A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. One important step for such a transition is to identify the enabling technologies from a value-creating perspective. The existing corpus of literature has discussed the benefits and applications of smart technologies in overall manufacturing or logistics. However, there is limited discussion specifically on a production logistics level, from a systematic perspective. This paper addresses two issues in this respect by conducting a systematic literature review and analyzing 142 articles. First, it covers the gap in literature concerning mapping the application of these smart technologies to specific production logistic activities. Ten groups of technologies were identified and production logistics activities divided into three major categories. A quantitative share assessment of the technologies in production logistics activities was carried out. Second, the ultimate goal of implementing these technologies is to create business value. This is addressed in this research by presenting the “production logistics data lifecycle” and the importance of having a balanced holistic perspective in technology development. The result of this paper is beneficial to build a ground to transit towards a data-driven state by knowing the applications and use cases described in the literature for the identified technologies.

Nowadays, digital twins exist everywhere in various fields. However, an analysis of existing applications in manufacturing and logistics revealed that many entirely apply the concept. To identify when a complete implementation of the concept is beneficial, we analyse the need and the implications within production logistics. This study also presents an architecture supporting integrating a digital twin into production logistics and a corresponding application scenario. Based on this, we have derived practical applications. Each application is applied to different situations, and actual benefits can overcome the limitations of the previous studies.

Digitalisation and automation of existing processes are key factors for competitive industry, but still logistics operations are often dominated by manual work. A shift towards higher degree of automation within existing infrastructure is often challenged by high cost and complex processes, thus a return-on-investment is hardly achievable within decent time. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put into operation. This paper presents and discusses if the usage of digital twins representing and simulating a physical part can support the related assessing and decisionmaking processes. In this context, this paper presents a production logistics testbed includes physical devices, an IoT-infrastructure and simulation software for innovation as well as operational management purposes.

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and uptake of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope.

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.