Digital transformation at Maersk: the never-ending pace of change

ABSTRACT

This teaching case describes, based on publicly available material, the transition of the A. P. Moller Maersk shipping company from a logistics-based organization to an information and technology-based organization. Multiple digital technologies, including distributed ledger, robotic automation, digital platforms, and big-data analytics offered opportunities for Maersk. Although the company was a relatively late starter in digital transformation, its journey so far has been successful. The challenge is how to enact sustained digital transformation in an environment of rapid technology change. Some theoretical lenses are offered to assist with analyzing and framing Maersk’s digital strategy, including business models, organizational ambidexterity, and dynamic capabilities.

The way we think about technology! – “not long ago we thought about technology as the engine room that keeps everything going” but now “Technology is really an integrated part of our strategy,” said Søren Skou, CEO of Maersk.¹

Case summary

Maersk is one of the biggest and oldest shipping companies in the world. Maersk has been involved in a major digital transformation program that has so far seen its customer-base grow, and this case follows its digital transformation journey. Maersk is an integrated container logistics company with a vast ecosystem, including a strong international network of sub-brands, incorporating the container industry, supply services, and multiple partners, collaborators, and distribution channels across regions, countries, global supply chains and logistics. Maersk is deeply committed to digital transformation and has already implemented several initiatives, including leveraging data analytics and redesigning its customer interface. However, the rapid pace of development of new technologies such as distributed ledgers, robotics, internet of things, machine learning and artificial intelligence continues to offer tremendous opportunities, as well as threats and risks, for Maersk, if it wished to maintain its leading position. The challenge is how Maersk can absorb new technologies and engage in continuous transformation. The key characters are Maersk’s leadership team, especially Søren Skou the CEO, who has been widely quoted in the media. We infer the challenges posed for the leadership team based on publicly available information about Maersk, the logistics and shipping industry, and the opportunities and threats of new technologies. The case illustrates the scope and complexity of the changes that digitization, digitalization, and digital transformation can have on a large logistics organization, especially at a time of rapid technology change. Students will gain an understanding of (1) the ways in which digitalization combined with contemporary technologies create a wide range of opportunities, new products and services, and new business models for Maersk, and (2) the challenges of developing, maintaining and implementing a digital transformation strategy that continues to incorporate and be responsive to rapid technology change.

The case: sustaining digital transformation at Maersk

The shipping industry (including Maersk) has traditionally been slow to capitalize on the benefits of new information technologies, as there has been a persistent disconnect between them and the IT industry, based around different perspectives, expectations and priorities (Chua, 2021). This is changing. Maersk has been involved in a major digital transformation program that has so far seen its customer-base grow. However, the potential opportunities arising from digital technology-based business transformation are rising exponentially, often outstripping the ability of even agile organizations to change rapidly enough to capitalize on all of them (this phenomenon is sometimes described as Martec’s law (Brinker, 2016)). Maersk needs to plan for a future of continuous digital transformation. This is not confined to the shipping industry – industries such as construction have enthusiastically embraced digital technologies after a slow start (Prakash & Ambekar, 2020).

Company background

The A.P. Moller-Maersk group established the company in Denmark in 1904, making Maersk one of the oldest companies in the maritime market with a long history of over 100 years (Anwar, 2020). The history of the company shows several previous instances of re-conceptualization of its business model(s) and the adoption of new technologies. Moller’s desire to expand the business started in 1912 with its first acquisition of a steamship company. The company subsequently became involved in other major business sectors including oil and gas exploration and drilling.

In 1962, the company was granted the sole concession for exploration and extraction of raw materials from the Danish underground for a 50-year period. The venture sparked the beginning of Maersk’s engagement in the oil and gas industry as well as providing shipping and supply services to the offshore sector. To achieve this, the company established a joint venture with Shell and Gulf in 1962. This injected valuable knowledge about exploration and extraction activities, where the company had no previous experience (Maersk, 2022a). By 1986, Maersk Oil became the sole operator for DUC (a joint venture with the Danish state in charge of oil and gas offshore exploration).

In the 1960’s containerization of shipping revolutionized global trade. Maersk was not an early adopter; the company history notes that Maersk was no forerunner in containerization (Maersk, 2022a). However, during the period from 2001 to 2017, the company invested extensively in large, state of the art container ships and port facilities, including, in 2006, the largest container ship in the world (Maersk, 2022a).

More recently, the company decided to separate the oil and gas related parts of the business from the logistics function, and Maersk Oil was sold in 2018. The origins of a new business strategy can be observed in 2016, when the company made a strategic decision to move from being a corporate conglomerate to being “a focused, integrated transport and logistics company” (Maersk, 2022a, Integration − 2016-Present). The new business vision aimed at becoming a leading global integrator of container logistics and related services.

The company’s approach to previous technology innovations and investment suggests a measured, cautious, risk averse culture, based on initial capability building and monitoring of the market, followed by large-scale investment in leading-edge technology when the market was mature. While this served the organization well regarding large capital investments in container ships and container terminal facilities, it was unlikely to be appropriate for a fast-moving digital technology environment. A new approach was required.

Challenges in the logistics sector

In addition to these systemic issues, global supply chains had been facing significant challenges for several decades. These include manual tracking, fragmented communication, “empty” miles (miles traveled with no financial benefit), delivery delays resulting in unpredictable arrival times (Norkin, 2023), difficulties with regulatory compliance, and excessive operational complexity (Donepudi, 2014). The years from 2004–2010 have been described as “turbulent” in the shipping industry, with an unstable business environment creating particular challenges for technology managers (Denstad & Bygstad, 2012) and other senior managers. The shipping industry experienced slow growth for several years in the 2010s. Although its profitability has recently rebounded, problems remain. In the second half of the year 2020, a continuous rise in demand generated supply chain bottlenecks, including vessel and container shortages. In 2021, supply chains were described as “a mess” with ports and warehouses jammed with containers, and long delays occurring in moving them (Hsu, 2021).

As well as immediate issues of complexity and inefficiency, there are significant micro-economic challenges that have been systemic in the shipping industry. While demand patterns in global trade can change very rapidly, capacity in international shipping changes slowly. This tends to result in cycles of under and over-supply, where freight charges increase rapidly due to increased demand and insufficient supply, or situations where vessels are scrapped due to over-supply. For example, in 2016, fewer exports were coming from China, while at the same time, large fleets of huge new ships were being delivered to major shipping companies (Northam, 2016). This led to significant over-capacity, driving down the cost of freight and slashing profits (Northam, 2016). The supply and demand balance improved after 2016, with fewer vessels ordered.

