Supply chain trends 2024: The digital shake-up

Enabled with a raft of technology developments, a new paradigm is emerging in supply chain management. One where organizations can respond quicker to day-to-day requests, proactively address problem solving, and reduce errors and inefficiencies. It can also provide greater visibility, transparency and traceability. Most importantly, organizations will be more resilient to future supply chain shocks.

With a future that promises autonomous, self-learning machines seamlessly managing the broader supply chain process, now is the time for organizations to overcome the inherent silos and enterprise systems that will restrict their progress.

To get started, organizations need to first embrace the trends that will define 2024. This includes learning about emerging technologies from AI to distributed ledger technologies, low-code and no-code platforms and fleet electrification. This will need to be followed by managing the migration to a new digital architecture and executing it flawlessly.

Organizations will need to intensely focus on mining relevant, clean and well-governed data if they want to make the most of their new technology investments. Data will also be crucial as organizations are pressured to meet evolving ESG and Scope 3 commitments.

These structural trends will shape new operating models and improve broad processes. To avoid being left behind, it is important for organizations to understand these trends and apply specific actions to begin their transformation sooner rather than later. This way they can create a more agile and responsive supply chain that can capture the promise of value creation, cost reduction and improved shareholder value.

Generative AI (GenAI) is a subset of AI that has the potential to revolutionize supply chain management, logistics and procurement. Software engines powered by GenAI can process much larger sets of data than previous forms of machine learning and can analyze an almost infinitely complex set of variables. GenAI can also learn —and teach itself — about the nuances of any given company’s supply chain ecosystem, allowing it to refine and sharpen its analysis over time.

The list of opportunities for GenAI is extensive. It can help ensure procurement and regulatory compliance, streamline, and enhance the efficiency of manufacturing production workflows, or enable virtual logistics communication by using virtual assistants to handle routine inquiries and provide quick responses.

The use of AI is an enterprise-wide consideration, organizations must avoid dissipating effort across several single point disconnected AI implementations. Core business processes should be strategically rethought and redesigned to effectively leverage GenAI.

With the continued focus on resilience and ESG coupled with the expansion of sites, flows, and partners, the pressure on supply chain planning is increasing. Existing planning capabilities have been unable to meet the demands of a more complex, multi-tiered, more nuanced world. The result is few companies can run effective scenario analysis to determine the financial consequences of important decisions.

AI enabled sales and operational planning (S&OP) and integrated business planning (IBP) applications will help eliminate the gap between supply chain planning and execution. Low touch planning will take large swaths of manual work out of the end-to-end planning process and leverage the power of advanced analytics to answer deeper questions with minimal human intervention. AI will be able to analyze data at scale, identify anomalies, search for patterns that lead to unexpected disruptions, and make suggestions on how to solve them—almost instantaneously.

From a technology perspective, the capabilities to enable low touch planning are like a control tower or its more advanced counterpart, the cognitive decision center which includes digital twin capabilities. These promise improved predictability, enhanced gross margins and free up resources to focus on value adding activities.

Data is still one of the core challenges facing supply chain management. Each day millions and millions of date records are generated across the supply chain from multiple systems. The proliferation of digital technologies, IoT devices, and advanced tracking systems have compounded the problem. This wealth of data has given rise to greater silos of data within the organization which in turn has led to disconnected data sets. Duplication and misinterpretation will become increasingly problematic, too. Critically, the fragmentation of data impedes the creation of a holistic view of the organization’s supply chain.

Consequently, data availability, quality, cadence, and consistency – are now critical considerations. Supply chain professionals must manage the complexities within their data landscape efficiently; to be able to make informed decisions and enhance their operations.

A solution is to adopt a use case-driven approach to proactively address data quality issues. By focusing on specific use cases, organizations can prioritize data quality improvements where they matter most, thereby gradually refining and improving their datasets.

The lack of visibility across the layered tiers of a supply chain has major implications for organizations across industries, particularly for meeting regulatory requirements, and for the identification and mitigation of supply chain risks.

Breaking the barrier of visibility beyond Tier 1 allows organizations to look across their extended supply chain into partners, build greater and deeper insights into root causes, identify new risks that occur further into the supply chain and drive ESG goals through better traceability and transparency.

Technology tools such as control towers and digital twins can surface critical sub-tier supplier relationships, highlight common sub-tier suppliers, factory locations and provide clear insight into the depth of an organization’s supply chain. When implemented at scale they can improve supply chain resilience.

A supply chain is a dynamic and complex process that includes provisioning, raw material supply, warehousing and the distribution of manufactured products to consumers. Historically, this has resulted in multiple systems and data sources. Implementing software change in this environment is time consuming with a high probability of errors.

Most supply chain tasks can be fully or partly automated through low-code platforms, which use a wide range of Application Programming Interfaces (APIs) and pre-packaged integrations to link previously separate systems. These cut the development time, enabling companies to swiftly react and adapt their applications to new market conditions, disruptive events, or changing strategies. It enables business users with little technical knowledge to quickly build, test and implement new capabilities.

Potential applications span planning, manufacturing, product life cycle, supply chain collaboration, and track and trace. Low-code platforms are not just a technological upgrade; they represent a paradigm shift in how organizations approach their operations providing a pathway to a more agile and adaptable future.

While many businesses have traditionally prioritized the collection of their Scope 1 (direct emissions) and Scope 2 (purchased electricity) emissions data, the focus has now shifted decisively toward Scope 3 emissions – that is, emissions incurred throughout the entire value chain. Although voluntary to date, the collection and reporting of Scope 3 emissions data is becoming a legal requirement in many countries.

Establishing a solid emissions baseline is essential for monitoring progress and setting ambitious reduction targets. Scope 1 and Scope 2 emissions are relatively straightforward to assess however, when extending this to the full supply chain, as in Scope 3, the complexity multiplies exponentially.

To target reductions in carbon emissions, companies need primary sources of information from their suppliers, and are starting to use hybrid carbon accounting methodologies to produce a more accurate assessment of Scope 3 emissions. Digital platforms are providing a centralized system for suppliers to input their emissions data, which can then be easily integrated into a company’s sustainability reporting.

The logistics sector is also undergoing rapid transformation. Some elements of future-ready transport and logistics networks are already in evidence such as the automation of warehouses and ports, and the increasing use of autonomous vehicles. Their adoption will expand as organizations commit to emissions reduction targets and battery technology evolves to extend distance limits for electric trucks, buses and delivery vehicles.

Organizations will continue to accelerate the electrification and automation of the logistics transport value chain – especially those that remain costly or manual, such as processing of air freight and last mile delivery. Similarly, the transition from autonomous vehicles overseen by humans to fully automated vehicles without human intervention is almost ready to expand from controlled closed-loop environments to public roads.

Smart logistics and transport will also be accelerated with the continued ramp-up of AI, IoT, data analytics and cloud across many use cases – improving traditional route optimization and applying machine learning, predictive and sensing capabilities to make material improvements to network efficiency, customer experience, risk reduction and sustainability targets.

As we stand on the brink of 2024, the supply chain landscape is on the cusp of profound transformation. AI and other advanced technologies are quickly reshaping the very core of supply chain management. KPMG professionals believe organizations with the right approach and culture can harness these seismic shifts.

In 2024 organizations could gain fundamental opportunity by focussing on the strategic application of GenAi, adopting a low-touch planning approach, striving for data excellence and transparency, adapting to low-code platforms, prioritizing Scope 3 ESG data reporting, and planning for the electric future. Time is of the essence, and those who are ready and willing to adapt quickly will be better able to unlock value, reduce costs and embrace new models of success.