New technology solutions could be transformative—but only if executives properly prepare their organizations.
In recent years, supply chains have become substantially more challenging to manage. Longer and increasingly interlinked physical flows reflect the rising complexity of product portfolios. Market volatility, which has been exacerbated by the COVID-19 pandemic, has elevated the need for agility and flexibility. And increased attention on the environmental impact of supply chains is triggering regionalization and the optimization of flows. As a result, companies and stakeholders have become more focused on supply-chain resilience.
Supply-chain management solutions based on artificial intelligence (AI) are expected to be potent instruments to help organizations tackle these challenges. An integrated end-to-end approach can address the opportunities and constraints of all business functions, from procurement to sales. AI’s ability to analyze huge volumes of data, understand relationships, provide visibility into operations, and support better decision making makes AI a potential game changer. Getting the most out of these solutions is not simply a matter of technology, however; companies must take organizational steps to capture the full value from AI.
The main inputs and contributions are:
In many organizations, supply-chain management has shifted to concentrate on dynamically optimizing the company’s global value rather than simply improving the performance of local functions.
Enhancing the relevance and size of supply-chain or business-plan teams is not enough to achieve better performance. Companies must tackle several additional challenges:
- Predicting demand across multiple product segments and geographies.
- Dynamically identifying trade-offs with hundreds or thousands of interlinked variables and innumerable technical constraints.
- Integrating AI solutions (such as processing optimization, predictive maintenance, or master data quality) to manage the wider value chain.
- Ensuring that plans get executed and can adapt to variability effects (such as demand shocks, production stoppages, and transportation disruption) in a timely manner.
The good news is that AI-based solutions are available and accessible to help companies achieve next-level performance in supply-chain management. Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors.
- Selecting the right AI-based solution is critical. To manage the complexity of today’s supply chain, new solutions need to be smartly designed and adapted to specific business cases. They also need to fit well with the organization’s strategy. This alignment enables companies to tackle key decision-making points with an adequate level of insight while avoiding unnecessary complexity. However, implementation can require significant time and investments in both technology and people—meaning the stakes are high to get it right.
- Clearly defining a digital supply-chain strategy helps support the company’s business strategy and ensures better alignment with its digital program.
- The complexity of supply chains means that finding one provider that can meet all of these needs is increasingly unlikely. Often, the best approach is a combination of different solutions from different providers, implemented by different systems integrators.
- Companies should take a holistic approach to implementation and systems integration. By optimizing the end-to-end value, companies can implement solutions that deliver value in the short term and are more sustainable over the long term.
- To ensure adoption of new solutions, companies must invest in change management and capability building. Employees will need to embrace new ways of working, and a coordinated effort is required to educate the workforce on why changes are necessary, as are incentives to reinforce the desired behaviors