The aim of this study is to develop a theoretical framework for blockchain, operations in particular. Furthermore, we aim to identify the main drivers and barriers of digital innovation and explore the general possibilities of blockchain applications within the maritime industry. A case study approach is applied: the Norwegian offshore industry. Primary data is collected through interviews, while secondary data is collected from industrial and company reports, the Internet, and national and international media reports. We have discovered that cost reduction intentions, the high level of regulation in the maritime industry, and the large amount of data that maritime companies should process, along with the intention to work more effectively, are the main drivers of digital innovation. On the other hand, the high cost of implementation, the bad quality of Internet connections offshore, the old age of decision-makers, the technology-oriented culture, the lack of investment initiatives, the low level of blockchain diffusion through the supply chain, and risk aversion are the main barriers. The results of the qualitative study show that some of the barriers and motives of digital innovation and the introduction to blockchain technology were pointed out by earlier studies. However, we have identified several unique drivers and barriers specific to the industry. Finally, the blockchain process framework is developed.

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This article presents the results of the third cycle of an Action Research (AR) study conducted in an offshore exploration and production (E&P) operator on the Norwegian Continental Shelf (NCS) that investigated a digital transformation (DT) in offshore E&P supply chain operations, with a focus on drilling operations. This study provides the results of a brief investigation of the main factors that contribute the success of DT and demonstrates the value of using technology roadmapping alongside systems engineering (SE) practices to create a path between identified "AS-IS" and "TO-BE" operational states. Applying SE methods to the adoption of the "T-Plan" roadmapping process resulted in a strategic communication tool that can be used among stakeholders to support them with the integration of business planning and technology adoption, and to help assess the impact that new technologies may have on their organizations in their journey toward a successful DT. In addition to the roadmap itself, which benefits the case company, this study contributes to building and enriching academic literature by providing insight from the oil and gas industry and demonstrating the use of technology roadmapping to create a strategic plan for DT in a well-established industrial domain.

Oil and gas offshore exploration and production (E & P) will remain necessary to meet increasing global energy demands. However, appraising and exploring these resources has a major impact on sustainability and faces many challenges. Improving the supply chain operations that support E&P activities presents opportunities to contribute to the United Nations (UN) Sustainable Development Goals (SDGs), but relies on organizations being able to adopt new strategies and technology and, innovate their current business models. Business model innovation (BMI) has not been actively pursued in this industry, partially due to the traditional operation management and due to the complexity in changing established models or adopting full-fledged archetypes. Thus, the present study proposes a more flexible and granular approach to BMI by defining elements to be adopted rather than proposing business models archetypes. To define the elements, an application of systems engineering (SE) is adopted through a morphological analysis (MA). They are presented in morphological boxes in three dimensions—technology, organization, and the human element—inspired by sustainable business model (SBM) literature. The elements are proposed as “bricks” for BMI where they can be adopted and re-arranged as necessary, providing granularity and flexibility to facilitate BMI for organizations of varying sizes.

The offshore oil and gas upstream supply chain operations are part of a complex system with many stakeholders and intricate relationships. Traditionally, these operations are managed manually, which leads to inefficiencies. Despite the innovative and engineering‐orientated approaches adopted in other technical operations of the industry, the supply chain activities remain unchanged, relying heavily on legacy systems. However, cutting‐edge technology opportunities are available for adoption in supply chain management systems in the oil and gas industry. Such a transformative upgrade relies on first understanding the operational inefficiencies and preparing an accurate picture for how these operations should be performed. This study adopts a systemic approach to examine the oil and gas offshore supply chain operations of a case company to identify areas for improvement. The objective is to address the following research questions: (1) what is the current “AS‐IS” supply chain operations support; and (2) what is the desired “TO‐BE” state for these operations. This research adopts a soft systems lenses applied in an action research project to capture and analyze existing operations. The research revealed that information exchange is a major barrier, and that technology and organizational gaps are the primary hindrance for a digital transformation. The conclusion is that there is a need for a higher level of data exchange and increased data quality in any proposed transformation.

Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are summarized in the first part of this study. Two applications of digital models and blockchain technologies are discussed and compared with their features to illustrate their similarities and differences in the second part of this study. A digital model represents a vector based mathematical structure derived from ship performance and navigation data sets and has categorized as a low-level information model. It is also believed that the respective data sets from industrial IoT (internet of things) should go through such low-level models to improve their quality. These data driven networks can be used to quantify ship performance and navigation conditions, where the outcome can also be used to improve vessel energy efficiency and reduce engine emissions in a local scale. A blockchain represents a decentralized, distributed and digital ledger system in a public domain and can handle and record transactions executed by many users. That has categorized as a high-level information model due the high quality data sets from industrial processes that these networks are handling. Such data driven networks can be used to formulate various logistics operations in shipping and optimize their operational conditions in a global scale. The outcomes of these data driven networks can be used to improve operational efficiency and reduce the respective costs in the shipping industry.