What really works in the digital supply chain?

From the July-August 2024 edition of Supply Chain Management Review.

July-August 2024

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In 2014, SAP Business Consulting conducted its initial study to understand companies’ visions for digital supply chain management. Based on numerous interviews with supply chain executives from different industries, these visions were exciting: many innovative technologies were emerging, a set of 120 use cases was compiled, and companies demonstrated a willingness to invest and experiment with these innovations. Companies explored interesting ideas like the 3D printing of Lego bricks, social media data for forecasting, mini-factories, super-fast bill of material explosion, and last-mile delivery by drones—many of them quite visionary at that time. Now, a decade later, we have completed a follow-up study in collaboration with Kühne Logistics University to examine the current state of digital supply chain journeys, the application of emerging technologies, and the evolution of corporate expectations on digital supply chain technologies as the hype has faded.

After discussing with numerous supply chain executives (old and new ones) their real-life experiences, we find that companies’ perspectives have changed. Our research indicates that successful companies adopt a pragmatic approach to their digital supply chain initiatives. We observed a scaled-down and delayed implementation of digital technologies, with fewer companies adopting them than expected. This shift can be attributed to an underestimation of the complexity of use cases, a misunderstanding of the capabilities and limitations of digital technologies, and the companies’ level of technological readiness. Organizations often struggle with immature technologies, a shortage of skilled personnel, and inconsistent data management when embarking on a digitalization journey. In contrast, a few “early adopters” have found ways to leverage certain digital innovations quickly to gain competitive advantage—predominantly driven by industry clock speed and significant upfront investment in technology and skills.

Contrary to the broad digital ambitions initially envisioned, firms are now prioritizing technologies that address clearly defined problems and provide rapid returns. Companies in our interview sample focused on projects that aim to improve fulfillment reliability, minimize supply chain disruptions, or add immediate value to the customer. The changing corporate environment, characterized by heightened volatility, stronger competitive pressures, and complex operations, constrained by limited resources, had contributed to this change in ambitions.

 

In this article we aim to provide a realistic perspective on what is needed to successfully deploy new digital technologies for better supply chain management. We take a closer look at the technologies that offer the biggest potential and outline key factors for the success of digital projects. Finally, we provide insights into the practical realities of adopting digital technologies in supply chains, underscoring the need for focused strategies, clear management support, and thorough execution.

The supply chain world has become more interesting, and more difficult

The digitalization of a supply chain cannot be done in isolation from the dynamics of the environment surrounding the supply chain. And, these dynamics have been very challenging for most companies over the past several years. External challenges resulting from the COVID-19 pandemic, geopolitical disruptions, climate change, and volatile economic conditions have forced supply chain managers to re-evaluate their global footprint, diversify risk, and shorten supply lines to create more resilient supply networks. Organizations are struggling to find qualified staff, both for new roles such as data scientists and AI specialists, and for more traditional supply chain roles such as transport drivers and warehousing personnel.

If the challenges posed by these external events weren’t enough, implementing solutions to address their impacts—such as near-shoring, increased safety stock, network redesign, and improved planning—has put yet more pressure on today’s supply chain professionals. To cope with the complexity of this new normal, supply chain managers are increasingly turning their attention to new technology. Thanks to technological advances (e.g., the maturity of cloud computing, improved algorithms, increased computing power), supply chain technology is becoming more sophisticated and capable. At the same time, budgets and resources for experimenting and implementing these technologies are often limited.

Most managers understand that aiming to revolutionize entire supply chain processes with new and potentially untested technology is extremely risky. Even use cases that seem simple can easily fail (see Table 1: Examples of successful and failed SC technology projects). A key question that managers need to answer is where to start deploying emerging digital technologies in their supply chain. Given the number of digital technologies and the different processes in the supply chain, this is certainly not an easy question to answer.

We solicited the insights of experts, asking them to suggest promising combinations of processes and technologies that could yield the greatest potential for practical use cases. With eight business processes such as design, planning, and sourcing, and 14 prominent digital technologies under consideration, we developed a total of 112 possible combinations. From the experts’ input, we constructed a heat map using a process-technology matrix to represent the use cases that companies were either exploring or considering (see Figure 1).

