The seemingly endless possibilities in manufacturing and logistics arrive with unprecedented challenges. The relentless pace of technological change and constant oscillations of markets put pressure on businesses to remain relevant. Leaders need to be “antifragile” and “smarter” in using data to maximize the benefits of this era’s technological advancements and reap the returns on their investments.
In this next part of our series on technology and analytics trends for 2024, experts at Lingaro’s supply chain analytics (SCA) practice discuss developments in logistics, warehousing, and transportation as well as manufacturing, and how Lingaro can enable businesses to keep pace with evolving trends and technologies.
The ‘antifragile’ supply chain: Elevating resilience in logistics
Further increases in logistics costs (e.g., labor, electricity, energy, fuel, transport) and constant disruptions in the flow of goods make uncertainty a norm in managing warehousing, transportation, and logistics operations. In fact, according to Gartner® 89% of chief supply chain officers (CSCOs) believe that this uncertainty will remain and even become more unpredictable. Moreover, 63% of supply chain professionals anticipate decreases in revenue due to their susceptibility to uncertainty.
While resisting shocks, avoiding risks, and withstanding or cutting losses are still crucial, reinforcing the supply chain’s resilience — realizing the best possible outcomes while proactively learning from adapting to change — will be a key theme for logistics in 2024. This fil rouge will be the juncture toward “antifragility,” which, instead of avoiding uncertainty, uses it as leverage for growth.
“The idea of an antifragile business is not new,” said Lingaro lead supply chain consultant Konrad Rosiński. “Its underlying principle views uncertainty as a catalyst for growth, and a compass for finding opportunities. When applied to supply chain management, it entails investing in the right digital capabilities to sense disruption and swiftly change and adjust plans as conditions evolve. This is particularly true for logistics, where costs, resources, and capacity hinge on fast-changing market conditions, shifts in supply and demand, consumer preferences, competitor behaviors, economic trends, and geopolitics.”
Antifragility in logistics means reinforcing its resilience, which, in turn, will require leaders to gain full visibility into their supply chain. “Large international companies will look more closely at the end-to-end logistics process, looking for comprehensive solutions, not just individual elements to optimize — a chance to design and implement strategic solutions covering the entire supply chain,” Konrad said. Indeed, a McKinsey study revealed that organizations that improved end-to-end supply chain visibility are twice as likely as others to prevent problems brought about by supply chain disruptions.
This visibility, Konrad furthered, will enable CSCOs and logistics decision-makers to better monitor and manage activities that affect their supply chain’s operations or business’s revenues. Full transparency and insight into these activities strengthen antifragility by enabling the logistics chain to evolve — to learn and adapt — and transform exposure to volatilities into a competitive advantage.
“In resource management, for example, a CPG or manufacturing company can base on seasonality to dynamically scale or reallocate labor. The workforce isn’t just a cost but becomes an adaptable resource that can be grown and upskilled during different business cycles. In warehousing, a demand-driven layout can be reconfigured in real time. For instance, fast-moving goods are placed closer to packing stations, staging areas, or loading docks especially during peak periods, which minimizes distance and travel time for pickers,” Konrad illustrated. A warehouse optimization system can make decisions about the scheduling of tasks in the warehouse, determining not only the start and end time of each task, but also prioritizing tasks that are more important.
Another example would be in transportation, where decision-makers can use a transport optimization system that dynamically adjusts to changing demands. For example, if a certain region faces a truck shortage due to unforeseen spikes in demand, they could automatically reroute vehicles from less busy areas. The system is also ideal for efficient fleet utilization and fleet synchronization model that takes into account the product volume, routes, and optimal mix of company-owned and 3PL (dedicated and spot) trucks. The system could also predict and manage fleet maintenance issues (e.g., regular checks, repairs, wear and tear) to ensure the vehicles’ availability.
These antifragile strategies ultimately converge on one essential element: data.
“Being resilient and antifragile means having the right data intelligence to dynamically adapt and continuously improve,” Konrad reiterated. “It’s in this context that the vast majority of large, multinational companies with global operations will seek savings and improvements in logistics, mainly by using technologies or solutions that enable them to effectively use data.”
To corroborate, IDC predicts that by 2026, 75% of supply chains will invest in solutions that better connect supply chain planning and fulfillment systems. The latest third-party logistics (3PL) study also found that both shippers (companies that ship goods or carriers/forwarders) and 3PL providers included advanced analytics in their top three technologies that have the greatest potential for their organization. AI and machine learning (ML) will play vital roles, too: AI-powered automation in the warehouse, for example, is predicted to increase tenfold by 2028. By 2026, 55% of the Forbes Global 2000 original equipment manufacturers (OEMs) will redesign their service supply chains around AI to ensure the efficient and sustainable flow of their goods and materials.
