Benefits and Challenges of Big Data

Industry 4.0, Big Data and Internet of Things (IoT) are continuously attracting the academic scholars towards its opportunities and the challenges. This is indeed very difficult to say anything about whether these advancements are good or bad. These advancements can possibly improve the method of manufacturing or production by meeting the rising demands for mass production. However, the environment that it creates is very complicated indeed. The utilization of Big Data can be effectively done only if it is able to understand the potential leads. There are high chances that most of the data remain as if not being utilized. In such cases, reliance over Big Data while not ensuring the appropriate use of it can only mean a less profitable practice to commence. Apart from productivity, an ever-increasing reliance on ‘Big Data’ can also be questioned for data security (Waller & Fawcett, 2013).

In an Industry 4.0 environment, data are being shared between the interconnected businesses. However, an ethical and appropriate utilization of the data is hard to be realized at this point in time. These few questions must be answered before concluding anything about the benefits of ‘Big Data’ (Saarikko, Westergren & Blomquist, 2017). Opportunities are there; however, a lot depends on how firms across the globe reap the benefits from it. The biggest impact of ‘Big Data’ will expectedly on manufacturing; however, supply chain being one of the parts of it is also expected to be impacted by it. Advancement in the form of ‘Big Data’ can revolutionize manufacturing by providing the insights into maintenance cycles, market trends, customer buying patterns and the targeted business decisions (Wang et al., 2016). Therefore, opportunities can be availed entirely if the list of challenges is mitigated with effective measures.   

The study mainly aims to identify how firms are able to utilize the ‘Big Data’. This is because few of the barriers in regards to utilization of ‘Big Data’ has already been mentioned above. 

There are a few examples of factories which have become the smart factories. However, even those few companies have very fewer clues on how to utilize such large-scale data. Even those few factories probably won’t deny the complexities of ‘Big Data’. In an Industry 4.0 environment, people, process and technology are shared together. This is how such large-scale data is produced. However, it is really challenging to identify or prioritize one data over another. Some of the important data may get missed due to not having a system to conduct the data validation. However, Zhong et al. (2016) have argued that such circumstances can effectively be handled by ‘Apache Hadoop’. Hadoop is an open-source software framework written in Java. According to the authors, the software can help to effectively deal with large data sets. The software contains several modules each having a different role to play. Hadoop Common, Hadoop MapReduce, Hadoop YARN and Hadoop distributed file system (HDFS) are the modules. These modules can help to automatically detect any hardware related failure. Few examples of successful applications have been given as evidence to the effectiveness of the software. Hence, Facebook and Yahoo are those few companies that have been benefitted from using the Hadoop. MapReduce is a programming paradigm. Data processing is programmed here. Few of its applications include web analytics applications, social networks and scientific applications. IBM, Oracle and Microsoft now support the software.

Utilizing Big Data with Different Software Frameworks

Another framework to effectively utilize the large-scale data is IBM’s SPSS Modeler. The software can help in making the decisions to the individuals, groups, systems and enterprise through its forecastable intelligence. The framework can support in making appropriate decisions through its entity analytics and a set of advanced algorithms. The model comes up with embedded techniques on ‘Big Data Analytics’ and is being tested both in case of software and hardware failures. The best part of this framework is that it allows utilizing the right data at right time for the right customers. It means that the framework can help to avoid the data wastes (Leventhal, 2010). The “3C framework” which is currently being used in the IoT environment can be replaced with an improved framework such as the “6C framework”. The current 3C framework is divided into three stages the ‘context’, ‘configuration’ and ‘capability’. However, it fails to address the shared objectives of co-evolving bodies. This can be resolved by adding one stage to the existing 3C framework such as ‘cooperation’ (Rong et al., 2015). ‘Construct’ and ‘change’ will be the other stages need to be included in the existing 3C framework.  The infrastructural developments lack the competency in effectively utilizing the right data. ‘Construct’ and ‘Change’ can give ways to such improvements. Hence, ‘6C framework’ rather than the ‘3C framework’ will be much more effective in helping the firms to fulfill the shared objectives.

