Supply chain management is due for a renaissance. We’ve seen technology revolutionize everything from bookkeeping to phone calls, and we are only beginning to see AI’s possibilities. It’s time for companies to end their reliance on legacy systems and invest in modern supply chain management technology.
Emerging technologies powered by AI turn data into actionable insights that strengthen business models. You gain supply chain visibility and streamlined workflows for improved efficiency, risk management and collaboration to deliver a better customer experience. Companies already using AI-powered solutions have achieved:
● 15% savings in logistics costs.
● 35% increase in inventory levels.
● 65% increase in service levels.
AI-powered solutions can transform inventory management. A failure to upgrade your systems puts you at a disadvantage.
Demand forecasting
Product overstock and stockout costs quickly add up, but accurately forecasting demand amid market volatility and rapidly evolving buyer habits is challenging. Legacy systems often lack integration, leaving you with siloed data and a limited view of the current situation.
Modern systems can integrate data from many sources, including other supply chain partners, to gather a more holistic perspective. AI analyzes the information to enable decision-makers to anticipate consumer demand and optimize stock levels, avoiding expensive stockouts and overstocks. The technology moves beyond human capabilities to recognize inventory data patterns and trends, such as seasonal or TikTok-inspired demand spikes.
Inventory tracking
AI solutions automate manual inventory management tasks, like inventory counts and receiving scans. Integrating an AI-powered solution with other sales channels or order management systems enables companies to automatically track the product journey from raw material to customer delivery. For instance, the platform may follow warehouse inventory progress using RFID (radio-frequency identification) tags and sensors for real-time inventory levels and locations. Companies can use this information to identify bottlenecks and make strategic procurement, production and shipping decisions.
AI also powers robotic automation in warehouses. The technology leverages data from integrated systems and robots’ sensors to make tracking, transporting and delivery decisions based on the current physical environment and operational needs. This capability reduces manual labor while increasing safety, speed and efficiency.
Returns
With the rise of online shopping, businesses face the complex and growing problem of returns. New supply chain technology provides solutions. In addition to enabling reverse logistics, AI predicts returns by analyzing customer data like reviews, reason codes and purchase histories. This insight also helps determine the reason behind a return — like customer preference or a larger issue with the product — allowing you to take the appropriate action to meet customer needs.
How to Implement AI
All of these benefits can be yours with proper implementation. Preparing for a new system involves five key factors.
Quality data
AI is only as good as its training data. Limited or low-quality data create less reliable AI outputs. Prioritize gathering accurate, up-to-date information that is accessible and well-organized. A comprehensive repository should comprise historical and real-time data from multiple sources, including suppliers, logistics providers and customers.
Before launching AI, research your data sources and determine quantity and quality. If you uncover holes or inadequacies, work to fix them to leverage AI more effectively.
Clear objectives
What do you want from your AI? If you don’t set expectations, you can’t tell if it’s delivering desired results.
Technology is not a silver bullet for supply chain challenges. Review your ongoing operational issues to identify clear use cases and desired outcomes. Set measurable, achievable, relevant and time-bound objectives. Do you want to increase shipping speed by 20% within the next quarter? Or reduce overstock costs by $1 million in the next year? These defined metrics ensure AI is effectively helping you meet business goals.
Skilled personnel
Integrating advanced tools into complicated supply chain processes requires knowledgeable and skilled personnel. Your team should include data scientists, software developers, supply chain experts and IT professionals to design, implement and manage the AI system. Initially, consider outside expertise to get the technology off the ground. As you upskill employees, you can transition to in-house specialists.
AI dramatically shifts workflows and is often intimidating. Training and supporting all employees is imperative to prepare them for the new processes.
Adequate infrastructure
AI requires significant computing power and storage capacity, which may overwhelm your current infrastructure. Before adopting AI tools, invest in a robust supporting system that includes:
● High-performance computing systems designed to handle large-scale data processing tasks.
● Storage systems to maintain large volumes of data.
● Networking infrastructure providing high-speed connectivity to support data transfers.
● AI software tools like programming languages, libraries, frameworks and developing environments.
● Data integration and management tools.
Change management plan
AI will change the way your business operates. Adjusting appropriately requires an extensive change management plan. The strategy mitigates workflow and supply chain disruptions by providing a structured approach to the transition. An effective plan involves:
● Stakeholder engagement, including employees, suppliers, customers and partners.
● Good organizational communication outlining the timeline and potential impacts.
Without these data, objectives, personnel, infrastructure and a plan, you may flounder during the transition, losing valuable opportunities to gain efficiency and optimize the supply chain.
Cybersecurity considerations
While integrating systems provides many benefits, it does introduce new vulnerabilities. Companies require robust cybersecurity measures for their systems and platforms to protect their supply chain. Mitigate risk by implementing:
● Secure data sharing.
● Identify verification.
● Access management.
● Continuous monitoring.
● Threat detection.
Cybersecurity involves third-party vendors and logistics providers. Work with them to implement adequate security measures for their systems as well.
Leveraging data and technology in your supply chain has never been more critical. Antiquated legacy systems can’t keep up with increasing supply chain complexity. To execute a competitive supply chain strategy, systems must provide flexibility, adaptability and greater visibility into the entire ecosystem.
Gartner predicts more than 75% of commercial supply chain management application vendors will deliver embedded advanced analytics (AA), artificial intelligence (AI) and data science by 2026. With ever-increasing AI capabilities, companies clinging to legacy systems will quickly fall behind the competition.
Padhu Raman is the co-founder and chief product officer of Osa Commerce, an innovative supply chain technology provider for brands, retailers, and the third- and fourth-party logistics providers (3PLs and 4PLs) that support them.