Competitive pressure has been increasing with changing patterns of customer demand and rising costs (Mongelluzzo, 2021). Non-traditional competitors, such as Amazon, have emerged as major players, and its competitive threat is based on both big data and digital platforms. The term “big data” was coined in the 1990’s to capture the differences in characteristics of the data sources that were becoming available from previous data sources. Big data was distinguished from previous data sources by “volume, velocity and variety,” with volume describing the quantity of data, velocity the speed at which it can be processed and analyzed, and variety representing the fact that digital data comes in many formats or data types that need to be integrated (Lutkevich & Wigmore, 2023). Big data is often made available on platforms that offer the ability to collect, store, analyze, manage and govern these enormous data resources (Visconti et al., 2017). Organizations such as Amazon, that developed and controlled important data platforms, were able to gather large quantities of raw data and perform analysis that supported organized, actionable business decisions. Therefore, companies leveraging big data platforms could produce insights about markets and customers, identify new opportunities, identify trends, and make predictions.

For example, Amazon does not own or operate any ships. Instead, they leverage its strengths in order fulfillment and data analytics to book space on vessels and find the fastest and cheapest way to move goods (Schoolov, 2013). Already the digital platform model has proved extremely successful in other industries, such as local transport and delivery such as Uber, and travel and accommodation providers such as AirBnB. Successful platform providers have forced service providers (such as Uber drivers) to be price takers, while reaping the benefits of applying analytics to the large amount of data they collected. COVID-19 also forced a digital agenda upon many sectors in the marketplace, and many organizations have adopted digital solutions. There was a gap in the market for a shipping and logistics platform, and a risk that a more “digitally savvy” competitor would move into this niche.

The shipping industry is plagued by challenges in achieving simplification, standardization, connectivity, efficiency and predictability (Chua, 2021). While this should create opportunities for technology solutions, there remains a disconnect between maritime companies and technology providers in the shipping industry. Simply put, the “big issues” in the maritime industry, such as low freight volumes, regulatory changes, and managing operational costs (such as port costs) have not been well aligned with the value propositions of new technology such as lack of data standardization and usage, or lack of digitization (Chua, 2021). This has resulted in a lack of trust, and an unwillingness to try to capitalize on the benefits of new technology. To some extent, this problem still persists (Chua, 2021). However, we note that a degree of caution was likely appropriate. Previous experience has warned of the dangers of the unquestioned assumption that more digital is always better (Grover, 2015).

Nevertheless, despite this disconnect, the wider logistics and freight-forwarding industry had reached the point where it was ripe for digitization and automation. Manual handling and paper-based documents resulted in inefficiency, risk, and constraints on any future performance improvement initiatives.

Manually entering and transferring data between … different systems and operators is time-consuming, and the potential for error increases every time information is re-keyed. Maintaining job visibility across an organization becomes almost impossible, resulting in double handling of work. (Global, 2022, Manual data entry productivity paralysis)

Even in 2022, global trade sometimes required more than 20 documents to be exchanged between various stakeholders in the supply chain for a single shipment of goods (Vlacic, 2022). Some physical documents, such as bills of lading, are still required by some administrations (Vlacic, 2022). Document formats are inconsistent, and even the terms used can have slightly different meanings in different administrations. Not only was it necessary to move paper-based information to a digital format (digitization), but there was a pressing need for standardization in the industry as well (Vlacic, 2022).

A diagram of typical information flows required for goods handling is included in Figure 1 (adapted from Gavalas et al., 2022). In brief (as many of these processes have multiple sub-processes, variations, and information flows), the exporter will engage a freight forwarder, who will arrange a haulage contractor for the goods to be transported to a depot, where they will then be loaded by stevedores and prepared for shipping. Customs clearance will be obtained, and the harbor masters and port authorities will organize the dispatch of the vessel (including the use of pilots to guide the ship out of the harbor). On arrival the process is repeated in reverse, with the goods off-loaded and inspected, customs clearance obtained, and the goods transported to their destination. There are multiple interactions between exporters, carriers, forwarders, warehouses, and authorities that require documentation and compliance. These are complicated by inconsistent standards and delays in handling (Executive, 2018b).

Figure 1. Typical information flows for internal shipping (adapted from Gavalas et al., 2022).

These inefficient processes were partly a result of Maersk’s internal systems, and partly a symptom of conditions in the industry. A great deal of diversity existed between different countries and authorities in terms of underlying business activities, procedures, and handling processes. For example, the UK alone has two different types of ports requiring different processes (Customs4trade, 2021). Further, many port processes remained primarily manual, so that key activities were accomplished with pen and paper.

COVID-19 also forced a digital agenda upon many sectors in the marketplace, and many organizations have adopted digital solutions. A major impact in the maritime industry has been the extent to which the pandemic has accelerated demand for digital solutions throughout the ecosystem. “There is now greater appetite and acceptance of digital solutions across the industry. A data driven transformation that extends beyond ship-owners, ship-managers to charterers, financiers, and insurers is very much underway” (Gavalas et al., 2022, p. 1).

Implications for Maersk

In 2017, Maersk still operated with mainly paper-based processes and a complex infrastructure typical of the industry. Traditional complex operations meant that “for each container shipped, there may be up to 30 different parties involved, communicating up to 200 times.” (Vedlikehold, 2018, para. 7). One study discovered that in some instances an empty container traveled back and forth 20 times on 10 sailings, with Maersk typically spending about $1 Billion per year just to move empty containers (Oyku, 2017).

Despite this, Maersk has remained dominant player in the industry (Placek, 2021). In 2022, the company owned more than 600 vessels. It operated in 130 countries with a dedicated team of over 95,000 employees transporting $675 billion worth of goods per year. It is estimated that every 15 minutes, a vessel arrives at one of the 343 Maersk ports worldwide (Maersk, 2019b). The challenge for Maersk was to leverage this dominant position in container shipping and logistic into a strong and agile digital business strategy. The focus adopted in 2016 on integrated logistics services represents a shift toward new information-based business models.

Digital transformation initiatives at Maersk so far

Maersk found that the transport industry, ways of doing business, and business infrastructure were becoming progressively more complex. In 2017, Maersk embarked on its transformation to become the global integrator of container logistics. Maersk appointed a new Chief Information and Technology Officer from within the company, someone with excellent knowledge of the business (Logan, 2021). Over the last few years Maersk was gradually transformed into a technology-oriented company. As Managing Director for Maersk South Asia, Vikash Agarwal, noted:

We were not born a tech company, but we are getting more and more tech-enabled. It remains a high priority for us because thousands of companies, big and small, around the world, depend on us transforming to support their ambition and their growth for the future.

