are in the manufacturing sector and have been undergoing a period of shifting and adopting BDBA. However, they were also selected on the basis of their different stages of involvement in BDBA for agile manufacturing and diverse environments (contexts) in terms of their turbu- lence. Thus, their selection would help understand the role of BDBA within agile manufacturing.

Initial contacts were made via a letter addressed to the CEOs. The letter explained the purpose of the study and pro- mised useful feedback on the results as well as confidentiality (Nachmias and Nachmias 1992). Some case study materi- als were thereafter mailed to the appointed facilitators and were returned to the researchers ahead of the plant visits, which were conducted by a team of two researchers. Activities during the visits started with structured interviews, a guided walk through the plants, observations and the collection of available published materials (Vázquez-Bustelo and Avella 2006). In two of the four companies, the main interviews were held with the CEOs, while they involved two senior managers in manufacturing in the two other companies.

The main tool consisted of two structured interview documents. The first structured interview document consisted of 11 questions directed at the head of manufacturing, while the second contained 6 questions for two other departmental heads outside the manufacturing function. The first focused on agile manufacturing initiatives, while the second investi- gated work process changes in other departments. Both documents included questions on how BDBA has enabled them to start a journey towards agile manufacturing and transformation.

The responses of the interviewees were tape recorded and transcribed verbatim. The analysis followed the thematic analysis method by Miles and Huberman (1994). Due to the nature of the study, which included a relatively small num- ber of interviews, and following Rymaszewska, Helo, and Gunasekaran (2017), the coding was performed manually on the hard copy printouts of the interview transcripts (Saldaña 2015). This way of coding was guided by our framework (informed by the literature review) and by our aim to explore the relationship of BDBA and agile manufacturing. The coding was conducted by the third author, while the other authors (first, second and fourth) did additional coding, con- firming and ensuring the reliability of the codes. In cases of minor disagreements, the codes were further discussed until agreement was reached. In this vein, reliability of the analysis as well as validity (checking interview data with reports by the companies) was secured.

3.1 Company profiles

Table 1 presents the profile report of the companies studied. Company A was founded in 1961 as an edible salt packag- ing business, but has extended to a range of edible and non-edible products. Twenty-five per cent of current business is in packaging salt for dishwater. The company has three factories, but only one was visited. The site visited has annual sales of over £11 million and 130 workers.

Company B is a leading manufacturer of refrigerated cooling systems for vegetable crops storage. Its products were designed for long term and bulk storage of potatoes, onions and vegetables to keep quality close to harvest condition.

Table 1. Brief profile of companies studied.

Plant visited Founded Sales (million) Workers Major business

A 1961 Over £11 130 Packaging food/non-food products B – £45 50 Refrigerated cooling system for bulk storage of potatoes/vegetables C – £320 550 Tablet and liquid pharmaceuticals D 1991 £81 230 Manufacture of medical and electro-surgical equipment

International Journal of Production Research 389

The company has two sites but only one was visited. The site studied had 50 employees and an annual turnover of over 45 million pounds.

Company C is a chemical and pharmaceutical products manufacturer, trading in more than 130 countries, with staff strength of over 57,000 and sales turnover above £13 billion. The site visited has an average annual sales turnover of about £320 million and a workforce of 550. The plant makes 15 basic products, mainly tablets, reconstituted granules, capsules and chemicals in about 50 different formulations. Seventy-five per cent of turnover was exported.

Company D manufactures specialist medical utilities, such as electro-surgical equipment, operating tables, autoclaves and pumps. The products had over 180 models, all manufactured to stock. Operations were in low volumes and con- sisted of the manual assembly of hundreds of components into life equipment used by hospitals and surgical laborato- ries. The leading model was launched in 1991, and it currently accounts for 10% of sales. Annual sales turnover is about £81 million pounds, and employee strength is 230. Major customers are NHS Trusts and independent distributors around the world.

4. Data analysis

The section presents the results of the study. The analysis is structured around the role of BDBA within agile manufac- turing and, in particular, its influence on agile manufacturing enablers, competitive objectives and performance out- comes, considering market turbulence (Table 2).

4.1 Implementation of the agility enablers

Following the literature, the study looked for agility enablers, including system design, supply chains, manufacturing technology and organisational empowerment. This section presents case-by-case analysis and a summary of the level of adoption of the agility enablers in Table 2.

4.1.1 Company A

The factory in Company A consists of simple machines arranged sequentially into workstations. Equipment changeover times and costs were relatively high, and in effect, low volume production of customised labels was relatively expen- sive. Manufacturing technologies were not elaborately applied, but the CEO claims that the principles underlying their use were understood and applied. The CEO claimed, ‘There is nothing really high-tech in all that we do’.

