Market globalisation has facilitated the growth of supply chains across jurisdictions, which brings about increased opportunity and risk. Global supply chain disruptions at the outset of COVID-19 have highlighted the need for businesses across industries to evaluate current practices in place to mitigate disputes risk, ensuring that it is sufficiently resilient against sudden and unforeseen disruptions.
This post considers how businesses can employ AI to supplement existing mechanisms to anticipate and resolve conflict within supply chains to prevent escalation into contentious disputes.
Facilitating negotiations regarding alternatives
As a result of the pandemic and geo-political tensions, supply chain disruptions ranging from semi-conductors to food continue to persist. This climate presents both a challenge and an opportunity for businesses to adapt and enhance their risk management strategies.
As The Economist comments, ‘Resilience comes not from autarky but from diverse sources of supply’.When a potential dispute arises, early and proactive communication between the parties can play a vital role to explore mutually beneficial solutions, such as the viability of identifying alternative sources of supplies. Being able to find alternatives early on can help to preserve the commercial relationship and minimise business disruptions. If alternatives are not possible, the parties may attempt to revise the contract as necessary. Early negotiations in this regard can also minimise the need for contract termination or to enforce force majeure clauses.
The negotiation process might involve challenging conversations, which can be enhanced with AI-powered tools. Natural language processing-based AI programmes presents an opportunity to change the narrative. By monitoring the development of sentiments throughout email correspondences, data analytics offer insight to monitor the risks of disputes and potential legal liability.
For instance, increasing awareness on how the language of correspondences influences the other party’s receptivity to the message communicated. Insights into the sentiment of correspondences can also encourage the focus of the dialogue on reaching mutually satisfactory outcomes, by emphasising shared interests, acknowledging shared challenges and the need for collaboration.
Additionally, data analysis from AI tools, particularly machine learning, can supplement risk management by integrating processes that connect previously isolated data points, for instance, by bringing together chains of past email correspondences and product supply spreadsheets. Such tools can integrate data already available within the enterprise to determine the type and level of risk to the company’s supply chain and help managers make more informed decisions in relation to their suppliers and vendors.
Where there is a high risk that a dispute will cause supply chains to become severely disrupted, the parties’ communication at these junctures are crucial because they can affect their rights and remedies within the relationship.
AI data analytics can offer assistance at this stage to supplement the business’ existing dispute resolution tools. The relevant data can help a business identify the earliest sign of disagreement, the source of the risk, track the development of events chronologically. Importantly, the data can help inform future mitigation strategies by predicting and assessing the risk based on past data. Being able to anticipate conflicts allows companies to plan ahead in response.
As global supply chains remains uncertain, it is especially valuable for businesses to have the right tools to derisk and manage commercial conflicts in their supplier relationships. Harnessing big data from supply chain systems with the use of AI tools can help businesses predict, mitigate and manage risks, settle conflicts efficiently and very early on and reduce the potential for litigation.
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