Practical implications, feasibility, justification and limitation 

Implications:

As argued by Almada-Lobo (2016), there is numerous definition of Industry 4.0. It is more a logic which firms utilize to enhance their competencies. The use of ‘Big Data’ due to the fourth industrial revolution is more of a shift in manufacturing logic. It is a shift towards a rapidly growing decentralized and self-regulated approach to value creation. The shift has its high impact on manufacturing for meeting the growing for mass production. The change can also impact the supply network by enabling the demand assessment and shortening the cycle times. The benefits can also be observed in terms of highly integrated & transparent supply network. Production planning will also improve. The logistics operation will be majorly impacted as due to real-time information flows, end-to-end supply network transparency and flexibility of operation. Moreover, these benefits can impact the value creation. According to Wang, Törngren & Onori (2015), ‘Big Data’ can be advantageous to developed countries such as Germany. Until recently the country has struggled to reduce the gap between product qualities and the pricing. Hence, German manufacturers are moving to competitive regions. The automotive, machine and plant industry have been in major troubles. However, the country is moving towards automation in manufacturing and is expected to reduce the identified gaps. The authors suggest that by balancing between automation and labor contribution, issues can be effectively resolved. Manufacturers need to identify the domains of operations for automation to avail the manufacturing benefits of ‘Big Data’ (Gandomi & Haider, 2015). 

Trends Impacting the Operations Management

Feasibility:

The utilization of ‘Big Data’ is still not a very feasible concept. As argued by Xia et al. (2012), there have been studies to understand the benefits of large-scale data; however, very less on the relevant skills. The adoption and identification of appropriate measures to effectively utilize the ‘Big Data’ require the leadership skills in managers. Managers should know their ability in terms of physical, human and financial resources to appropriately utilize the fourth industrial revolution. Additionally, ‘Big Data’ is largely being researched for manufacturing advancement but not for the service sector which is an indispensable part of the supply network. There are a lot of issues in value chains network such as mass customization, smart working & service, digitalization and others. Innovative capability needs to be improved along with the knowledge management. According to Wang et al. (2016), managers must have the ability to diagnose the reasons for failure. The adoption of ‘Big Data’ can challenge the ability to remain cost-effective and mitigate the risks due to external influences. The approach to ‘Big Data’ misses on identifying the consequences of improper decisions and its impact on automation cost.

Justification:

It is being highlighted in this study that the fourth industrial revolution can bring the potential changes to industries provided that the shared objectives are effectively maintained. The fact can be justified by academic scholars. As opined by Bokrantz et al., (2017), recent advancements in terms of digitalization have produced ample of expectations from the manufacturing system and other services. However, it also increases the adjoined need for maintenance management. Moreover, there is a lack of actionable guidance for the maintenance. The authors mean to say that the future of industries is surrounded by uncertainties. 

Limitation:

The favoritism of digitalization in the manufacturing processes and the service sectors are giving way to the large-scale changes in the entire value chain. The way raw materials are acquired and their final usage are all set to undergo the changes from Industry 4.0. As argued by Roblek, Meško & Krapež (2016), the concept is a potential move; however, despite a collective backup from organizations, government & academics and the proven examples of success, there is still a long way to go in regards to few areas. Those few areas include but are not limited to like management of works, digital & security protection, communication interfaces’ standardization, accessibility to cognitive ability and the SMEs. A smooth collaborative business between shared partners is another limitation of Industry 4.0 (Prause, 2015).   

There can be the list of trends impacting the operations management; however, to name a few there are following trends in the operations management (Clegg, MacBryde & Dey, 2013):

Operation Management Areas	Emerging Trends
Digitalisation of Manufacturing	Cyber-Physical System (CPS), 3-D Printing, Use of Robots etc.
E-operations	Increasing Spend on Warehouse Management, Appropriate Utilisation of Big Data, Revolution in the Business Processes, Elevating Speed of Supply Chain, Using a Fully Integrated EDI to Maximize the Efficiency etc.
Leanness and Agility	Use of Software in Supply Networks, Use of Agility in Supply Chain, Change of Perception in BRIC countries, Customers’ Inclination to Sustainability, Importance of Knowledge is growing etc.
Outsourcing	Cloud Computing, Data Security, Use of Artificial Intelligence & Automation through Robots, Digitalisation of Traditional Communication and Growth in Freelance
Performance Management & Quality Control	· The shift of Attention from Quantity to Quality, Simpler Process Management, Change in Pay & Bonuses related strategies, Enhanced Focus on the Impact of Performance Management Interaction between Managers-Employees and a Continuous Preference to Management Software over the Software for Appraisal etc. · In Terms of Quality Control, Increased Regulations, Rigorous Audits, Incomparable Product Quality and Internal Productivity Goals