Supply chain practices are defined by open competitive tendering and punitive rationalisation underpinned by the ‘ability to supply specifications and cost’. Employee empowerment was weak, as top management controls routine pro- cesses. The CEO concurred that ‘Top managers are responsible for everything’.

The foregoing discussion shows that Company A has been relatively weak in deploying BDBA for the agile manu- facturing enablers. Specifically, enhancements to product features and data used focused on size and shape (cosmetic customization) rather than technical functions (transparent customization).

4.1.2 Company B

The factory in Company B, the refrigerated cooling solutions provider, consists mainly of small-scale and general- purpose machines, aside from a semi-automatic band saw that was bought recently. Separate but interdependent

Table 2. Summary of the level of deployment of the agile manufacturing enablers.

Agile manufacturing enablers

Companies

A B C D

1. Intelligent automation Modest High High Modest 2. Total empowerment of workers Low High Very high Modest 3. Agile supply chains Modest High Excellent Low 4. Mass customization Modest Very high Very high Modest 5. Operational flexibility Low High Very high Modest

390 A. Gunasekaran et al.

workbenches engage in cutting, shaping, filing, painting, imprinting, wiring, assembly and testing, each of which is operated by highly skilled engineers and technicians. To enhance the efficiency of stocks and parts costing and control as well as job tracking and scheduling, a proprietary production management system, Job Boss, was purchased recently.

On worker empowerment, the company stressed team spirit among employees, scoring highly on the quantitative measures of the power of team members relative to team leaders, on-the-job training, free flow of knowledge among workers, individual responsibility for job completion and quality, implementation of suggestions and responsibility-based wages. A weekly production control meeting that specifies the rolling production sequence over the next two to three weeks was the strongest tool for coordination and communication.

The company nurtured customer and supplier confidence and rapport through several means, such as emails, a quar- terly newsletter and the website. These were in addition to some advertising and editorials in specialist media and exhi- bitions in national and international events. The company opined, however, that reaching out to suppliers and sales agents was much easier than reaching out to end users and customers, and they were harnessing BDBA to this purpose: ‘We build a stronger relationship with our suppliers as a means of shortening lead times, and improving delivery relia- bility and certainty’.

On mass customization, the company had started customising its own component labels as a means of enhancing value added. Furthermore, stock control, parts costing, production planning, dispatching and invoicing were being reor- ganised in support of the shift from a project-based system to a more flexible batch processing system. As such, the pro- cesses of metal cutting, painting and wiring were to be automated soon. According to the CEO, automation would evolve in the light of new emphasis on fully packaged, factory-built systems combining refrigerated cooling with ambi- ent ventilation and air mixing. The overriding determinant will be ‘Time saving and payback in relation to volume and employees’ costs’.

4.1.3 Company C

In Company C, the factory consists of several product line families of related tablets and reconstituted granules, inte- grated into flow lines connected by pipes for moving in-process materials. The range of skills within each line was high.

Manufacturing automation is significant:

(a) The use of key-on, key-off computerised control panels to monitor quality, safety and tablet force information every 2 h.

(b) The use of a camera system in the packaging lines, which detected and rejected blisters and other packages con- taining missing and under-counted tablets.

(c) Examination of in-process tablet samples every 15 min.

The company emphasised demand planning and control, especially through the use of BDBA, and in particular:

(a) Dedicated staffs in charge of demand management for specific products. (b) The SAS BPCS software bought last quarter but still being modified. (c) The use of a computerised Conformance Measures Status Chart. (d) A modest amount of contingency stocks in anticipation of winter disasters, and (e) A large stock of materials as well as pre-printed cartons and user instructions.

Employee empowerment was total. Operators and technicians were considered the most crucial asset, trained in mul- tiple skills, including BDBA, and motivated for teamwork to handle several support jobs while on the line. There was the evidence of suggestion schemes, reward for project completion, performance-based pay, ownership through share- holding and career stability.

As well, supply chain practices featured electronic enquiries, open competitive tendering, long-term contracts and long delivery lead times, but limited collaboration in design and manufacture. Approved supplier status had been intro- duced as well as several customer-focused initiatives, including:

(a) ‘Establishment of a new Communication department, which is to ensure that the company is more closely aligned with the changing NHS’.

(b) ‘Creation of two special units for sales and marketing in new franchises’. (c) Appointment of an administrator to manage demand for named patent supplies of a recently launched new chem-

ical entity in the field of HIV.

