In recent years, supply chains have become substantially more challenging to manage. The COVID-19 pandemic has exposed the industry for its archaic processes, and caused rushed efforts to catch up on lagging digitisation and amplified brain drain challenge from mass resignations.
In this new world where uncertainty is the only certainty, it will be crucial for organisations to harness the combined intelligence of humans and machines to drive better decision quality in supply chains.
Per McKinsey, successfully implementing AI-enabled supply-chain management has allowed early adopters to improve logistics costs by 15% and inventory levels by 35%. Gartner states that by 2025, more than 80% of newly created supply chain applications will leverage artificial intelligence and data science resulting in additional insightful information, predictions and suggestions.
AI-enabled decision-making is the next step in the supply chain digitisation journey and the use of advanced analytics could be transformative - but only if organisations properly involve the human component.
Growing labour shortages and rising labour costs are forcing companies to increase automation by applying technologies like robotics to supplement their human workforce. But increasing the degree of automation leads to a shrinking of collective human domain knowledge: Fewer individuals obtain hands-on experience and on-the-job training. A higher reliance on analytics and automation is decreasing companies' domain knowledge.
A key objective therefore is to capture this shrinking domain expertise, enabling mutual sharing of knowledge between humans and machines, with the goal of increasing the collective ability to make better decisions. Humans and machines should be seen as equal contributors, coming together to increase overall wisdom and effectiveness.
The good news is that such AI-based solutions are available and accessible to help companies achieve next-level performance in supply-chain management.
At Solvo.ai, we are building an AI optimisation suite of products that operate as a cross-functional (human-machine) brain to help bring supply chain decision-making to the next level. Our AI engine uses models that effectively work with sparse and often small data sets, applying reinforcement learning technology. Solvo.ai’s solutions empower logistics specialists to make better decisions under uncertainty and in highly complex business environments.
The first product in Solvo.ai’s optimisation suite has been pioneered with a globally leading logistics service provider, aimed to improve yields and truly enable dynamic pricing. The solution allows trade and pricing managers to configure business preferences and simulate outcomes, in order to define pricing policies that optimise KPIs such as market share or conversion rate.
The machine provides the human experts with transparent and explainable recommendations that allow them to drive better decisions. This cross-functional brain learns and improves itself from an ongoing cycle of historical and real-time market and business analytics data on one hand, and human re-configuration and intervention on the other.
It is our mission to solve real-world challenges of the supply chain industry.
As we continue to build out our AI suite, we will focus on helping supply chain leaders and logistics specialists optimise networks, while enabling the evaluation of tradeoffs such as revenue and sustainability goals. We aim to empower decision makers instead of replacing them.
Companies are right to balance enthusiasm with scepticism, but caution should not hinder progress. Evidence of impact, integrity, value, and resilience must be the primary considerations in adopting any new solution. While human skill will always be at the heart of logistical operations, supply chain participants must now explore how AI technologies can enable human specialists to make real-time, data-driven decisions to traverse this complicated landscape.
Companies that future-proof their operations against tomorrow’s difficulties and establish long-term resilience will be those that harness AI-driven decision-making capabilities.