(Agarwal, 2022, para. 15)

Digital transformation can be seen as a three stage journey of digitization, digitalization, and digital transformation (Bloomberg, 2018; Udovita, 2020). Digitization converts the products and information in the business into a digital format and acts as a platform for subsequent initiatives. Digitalization re-engineers processes, moving them to a digital platform, and introduces automation. This can have a profound impact on business at an operational level. Finally, digital transformation involves integrating digital technology into all areas of business with a focus on delivering customer value. In sum:

… we digitize information, we digitalize processes and roles that make up the operations of a business, and we digitally transform the business and its strategy. Each one is necessary but not sufficient for the next, and most importantly, digitization and digitalization are essentially about technology, but digital transformation is not. Digital transformation is about the customer.

(Bloomberg, 2018, pp. 5–6)

We can see how digitizing a paper-based system, particularly one where individual documents were often handled many times, was an essential first step. However, simply moving to a digital format would not address problems of inconsistent formats, rules, and definitions of key terms. To achieve significant benefits, digitalization was required – improving processes and standardizing documents and data to support the development of new business models.

Maersk leveraged the digitalization of its processes in conjunction with cloud-based computing applications to offer value propositions based on integrated, end-to-end and “seamless” transport and logistics services. These greatly reduced the time taken in multiple interactions between exporters, carriers, forwarders and warehouses, and in the handling of documents and compliance requirements (although some partners were not yet equipped to deal with digital systems and processes). Digitalization enabled Maersk to offer users online value-added services including ordering, invoicing, and parcel tracking in real-time.

A major benefit of digitalization is the opportunity to develop new, customer-centric business models. These included door-to-door services and solutions like Maersk Flow (Maersk, n.d.-c), a digital supply chain management tool. Maersk Flow offers collaboration, visibility of the various stages in the supply chain, reduced handling and manual work, reduced errors, and performance analysis. They have also a developed a “one-stop” fulfillment solution that manages goods for a major customer from the point of origin to delivery to its final destinations (Maersk, 2021).

Internally, the organization’s size enables the potential for economies of scale, but the complexity of the ecosystem means these are difficult to identify and realize without sophisticated information systems. As well as providing an integrated logistics service to customers, the digitalization was leveraged to improve internal processes and asset management. Better information was supporting optimization of the company asset utilization. For example, predictive maintenance can be improved by information systems to create cost savings. Instead of waiting for ship engines to break down, sensors alert when engines need care, preventing longer down times and increasing efficiency. The management team aim to simultaneously increase efficiency and safety, and reduce emissions, while also reducing cost, based on detailed analysis of asset usage.

The enhancement in Maersk’s competitive position in the marketplace after implementing digital transformation was not solely dependent on the technology they adopted. It was also the result of strategies its senior leadership team implemented, which were value and customer focused, consistent with best practice in innovation. Maersk has moved from digitalization to digital transformation. This has enabled new technology-based value propositions, including a suite of digital tools for international shipping and supply chain management. This includes (for example) a logistics hub, that predicts arrivals on an interactive map supported by AI. We elaborate on this tool in the discussion of Maersk’s use of AI below.

Better management of the enormous physical assets owned by Maersk is another potential area where the company can benefit from digitalization and the combination of purely digital technologies with advances in technologies such a robotics. These advances concentrate on performing functions that the organization already carries out, but at a reduced cost and with increased accuracy and efficiency. In particular, robotics and sensor technologies offer opportunities for improvements in warehouse management.

Port and warehouse automation

Digitalization is already transforming warehouses, with automated and optimized warehouse technology. This uses identifiers on goods such as radio-frequency identification (RFID) tags and QR codes, in conjunction with sensors-enabled technologies. Goods can be picked and packed by robotic warehouse workers and moved using automated guided vehicles (AGVs). All the assets, including the vehicles, can be tracked and plotted using telematics (a method of monitoring assets in real time using GPS technology and on-board diagnostics) (Dujak & Sajter, 2019).

Robotics offers the potential to revolutionize freight handling at ports. Maersk ports could be maintained by robots; enabling rapid handling of container, continuous operation and faster cargo turnaround time. The expected turnaround time using robotics is 35 minutes for a truck at port, while the current average time is more than 90 minutes (Roosevelt, 2019). It is expected, with robotics the turnaround time will cut down to half for both cargo and trucks. While post automation has experienced a number of challenges, including lack of skilled staff to manage the automated facilities, difficulty handling exceptions, and poor data quality (Chu et al., 2018), it is still widely predicted that robotics and automation will be a central component of the future of port design and management.

Capitalizing on this opportunity, in 2015, Maersk launched the first fully automated container terminal in the Netherlands. This was developed by an independent subsidiary of Maersk, APM terminals. Full automation of ports has been a slow process, with most still operating in a manual or “semi-automated” manner (Kim et al., 2022). The COVID pandemic has increased interest in port automation after the disruptions to supply chains that were associated with the pandemic (Kim et al., 2022).

In another recent development Maersk partnered with a Danish company, Sea Machine Robots, to develop a container ship with situational awareness powered by artificial intelligence. The system:

… uses advanced sensors to collect a continuous stream of information from a vessel’s surroundings, identifying and tracking potential conflicts as well as displaying information in the wheelhouse, facilitating safer and more efficient maritime operations…[it] is meant to improve at-sea, object identification and tracking capabilities.

(Gondelles, 2018, p. 1)

A data-driven platform

Maersk was well-aware of the challenges of managing and integrating data. Access to data, and the laborious processes of manually integrating and analyzing it, were major problems that a new data platform intended to resolve (Rios, 2021). Maersk was in a position to leverage the amount of data generated by its dominant position into new information-based products and services. In 2018, IBM and Maersk announced the launch of the TradeLens platform (Newsroom, 2018). TradeLens is an open and neutral industry platform, underpinned by blockchain technology, and supported by major players across the global shipping industry.

Creating a new open platform would establish collaboration and enable the development of an ecosystem for the shipping industry. The initiative developed a standardized open IT platform interoperability and data standards throughout the shipping industry (Lens, 2020). This addressed the administrative challenges discussed earlier, as well as providing opportunities for Maersk and other players in the ecosystem. Integration via digital technology allows increased transparency, visibility and more efficient information exchange saving time and costs for all players across the industry. For example, truckers could see its entry/exit times at the terminal go down from 105 minutes to 35 minutes (Maersk, 2020b). The platform supported resilience in the supply chain, aiming to increase customer satisfaction, and open new opportunities for Maersk and other ecosystem members. Small businesses (for example) could participate by renting containers, which would allow them to offer niche services like providing fragile goods handling, special requirements and temperature control requests by renting the containers. Newcomers to the market could potentially develop its shipping business without a needing to own a container ship with the associated capital investment. The platform would allow rival shipping companies to connect, share data and work together across the shipping supply chain ecosystem.