International Journal of Production Research 391

Direct relationship with competitors is now being compelled by ‘Global initiative to reduce cost in the purchase of materials and engage in collaborative research’.

On mass customization, the company is committed to paced introduction of advanced formulations ahead of com- petitors and protecting them with patents. Packages and blisters have been customised for patient convenience rather than attraction, even as some generic and cheaper formulations are now available for lower income nations. This is in addition to emphasis on routing, operational and capacity flexibility within the limits of regulatory controls such as total batch segregation. An informant stated, ‘We use job sequencing to minimise machine changeovers, which took between 24 to 30 h, but have been replaced by complete off-line re-tooling’.

4.1.4 Company D

The factory of Company D, the specialist medical equipment manufacturer, is operated as a job shop and is structured into two major units: the operating tables unit and the electro-surgical assembly unit. They both run as low-volume batch processes, with operations terminating in an offline assembly and testing unit. Within each unit, the company also repairs and services products already sold to customers.

The company scored relatively low on process automation, because ‘the products are complex and cannot easily be automated to a high degree’. However, further questioning revealed that certain processes would be automated apprecia- bly after current efforts to simplify product design and assembly through ‘kit component parts’. In addition, automated storage and picking systems as well as in-house automated production of printed circuit boards (PCB) were being evalu- ated.

Employees were highly educated and skilled in BDBA, but team disposition was low due to a compartmentalised structure and the putting of round pegs in square holes. For instance, an accountant was said to have headed manufac- turing until recently. In addition, material stores were decentralised on a product basis and poorly organised and docu- mented, while overall authority for stores rested in the head of personnel. The interviews revealed mutual suspicion, lack of trust, limited functional integration, a culture of ‘us’ and ‘them’, and emails being used as an instrument of ‘trench warfare’.

Supply chain collaboration with suppliers, customers and competitors was miserable, and therefore any mutual BDBA attempts were futile. The company has seven major competitors in the UK and several others in Europe, but no direct relationship of any form. The same applies to major customers who were independent distributors around the world and NHS Trusts in the UK. Nevertheless, NHS databases were remotely accessed for contracts available and bid specifications. On the NHS as the most valued customer, a principal informant stated that ‘bureaucracy does not allow for personal relationships’.

Supplier numbers are often rationalised and new agreements entered into, in line with new product development ini- tiatives. A Direct Alliance Scheme under which quality assurance personnel will visit and monitor implementation of specifications in major supplier companies was being planned.

The following extracts from interview reports reveal several supply chain problems:

• ‘Recently, two major projects ran out of control, partly due to software design problems with sub-contractors’. • ‘Emphasis on a better quality of suppliers limits options and leads to a greater reliance on Grade 1 core suppliers, who now demand better and closer support’.

The preceding report on the degree of implementation of the agile manufacturing enablers shows significant efforts in all the companies to cope with market turbulence and survive rather than perish. However, greater progress was reported for B and C, even as robust plans to implement the agile manufacturing enablers of competitive advantage were reported. To this extent, Hypothesis 2, which proposes that there is no relationship between the intensity of market turbulence and the deployment of agile manufacturing enablers, is not justifiable, and is hereby rejected.

4.2 The impact of the agile manufacturing enablers

The case studies collected data on the current attainment and future aspirations on seven competitive objectives. The companies generally returned high levels on some, while future aspirations for all were high (Table 3).

Also, Table 4 presents the degree of market turbulence (low vs. high) for each of the companies (and, hence, case studies).

Focusing only on the two extremes of low-end and high-end attainment, all four companies were identified with high-end attainments on a range of competitive objectives.

392 A. Gunasekaran et al.

The competitive objectives and performance were realised after the implementation of enablers. Table 5 reports the direction of change in performance outcomes after the adoption of the agile manufacturing enablers to achieve competi- tive objectives. Companies B and C that returned high-end current attainment as well as high-end future aspirations on a wider range of competitive objectives in Table 3 are shown in Table 4 to have experienced a sharp increase in the last five years on several business performance outcomes. This differs sharply from the evidence facing A and D.

Some other case study reports on Company B show that the implementation of agile initiatives has positively impacted reliability and flexibility, hence, the value added to throughput, repeat orders, sales turnover, net profit and return to shareholders has been positive in the last five years. In Company C, as well, there is more convincing evidence of swift responses to large order volumes during epidemics, scientific breakthroughs and regulatory controls through agile manufacturing initiatives. Consequently, sales turnover, net profit, market share, value added to throughput, asset utilisation and expansion prospects have been positive over the last five years. One principal informant in Company C opined, ‘Fortunately, on average, we have been on the winning side, not on the losing side’.

