The industrial sector and global supply chains are living through unprecedented times. From global pandemics to geopolitical upheaval, industry's traditional beliefs are being challenged and it has no choice but to re-evaluate long-standing practices.
Two recent examples highlight the need for industry’s supply chain re-evaluation:
- The Suez Canal and the Red Sea witness 30% of global container trade and facilitate the transport of nearly $1 trillion worth of goods. Disruption in these key supply chain channels is causing serious trouble for the industrial sector as it copes with shipping delays and profit margin pressures.
- * Unforeseeable accidents such as the Baltimore bridge collapse add to the pressure, creating a ripple effect on an already fraught supply chain.
While I am confident our industries are learning how to withstand these shocks, the setback of missed revenue opportunities from these events are estimated to be $1.6 trillion. While one solution to these issues is to establish regional manufacturing hubs and prioritize regional suppliers, I believe the paramount goal now is to use generative AI (artificial intelligence) to exponentially increase operational efficiency, dramatically reduce cycle times and rapidly identify and respond to potential supply disruptions.
Applying generative AI effectively
The application of advanced technology, particularly generative AI, has ignited an unprecedented surge of interest. Accenture research found that generative AI can affect 58% of processes in industry supply chains. The magnitude of the scale is staggering.
Generative AI can help identify risks and enable transparency in the supply chain like no other technology before it. As such, it serves as a catalyst in strengthening the supply chain's resilience against unforeseen disruptions and can be used to predict risks like geopolitical shocks or changing market conditions. Furthermore, generative AI has the power to enhance digital twins, taking them beyond their current capabilities.
By feeding real-time data into these virtual representations of machines, products or processes, businesses can test various response scenarios without disrupting day-to-day operations. For example, Mars, the global confectionary chain, in collaboration with Accenture, is using digital twins to simulate and validate the results of product and factory adjustments before allocating time and resources in physical space. Using digital twins, companies can quickly identify bottlenecks and quality issues, and proactively respond to unexpected shifts in demand.
What sets generative AI apart is its potential to automate or augment a significant piece of supply chain processes. By analyzing unstructured data from various sources like text, images, videos and social media posts, generative AI equips supply chain managers with contextualized insights for better decision-making. It can gather cross-functional insights and consumer sensing analysis for improved demand forecasts.
Procter & Gamble has leveraged generative AI technology to improve the precision of its demand prediction. By evaluating past data and external variables like weather patterns and industry shifts, the company has enhanced its capacity to forecast consumer demand more effectively. This strategy enabled P&G to optimize their inventory management and reduce expenses.
Efficient production and supply management
We know that way too many manual processes and complex, decentralized data in supply chains are hindering productivity. Generative AI brings structure and simplification to complex procurement and vendor management processes.
One of the key challenges these days is the lack of ESG (environmental, social and governance) data measurement across the value chain. Generative AI can offer solutions in this area by trawling through thousands of supplier websites and deliver near-instant insights. In collaboration with a global pharmaceutical company, Accenture facilitated the acceleration of that company’s supply chain decarbonization initiatives. The company had dedicated significant time and effort to gather data on the number of suppliers with science-based targets (SBTs). Within just one hour, our generative AI provided reliable insights, confirming that the company had already surpassed its supplier SBT. This successful application of generative AI showcases its potential in sustainability, such as generating customized decarbonization plans for organizations and improving Scope 3 emissions reporting.
Companies can also save time and effort. Generative AI is streamlining historical contracts, procurement policies and product specifications to identify common patterns and requirements. For example, a generative AI-powered import/export document generator can automatically fill in shipping and export documents by tapping into a wealth of information. Another area where generative AI excels is in supply chain nerve centers. Leveraging cloud, data, AI and analytics, these nerve centers provide multi-tier transparency, offering deeper visibility into supplier and manufacturer networks.
At this year’s Hannover Messe, we demonstrated a generative AI software for real-time visibility and AI-powered analytics that can foresee and neutralize disruptions and simplify your decision-making process. This Supply Chain Nerve Center integrates technology from our ecosystem partner SAP to identify potential supply chain risks, quantify the commercial impact of those risks and present mitigation options, including a risk score for each option. This implies a 4.5 times faster recovery time from supply chain planning disruptions and shortages with enhanced asset management capabilities that can reduce the incident frequency by 70%. With generative AI, manufacturers have the potential to start acting in minutes to situations that would typically take hours, if not days.
Generative AI enables workforce empowerment
In my view, building resilient capabilities is not just paramount for processes, but especially for people. When you look beyond enterprise transformation, generative AI enhances worker know-how by simplifying demand and capacity planning to make complex information more easily accessible. Workers can query recommendations and receive explanations in everyday language, thanks to generative AI's ability to break down complex information into easily understandable insights.
The digital workplace will be one in which productivity can be boosted with the help of generative AI. Accenture research found that potentially 43% of working hours among supply chain professionals could be transformed with the help of generative AI, with roles for production, planning, expediting and procurement clerk having the highest potential impact of up to 75% working hours.
Companies should therefore invest more in developing a multiskilled workforce that can work with generative AI and other new technologies to make better-informed decisions. The good news is that our research found that 60% of the industrial companies worldwide are already planning to upskill their workforce by 2026.
The success of businesses in an uncertain world depends on their ability to navigate complex supply chains and to reskill their workforce by leveraging advanced technologies. Fortunately, with the emergence of generative AI, industrial companies have the chance to position themselves as frontrunners in the pursuit of future profitable growth.
Brian R. May is managing director, Industrial North America, at Accenture.
