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Digital tsunami reshaping supply chains
The supply chain landscape is experiencing a technological revolution. With the rapid development of GenAI, data analytics, automation, machine learning, Internet of Things (IoT), blockchain, and other innovations, the transition to a 'smart' supply chain has quickly gained momentum.
In this new paradigm, organizations can swiftly address daily demands, take proactive measures in problem-solving, and mitigate errors and inefficiencies. This approach not only enhances visibility, transparency, and traceability but also strengthens organizations to navigate future supply chain challenges with greater resilience.
The trends that will shape 2024
Embarking on the path to supply chain digitization begins with organizations recognizing and incorporating the trends that will shape 2024. This entails dedicating resources to understand a spectrum of technologies, spanning from AI (artificial intelligence) to control towers and digital twins. By doing so, companies can attain improved visibility, heightened agility, and set the foundations for building "antifragile" supply chains.
To maximize the benefits of their new technology investments, organizations must also dedicate significant attention to extracting pertinent, clean, and well-governed data. The importance of data is further underscored as they face increasing pressure to fulfill evolving ESG and Scope 3 commitments.
Potential benefits of the trends outlined below include increased agility and responsiveness, as well as cost reduction, greater value creation, and enhanced shareholder value.
Generative AI
As traditional supply chains grapple with increasing complexity and volatility, will GenAI be the beacon of hope? This transformative technology, capable of powering novel solutions with its approach to quickly surfacing relevant data, is poised to revolutionize the way we manage logistics in 2024. Some organizations have already embraced GenAI to optimize route planning, predict disruptions, and facilitate virtual logistics communication. Through the use of virtual assistants, they can handle routine inquiries more efficiently by providing prompt responses.
“One of our marquee customers, an automotive OEM, has worked with us to build out their "logistics control tower" which automates the disruption management process and provides next-based action recommendations with potential cost implications in real-time based on latest available data,” says Anand Medepalli, Chief Product Officer at Shippeo. “We believe there is significant interest in our customer base for this technology.” Their next evolution is to leverage GenAI to support queriesQ&A from their users instead of just receiving next best action recommendations.
In order to reap the benefits of generative AI technology, it’s important for organizations to:
- Achieve proficiency in effective data management: AI systems depend on precise, reliable, and current data to formulate predictions and produce valuable insights. Identify superior internal and external data sources, both structured and unstructured, to ensure the robust foundation required for successful AI initiatives. It's important to recognize that maintaining data quality throughout the supply chain poses challenges, particularly when dealing with diverse suppliers, varied geographical locations, and different data formats.
- Identify business needs and build a benefits case: Recognize specific business requirements that warrants the integration of generative AI and construct compelling benefits cases around them. By identifying distinct business needs in areas such as planning, sourcing, manufacturing, or delivery, leverage the capabilities of generative technology to fortify and enhance the operational processes associated with fulfilling that need.
- Facilitate collaboration within the organization: Address implications of generative AI and identify the essential skills needed across various functions, extending beyond merely technical roles.
Big data and analytics
Unlocking the potential of AI requires watertight data management best practices, as effective digital transformation projects are contingent on the quality and consistency of data. The challenge therefore arises from the sheer volume of data and information, presenting a difficulty for humans to navigate through.
Smart technologies are required to discern and alert the relevant aspects that demand attention. Take the automotive supply chain, for instance, where inefficiencies surface when trucks arrive early but encounter delays in pickups. The question arises: Are we prepared to shift our mindsets, adopting a management-by-exception approach and entrusting technologies to handle specific tasks, allowing us to concentrate solely on essential areas? Is the time ripe for this shift in trust?
The tech that exists now facilitates the consolidation of master data, requiring organizations to prioritize proper data management and the integration of market data. The challenge lies in navigating through the vast dataset and ensuring that every member of the organization utilizes the data accurately.
According to Anand Medepalli, “creating solutions involves seamlessly navigating data to extract relevant information. These solutions should offer the flexibility to explore data, retrieve desired information, and swiftly recover from any mistakes.
An emphasis should be placed on providing the freedom to modify data parameters, allowing users to reach their intended destination. For instance, encountering a delay in shipment is one thing, but understanding what actions to take based on that information is equally crucial.
The growing significance of process automation, decision support, and effective decision-making underscores the evolving landscape.”
Ultimately, crafting a coherent narrative around data and ensuring its accuracy is paramount for the success of any digital supply chain initiative.
“ Unlocking the potential of AI requires watertight data management best practices. Data quality and consistency is essential for successful digital transformation projects.”
Building Antifragile Supply Chains with Visibility
According to a McKinsey survey conducted in 2022, 45% of companies admitted to lacking visibility into their upstream supply chain, with some indicating visibility only extending to their first-tier suppliers.
The persistent absence of visibility into the performance of tier one suppliers remains a concern, posing a threat to the effective identification and mitigation of supply chain risks.