Table 3. Companies’ current attainments and future aspirations.

Competitive objectives

Companies’ attainment Companies’ aspirations

Low end High end Low end High end

1. Lower production costs A, D B, C B, C, D 2. First to market with new products A, B A, B, C, D 3. Leading technology products A, C A, B, C 4. Better after sales/tech support D A, C A, C, D 5. Superior quality of design D B B, C, D 6. Higher quality conformance B, C B, C, D 7. Swift response to demand surges C A, C, D

Table 4. Degree of market turbulence (low vs. high) for each of the companies.

Company

Degree of market turbulence

Low High

A √ B √ C √ D √

Table 5. Attainment on business performance outcomes.

Direction of change in the last five years Sharp decrease Sharp increase

Sales turnover D B Profit after tax A, D B Market share D Customer loyalty (repeat orders) D B Value-added to throughput B, C Utilisation of fixed assets, e.g. machines D C Returns to company owners B Process and product innovation C Growth and expansion prospects A, C Support to community or environment B Performance relative to competitors D

International Journal of Production Research 393

5. Discussion

Successful deployment of agile manufacturing requires acknowledging the role of BDBA in harmonising and applying the whole range of agility enablers. Our previous discussion illustrates that Companies B and C implemented a wider range of initiatives. Responses to the questions on agile initiatives towards enhanced operational flexibility show that Companies B and C are more consistent over the whole range.

• Contingency inventories [Companies A, B, C and D]. • Customer site and in-plant assembly in parallel [Company B]. • JIT purchasing and scheduling [Companies B and C]. • Demand planning and forecasting [Companies B and C]. • Complete offline retooling [Company C]. • Flexible supply chain [Company C]. • Dedicated communication and marketing [Company C]. • Modular kit parts [Company D]. • Strategic purchasing [Company D].

Anything less than integrative and harmonious deployment would not achieve significant results (Yusuf and Adeleye 2003; Vázquez-Bustelo, Avella, and Fernández 2007; Vinodh, Sundararaj, and Devadasan 2009; Oyedijo 2012). This accounted for the relatively poor results reported for Companies A and D, especially on operational and volume flexibil- ity, which is the agile manufacturing conundrum (Upton 1995; Yusuf, Adeleye, and Sivayoganathan 2003; Thomas 2014). The study therefore advances manufacturing research in that it illustrates that more integrative deployment of BDBA leads to agile manufacturing enablers and greater competitive and business performance outcomes.

Deriving from the foregoing findings, the deployment of BDBA to agile manufacturing should be underpinned by integration of the appropriate range of enablers. The starting point is to enhance the role of BDBA for the enablers of agile manufacturing. Next is the determination of the impact of BDBA on competitive factors and performance. The role of BDBA needs to be grasped by understanding the role of the market turbulence facing the specific company or indus- trial sector as the basis for understanding whether agile manufacturing is feasible. Before eventual deployment, it is important to compute the interaction index of the agile manufacturing enablers, so that harmony and synergy can be ensured. Therefore, another contribution of this study towards the advancement of manufacturing research is that it high- lights that quantification (Vinodh, Devadasan, and Rajanayagam 2008; Routroy, Potdar, and Shankar 2015) and total agile design (Vinodh, Sundararaj, and Devadasan 2009) would need to be considered (Jadhav, Mantha, and Rane 2014) for increased agility and performance.

From a theoretical point of view, this study contributes to the literature on the role of BDBA for agile manufacturing (Vázquez-Bustelo, Avella, and Fernández 2007; Zhang 2011; Leite and Braz 2016). It underlines the role of big data in achieving agile manufacturing, and competitiveness and performance. In this vein, it adds to the existing literature that states the benefits of BDBA (Wamba et al. 2015; Akter et al. 2016; Amankwah-Amoah 2016; Dubey et al. 2016; Wang et al. 2016; Matthias et al. 2017), highlighting the capability of BDBA that leads to better performance (Akter et al. 2016; Wang et al. 2016; Wamba et al. 2017) through agile practices. Finally, our study contributes to further conceptual- isation of BDBA (Akter et al. 2016; Wamba et al. 2017; Dubey et al. forthcoming) by looking explicitly at the impact of BDBA on agile manufacturing and its enablers. These studies have looked at benchmarking performance with regards to BDBA (mostly looking at BD as a capability that organisations should have or are working towards attaining) but not with reference to agile, where is the focus of this study. Therefore, our study highlights the role of BDBA on achieving agile performance measures, given a particular type of market turbulence (i.e. low vs. high). For instance, our study has highlighted the role of BDBA in achieving agility for companies B and C related to the performance measures of ‘sales turnover’, ‘profit after tax’ and for companies A and C in terms of ‘growth and expansion prospects’. Thus, this study highlights the role of BDBA in performance measures but with a reference to market turbulence.