The platform intended to offer added value based on data analytics and create opportunities for other entities including ports, shippers, freight-forwarders, and carriers. The electronic shipping ledger holds information about cargo consignments such as its origin, arriving/leaving port, movement (local or overseas) and finally receipt by the recipient. The benefits to ecosystem members from data analytics services can include a comprehensive view of the information for better decision making, the ability to analyze trends, improved forecasting and risk management, better service quality, and cost reduction.

Compared with the traditional information flows identified in Figure 1, a data-driven platform offers integrated, consistent, timely, and trusted information (Figure 2).

Figure 2. Data-driven e-shipping platform (adapted from Gavalas et al., 2022).

However, as we noted previously, non-traditional competitors are emerging as a threat in the digital space. A competitive logistics platform could emerge from a technology-based company that does not own ships. Maersk needed to consolidate and maintain its dominant position in the cyberspace of shipping and logistics, as well as the physical space and movement of goods. It was also essential that other stakeholders in the network were prepared to subscribe to the open standards used in the platform. This turned out to be a challenge.

Distributed ledger technology

Distributed ledger technology is a devolved system of recording and verifying transactions securely without the need for a central agency like a bank. Each new transaction is recorded and linked cryptographically to a chain of transactions, and the records on the chain are decentralized to every node in the ledger so there is no single point of failure, and no single point where crime or hacking can take place. Distributed ledgers are considered in general to be a very trustworthy method for carrying out transactions securely. The same technology can be combined with “smart contracts” that will take place automatically when certain conditions are met. For example, the contract can be updated so that payment is made when goods are received.

In international shipping, the complexity of obtaining the necessary approvals and managing credit, insurance, and payments for goods in transit is one of the major challenges of international trade. Incompatible processes, systems and data standards are responsible for errors and delays. Apart from the processing delays that frequently occur, genuinely complex situations can sometimes arise. One of the authors was involved in a situation where a major bank ended up owning several containers of bananas that were sitting on a wharf when an export/import payment arrangement went awry.

The TradeLens platform used distributed ledger (blockchain) technology. The platform allowed a clear, transparent, and immutable record of exchanges of information and money. Compared to the complexity shown in our Figure 1 above, TradeLens offered a single, consistent, and secure end-to-end solution for recording and exchanging each step required for contracts, payments documents, and other shipping information (Executive, 2018a).

Unfortunately, in 2022 the following announcement was made:

TradeLens was founded on a bold vision for global supply chain digitalization as an open and neutral industry platform. The vision centred on the ability to enable true information sharing and collaboration across a highly fragmented industry globally. Unfortunately, such a level of cooperation and support has not been possible to achieve at this time and A.P. Moller-Maersk and IBM have announced the discontinuation of the TradeLens platform.

(Tradelens.com, 2023, p. 1)

A more targeted service launched in 2019 by Maersk is Captain Peter^TM. “Reefer,” or refrigerated cargo, is often perishable, and frequently vulnerable to changes in external conditions. Captain Peter^TM allows organizations to have “eyes on your cargo from the moment your goods are locked inside the container right up to delivery at your destination (port and/or store door).” (Maersk, n.d.-a, para. 2). This includes monitoring (among other things) the temperature and conditions inside the container and its current location. Captain Peter^TM offers a range of different services and pricing options to allow customers to develop a customized service.

Analytics and artificial intelligence

The increasingly large volume of data created at all stages of logistics and shipping processes offer opportunities for value-added services that are artificial-intelligence and analytics-based. Despite something of a false start with the TradeLens platform, Maersk remain committed in principle to the use of data to support improvements in supply global supply chains:

Our aim is to contribute with technology solutions that allow us to digitise, integrate and decarbonise global supply chains. To achieve that, we are connecting physical assets with the digital world, leveraging the power of digital platforms, IoT [Internet of Things] and data. Our vision is to use this rich data set to provide end-to-end visibility across global supply chains that allow our customers to make the best possible decisions for their businesses.

(Maersk, 2022b, p. 7)

In addition, Maersk has incorporated AI into a number of new products and services. Maersk’s logistics hub² (Maersk, n.d.-b) provides visibility for logistics information. It uses machine learning from previous voyages, in conjunction with GPS data to carry out predictive analysis on the estimated arrival time for vessels. Another tool we previously described is the autonomous vessel initiative conducted in partnership with Sea Machine Robots, which uses AI based on the functionality developed for autonomous vehicles to improve maritime safety (Dotzer, 2021). An area for growth in the use of AI is the ability to combine a range of AI tools into integrated solutions. Currently, Maersk’s use of AI is restricted to specific, targeted areas such as a tool that calculated the shortest route for goods, and a tool that optimizes the work carried out in warehouses. At present, it is estimated that Maersk group uses AI for 15–20% of its logistics tasks, but this is expected to increase greatly in the next 5–7 years, to incorporate 70–80% of all logistics tasks (Lindhardt, 2023).

Other technologies

A current initiative involves developing a “digital twin” (a digital model) of a vessel to simulate a voyage (Dotzer, 2021). Each vessel is unique in certain ways, and digital twin technology allows a digital version of the vessel to be developed, allowing its performance under a range of conditions to be modeled and optimized. The digital twin simulation uses data from vessel reports and historical voyages, as well as external sources and weather information. As well as assisting organizations to select the best vessel and route, this technology aims at reducing emissions by optimizing routes and vessel performance.

Maersk has also been carrying out exploratory work with using virtual reality for safety training. It is especially valuable for situations where physical and cognitive learning need to take place simultaneously, such as the situation of a docking ship. Initial indications are that virtual reality-based training can be cheaper and more effective than alternatives (Virsabi, 2022). Environmental changes that have safety implications, for example, increasing wind, can be experienced, and responded to, in a virtual environment.

The dilemma

Despite its leadership role and dominant market position, many challenges remained. The digital transformation carried out so far has been successful, even though it involved some “growing pains” (e.g., the discontinuation of the TradeLens platform). The year 2020 was another year of solid growth for the company, and revenue increased to $39.7 billion in 2020 compared to $38.9 billion in 2019. Maersk dominates the global logistics and supply-chain shipping ecosystem.

