The attention of the US president administration to both artificial intelligence and the supply chain shows serious recent changes in their policies.
In 2023, the president signed orders on AI regulations and supply chains. In the EU, the upcoming AI Act also mentions the necessity for all vendors within the supply chain to comply with AI regulations.
Statistics indicate that more companies that are running supply chain businesses will adopt artificial intelligence by 2025, decreasing the percentage of those not using AI to only 4%. However, a blind rash for hyped new technologies will not bring a business to the goals set. Conversely, investing in AI while carefully analyzing its consequences for business operation processes pays off.
Data analytics for improved performance
The world supply chains are currently witnessing drastic changes due to the consequences of the pandemic, political conflicts, shifting powers in geopolitics, climate change, and natural disasters, as Deloitte mentions. Besides those, supply chains are subject to several roadblocks. Clients are looking for fast deliveries and transparency, while businesses face rising costs of raw materials and logistics.
Software development companies like Belitsoft see the treatment for procurement pains in big data analytics coupled with business intelligence tools. BI tools suggest businesses a variety of measures to cope with routine tasks:
- Timely reordering of out-of-stock items is necessary to avoid a decrease in sales.
- Over- and understocking prediction with customized inventory intelligence.
- A single and easily accessible workspace for inventory managers on sites and in the head office.
Companies that implement BI tools for their supply chain business admit improvements in the average monthly active inventory that leads to increased revenue and gross margin ROI.
AI Potential for the Supply Chain
Among the main causes of discrepancies in supply chains, experts mention unexpected shipping costs, contingency planning, and item shortages. AI and machine learning tools can facilitate the procedures. Big data influences all the stages in the supply chain, from developing item categories and evaluating suppliers to discussing procurement conditions. If everything is done carefully and with proper preparation, the performance can bring businesses to 200% effectiveness. Here are the areas where data and AI work best together.
Procurement processing
AI tools analyze historical data and can extract, summarize, and compile documents. Startups like Didero offer software that can find and qualify new vendors, update orders, and process invoices. AI in procurement suggests the following assistance:
- Deep analysis of the market, patterns, and trends in pricing. These facts provide procurement managers with an advantage in negotiating fair prices for commodities.
- Procurement leads are able to estimate the fluctuation of prices for raw materials and its influence on product margins. AI tools can predict possible ways to keep those margins unchangeable. Such ways may include selecting alternative materials, adjusting value chains, hedging commodities, or passing price changes to customers.
Risk management
AI technologies analyze the data and predict global and local events and weather conditions that might affect deliveries. Besides, predictive analytics can suggest possible scenarios in case of delivery delays or material shortages. For example, pharmaceutical companies foresee scenarios if some chemicals required for drug production become unavailable.
McKinsey predicts the development of digital twins of supply chains by 2030. Those are models of global supply chains that will include main and additional suppliers of raw materials to the production and further to customers, with the logistics routes connecting those points. Each participant in the chain will be assessed from the point of view of possible risks in its operation, costs, and carbon footprint. Procurement departments will, therefore, possess a visual model of the processes and a tool to simulate risks and test alternative options. Companies with similar technologies will be able to receive the required product faster, cheaper, and with a minimal carbon footprint in comparison with their competitors.
Demand analysis
Statistical data is insufficient for predicting demand for a particular product. Demand patterns depend on historical data, seasons, holidays, and promotion plans. AI tools can gather information from review platforms and blog posts on social media and extract insights regarding future buying patterns.
Tools like Google Video AI collect videos, text, and images. After analyzing this data, the tool creates a supply chain dashboard that can predict demand changes due to different factors, like panic buying caused by natural disasters or pandemics.
Generative AI can optimize spend and demand in the following ways:
- Creating cost cubes that explain what products customers are ready to buy and from which vendors.
- Procurement managers can use the data generated by AI to foresee price fluctuations due to changes in oil prices or geopolitical events.
- AI algorithms can perform contract automation, allowing for streamlined workflows and scalable contracting processes.
Tracking and assessing suppliers
Online e-commerce dashboards compile data regarding contract terms and conditions, invoices, and delivery documents. Such data concludes the supplier’s adherence to the preset agreements. The system alerts users to any deviations from the contract and suggests further steps.
Data analysis also aids in negotiating with potential suppliers. Firstly, sophisticated calculations for thousands of items perform automatically, allowing companies to have fact-based agreement discussions. Secondly, AI allows for careful investigation of social media data about a supplier and their public profile. Such information enables businesses to estimate the risks of dealing with a particular supplier. Thirdly, Gen AI creates content for conducting negotiations, such as emails, contract terms, call scripts, and reports.
Final thoughts
AI offers valuable insights only if qualitative input data is applied. According to McKinsey, the transition of supply chain functions into a data-driven and AI-powered process usually takes six to eighteen months. A business vision, clear intentions, and the mutual engagement of all stakeholders are required for such a substantial process. To develop an effective road map, businesses need to analyze the present position, align with the expectations of involved parties, note early progress, and regularly revise priorities. Therefore, companies will be able to meet the goal and develop a scalable system that will demonstrate improved performance in the long run.