From a managerial perspective, this study highlights the importance that needs to be paid by managers to BDBA in order to become agile and increase their performance. In particular, managers should invest time and resources in devel- oping tools and methods for understanding and enhancing BDBA within their organisations and their supply chains. Fur- thermore, it is crucial that managers understand the negative role of market turbulence and take appropriate measures in order to be able to predict this through BDBA. Finally, this study highlights to managers particular factors that need to be considered when it comes to BDBA and agile manufacturing, by relating these to business performance outcomes. By adhering to the enablers of manufacturing and harnessing the power of BDBA, managers could enhance agility and performance in their organisations and supply chains.

394 A. Gunasekaran et al.

6. Conclusion

This study investigated the role of BDBA within agile manufacturing. A theoretical framework based on the extant liter- ature was developed and further validated using qualitative data (structured interviews) from four companies.

Two of the four companies studied seem to have been more successful in the use of BDBA for agile manufacturing and operational flexibility. The two companies delivered widely on competitive objectives, and, consequently, over the last five years, sharp increases in business performance measures were documented. In contrast, the two other companies were identified as less positive towards the agility enablers. They did not deliver widely on competitive objectives, and over the last five years, sharp decreases in business performance measures were documented.

Therefore, BDBA plays a major role in the agility of an organisation, helping in gathering and analysing data on product demand (demand planning) and quickly designing and developing a supply chain network, product and process and collaboration among partnering firms. BDBA could also be accompanied by future research on: (i) the Internet of Things (IoT), which enables organisations to have an integrated information system in support of supply chain opera- tions and visibility, and, in turn, flexibility and speed of responding to customer or market requirements. Agility requires real-time information in order to act quickly to ensure the whole supply chain’s activities are completed, so as to reach the market as quickly as possible and with minimum cost and high-quality products and services. (ii) Industry 4.0 includes the IoT, cloud computing and RFID to support automated business processes in manufacturing in order to facil- itate real-time information-sharing along the supply chain and, in turn, support the flexibility and speed of an organisa- tion to respond to changing market and customer requirements. (iii) Blockchain technologies could offer provenance to big data within supply chains, capturing data regarding products, services and supply chain tiers in real time, thereby enhancing supply chain agility.

Disclosure statement No potential conflict of interest was reported by the authors.

References

Adeleye, E. O., and Y. Y. Yusuf. 2006. “Towards Agile Manufacturing: Models of Competition and Performance Outcomes.” Interna- tional Journal of Agile Systems and Management 1 (1): 93–110.

Adeleye, E. O., Y. Y. Yusuf, and K. Sivayoganathan. 2000. “Leanness, Agility and Manufacturing Performance in the UK.” Proceed- ings of the 4th International Conference on Managing Innovative Manufacturing, Aston Business School, Birmingham, July 17–19, 19–27.

Akter, S., S. F. Wamba, A. Gunasekaran, R. Dubey, and S. J. Childe. 2016. “How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment?” International Journal of Production Economics 182: 113–131.

Almahamid, S., A. Awwad, and A. C. McAdams. 2010. “Effects of Organizational Agility and Knowledge Sharing on Competitive Advantage: An Empirical Study in Jordan.” International Journal of Management 27 (3): 387–404.

Amankwah-Amoah, J. 2016. “Emerging Economies, Emerging Challenges: Mobilising and Capturing Value from Big Data.” Techno- logical Forecasting and Social Change 110: 167–174.

Bartezzaghi, E. 1999. “The Evolution of Production Models: Is a New Paradigm Emerging?” International Journal of Operations and Production Management 19 (2): 229–250.

Bhattacharya, A. K. 1996. “Product Market, Turbulence and Time Compression: Three Dimensions of an Integrated Approach to Manufacturing System Design.” International Journal of Operations and Production Management 16 (9): 34–37.