Maersk cannot rest on its current success. We can infer that the CEO would have strong reasons to be concerned about improving internal performance and underlying supply chain performance by focusing on costs and agile capacity management, the pace of change, and how to sustain its marketplace position. It is evident that the CEO was still driving the senior leadership team to improve company returns:

While we still need to improve returns, we delivered solid progress in our financial performance in 2019 while progressing the business transformation, in spite of weak trade growth, ongoing trade tensions and geopolitical uncertainty in many markets, explains Søren Skou.

(Maersk, 2019a, para. 2)

We infer that a major focus for the senior leadership team will be sustaining the digital transformation they have started, especially now profitability in the shipping industry has recovered. Maersk’s challenge is to develop a culture of continuous innovation while offering sustained excellence in its core business (also described as organizational ambidexterity). The technology team in conjunction with senior leadership team will face the challenges of modernizing Maersk’s technology estate to become more efficient, faster, and automated, and continue to sense and leverage the opportunities of new technologies, including some, like robotic warehouse automation, that are controversial.

There are so many opportunities, that one of the challenges will be to determine the level of investment to commit to ongoing technology development, launching new digital offerings and innovation. New technology exploitation needs to be fully integrated into the business strategy (Figure 3). Given the rate of change, this may sometimes need to be based on a “best guess” about future technology trends, including consideration of who should benefit the most, where the impact should be, and most importantly, how to achieve the desired business values. Maersk’s traditional approach of monitoring the market and developing capabilities before making large technology investments is unlikely to be fit for purpose in this dynamic environment.

Figure 3. Opportunities need to be integrated with the business strategy.

These challenges are exacerbated by the fact that while new business models based on the organization’s digital assets can be developed in a rapid, agile manner, changes in the physical infrastructure of ports and container ships to incorporate and leverage digital technologies are much slower to execute and require large capital investment.

Some mechanism is needed to access knowledge of the latest technology innovations, rapidly trial and evaluate the opportunities they represent, and develop new business models before they could be incorporated into the mainstream of the business. New organizational forms and arrangements could be adopted. The TradeLens venture with IBM is an example of a technology-based spin-off initiative. Absorbing new technology and innovating, while optimizing business-as-usual services and minimizing potential brand risk incurred by new initiatives will be an ongoing challenge. The organization will also need to consider how to obtain social license for initiatives that involve increased automation and job losses.

Despite these challenges, the Maersk CEO remained optimistic:

2020 will forever be remembered for the COVID-19 pandemic that negatively impacted our lives, jobs, businesses and the global economy. I am proud that we have accelerated our transformation and delivered earnings growth during every quarter of 2020, despite very different market conditions, beginning with negative COVID-19 impact in the first half to a rebound in Q4, says Søren Skou.

(Maersk, 2020a, para 1)

Recently, Maersk has been delivering a range of shipping, customs, supply-chain management, and warehousing services through its web portal. After a relatively slow start, sustaining IT innovation has become a core competency in the shipping industry. In fact, Maersk’s entire vision of itself as a company changed, from a company mainly engaged in moving goods, to an organization that manages information about logistics, enabling Maersk, and other organizations in the ecosystem to trade more effectively.

Implications and IT-related learning objectives

The company faces many challenges with harnessing the opportunities of new technologies, which include warehouse automation, IoT, robotics, distributed ledger technology, machine learning and artificial intelligence. The learning objectives for this case include:

1. Understand what current and emerging technologies (and combinations of technologies) offer to significant challenges and opportunities for Maersk.

2. Determine what strategies Maersk can adopt to support a sustained program of innovation and transformation.

3. Develop new business models where Maersk can develop and leverage from its digital transformation.

4. Establish the differences between developing new products and services based on digital assets, as compared to combining digitalization with physical assets.

Students should be able to understand and explain:

1. The impact of new technologies, including the combinatorial effects of technologies and how they can create opportunities and challenges for Maersk;

2. How Maersk can sustain a program of digital transformation; and

3. How digitalization, digital transformation, and digital platforms can support the development of new business models for Maersk.

Case analysis: opportunities and suggested readings

This case can be analyzed using a range of different theoretical lenses. These are not mutually exclusive, as the many facets of managing a successful and sustained digital strategy are closely intertwined.

There are several theoretical approaches to case analysis that we recommend. First, we suggest an understanding of the combinatorial effects of technology to understand the new opportunities and challenges that exist for Maersk. Next, we suggest a strategic analysis, considering how Maersk can develop and maintain new technology-based resources and capabilities, and maintain a balance between “business as usual” (BAU), and innovation. Finally, we consider digital transformation and the opportunities for new digital and platform-based business models.

Combining digital innovation and information-based business models with excellence in BAU, using digitalization and new technologies to improve the performance of its physical infrastructure is also a major challenge.

Combinatorial effects of technologies

While it is widely recognized that specific technologies such as robotics and artificial intelligence offer increasing performance and sophistication at historically unprecedented speeds, it is equally clear that combinations of technologies are also having an enormous impact.

The combined value delivered by multiple emerging technologies is multiplicative. The impact to business innovation and the transformative effect of a combination of emerging technologies is far more profound than what a single technology can provide alone.

(Alairys et al., 2018, p. 1)

For example, a combination of sensor technology and artificial intelligence has been driving the evolution toward the components required for fully autonomous vehicles (Ahangar et al., 2021). A business-oriented overview of combinatorial effects can be found in Alairys et al. (2018). More sophisticated economic analyses of this effect can be found in, for example, Youn et al. (2015) and Zaytsev et al. (2021). The innovations occurring in shipping and port handling industries require similar, sophisticated technology combinations.

Organizational ambidexterity

Existing organizations, especially those that occupy dominant market positions and have significant existing infrastructure can be very vulnerable to disruptions arising from technology innovation. Most business students should be familiar with examples such as Kodak, who failed to respond appropriately to the changes associated with digital photography (Lucas & Goh, 2009). However, existing businesses need to sustain and improve its BAU offerings, as well as seizing the opportunities offered by technology innovation.

One approach for achieving this is to develop organizational ambidexterity. This describes the capabilities and organizational forms required to balance excellence in BAU with continuous innovation (O’Reilly & Tushman, 2004). It is often described as exploitation and exploration, where exploitation is the pursuit of incremental innovation based on known factors, while exploration involves investigating radical, discontinuous innovations that will result in major structural and business model changes (Osterwalder & Pigneur, 2010). Ambidexterity can take a number of forms, including structural ambidexterity, where the organization maintains separate units for innovation and BAU operations; contextual ambidexterity, where each member of the organization can switch between competing tasks or exploration and exploitation depending on need; sequential ambidexterity, where exploitation and exploration are sequenced over time and constitute a natural cycle; and managerial ambidexterity, where a manager combines exploitation and exploration activities as part of their role (Andriopoulos & Lewis, 2010).