Linkages between practical & theoretical aspects 

A theory can be defined as a set of ideas which are being used to improve the practical outcomes. In a likewise manner, several ideas are being used to improve the operational outcomes and also to reduce the operational complexities. Following is the list of theories which will be shown to have a linkage between theoretical aspects and the current practices:

Management Systems Theory: In this theory, the systems approach is based on evaluating the internal and external factors which are affecting or can affect the performance. Market trends are observed to see or realize the changes in practice. Once, any significant change is noticed, the approach dictates to evaluate both internal and external environment, so that, the feasibility of the changes can be identified (Schuh et al., 2014).

The Chaos Theory: The theory gives emphasis on acquiring an adaptation with complex external changes. The theory suggests that organizations should be into the learning. It also suggests that an effective feedback system on a continuous basis is a good way to be updated with the internal capabilities of firms (Prause, 2015). Moreover, this also gives the opportunity to compare it against the external changes and realize the changes that are required. Indeed, the Industry 4.0 is full of complexities which can be sorted only if firms especially the SMEs learn to cope with the challenges (Faller & Feldmüller, 2015).

The Open System Approach: The theory commands that firms must adapt to complexity in external changes to maintain their competency and sustain the market performance. The theory highlights what is more required for Industry 4.0 environment. Firms are expected to face the challenges; however, it must strive to cope by using suitable ways (Closs et al., 2008). Organizations can also learn from firms which have successfully utilized the fourth industrial revolution. The theory emphasizes the importance of adaptability which is also a key factor in an Industry 4.0 environment.

Contingency Theory: It rejects the traditional approaches of management and finds inadequate with current practices. It is a very important theory as it highlights the importance of managers’ role in everything that takes place in firms. It says that managers must continuously work on their strategy making capabilities to identify whether it is feasible with the current changes. There must be the different approach in regards to a different situation in which managers are expected to be in. It is, therefore, advocating the importance of managerial skills to effectively implement the external changes (Sauser, Reilly & Shenhar, 2009). Managers must know the ways to head towards the changes and the learning needs to adapt it.

As argued by Erol et al. (2016), the suggested framework can be utilized to make the effective moves towards the industry 4.0 environment. This framework will not just enrich the firms’ learning capabilities but will also enhance its capabilities to guide & standardize the intelligent manufacturing.

According to Erol, Schumacher & Sihn (2016), the proposed framework in this study is aimed at raising the standards of organizational practices for innovation and learning. It is being said that the framework will make changes in internal practices and also create the abilities to effectively adjust with the external changes. 

In the opinion of Thames & Schaefer (2016), the fourth industrial revolution will expectedly enhance the efficiency of manufacturing and service process. It is suggestive to improve the learning capabilities of firms especially the SMEs to become competitive with Industry 4.0. The fourth industrial revolution is exciting for those which have the adequate internal capabilities. Organizations have faced the challenge of prioritizing the data. Skills are required to identify or prioritize the data in different circumstances.

As argued by Mrugalska & Wyrwicka (2017), the fourth industrial revolution is a fact and is there to stay for those that have in them the relevant adaptability level. Developed countries such as Germany and Japan have already shown the sign of development. The United Kingdom can also be benefitted from as it has a rare status in the field of innovation and technologies. Emerging countries such as India and China can take the advantage from.

As stated by Majeed & Rupasinghe (2017), the fourth industrial revolution is happening and the future of manufacturing & service process will be a lot more efficient. The efficiency of warehouses in terms of picking orders and packing will improve as Robots will occupy a significant existence in warehouses. Firms need to identify ways to deal with such large-scale data. Appropriate data should be utilized at the time it needs to be delivered.

Conclusion 

In summary, it can be concluded that Industry 4.0 is a logical advancement in manufacturing and service process to a few. It is also a fact to many as highlighted in this study. However, it is undeniable that Industry 4.0 and the utilization of ‘Big Data’ is full of barriers in the form of effective communication between shared businesses, organizational capabilities to adapt to changes and the policy designs for the managerial skills. To get the most out of the benefits, it is important that managers have the relevant skills in them to identify the needs for changes and a relevant preparation to attain those. There should be an adequacy with such a large-scale of data to make the better impacts. 

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