 

Four key application areas are emerging

The distribution of company interests across this matrix is decidedly uneven. For instance, while technologies like 5G garner limited attention across the board, others, such as augmented reality and computer vision or autonomous vehicles and robots, stir up more interest within a few specific processes. As anticipated, technologies such as artificial intelligence and advanced analytics attracted interest across virtually all processes. Based on the experts’ responses, we have identified a particularly intense focus on digital adoption in four key application areas. Owing to their diverse yet integral roles in supply chain management, these appear to be the frontrunners for the adoption of digital technologies in many companies.

Advanced analytics in planning and operations is leveraging big data, machine learning, and artificial intelligence to increase knowledge of—and visibility into—supply chain operations. Advanced analytics is capable of processing vast data volumes, enhancing forecasting accuracy for demand, yield, and lead times. Companies prioritizing improving demand forecasts and identifying service failure causes, find machine learning solutions particularly promising. For example, a high-tech company analyzes customers’ reactions to service failures to prioritize production plans accordingly. However, given all the potential benefits of advanced analytics, managers need to deepen their understanding of its limitations, recognizing that unpredictability remains and not all decisions can be improved through analytics.

 

What really works in digital supply chain?

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Integrated planning with digital platforms connects demand, supply, and distribution planning within the organization and between its upstream and downstream partners. In response to the often still unpredictable nature of supply and demand, companies are increasingly adopting digital technologies to enhance planning agility. Utilizing digital platforms and digital twins facilitates a unified data source, enabling more responsive adjustments to market shifts. For example, a leading semiconductor manufacturer has developed a digital twin of its entire supply chain that allows it to simulate and finetune operations end-to-end. This move, from isolated to integrated planning, supported by real-time supply chain status data, exemplifies modern strategies for overcoming volatility.

Technologies for improving the customer experience enhance the customer journey and improve their experience of interacting with the organization. To meet the dual challenges of intensifying competition and growing customer expectations, numerous firms are investing in technologies directly affecting customer experience. Innovations such as tracking and tracing, chatbots, and customizable digital platforms are transforming how customers interact with companies. A notable initiative is a paint manufacturer’s app that lets customers virtually test paint colors at home using augmented reality, coupled with an automated production process for customized orders. This approach not only revolutionizes the customer experience but also offers significant implications for the supply chain and e-commerce models.

Automating digital operations enables an organization to streamline tasks along the supply chain by, for example, using robotic process automation for order management. Given the surge in decision-making complexity (e.g., with more granular planning on product and location level) there is a need to manage escalating supply chain workloads amidst limited human resources. By automating processes and reducing the need for manual intervention, companies can handle increasing system and product complexity more efficiently. An example is an e-commerce company that has automated inventory management to minimize reliance on manual decision-making. These innovations reflect a strategic shift toward enhancing system-driven decision-making and efficiency in supply chain management.

Viability and feasibility of technologies

To deepen our understanding of the various technologies’ potential, we invited the participating supply chain experts to share their experiences with the technologies they have implemented. We asked them to characterize each implementation in two dimensions: viability and feasibility. The results are shown in Figure 2. Viability (on the x-axis) refers to the expected value of a technology implementation in terms of costs and benefits. Feasibility (on the y-axis) refers to the perceived ease of implementation.

 

We find that autonomous vehicles and robotics technologies have demonstrated high value and feasibility. Companies are effectively leveraging AGVs, robots, cobots, and autonomous forklifts realizing substantial benefits in shop floor and logistics operations, showcasing the maturity and practical applicability of these solutions. While AI and advanced analytics are viewed as highly promising, their application has yielded mixed results. High expectations are often met with challenges in implementation, primarily due to the need for skilled experts like data scientists for developing and training models. This situation has resulted in a split between the anticipated value and actual feasibility, highlighting the complexities involved in harnessing these technologies successfully. On the other end of the spectrum are technologies such as blockchain and digital twins: They have considerable challenges in terms of feasibility and practical application. Blockchain projects often lack a clear business case, and digital twins have struggled with the complexities of modeling and simulation, making these technologies less viable in the current landscape.

