At the beginning of 2021, many studies pointed out the supply chain management industry’s problems in the post-pandemic scenario. Today, the uncertainty generated by operational risks and market fluctuations has become a testing ground for AI-enabled solutions for the sector. However, AI adoption has been low in supply chain management for a while. At the same time, sustainability adoption has also become a buzzword in the sector since the pandemic. This article aims to pinpoint the major problems for AI adoption in the SCM sector, with a special emphasis on sustainability practices. How can sustainability be leveraged in such uncertainty, and how can AI help make supply chains more sustainable?
In 2020, as Covid-19 restrictions started to dent industries around the world, the impact of the pandemic on supply chain management became a critical subject of discussion across automotive, plastic, semiconductor and energy sectors, amongst others. In everyday life, the shortage of equipment for frontline healthcare workers became just as evident as the expiration of supplies of essential goods like food and sanitary accessories. The shortage of suppliesOpens a new window affecting consumers and patients around the world reflected the results at the end of supply chains dealing with unpredictability issues in operations and market volatility. A general sense of chaos ensued across many sectors, and many national and industrial enterprises struggled to adapt to this unexpected scenario. Even though industrial supply chains have long relied on the infallibility of their value stream and their resilience to failure, these core principles were greatly challenged by the Covid-19 supply chain crisis.
See more: IT is Ripe for Modernization, But Supply Chain Issues Will Continue to Persist
Pandemic-driven Unpredictability Across the Value Chain
Why then was the Covid-19 disruption of supply chains such a big deal? Firstly, the value stream in lean manufacturing traditionally expects both value and risks to move from upstream to midstream and downstream. Indeed, in many dynamic and learning-based organizations, big failures at any value point are looked at as unplanned investments that help further optimize their safety culture. However, none of these incidents have been comparable to the global shutdown that the Covid-19 scenario brought. This led to disruption in operational management in each point of the supply chains, often simultaneously, leading to a complete collapse for many of them.Â
For example, during the Covid-19 crisis in India, oxygen supply was thus critically low in hospitals, even though industrial plants in India had sufficient capacity to produce the required oxygen. On the other hand, demand in the market became highly volatile for many products, especially in the services, wholesale, manufacturing and retail sectors, but also in the medical sectorOpens a new window as shown. Short-term and long-term demand thus needed to accommodate production loss in the wake of factory closures and economic slowdown, parallel to the large-scale inventory depletion across industries.
As per the May 2020 Gartner Report on Supply Chain ResilienceOpens a new window , the post-Covid global shutdown exacerbated the continued disruption of globalized supply chains that the U.S.-China trade war and Brexit had massively triggered. As Harvard Business School professor Willy Shih’s article pointed out, the risks associated with supply chain fragmentation and globalization had thus been ignored for a long time. It was the combination of lean production and global multistage supply networks that led to the worldwide crisis. One can say that the outsourcing model, that facilitated inexpensive labour and efficient, cost-optimized logistics, had thus already begun to be challenged by the reduction of options in distributed sources and the increased reliance on local resources. Paradoxically, the latter situation also became an important motor for sustainability adoption across many industries. Therefore, the worldwide supply chain disruption during the pandemic simply exacerbated the problems already becoming prevalent with massive geopolitical trade changes. And soon enough, two solutions had started to emerge: sustainability and AI adoption in supply chain management.
The Complexity of Sustainability Management
Supply chain management has been a part of the core principles of sustainability ever since its foundational manifestos like the 1987 Brundtland report. The depletion of natural resources has for a long time been seen as a primary danger. A range of other parameters, such as the energy consumed in harvesting natural resources started to be considered, and sustainability became more of an integral vision of the supply chain. The impending costs of globalization made self-reliance an important criterion in sustainability evaluation. In the pandemic scenario, however, self-reliance was no longer an option for many industries. Sustainability adoption was thus more often an operational obligation than a strategic initiative during the pandemic because of the trade and logistical transport failures of the globalization model.Â
Nevertheless, the importance of sustainability is far from just being a factor of operational optimization. In today’s world, when the climate change crisis has affected livelihood, industry, and human survival all over the planet, institutions, governments and industries are joining hands to bring sustainability to everyday life, with the hope for a better future for everyone. The pandemic has undoubtedly been a collective wake-up call in this regard for everyone. Today, ambitious targets like the UN SDGs are being set on the geopolitical scale to reverse the depletion of natural resources and make life on earth sustainable for mankind. To meet these targets, there has been an ever-increased focus on managing the necessities and demands of an ever-growing human population and ensuring that these can be met through better management of supplies.Â
Global strategic players had identified sustainability as a primary point of interest pretty early on in the crisis. A BCG report in July 2020 thus looked at sustainability adoption in supply chain management as “an historic opportunity for companies to build back better†by demonstrating such efforts as a high payback investment. These paybacks often rely on the ability of companies to ensure sustainability all along their value chains and prove it on both environmental and social counts to governing and regulatory institutions. This is how environmental, social, and governance (ESG) criteria have started to become a standard in consumer goods, with companies adopting them demonstrating markedly higher sales and valuation over their competitors.Â
In the agri-food industry, sustainability adoption was thus touted as a win-win scenario for major market players. However, meeting these criteria all along the supply chain is just as challenging when supply and demand are highly fluctuating for the same reasons as highlighted above. Indeed, increased localization of sourcing has a significant impact on carbon footprint and improving economic indicators in local economies, but the social and logistic aspects of sustainability, along with depletion of non-renewable resources, have all been heavily impacted by the pandemic crisis.
