Increased computing powers and scale of cloud technologies are making AI tools increasingly sophisticated. Powerful processors are becoming more capable of crunching huge, complex data sets much more quickly than a person could manually, and often in real-time. This is helping distributors to catch up with other industries in their journey towards automation and becoming data-driven businesses, as they look to leverage AI distribution tools to unlock value through optimising data and supply chains, streamlining processes and improving customer experience.
In Deloitte’s annual State of AI in the Enterprise report, 82% of survey respondents indicated that their employees believe working with AI technologies will enhance their performance and job satisfaction. Adding AI to a distributor’s toolkit can unlock quick wins by making menial tasks quicker and easier, scoring businesses the luxury of additional time, which employees can utilise to focus on their expertise and on more value-added activities elsewhere in the business to contribute to long-term business growth.
“I think we are still in a very early phase of implementing AI in distribution, with even the most progressive tools still in a sort of piloting or testing out phase,” says Håkan Strömbeck, industry and solution strategy director at Infor. “Some businesses have implemented AI in specific areas, such as production. They might have one, two or three use cases at present, but in most organisations, the number of potential cases is probably more.”
Getting more from data
AI is helping businesses to get more from their data, by linking it to sensors, or having the capabilities of processing it, learning from it, and generating effective or insightful outputs at a much quicker pace. These may be the ability to draw actionable insights from vast volumes of complex data, or to predict supply variations which help to reduce waste and excess costs, and so on. From predictive analytics to smart learning, AI can help distributors optimise processes and increase operational efficiencies, ultimately increasing speed to value. Using cloud solutions like Infor CloudSuite Distribution Enterprise and Infor CloudSuite Distribution, the most up-to-date AI tools are poised for users to experiment with and extract value from.
“We tend to talk about applied machine learning or applied AI because that is what matters. It’s not just a high-level concept or something to talk about,” says Strömbeck. “Whether it’s used to automate or to help an employee get recommendations, decision-making is still largely down to the employee, but with the help of AI they can make a more data-driven decision.”
Distributors can strengthen supplier relationships with the help of AI and machine learning to inspect the data behind the service they’re offering. Digital tools can help with suggesting ways to deliver improved supplier performance while ironing out process inefficiencies or weaknesses in products that may not be immediately visible.
Improving sales with AI automation
“A key benefit of AI in sales is organising the huge number of articles a business might have on an interface so that an employee knows what to recommend to a customer alongside a particular product,” says Strömbeck. “If it’s a contract customer, it will also suggest whether the customer can receive a discount.”
AI can also rely on market data to constantly tweak product or service prices while displaying dynamic recommended buying options to increase competitive edge, while also making the likelihood of completing a sale greater. The AI-powered sales application can utilise sales data and sentiment analyses based on customer history and market conditions to suggest volume discounts which increases the likelihood of customer engagement and retention.
“Suddenly, new employees were behaving as though they’d been working in the company for five years and were experts in everything,” says Strömbeck. “I would guess that such a new employee would feel great satisfaction being perceived as a pro from day one.”
Optimising inventories with AI distribution tools
AI tools can also monitor transport costs and supplier charges, finding the most cost-effective ways to purchase, deliver, and distribute goods to and from a distributor’s warehouse operations. It can provide buyers with recommendations, and update routings, ensuring the fastest service for customers, which will contribute to their loyalty long-term.
Running inventory applications to retain visibility on stock orders is nothing new for distributors, but AI can monitor sales patterns and orders more closely in real-time to ensure that the right stock is in the right warehouse at the right time, particularly as markets, seasons and products or supplies change.
AI is helping to strengthen customer relationships
With AI helping to streamline processes and analyse data at a fast pace, it is helping to replace many of the mundane and time-consuming tasks that employees would otherwise be required to complete. But freeing up this time means workers can now focus on building strong and effective relationships with customers along the entire supply chain, providing valuable data-driven insights and differentiating the business from competitors. AI is also helping to optimise customer experiences with the help of chatbots and automated recommendations which allow around-the-clock engagement, turning time into value for distributors.
“Machine learning and AI tools can take away work, but the human element is crucial for their success,” says Strömbeck. “Change management is necessary. Identifying good use cases is important, but it’s crucial to prepare the workforce and manage change effectively.”
Creating value for everyone
Whether they are warehouse staff or the CIO, AI is maximising value for everyone across businesses. Leveraging a platform with preconfigured AI models like Infor’s Coleman AI is making machine learning more accessible through low-code and no-code implementations. Automated retraining enables hands-off maintenance, meaning that the system can be updated without human intervention. But effective change management, careful implementation processes and clean data will be essential for an effective transition towards an AI-automated workflow.
“Our approach is to rely on AI more often, but we should be cautious about the risks involved in feeding AI with incorrect assumptions and data,” says Strömbeck. “Although it may seem easy to deploy, we need to assess whether it is doing what it is expected to do. Data scientists will continue to play a significant role in this assessment.”
While risk, transparency and security remain key priority areas of focus for AI in the near future, these insights and opportunities to accelerate automation will empower distribution companies to take a greater interest as AI services become a feature of all applications and services.