One key focus in 2024 will be on managing supplier risks and boosting transparency in this area. Supplier risk management is gaining traction as a rising category, offering insights into the risks involved in supply chain operations—something hard to handle manually. With the help of a digital twin, organizations can model different scenarios, consider the risks, and always have a backup plan at their fingertips to enhance supply chain resilience.
However some believe resilience doesn’t go far enough. Organizations are now reaching for a level beyond resilience, that goes further than merely protecting themselves from a disruption to get back to the position they started from – the classic bend but not break scenario. It refers to a state where disruptions and uncertainty are embraced, one where supply chains thrive, strengthen, and ultimately land in a better position compared to where they started.
Such a framework comes from Nassim Nicholas Taleb, who in 2012 published the New York Times bestseller ‘Antifragile: Things that gain from disorder’, and coined the term antifragility, which he defined as a property of systems that can increase their capacity to thrive as a result of shocks, attacks, failures, or any kind of volatility. As the old adage goes, antifragility embodies the spirit of ‘what doesn’t kill you makes you stronger’.
In the context of supply chains, when high quality visibility data is combined with the right platform capabilities, supply chains can go beyond merely coping with uncertainty and volatility (seeking to return to their normal state), to thriving, becoming stronger and better off with each obstacle or challenging operating environment faced.
Two key attributes are needed to enable antifragile supply chains.:
1. High data quality: Reliable, high-quality data is crucial for enhancing agility, responsiveness, and understanding the potential impact of uncertainties within your internal teams and ecosystem partners. It forms the foundation for trust, enabling end-to-end decision alignment and process orchestration, providing continuous visibility into the supply chain.
2. A true system of engagement: Relying on a system of information won’t suffice; the ability to assess the material impact of disruptions is essential. Transforming visibility data into actionable insights and probability calculations is crucial for urgently addressing current issues and proactively preparing for future challenges.
In the wake of ongoing geopolitical shocks such as Red Sea maritime terrorism and the Russia-Ukraine war, events unlikely to settle down soon, the opportune moment has arrived to invest in establishing the groundwork for constructing antifragile supply chains.
Talent Investment to drive adoption and automation of new tech
The increasing use of digital tools in supply chain, particularly AI-based solutions, mean leaders must create digital mindsets at work, which includes focusing on ongoing learning and upskilling. Supply chain leaders can capitalize on the chance to attract and recruit members of Generation Z, leveraging their influence to maximize the process of supply chain digitalization.
According to Gartner, between 2025 and 2030, many hyperautomation technologies, such as machine learning, are expected to mature and enter mainstream adoption. They will help automate supply chain decision-making by augmenting human judgment. Therefore, today’s supply chain leaders expect Gen Zers to be innovators that accelerate supply chain digitalization and pave the way towards hyperautomation.”
The progression of Generation Z in the workforce serves as a catalyst for transformative change. This evolution involves overcoming various challenges, including cultural resistance, a shortage of digital skills, insufficient top management support, and misalignment between IT and business goals—issues that necessitate a clear articulation of ROI and effective stakeholder persuasion.
While embracing technology is essential, there should be a balanced approach that avoids over-reliance. Additionally, emphasis should be placed on aspects like change management, process alterations, comprehensive training programs, and the cultivation of a culture that embraces organizational shifts. These considerations collectively contribute to a holistic and effective transformation within the workplace that can that can accelerate supply chain digitalization
ESG & Scope 3 emissions reporting
Accurately measuring Scope 3 emissions, which refers to indirect emissions throughout a company’s value chain, is a serious challenge. However, its importance continues to grow as companies prepare for increasing regulation on supply chain transparency, due diligence and environmental impact. The inclusion of scope 3 allows for a more comprehensive understanding of the organization’s impact on the environment and can inform efforts to reduce emissions throughout the value chain. Moreover, companies’ scope 3 emissions account for 75% of total emissions on average, and in some cases up to 100%, making them critically important to monitor and improve upon.
To reduce carbon emissions, businesses must acquire detailed information from their suppliers. Employing a combination of various methods to quantify carbon, companies are striving for a more precise understanding of their Scope 3 emissions. Digital systems play a crucial role in facilitating this process, allowing suppliers to seamlessly share their emissions data, which is subsequently integrated with a company's ESG reports.
Moreover, fostering employee understanding of Scope 3 emissions, along with providing education and support for carbon reduction methods and technology solutions in the collection and management of carbon data, is essential. Integrating a change management strategy into the decarbonization action plan is crucial for ensuring its successful implementation.
In summary
As we advance in 2024, the supply chain landscape stands at the brink of significant transformation, with AI and other cutting-edge technologies rapidly redefining the fundamental aspects of supply chain management.
Organizations stand to gain significantly by strategically unlocking new opportunities through the deployment of GenAI, the construction of antifragile supply chains, prioritizing Scope 3 ESG data reporting, and fostering digital mindsets within the workplace. Those prepared to adapt swiftly and establish the foundation will not only unlock new possibilities but also have the chance to pave the path towards supply chain excellence.
