Data-Driven Production Logistics – An Industrial Case Study on Potential and Challenges

Abstract

Production logistics is typically considered a nonvalue-adding activity with a low level of automation and digitalization. However, recent advancements in technology infrastructure for capturing real-time data are key enablers of smart production logistics and are expected to empower companies to adopt data-driven strategies for more responsive, efficient, and sustainable intrasite logistic systems. Still, empirical evidence is lacking on potential and challenges in industrial transitions toward such systems. The objective of this article is to analyze the potential and challenges of adopting data-driven production logistics based on an industrial case study at an international manufacturing company in the pharmaceutical industry. The industrial application is analyzed in relation to established frameworks for data-driven manufacturing, and key technology infrastructures are identified. The potential of adopting a data-driven solution for the industrial case is quantified through simulating a future scenario and relating the results to the five SCOR performance attributes: reliability, responsiveness, agility, cost, and asset management efficiency. The findings show that deploying a data-driven approach can improve the overall performance of the system. The improvements especially concern lead-time, utilization of resources and space, streamlining logistics processes, and synchronization between production and logistics. On the other hand, challenges in adopting this data-driven strategy include a lack of relevant competence, difficulties of creating technological infrastructure and indistinct vision, and issues with integrity. Key contributions of the article include the analysis of a real industrial case for identification of potential and challenges while adopting a smart and data-driven production logistics.

Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions

“…The integration and optimization of the operation of the production logistics system, and then the improvement of production efficiency and the reduction in logistics costs in the operation process, have become the key issues urgently to be solved by manufacturing enterprises [16,67,68]. At present, the research on the management model and decision-making method of production logistics mainly focuses on the following aspects [65,66,[69][70][71][72][73]: First, the research on the influencing factors and mechanism of resource allocation of production logistics system; the second is the design and research of production logistics system platform, and focuses on improving the intelligent service level of operation system; the third is the optimization research of production logistics system, including the optimization of production process flow and production logistics system. At the same time, it was gradually noticed that the production logistics system is essentially a social technology system, and human factors play an important role in improving the system operation performance [74][75][76][77].…”

Analysis on the Application of Artificial Intelligence in the Field of Logistics

“…Data-driven intelligent logistics management will become an important trend in the logistics industry [5] .Through big data analysis and artificial intelligence technology, logistics enterprises can extract valuable information from massive logistics data to achieve refined transportation planning and resource allocation. The logistics network will become more visual and intelligent, and can monitor the flow of goods, traffic conditions and transportation environment in real time, providing real-time decision support and early warning functions.…”

Assessment of Digitalized Logistics for Implementation in Low-Income Countries

“…However, they are also associated with some challenges, including uncertainty, cost, and complexity [64] (Table 5). Energy consumption Singh and Bhanot [22] Investment cost Singh and Bhanot [22], Tu [13], Horvath and Szabo [20], Zafarzadeh et al [66], Kshetri [67] Big data management Zafarzadeh et al [66] Government support Tu [13], Ghadge et al [31] Internet connectivity Sriram and Vinodh [23] Technology access Mathauer and Hofmann [81] Opportunities and challenges mentioned in Tables 4 and 5 that fall into similar categories were merged together to form eight criteria that low-income countries need to consider for the selection of digitalized logistics practices. The criteria were: i.…”

“…The use of digital technologies results in an improvement in the performance of the logistics system. It facilitates better resource utilization and improved asset management [66]. Improved performance can also result in cost savings due to operational efficiency and reduced lead times.…”