Bodine, W. E. 1998. “Making Agile Assembly Profitable.” Manufacturing Engineering 121 (4): 60–68. Booth, C. L., and M. P. Harmer. 1995. “Agility, the Future for Ceramic Manufacturing.” Ceramic Engineering Science Proceedings

16 (1): 220–225. Browne, J., J. Sackett, and J. Wortmann. 1995. “Future Manufacturing Systems – Towards the Extended Enterprise.” Computers in

Industry 25: 235–254. Carvalho, H., S. G. Azevedo, and V. Cruz-Machado. 2012. “Agile and Resilient Approaches to Supply Chain Management: Influence

on Performance and Competitiveness.” Logistics Research 4 (1–2): 49–62. Chen, H., Chiang, R., and Storey, V. 2012. “Business Intelligence and Analytics: From Big Data to Big Impact.” MIS Quarterly 36

(4): 1165–1188. Collins, R., and C. Cordon. 1997. “Survey Methodology Issues in Manufacturing Strategy and Practice Research.” International Jour-

nal of Operations and Production Management 17 (7): 697–706. Dowlatshahi, S., and Q. Cao. 2006. “The Relationships among Virtual Enterprise, Information Technology, and Business Performance

in Agile Manufacturing: An Industry Perspective.” European Journal of Operational Research 174 (2): 835–860. Duan, L., and Y. Xiong. 2015. “Big Data Analytics and Business Analytics”.” Journal of Management Analytics 2 (1): 1–21.

International Journal of Production Research 395

Dubey, R., and A. Gunasekaran. 2015. “Agile Manufacturing: Framework and Its Empirical Validation.” The International Journal of Advanced Manufacturing Technology 76 (9): 2147–2157.

Dubey, R., A. Gunasekaran, S. J. Childe, S. F. Wamba, and T. Papadopoulos. 2016. “The Impact of Big Data on World-Class Sustain- able Manufacturing.” The International Journal of Advanced Manufacturing Technology 84 (1–4): 631–645.

Dubey, R., A. Gunasekaran, S. Childe, T. Papadopoulos, Z. Luo, S. F. Wamba, and D. Roubaud. Forthcoming. “Can Big Data and Predictive Analytics Improve Social and Environmental Sustainability?” Technological Forecasting and Social Change. doi:10.1016/j.techfore.2017.06.020. http://www.sciencedirect.com/science/article/pii/S0040162517305668.

Eisenhardt, K. M. 1989. “Building Theories from Case Study Research.” Academy of Management Review 14 (4): 532–550. Elkins, D. A., N. Huang, and J. M. Alden. 2004. “Agile Manufacturing Systems in the Automotive Industry.” International Journal

of Production Economics 91 (3): 201–214. Feitzinger, E., and H. Lee. 1997. “Mass Customisation at Hewlett-Packard: The Power of Postponement.” Harvard Business Review

75 (1): 116–121. Fisher, M. L. 1997. “What is the Right Supply Chain for Your Product?” Harvard Business Review 75 (March–April): 105–116. Fitzgerald, B. 1995. “Mass Customisation – At a Profit.” World Class Design to Manufacture 2 (1): 43–46. Gunasekaran, A. 1998. “Agile Manufacturing: Enablers and an Implementation Framework.” International Journal of Production

Research 36 (5): 1223–1247. Gunasekaran, A. 1999. “Agile Manufacturing: A Framework for Research and Development.” International Journal of Production

Economics 62: 87–105. Gunasekaran, A., T. Papadopoulos, R. Dubey, S. F. Wamba, S. J. Childe, B. Hazen, and S. Akter. 2017. “Big Data and Predictive

Analytics for Supply Chain and Organizational Performance.” Journal of Business Research 70: 308–317. Gupta, M., and J. F. George. 2016. “Toward the Development of a Big Data Analytics Capability.” Information and Management 53

(8): 1049–1064. Hoffman, E. 2017. “Big Data and Supply Chain Decisions: The Impact of Volume, Variety and Velocity Properties on the Bullwhip

Effect.” International Journal of Production Research 55 (17): 5108–5126. Islam, M., and A. Karim. 2011. “Manufacturing Practices and Performance.” The International Journal of Quality and Reliability

Management 28 (1): 43–61. Jadhav, J. R., S. S. Mantha, and S. B. Rane. 2014. “Development of Framework for Sustainable Lean Implementation: An ISM

Approach.” Journal of Industrial Engineering International 10: 1–27. doi:10.1007/s40092-014-0072-8. Jain, S., G. Shao, and S.-J. Shin. 2017. “Manufacturing Data Analytics Using a Virtual Factory Representation.” International Journal

of Production Research 55 (18): 5450–5464. Ji-fan Ren, S., S. F. Wamba, S. Akter, R. Dubey, and S. J. Childe. 2017. “Modelling Quality Dynamics, Business Value and Firm Per-

formance in a Big Data Analytics Environment.” International Journal of Production Research 55 (17): 5011–5026. Ketokivi, M., and T. Choi. 2014. “Renaissance of Case Research as a Scientific Method.” Journal of Operations Management 32 (5):

232–240. Kim, K.-J., and H. Ahn. 2017. “Recommender Systems Using Cluster-Indexing Collaborative Filtering and Social Data Analytics.”