Konrad also cited the various data projects that Lingaro’s SCA practice has built for Fortune 500 CPG and FMCG companies. These include a warehousing operations solution serving as a holistic orchestrator for optimizing space and resources, including labor and material handling equipment (MHE) allocation, outbound tasks scheduling, and work queue management. It first assesses the warehouse’s manual operations then qualifies the level of automation to improve warehouse throughput, cost, and service.
Another is in transportation, using advanced analytics (i.e., AI and other techniques, including statistical and predictive forecasting) to consolidate internal and external data to accurately plan transportation capacity, optimize routes, calculate costs, and run simulations and comparisons. The SCA practice also develops solutions that optimize logistics costs using variables that could fluctuate sharply and regularly — forecasted vs. actual costs/accruals, cost to serve, cost per unit, and freight bill audits, to name a few.
“Data might be the heart of a resilient or antifragile logistics, but its soul rests in the mindset of the organization and its leaders,” Konrad said. “Organizational issues can deter the business from capitalizing on the inherent lessons and opportunities presented by uncertainty. Achieving resilience, let alone antifragility, represents a fundamental shift in the cultural fabric of how CSCOs operate and evolve their strategies.”
Konrad recommends adopting a holistic, long-term perspective on ROI. This involves evaluating and embracing technologies and initiatives that, despite their significant initial investment, promise substantial returns through savings and efficiency gains. Indeed, Gartner’s study on antifragile supply chains indicates that in periods of uncertainty, assessing investment values can be 4.5 times more effective in positively affecting revenue. Similarly, focusing on end-to-end supply chain planning during these times is 2.5 times more likely to yield favorable financial outcomes.
‘Smarter’ manufacturing: Accelerating digital transformation with AI
The past years have been fraught with disruptions in the global supply chain, affecting both manufacturers and customers. Global challenges caused organizations to tighten their industrial belts by reducing costs and eliminating waste. These contexts made the shift to digitalization more compelling for businesses.
In fact, smart manufacturing — the use of technologies utilizing automated, connected, and intelligent machineries and systems — will play a significant role in scaling improvements in manufacturing across disparate people and processes. The priorities of 80% of C-level executives surveyed by Gartner® also cited business growth, cost optimization and efficiency, and digital transformation as their top priorities for increasing their investment in digital technologies for manufacturing.
Yassin Ibrahim, business and enterprise solutions architect at Lingaro’s SCA practice, expounded: “In order to be better equipped to deal with disruptions more effectively and become more resilient, organizations need to collect, centralize, and analyze machine and supply chain data, relying more on technical solutions and AI to process information and translate it quickly into actionable insights — in real time, if needed.”
Data and analytics will be instrumental to digital transformation. Manufacturing control towers, digital twin technology, and advanced analytics provide capabilities to gain insights, optimizations, and simulations to enhance planning and processes. AI and ML will complement other technologies such as cloud computing, computer vision, the internet of things (IoT), and manufacturing analytics for quality assurance, anomaly detection, and predictive maintenance.
Connectivity advancements, in particular, will have a symbiotic relationship with analytics when it comes to transformation. Cloud computing will allow various use cases, AI-powered apps, and data transformations for control towers or digital twins. About 70% of companies are expected to harness hybrid and multicloud solutions while more than half will use more flexible and specific industry cloud platforms according to Gartner®. Increased LTE speeds via 5G networks, on the other hand, will enable faster sensor data collection and real-time processing, tracking, and tracing in IoT and industrial IoT (IIoT) implementations. Lingaro’s SCA practice, for instance, is working with CPG and FMCG manufacturers on their digitalization efforts, designing and building a road map for meeting unique business objectives. The practice also delivers solutions using digital twin technologies as well as use cases that unlock the full potential of cloud data.
Radical and rapid transformations will, of course, entail new systems, processes, and ways of working, not to mention significant investments. Leaders and decision-makers must ascertain organizational readiness from a change management perspective to prevent further costly disruptions to the business. In fact, in 2024, 50% of manufacturers will prioritize digital literacy as a key skill in supporting digital transformation and adopting modern technologies across the enterprise. When executed with scalable resources and workflows, accompanied by the right digital and data competencies and capabilities, the impact should be positive, including productivity enhancements, reduction of operational expenditure, and operational excellence.
“Now, with emerging AI-powered technologies coming into the picture and enterprises becoming more mature in data strategy and infrastructure establishment, organizations see the positive impact of transformation,” Yassin observed. “It’s no longer a luxury but rather a necessity to maintain competitive advantage in a super fierce market.”
The Industry 4.0 landscape is experiencing a dynamic shift driven by smart innovations and contentious contexts. As changes reshape how products are made, transported, and delivered, business leaders and decision-makers must also reshape their processes and systems. Data-driven optimizations, digitalization, automation, and AI can help businesses prepare for the new horizons they aspire for — and reach.