Reduce costs in your supply chain with AI and automation programs

Throughout the pandemic, supply chain professionals were asked to manage extreme volatility and supply chain disruptions. Consumer demand was shifting, alternative sales channels (such as e-commerce) were booming, new delivery models were established, sourcing of supplies became more regional, and on and on.

The number one task was to create a more resilient and sustainable supply chain with a strong focus on supply chain risk management. Companies even accepted higher inventory levels, which increased the cost of running supply chains.

Now we are in a very different situation. Taking cost out of the supply chain is the top priority for supply chain professionals. At the same time, supply chain disruptions continue to be the norm. Therefore, just reducing inventory levels will not be enough to reduce costs efficiently and effectively for supply chains.

Many chief supply chain officers are looking for artificial intelligence (AI) solutions and automation of business processes to achieve their goals. So, are AI and automation solutions ready to deliver on the promise? This blog post focuses exactly on this question.

The value hypothesis

In previous blogs, I talked about the transformative potential of AI in the supply chain as well as the possibilities to build more agile supply chain organizations. Now it’s time to talk about the adoption of AI and automation in the supply chain.

For this purpose, I want to differentiate between three types of AI solutions:

• Firstly, applying intelligent document processing for documents (e.g. invoices, compliance certificates, etc.), communication processes (e.g. emails), and for generating content (e.g. instructions, quality documents, etc.). The entrance barrier for the adoption of intelligent document processing is very low, but it can deliver very high value—especially in the supply chain. Why? Because supply chain professionals are dealing with thousands of business partners (suppliers, carriers, outsourcing partners, etc.), thousands of documents (such as orders, invoices, freight documents, etc.), and communication and collaboration is key to running a successful supply chain.

• Secondly, using specialized AI models and solutions for pricing, forecasting, inventory management, transportation, etc. The specialized AI models for forecasting, inventory management, etc. are promising very high value. However, the prerequisite is “data.” Collecting, cleansing, and organizing data and building data lakes on which the AI models can be trained on can be cumbersome and time consuming. However, many companies started on this journey years ago and we see successful adoptions of these models.

• Finally, building autonomous supply chain processes by combining robotic process automation (RPA) with agentic process automation, i.e. using intelligent agents to orchestrate business processes in the supply chain. Typically, companies use over 20 different systems to run their end-to-end supply chain. Enabling a more autonomous supply chain by leveraging agentic process automation will make a huge difference in orchestrating business processes across these systems. This has by far the highest value proposition and UiPath is investing in this technology to realize this vision.

So, to answer the question about the readiness of AI and automation, the answer is “yes.” AI, in combination with automation, can support the goal of reducing cost in the supply chain.

Now, how are successful AI and automation programs being established? 

Traditionally, companies established a center of excellence for automation as part of their IT organization. The team analyzed business processes (e.g. by using process mining) and reached out to business organizations across the company to come up with a prioritized list of use cases for AI and automation and started to implement them. The main goal was to reduce the number of hours spent on manual, repetitive and document-based processes. 

Companies who have adopted AI and automation programs more successfully are combining this “bottom-up” approach (i.e. creating a prioritized list of use cases) with a “top-down” approach. “Top down” creates a focused and strategic approach for the adoption of AI and automation.

Let’s first discuss the most important KPIs for supply chain automation programs. 

The promise of automation is to reduce and/or eliminate the time people spend on manual, repetitive, and document-based business processes. Many supply chain processes meet these criteria. Just think about all the Microsoft Excel spreadsheets and documents (such as invoices, freight documents, bill of lading, etc.) being processed every day. A significant 64% of businesses believe that artificial intelligence will help increase their overall productivity, as revealed in a Forbes Advisor survey. Therefore, productivity needs to be a top priority for supply chain programs.

But automation in the supply chain needs to go beyond the enterprise. Every Fortune 2000 company works with over 10,000 business partners, such as suppliers, logistics service providers, and outsourcing partners. While electronic data interchange (EDI) is still the standard for exchanging data between companies, the day-to-day and ad hoc communication between all the partners is email based. Leveraging intelligent document processing to automate the unstructured (i.e. email) communication process will increase the Net Promoter Score (NPS) with business partners, because of faster processing and higher accuracy of the data exchange. A higher NPS leads to better service, which in turn leads to reduced costs.

In summary, automation programs in the supply chain increase productivity internally and the NPS with business partners. Both result in significantly reduced costs in the supply chain.

The automation program 

Now the question is, how can this be done?

As discussed, most successful automation programs are not run from the bottom up. Instead, organizations take a “top-down” approach to deliver the impact on productivity and NPS. 

The program needs to be structured from the C-level down to the functional unit, which defines the desired outcome and the initiatives necessary to achieve the outcome. Only then are use cases defined which support these initiatives. 

Here is an example:

• The C-level can be the chief supply chain officer.

• The functional unit is supply chain planning.

• The desired outcome is to create an autonomous demand planning process to increase sales productivity by 20%. This means eliminating the weekly, manual forecasting process from the sales and supply chain planning teams.

• The initiatives to achieve the outcome start with automating the consolidation of sales information (e.g. pipeline information, campaign information, new product introduction, promotions, etc.). Then it leverages an AI-based forecasting algorithm. It also includes a human in the loop to manage exceptions and verify results.

• The use cases are defined to support these two initiatives (consolidating sales information and executing the forecasting algorithm). 

This simplified example demonstrates that it is much more effective to leverage a “top-down” approach for AI and automation programs. Having a clear goal in mind helps to exceed expectations and allows everyone involved to be more successful.

Conclusion

Supply chain disruptions are the norm; managing risk and creating resilient and sustainable supply chains are still the goals. However, taking costs out of the supply chain is the top priority for companies across industries and regions.

Benchmarks have shown that AI and automation programs deliver real value in supply chains. The most prominent KPIs for automation programs are productivity gains for internal processes and NPS for external processes.

Automation programs need to become a strategic initiative for supply chain organizations. And the programs need to be driven from the C-level down to deliver the expected value—and, in many cases, this means reduced supply chain costs.

Join us at FORWARD + TechEd in Las Vegas to hear more about successful supply chain AI and automation programs.