International Journal of Production Research 55 (17): 5037–5049. Lampel, J., and H. Mintzberg. 1996. “Customising Customisation.” Sloan Management Review 38 (Fall): 21–3121–31. Leite, M., and V. Braz. 2016. “Agile Manufacturing Practices for New Product Development: Industrial Case Studies.” Journal of

Manufacturing Technology Management 27 (4): 560–576. Lewis, M. A. 2000. “Lean Production and Sustainable Competitive Advantage.” International Journal of Operations and Production

Management 20 (8): 959–978. Ling, X. 2000. “Manufacturing Capability Development in a Changing Business Environment.” Industrial Management and Data Sys-

tems 100 (6): 261–270. Mason-Jones, R., B. Naylor, and D. R. Towill. 2000. “Lean, Agile or Leagile? Matching Your Supply Chain to the Marketplace.”

International Journal of Production Research 38 (17): 4061–4070. Matthias, O., I. Fouweather, L. Gregory, and A. Vernon. 2017. “Making Sense of Big Data – Can It Transform Operations Manage-

ment?” International Journal of Operations and Production Management 37 (1): 37–55. Miles, M., and A. Huberman. 1994. Qualitative Data Analysis: An Expanded Sourcebook. Thousand Oaks, CA: Sage. Nachmias, C. F., and D. Nachmias. 1992. Research Methods in the Social Sciences. 4th ed. London: St. Martins Press Incorporated. Nandhakumar, M. K., N. Ghobadian, and N. O’Regan. 2011. “Generic Strategies and Performance- Evidence from Manufacturing

Firms.” International Journal of Productivity and Performance Management 60 (3): 222–251. Ocampo, L., and C. O. Ocampo. 2015. “A Proposed Sustainable Manufacturing Strategy Framework.” Verslo Sistemos ir Ekonomika

5 (1): 87–98. Oyedijo, A. 2012. “Strategic Agility and Competitive Performance in the Nigerian Telecommunication Industry: An Empirical Investi-

gation.” American International Journal of Contemporary Research 2 (3): 227–237. Oztekin, A. 2017. “Big Data Analytics for Creating a Marketing Strategy in Healthcare Industry.” Annals of Operations Research 1–

XX. Accepted for publication. Pagell, M., and Z. Wu. 2009. “Building a More Complete Theory of Sustainable Supply Chain Management Using Case Studies Of

10 Exemplars.” Journal of Supply Chain Management 45 (2): 37–56.

396 A. Gunasekaran et al.

Papadopoulos, T., A. Gunasekaran, R. Dubey, and S. F. Wamba. 2017. “Editorial: Big Data and Analytics in Operations and Supply Chain Management: Practical Challenges and Management Aspects.” Production Planning and Control 28 (11/12): 873–876.

Power, D. J., A. S. Sohal, and S. Rahman. 2001. “Critical Success Factors in Agile Supply Chain Management. An Empirical Study.” International Journal of Physical Distribution and Logistics Management 31 (4): 247–265.

Routroy, S., P. K. Potdar, and A. Shankar. 2015. “Measurement of Manufacturing Agility: A Case Study.” Measuring Business Excel- lence 19 (2): 1–22.

Rymaszewska, A., P. Helo, and A. Gunasekaran. 2017. “IoT Powered Servitization of Manufacturing – An Exploratory Case Study.” International Journal of Production Economics 192: 92–105.

Saldaña, J. 2015. The Coding Manual for Qualitative Researchers. Thousand Oaks, CA: Sage. Sarkis, J. 2001. “Benchmarking for Agility.” Benchmarking: An International Journal 8 (2): 88–107. Sharifi, H., and Z. Zhang. 2001. “Agile Manufacturing in Practice. Application of a Methodology.” International Journal of Opera-

tions and Production Management 21 (5/6): 772–794. Sheridan, J. 1998. “Pushing Productivity to New Heights.” Industry Week 43 (1): 1–16. Tan, K. H., G. Ji, C. P. Lim, and M.-L. Tseng. 2017. “Using Big Data to Make Better Decisions in the Digital Economy.” Interna-

tional Journal of Production Research 55 (17): 4998–5000. Thomas, E. F. 2014. “Platform-Based Product Design and Environmental Turbulence: The Mediating Role of Strategic Flexibility.”