Organizational ambidexterity in a digital context can be seen as a complex interaction of different structural factors (Park et al., 2020). These include (1) technical digitalization factors, such as information technology (IT) spending, IT training and skill levels, and IT usage intensity (the degree to which IT is embedded in organizational processes), (2) intrafirm factors, such as the degree to which the organization is centralized or decentralized, and the extent of collaboration between units, (3) interfirm factors, such as strategic alliances, and (4) contingency factors, such as firm size and the competitive landscape. All of these can interact in a range of different configurations to support organizational ambidexterity. These studies present a fairly high-level view of organizational ambidexterity. More detailed guidance on strategies for exploration and exploitation, based on a case in the insurance industry, is offered by Mitra et al. (2019).

Absorptive capacity

Another key organizational competency for supporting continuous innovation is the ability to learn. Absorptive capacity is a term used to describe a firm’s ability to recognize the value of new information, assimilate it, and apply it to commercial ends (Cohen & Levinthal, 1990). Absorptive capacity arises from knowledge and communication. A detailed conceptualization of absorptive capacity is offered in Zahra and George (2002).

However, not all organizations possess, or can easily acquire, knowledge about new and emerging technologies. This knowledge may also be in short supply. The result is that traditional organizations may struggle to digitally transform on their own (Siachou et al., 2021). This knowledge and learning gap can be filled by building strategic alliances with more tech-savvy partners. However, this also raises the possibility of unequal power and different levels of interdependence in the alliance. Organizations need to be able to make realistic assessments of their own capabilities and the nature of the strategic alliance.

A simplified model of the importance of absorptive capacity and organizational ambidexterity, based on Kranz et al. (2016) is shown as Figure 4. Organizations must have the potential to recognize and assimilate knowledge about new technology opportunities, and the ability to sustain excellence in their existing product and service catalog while also innovating. Especially considering the combinatorial effects of technology, this is not as simple as (for example) waiting for containerization to mature before making a large investment in container shipping. Many innovations require the ability to recognize promising combinations of technologies and apply them in a specific business context. This explains why (for example) autonomous vehicle and robotics technologies need to be developed specifically for the shipping context.

Figure 4. A simplified model of the strategic antecedents of business model change (adapted from Kranz et al., 2016).

Dynamic capabilities

A related lens for analyzing digital transformation efforts is the concept of dynamic capabilities. These are defined as the firm’s ability to integrate, build and reconfigure internal and external resources and competencies to respond to a rapidly changing business environment. A simplified model of dynamic capabilities based on Teece (2018) is included as Figure 5.

Figure 5. Dynamic capabilities (adapted from Teece, 2018).

In particular, dynamic capabilities reflect the speed with which the unique resources of an organization can be realigned or reconfigured to meet new opportunities or respond to threats. First, the organization must have the ability to recognize technology possibilities and opportunities. As we identified in our discussion of combinatorial effects, these may not be immediately obvious. The organization also requires the ability to engage in technology development (this may include partners, as with IBM’s TradeLens platform, or Sea Machine Robots). “Seizing” the opportunity involves product or service design and the commitment of resources. Competitive position in the market and the protection of new intellectual property created is also considered at this stage. Finally, delivering change requires the ability to both align existing organizational capabilities with the initiative, and the ability to develop (or source) new capabilities as required. This final step requires the ability to “orchestrate” major change initiatives. Organizations need the ability to add new resources, transfer existing resources, integrate resources, and shed resources that are no longer required (Sune & Gibb, 2015).

The ability to develop new business can be understood from the perspective of dynamic capabilities. Scholars (e.g. Franco et al., 2021) have argued that in a digital context, one of the most important capabilities of the organization is the ability to carry out business model innovation. The authors identify a specific range of technical, organizational and cultural strategies required to build dynamic capabilities in business model innovation, including a self-assessment tool.

Business models

The ability to carry out sustained digital innovation depends on the ability to develop new digital business models. The business model canvas (Osterwalder & Pigneur, 2010) is a light-weight tool for rapidly developing, evaluating and comparing business models that are particularly suitable for innovating in dynamic business environments. The canvas allows organizations to develop a “plan on a page” that considers nine key elements: partners, cost structure, key activities, key resources, value propositions, customer relationships, channels, customer segments, and revenue streams at a high level.

Partners are the alliances you need to form. These can be of various sorts, including buyer and supplier, complimentary noncompetitive alliances, and joint ventures. Cost structure is the collection of direct and indirect costs associated with the product or service. Key activities are the actions and activities that need to be engaged in, and they can be of several types, including:

• Actions directly associated with producing the product or service;

• Managing the infrastructure that is required; and

• Troubleshooting, planning and other offensive and defensive activities.

Key resources are the resources of all kinds that are required (e.g. human, knowledge, capital, physical and intellectual resources). Value propositions are what your product or service will offer, including such considerations as the specific value provided to the customer, whether it is a disruptive or incremental offer, and how it is superior to competing products or services. This may be slightly different for different customer segments. A detailed discussion of the components of the business model canvas is provided by Osterwalder and Pigneur (2010).

Recent studies (e.g., Gruchmann et al., 2020; Lambrou et al., 2019) have considered opportunities for digital business models in shipping, including improved internal management practices and digital partnerships, and new value propositions such as predictive maintenance tools, shipping personal assistants and distributed-ledger-based “smart contracts”. Other studies have examined specific transformations that can arise as a result of improved data management, access and transparency in the maritime supply chain, also known as maritime informatics. A wide range of potential opportunities are explored in the book edited by Lind et al. (2021).

Platform and eco-system-based business

Digital transformation needs to be considered in a wider context than just the target organization. The metaphor of industries as ecosystems has been borrowed from ecological research. This characterizes industries as composed of a range of players occupying different niches, where some complement each other, some compete directly, and some engage in coopetition, where supporting certain initiatives (for example, shared standards) may benefit all players, including those in competition with one another. Digital ecosystems have attracted extensive research attention, but industry ecosystems have existed since well before the digital era. It has been pointed out that shipping has operated as an ecosystem for centuries (Watson et al., 2021).