Pragmatic vs. strategic technology adoption

Most companies we talked to have elected to follow a pragmatic approach toward technology adoption that is driven by the necessity of a clear business case and a short-term return on investment. Despite the initial enthusiasm for new technologies, as in the early days of RFID, the current trend emphasizes technologies with clear and immediate business benefits. The unanimous sentiment across different sectors and roles is the need for new technologies to solve specific business problems and ensure a rapid ROI, underscoring a shift from technology-driven to need-driven implementations.

This pragmatic, results-oriented focus (see Figure 3) is critical for organizations aiming to navigate the hype and genuinely benefit from digital transformations. With all the media hype surrounding new digital technologies, one could be forgiven for assuming that organizations that do not immediately implement these cutting-edge technologies would be left behind. The reality is that organizations that follow a pragmatic approach to testing various digital technologies in their operations will be clearly aware of the opportunities and challenges. They are more realistic in where they might apply digital technologies and what benefits they might yield from an economic and operational perspective. As the supply chain manager of an agrifoods company states, it is the business need that drives technology implementation, not technology hype.

 

In sharp contrast, long-term investments in technology are mostly confined to a few industry pacemakers with advanced capabilities which are often categorized as strategic research rather than immediate operational problem-solving. The drive toward technological superiority is not just about keeping pace with competitors but about fundamentally altering the competitive landscape. These organizations, operating in sectors where technology directly influences market standing, are more inclined to invest in and experiment with cutting-edge digital solutions like AI, digital twins, sensors, and blockchain, even if these technologies do not promise immediate payback for the selected use cases.

Barriers and factors for success

While the promise of new digital supply chain technologies is large, many implementation projects fail due to some common barriers that are often underestimated. First, often immature technologies are hyped as being ready for prime time when they are at best in the beta testing stage. Frequently, a technology fails to meet the performance standards necessary for the harsh realities of day-to-day operations (e.g., augmented reality in picking). Second, projects often struggle due to a lack of skilled personnel for the implementation and rollout. New technologies often require new skill sets and new talents (e.g., data scientists for ML, simulation experts for digital twins) that are frequently unavailable internally and in high demand externally. Finally, poor data quality, lack of data and fragmented IT systems often drive tremendous implementation effort. Whenever advanced analytics is involved, the data foundation is key. This requires a high degree of data harmonization, which is difficult to achieve when IT systems are highly fragmented and data harmonization is still an unsolved problem.

Some companies that have been successful in supply chain technology projects credit their success to up-to-date and integrated IT systems and a strong global IT team—or even a separate department that drives the digitalization of the company. While not every company has the benefit of these luxuries, we identified multiple success factors that significantly increased the chances for success. First, companies require clear commitment from management and partners as setting strategic directions with achievable and consensus-driven goals is crucial for the successful completion of digitalization projects. Garnering support from internal business stakeholders and external partners is equally important. This often goes along with a clearly defined business need. If the business has identified a problem that can be realistically addressed with digital technology, the project definition becomes more straightforward and drives buy-in. Projects initiated in this way are more successful than those driven top-down or that are only technology-focused. While implementation costs are often difficult to forecast, a strong business case with a solid benefit-to-cost ratio is more likely to be supported by people in various lines of business as they become convinced that their time will be well spent. Finally, digitalization projects need to be supported with professional change management, spanning not just IT but also the whole organization and its business processes. For example, new tools for forecasting and planning are often black boxes and it is difficult to build trust into recommendations as explanations get lost for the users. Here, it is critical to train users, start small, and increase the portfolio of SKUs managed over time as buy-in increases.•

About the authors

Dr. Joerg Wilke is head of the SAP business consulting practice for supply chain management. He can be reached at [email protected].

Dr. Andre Brunner is a chief enterprise consultant at SAP in Walldorf. He can be reached at [email protected].

Dr. Kai Hoberg is a professor of supply chain and operations strategy at the Kühne Logistics University. He can be reached at [email protected].

Rod Franklin, Ph.D. is a professor of logistics practice at the Kühne Logistics University. He can be reached at [email protected].

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