Business analytics in manufacturing: Current trends, challenges and pathway to market leadership. (2019)

• Record Type: Journal Article
• Authors: Omar, Yamila M.; Minoufekr, Meysam; Plapper, Peter
• Is Part Of: Operations research perspectives. Volume 6(2019)
• Publication Date: 2019
• Subjects: Manufacturing -- Business analytics -- Big data -- Industry 4.0; 00-01 -- 99-00; Operations research -- Periodicals; Management science -- Periodicals; 658.403405
• DOI: 10.1016/j.orp.2019.100127
• Languages: English
• ISSNs: 2214-7160

Abstract

Highlights: A general definition of business analytics is upheld and completed by listing the building blocks and actors specific to the manufacturing industry. Five manufacturing domains are discussed and areas where business analytics can be a differentiating factor are highlighted. The challenges hindering wide adoption of business analytics in the manufacturing industry are identified. A pathway to market leadership is prescribed to guarantee business analytics returns Abstract: The manufacturing sector is under constant pressure to increase profitability in a growingly competitive international market in which differentiation is not tied to manufactured products or utilized technologies but to business processes optimization. In this context, business analytics offer the opportunity to harness the knowledge and value hidden within enterprise information systems to revolutionize innovation, enhance supply chain management and production, accurately target marketing and sales efforts, as well as develop and manage profitable after-sales services. While the literature to date presents numerous specific applications in which business analytics techniques were successfully deployed to improve specific business units, it is evident that a comprehensive enterprise approach is missing. In the present work, a pathway to attain market leadership through the effective use of business analytics is defined suggesting focus must center on three increasingly challenging barriers. Firstly, "standardization" of collection, aggregation and storage of data must be accomplished. Then, an "organizational culture evolution" that outgrows intuition and embraces data-driven decision-making is needed to create the perfect ecosystem for business analytics to produce actionable results and recommendations. In turn, these must guide "business model innovation" efforts to tackle new value creation, and capture and secure market leadership.