Ecosystems may develop around a central platform. Platforms are a place for exchanges of information, goods, or services to occur between producers and consumers as well as the wider community that interacts with the platform. Digital platforms can form the foundation of digital ecosystems. These typically consist of a platform owner (or multiple owners), and strategic partners, or “complementors”, who offer associated services (Hein et al., 2020). For example, banks could be considered complementors of a shipping platform, as they offer a range of financial instruments aimed at exporters. Collectively, these parties contribute to “value-creating mechanisms”, which include acting as an intermediary to facilitate transactions, creating the opportunity for innovation, and “generativity”, or the ability for new value-creation opportunities and services to emerge in an agile manner (Hein et al., 2020).

In the short term, an integrated digital platform (digitalization) should offer immediate advantages of data integration and quality, and reduced processing and handling of documentation, and improved flow of goods and information throughout the supply chain (Aiello et al., 2020). This should create value for all members of the ecosystem, although the dominant organization and platform owner may have a disproportionate influence.

Development of a platform can support, and be supported by, the second step in the journey from digitization to digital transformation – digitalization. In order to function in a platform environment, the information flows of all participants need to be digitized, consistent, and standards based. This creates opportunities for future efficiencies, the entry of new, complementary product and service providers, as well as information and analytics-based value-streams.

In the medium term, an effective platform can act as a “meta organization, neither possessing the hierarchical instruments of a firm, nor the largely uncoordinated decision making of markets. Successful platform ecosystems require coordination among multiple participants with possibly conflicting interests” (Kretschmer et al., 2022, p. 406).

Suggested teaching approach

This case is suited to a wide range of assignments relating to new technology-based innovation, digital strategy, digital transformation, and ecosystems.

This case can be used to illustrate and analyze multiple aspects of digital transformation and digital strategy in a large enterprise that is a major player in a key international market. It is suitable for using as a case analysis assignment at undergraduate or graduate level using a variety of theoretical frames. At undergraduate level, the instructor could select and introduce the theoretical framework that will be used for analysis. At the postgraduate level, students could be asked to select an appropriate theoretical framework or to select from a range of potential frameworks. Postgraduates could also be asked to source additional material to support their argument.

Suggested teaching plan

This case could be used as one of several teaching cases in a case-based course on digital strategy or digital transformation. It is also sufficiently rich, especially if it is supplemented with additional research carried out by students, or additional readings supplied by the instructor, that it could be used as a running case to illustrate several different theoretical concepts within a course on digital transformation. There is extensive publicly available information about Maersk, as well as academic and practitioner literature on digital transformation, including digital transformation of the maritime industry specifically, which could be used to supplement the case.

Assignment structure

The following general structure is recommended for assignments based on this case study:

Introduction and definitions

This section should introduce the problem to be addressed and provide key definitions.

Summary of the case

This section should briefly summarize the history of Maersk’s digital transformation so far and describe the problem to be solved. This could optionally include a contextual analysis of digital transformation of the maritime shipping industry.

Case analysis framework

This section should describe the theoretical framework or frameworks used to analyze the case. These might be selected by the lecturer or selected by students. If selected by students, then the selection of an appropriate theoretical lens that yields interesting insights about the case becomes part of the assignment criteria.

Case analysis results

In this section, the student will describe the experiences of Maersk based on the case (and optionally, supplementary resources), using the case analysis framework to structure the discussion.

Insights or recommendations

This section should present an analysis of the problem to be solved, problem insight, possible solution concepts, evaluation of possible solutions, and short and long-term recommendations.

Example assignment questions

Digital transformation strategy

1. What is organizational ambidexterity? How could organizational ambidexterity assist Maersk to continue to innovate and retain a leadership position in its market? What actions should Maersk take to improve its organizational ambidexterity? How can Maersk excel in BAU while engaging in transformational change?

2. What are some of the opportunities and risks of continuous digital innovation for an organization like Maersk?

3. What is absorptive capacity? How can Maersk improve its absorptive capacity for creating value from new technology?

4. What are dynamic capabilities? How can Maersk develop and maintain dynamic capabilities to sense, seize and respond to opportunities created by new technology?

5. How can Maersk sustain innovation with new technology?

Digital business model innovation

1. Develop one or more business model canvases for Maersk for new and innovative digital products or services

2. Select a specific technology (e.g., robotic process automation or distributed ledger technology). What new business models could be developed for Maersk based on this technology?

3. Using business models, develop, compare, and evaluate a range of initiatives to create a high-level technology development and investment plan for Maersk.

Digital platforms and ecosystems

1. Design a digital shipping ecosystem.

2. What data and services would be involved in a digital shipping ecosystem?

3. What partners and complementors would add value to a shipping ecosystem?

4. What new services and value-adding opportunities would be enabled by a shipping ecosystem?

Research note

The initial version of this case was developed in conjunction with teaching materials for masters-level, post-experience courses on managing information-technology-related change, innovation, new technology and digital transformation. It was initially developed by the authors based on published academic and gray literature sources, including extensive engagement with Maersk annual reports, newsletters, and other reports. While the case is intended for teaching purposes and not as an independent contribution to research literature, we were guided by grounded theory approaches in the tradition of Strauss (Strauss & Corbin, 1990), and supplemented by a discussion of the use of grounded approaches in conjunction with case studies by Halaweh et al. (2008).

Importantly, in order to sustain its digital transformation and market leadership, Maersk will need a strategy that supports continuous change, in an industry that has previously been characterized by “punctuated equilibrium” (Sabherwal et al., 2001) and the need for periodic, very large capital investment, as we observed in the transition to container shipping. While the requirement for large capital investment (e.g., container port automation, autonomous vessels) has not changed, the speed at which the external conditions are changing has, including the emergence of new threats and opportunities.

The case was extended for teaching purposes with a focus on digital transformation, digital business models and technology innovation in the shipping sector. The example assignment questions and learning objectives provided “theoretical sensitivity” for our key areas of focus and framed the approach to developing the case study. Insights about Maersk were complimented by “constant comparison” with additional sources (Halaweh et al., 2008). These included (mostly) gray literature on issues in the logistics and shipping industries in general, and (mostly) academic literature on the theoretical lenses that can be applied to the case. These allow students to situate the history of digital transformation at Maersk within the broader context of trends in technology innovation and digital transformation.

The case can be considered from a range of theoretical perspectives. In our earlier section on case analysis opportunities, we introduce a selection of theoretical frameworks and perspectives that could be applied. We note that the shipping industry is a very complex domain. There is a wealth of relevant material available in both academic and gray literature to supplement the information provided with this case. Some research notes follow, relating to how the suggested readings and case analysis opportunities we discussed earlier can be related to specific aspects of the case.

