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Information Management and Analytics Technology

Industry Sector Analysis

IT in the Manufacturing Sector

The application of IT (information technology) in the manufacturing sector has started in the early 1970s and constantly developing since then. At present days, companies used to store digital sorts of documents on storage devices and servers. These documents are stored in storage devices to make it accessible to everyone in the network. The data management systems are adapted to store and handle numerous amounts of previous data cautiously, and employees help from instant access they require. The manufacturing sector benefited by information management technologies by tracking productivity over a period, maximizing return on speculation, and recognizes areas of development. An industry uses data warehouses to collect and process information and circulating them to needed people. This information is required in decision making, coordinating, and handling a wide range of activities in the industry (Nikolic, Ignjatic, Suzic, Stevanov, & Rikalovic, 2017). Data warehouses are a centralized collection of databases in which data are associated. The IS managers transfer data from databases into a data warehouse.

PESTLE Analysis

Political factors

Political factor includes state policies, political stability, regulation activities, and petitioning groups that affect the industry through financial policies, legislation, guidelines, and rules. The industry using information technologies may face the issue with transparency and the sharing of confidential information with the concerned stakeholders. Furthermore, the data ownership is to be formally contracted and all the rights are reserved for the same.

Economic factors

Data warehousing does not have an instinctive secure value, particularly comparing to traditional resources. This fact makes defining the economic worth of data quite difficult, and even inappropriate in some cases. The unremitting obtainability of data regularly is a stiff requirement and hence costs are higher to ensure abundance (Uhlemann, Schock, Lehmann, Freiberger, & Steinhilper, 2017).

Social factors

Social factors which are to be faced by the industry using data warehousing and other information technology consists lack of transparency issue raised by employees, uncertainty about data prevention, absence of perceived benefits, issue of personal security, demand for higher accessibility (Kunz et al., 2017).

Technological factors

Data warehouses have the uppermost functionality and therefore have a more intricate technical operation. The connection remains a vital concern while having multiple users on a server and the technology does not reach the level to resolve this issue yet. Provide a standardized and consistent format for the data stored in the system. However, it has complexity in the filling, cleaning, and inspection before storing and may take a long time to finish the process. 

Legal factors

Legal factors may include restrictions in trade, protection of data, patents and copyright issues, e-commerce law and consume protection act. A consistent and covering legislation is necessary to ensure common rights and accountability while operating with IT management.

Environmental factors

IT is a helpful tool for achieving environmental objectives and endorsing sustainable growth. The digital method of storing data preserves the use of papers and contributed in controlling pollution. Big data handling warehouses can be used to monitor the activities within sustainable confines (Kiel, Müller, Arnold, & Voigt, 2017).

Trend Analysis

The world is enduring a foremost alteration in management and buying command, and it’s shifting the manufacturing businesses. So what’s coming up in 2020 and beyond may include the following:

Industry 4.0 is the present trend of data interchange and automation in the manufacturing sector that involves a cyber-physical network, cloud computing, and the internet of things (Sampathkumaran, Chandrasekaran, & Ramkumar, 2016). The European Commission in its funding program named The Horizon 2020 R&D, allocated 17 billion dollars approximately for industrial leadership. As a result of which the manufacturing sector is on boost in footings of sensor technologies, advanced computing, and robotics (Ghobakhloo, 2018). Moreover, sustainable development is another trend for the sector to achieve its goals. The manufacturing sector identifies the requirement for it to be innovative and inclusive socially.

Organizational Analysis

3M Corporation

To become customer-centric from product-centric, the 3M Corporation decided to change its documentation process to digital data storing. To address the future need, 3M Company started a significant enterprise to develop a data warehouse named GEDW (Global Enterprise Date Warehouse). Since its installation in 1996, 3M has made a capital investment of 3 million USD in the GEDW, counting software, hardware, and other applications. The on-going maintenance budget required per year is 2.6 million. The current team of data warehousing consists of general application development, data handling, e-commerce, and digital media. GEDW can sustain hundreds of simultaneous users (Watson, Wixom, & Goodhue, 2008). Customers, personnel, suppliers, distributors, and people across the world can access centralized information via the internet or any browser. There is an assortment of stable hierarchies for accessing data according to the need of the users. GEDW is used to design product-shipping tags and provide accurate information due to 24 hours of activation.

SWOT Analysis


  • Improved sales force and productivity in customer service
  • The process of product commercialization has improved
  • Reduction in marketing communication cost
  • Increased value of business globally


  • Requires high quality professional to handle the server
  • Requires more efforts in selling a warehouse to other organization
  • It is dangerous to benchmark a range of technology boards due to the large size of the data warehouse (Abdellaoui & Nader, 2015).


  • To analyse cross-selling data for the related products
  • Provide a platform to recognize the end customers as the POS data is in the warehouse
  • Ease in tracking and analysing sales to identify consignments outside of the US.


  • Hacking is the major threat for GEDW
  • US trade relations and isolationism

Trend Analysis

The European Commission put forward a digital agenda that targets to use potential information technologies to substitute creativity, economic development, and growth. Hence the trend of increased digitalization will bring substantial growth of the company in the next 5 to 10 years (Furtado, 2020). The millennial generation is attracted to services with durable supervision of environment and social problems. Using data warehouses enables the organization to contribute to sustainable development globally. Adopting new practices like storing data in digital lockers provides an opportunity for the sector to enhance its profitability with fewer efforts (Almada-Lobo, 2015). For the next 20 years, sustainability has been a central objective and the range of ICT has the prospective to accelerate the accomplishment of the set ends.

References for Information Management Technology

Abdellaoui, S., & Nader, F. (2015, February). Semantic data warehouse at the heart of competitive intelligence systems: Design approach. In 2015 6th International Conference on Information Systems and Economic Intelligence (SIIE) (pp. 141-145). IEEE.

Almada-Lobo, F. (2015). The Industry 4.0 revolution and the future of manufacturing execution systems (MES). Journal of innovation management3(4), 16-21.

Furtado, P. (2020). Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions. Pedagogy, 1355.

Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management.

Kiel, D., Müller, J. M., Arnold, C., & Voigt, K. I. (2017). Sustainable industrial value creation: Benefits and challenges of industry 4.0. International Journal of Innovation Management21(08), 1740015.

Kunz, W., Aksoy, L., Bart, Y., Heinonen, K., Kabadayi, S., Ordenes, F. V., & Theodoulidis, B. (2017). Customer engagement in a big data world. Journal of Services Marketing.

Nikolic, B., Ignjatic, J., Suzic, N., Stevanov, B., & Rikalovic, A. (2017). PREDICTIVE MANUFACTURING SYSTEMS IN INDUSTRY 4.0: TRENDS, BENEFITS AND CHALLENGES. Annals of DAAAM & Proceedings28.

Sampathkumaran, R., Chandrasekaran, K., & Ramkumar, A. (2016). U.S. Patent No. 9,519,695. Washington, DC: U.S. Patent and Trademark Office.

Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The digital twin: Demonstrating the potential of real time data acquisition in production systems. Procedia Manufacturing9, 113-120.

Watson, H. J., Wixom, B. H., & Goodhue, D. L. (2008). Data warehousing: The 3M experience. In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 2749-2761). IGI Global.

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