European Journal of Strategic Management 17 (1): 107–124. Upton, D. 1995. “What Really Makes Factories Flexible.” Harvard Business Review 73 (July): 74–84. Vázquez-Bustelo, D., and L. Avella. 2006. “Agile Manufacturing: Industrial Case Studies in Spain.” Technovation 26 (10): 1147–

1161. Vázquez-Bustelo, D., L. Avella, and E. Fernández. 2007. “Agility Drivers, Enablers and Outcomes: Empirical Test of an Integrated

Agile Manufacturing Model.” International Journal of Operations and Production Management 27 (12): 1303–1332. Vinodh, S., S. R. Devadasan, and D. Rajanayagam. 2008. “Quantification of Agility: An Experimentation in an Indian Electronics

Switches Manufacturing Company.” Journal of Engineering, Design and Technology 6 (1): 48–64. Vinodh, S., G. Sundararaj, and S. R. Devadasan. 2009. “Total Agile Design System Model via Literature Exploration.” Industrial

Management and Data Systems 109 (4): 570–588. Vokurka, R. J., and G. Fliedner. 1997. “Agility: Competitive Weapon of the 1990’s and beyond.” Production and Inventory Manage-

ment Journal 38 (3): 19–24. Vokurka, R. J., and G. Fliedner. 1998. “The Journey toward Agility.” Industrial Management and Data Systems 98 (4): 165–171. Wamba, S. F., S. Akter, A. Edwards, G. Chopin, and D. Gnanzou. 2015. “How ‘Big Data’ Can Make Big Impact: Findings from a

Systematic Review and a Longitudinal Case Study.” International Journal of Production Economics 165: 234–246. Wamba, S. F., A. Gunasekaran, S. Akter, S. Ji-Fan Ren, R. Dubey, and S. J. Childe. 2017. “Big Data Analytics and Firm Perfor-

mance: Effects of Dynamic Capabilities.” Journal of Business Research 70: 356–365. Wang, J., and J. Zhang. 2016. “Big Data Analytics for Forecasting Cycle Time in Semiconductor Wafer Fabrication System.” Interna-

tional Journal of Production Research 54 (23): 7231–7244. Wang, G., A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos. 2016. “Big Data Business Analytics in Logistics and Supply Chain

Management: Certain Investigations for Research and Applications.” International Journal of Production Economics 176: 98– 110.

Ward, P. T., J. K. McCreery, L. P. Ritzman, and D. Sharma. 1998. “Competitive Priorities in Operations Management.” Decision Sciences 29 (4): 1035–1046.

Wu, J., H. Li, Z. Lin, and K.-Y. Goh. 2017. “How Big Data and Analytics Reshape the Wearable Device Market – The Context of E- Health.” International Journal of Production Research 55 (17): 5168–5182.

Yin, R. K. 1981. “The Case Study Crisis: Some Answers.” Administrative Science Quarterly 26 (1): 58–65. Yusuf, Y. Y., and E. O. Adeleye. 2003. “Agile Operations and Simultaneity Concept – An Introduction.” The First International Con-

ference on Performance Measures, Benchmarking and Best Practices, Guimaraes, Portugal, June 11–15. Yusuf, Y. Y., and A. Gunasekaran. 2002. “Agile Manufacturing: A Taxonomy of Strategic and Technological Imperatives.” Interna-

tional Journal of Production Research 40 (6): 1359–1387. Yusuf, Y. Y., M. Sarhadi, and A. Gunasekaran. 1999. “Agile Manufacturing: The Drivers, Concepts and Attributes.” International

Journal of Production Economics 62: 33–43. Yusuf, Y. Y., E. O. Adeleye, and K. Sivayoganathan. 2003. “Volume Flexibility: The Agile Manufacturing Conundrum.” Management

Decision 41 (7): 613–624. Zhang, D. Z. 2011. “Towards Theory Building in Agile Manufacturing Strategies – Case Studies of an Agility Taxonomy.” Interna-

tional Journal of Production Economics 131 (1): 303–312. Zhang, Y., S. Ren, Y. Liu, and S. Si. 2017. “A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Pro-

cesses of Complex Products.” Journal of Cleaner Production 142: 626–641. Zhong, R. Y., C. Xu, C. Chen, and G. O. Huang. 2017. “Big Data Analytics for Physical Internet-Based Intelligent Manufacturing

Shop Floors.” International Journal of Production Research 55 (9): 2610–2621. Zhou, Z. H., N. V. Chawla, Y. Jin, and G. J. Williams. 2014. “Big Data Opportunities and Challenges: Discussions from Data Analyt-

ics Perspectives [Discussion Forum].” IEEE Computational Intelligence Magazine 9 (4): 62–74.

International Journal of Production Research 397

Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.