Recent technology innovations at Maersk involve multiple “combinatorial” technologies (Alairys et al., 2018). In particular, it can be observed that automation of the processes in container terminals and container ships require large investments in robust and sophisticated robotics, situational awareness and artificial intelligence that can be scaled. A major challenge is that since the combined capabilities of these technologies are increasing at an exponential rate, it is difficult to identify the point in the evolution of the technologies when large capital investment is appropriate and to avoid investing in technologies that may become rapidly outdated. The options available to Maersk could be investigated.

In order to maintain a leading position in the shipping industry, Maersk faces the challenge of maintaining its BAU operational leadership in a competitive industry that is plagued by complexity and fluctuations in supply and demand, while continuing to innovate. The need for sustained operational excellence combined with the need to innovate indicates that Maersk will need to develop a high level of organizational ambidexterity (Kranz et al., 2016). A review of the history of some of the innovations carried out by Maersk, such as the TradeLens platform, and the autonomous shipping initiative carried out in conjunction with Sea Machine Robots, suggests that Maersk has adopted a strategy of selecting partners and/or creating sub-brands to manage innovative projects. It is also clear that as with the move to container shipping, Maersk is not averse to making very large investments in new technology. The senior management team need to maintain the core business of shipping, while making appropriate investments in digital transformation and new technologies. Case analysis could investigate what types(s) of organizational ambidexterity have been practiced by Maersk, whether this has changed over time, and why, and what approaches to organizational ambidexterity are appropriate at various levels of the organization.

A closely related challenge, as we show in Figure 4, is developing the absorptive capacity of the organization. Particularly when many technology innovations are combinatorial, it may not be immediately clear what opportunities are presented by new technologies. For example, the technology used to develop autonomous vehicles has potential to be applied to autonomous shipping, but different information and decisions will be required. The AI used to optimize logistics and shipping routes can potentially be used for prediction and planning purposes, and to support a digital assistant. Case analysis could investigate how reconceptualizing the organization as integrated and technology-enabled has improved the absorptive capacity of the organization. Other areas of interest could include the extent to which partners were used as a source of learning, and the effectiveness with which Maersk has successfully identified opportunities based on digitalization.

Dynamic capabilities involve the ability not only to identify new opportunities but to design and implement changes. A major challenge for an organization such as Maersk is that while opportunities associated with digitalization such as the development of an AI assistant may be (comparatively) inexpensive to implement, the areas of the business where digital innovation intersect with physical logistics capability require massive capital investment. It has been estimated that the cost of the port automation carried out in Rotterdam was upwards of half a billion dollars (Petersen, 2015). Another significant challenge, as we observed in the history of the development and eventual closure of the TradeLens, is the complexity of the industry ecosystem, and the difficulty in negotiating standards, even for a dominant player such as Maersk. Considering the components of dynamic capabilities shown in Figure 5, we can see that the delivery stage for many innovations is especially challenging. While public statements from the CEO, as well as the divestment of some major parts of the business (for example, Maersk Oil), indicate a desire to focus on integrated logistics capability, many physical aspects of the business remain slow and expensive to digitize, automate and reconfigure. A more complete analysis of this issue is included in the report by Petersen (2015).

The history of digital transformation at Maersk so far, as well as many of its recent public statements, attest to a business vision that is based on information integration and the development of new business models in partnership with technology companies. They have also shown a willingness to develop new business models based on new technologies, such as distributed ledger technology and smart contracts. Its dominant position in the shipping industry also means that they capture enormous amounts of data that can be used in AI and analytics applications to develop new information-based services. This will only increase as additional data streams, supplied by (for example) sensors and the internet of things, become available.

It is interesting that the TradeLens platform did not, in the end, succeed in gaining the level of cooperation it required to succeed. Nevertheless, Maersk has shown a commitment to developing new, digitally enabled business models. Its current strategy seems to be to develop highly targeted solutions to specific logistics problems, rather than the ambitious and integrated TradeLens platform. Its website includes a dedicated section for digital solutions (Maersk, n.d.-b), which include a range of offerings including a spot price application, a supply chain optimization tool, a sustainability dashboard, and Captain Peter^TM, an assistant for monitoring perishable cargo. Maersk seems to have adopted an agile approach for enhancing Captain Peter^TM, based on feedback from customers and continuous improvement. The ability for customers to customize the services and pricing models based on their needs also indicates a commitment to customer-centric solutions. Further analysis of the case could include analyzing the business models for Maersk’s existing digital offerings and developing new business models for new digital products and services.

As discussed, the international shipping industry was already a complex ecosystem long before the advent of digital technologies. Digitization of documents and digitalization of processes offered many opportunities for new business models, as well as improved management of existing services. However, the size and complexity of the ecosystem, the involvement of multiple local and national administrations have acted as a brake on the development of a central platform. Maersk’s TradeLens platform did not achieve the central and dominant position that was originally envisaged.

When developing a digital logistics ecosystem, Maersk has one advantage that “digital only” competitors such as Amazon.com do not have, which is that much of the data about the physical movement of goods is created and managed in the normal course of its logistics operations. Solutions that improve the efficiency of their own operations also generate large amounts of valuable data that can be used to develop new niche offerings in the ecosystem.

Conclusion

Maersk is in the enviable position of being a dominant player in shipping industry, controlling the creation and management of large amounts of information, and with a large capital base. However, the very large investments required to effectively merge the physical and digital spheres tend toward a conservative approach to innovation, while creating new digitally based business models require an agile approach. Maersk needs to maintain its position as a leading shipping company and continue to use digital technology to improve its BAU services, while at the same time supporting a program of sustained innovation and developing new digital business models.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15228053.2023.2300921

Additional information

Notes on contributors

Manoj Dagar

Manoj Dagar manages the service desk team at Fujitsu New Zealand. He has experience in service management, capability-building and team leadership. Manoj has completed post-graduate study Information Management. He applies contemporary best practice and research expertise to his leadership roles.

Mary Tate

Mary Tate is an Associate Professor of Information Systems at Victoria University of Wellington, New Zealand. She has over 100 publications in leading outlets. Mary is interested in digitization, digital transformation, leadership, and digital service delivery in the public and private sectors.

David Johnstone

David Johnstone is a Senior Lecturer in information Systems at Victoria University. David has published over 50 articles in leading information systems journals and conferences. David is interested in data and information systems governance, platforms, crowdwork and design.

Notes

¹ https://www.youtube.com/watch?v=iThr7hI34-g

² https://www.maersk.com/digital-solutions/logistics-hub