See More: How Data Can Help the Supply Chain for Real-Time Disaster Preparedness
Sustainability and the Paradox: The Case of Agri-food Value Chains
However, there is a deeper reason why such sustainability adoption is hard to prove end-to-end in supply chains today. As detailed in the article “The sustainability paradox and the conflicts on the use of natural resources,†the use of local resources can be a huge source of conflict of ecological and economic interests. The global surge in demand for batteries producing electrical energy for driving vehicles has pushed the demand for lithium and vanadium, leading to an exponential increase of the number of exploration activities and mining in countries like Sweden, Bolivia and Chile, and which have finally led to profound conflicts at the local level against devastation of ecosystems, rural landscapes and local livelihoods for people. At the same time, these mining operations are defended by governments as promoting green energy, climate-smart solutions and offering employment opportunities.
Another important example is the case of agri-food value chains. As consumer interest in environmentally friendly food has increased over the years, the situation of smallholder farmers has not always improved. In the case of palm oil, as the WWF report on the subject highlights, replacement with sustainable options hasn’t always proved advantageous for farmers, millions of whom still depend on producing palm oil for their livelihoods. Also, palm oil produces more oil per land area than any other equivalent vegetable oil crop. Industrial production practices that involve massive deforestation have, however, tarnished the reputation of this essential commodity. Such a conflicting view of essential products or resources has led to a gap between ecological and economic sustainability. This growing gap affects the most vulnerable section at the origin of this supply chain.
Sustainable supply chain management thus requires a holistic, integral comprehension and monitoring of each of the value points of a supply chain, with the application of ESG metrics that consider economic, ecological and social criteria. Coincidentally, as stated before, such an integral view of supply chain management is also what most industries need to practice in today’s ever-unpredictable scenario.Â
AI as a Strategic Solution for Supply Chain Resilience
Today, artificial intelligence adoption in supply chain management is the major factor in improving resilience and sustainability.
In the post-pandemic scenarioOpens a new window , where supply chain disruption has become one of the major risks across all industries, resilience has become a buzzword. Nevertheless, the uncertainty in both demand and supply fluctuations is still prevalent. Resilience in supply chains helps reduce unexpected cost overheads during disruption and increases profitability when disruptions have been minimized. Developing resilience includes security culture upgrades like improving mean time to recovery and creating smarter sourcing strategies at every stage. Artificial intelligence applications to resilience management, such as applying insights from analytic engines, helps support adaptive decision making and coordination with internal and external operations to improve end-to-end performance.Â
Interestingly, as of August 2021, the same studies that highlight supply chain disruption risks predict ESG regulatory requirements as the second biggest risk by overall risk score across industries. Sustainability adoption with all its advantages is complicated, given the unpredictable and often very qualitative nature of the metrics that need to be managed, especially in the case of ethical and human rights issues. However, creating hyper transparency and accountability is becoming increasingly possible through innovative adoption methods for both AI and blockchainOpens a new window . Thus, artificial intelligence adoption helps supply chains both become resilient and sustainable.
Overcoming the roadblocks
There are, however, a few critical roadblocks in AI adoption for SCM. Because of high dependence on enterprise and legacy IT systems as well as more traditional logistic management, only 50% of supply chain organizations would have invested into AI by 2024 as per a Gartner report. In the case of ESG and sustainability management, apart from similar legacy dependencies, the tradeoff between the energy consumed by AI and blockchain and the energy saved by the solutions they offer is a delicate balance. The solutions will come from holistic, integral visions that build resilience and sustainability into both the overall strategy as well as each step of supply chain operations. Enabling observability and faster recovery time is finally a question of data governance and quality, that must thus be able to join the built-in resilience and sustainability approaches that the leaders in the sector are implementing today.
Do you think AI adoption is a sustainable solution for supply chain management? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!