Many factors come into play when a company opts to automate its processes. But as manufacturing metrics evolve, what worked previously may not be an optimum process today. In these cases, how does an organization identify its product lines or justify automation? More importantly, how can it zero in on the best solution for this process?
Solutions like QAD Process Intelligence can help companies answer those questions. The solution uses generative AI (Gen AI) for process mining, allowing organizations to visualize, optimize and monitor key business processes.
A real-world use case
Take the example of a North American pharmaceutical company that wanted to optimize the payment time to its suppliers as it saw inconsistencies and instability in its procure-to-pay (P2P) process. This process eroded over time, resulting in a greater frequency of late supplier payments. Global disruptions also slowed down the company’s P2P process considerably due to requisition blocks, checks, erroneous data entries and supplier changes.
Explore related questions
The pharmaceutical company chose QAD Process Intelligence to connect to its ERP, overcome this challenge, and monitor its entire P2P process. A global and detailed view allowed it to identify all the pain points, such as blockages, cancellations, controls, and manual updates.
Paying suppliers on time was the highest priority, and the company gained additional value in simplicity and fluidity as the solution allowed users to visualize all the flows involved in the payment process. Additionally, the solution identified three axes of analysis:
- Process analysis with the identification of nonconforming steps (invoices without purchase orders, loops, and bottlenecks)
- Temporal analysis: Slow approvals, late receipts, missed discounts
- Dimensional analysis: Benchmark by supplier, deviation by country, and type of invoice.
The solution helped the company improve cash flow in all the countries where it had operations by highlighting purchase orders (POs) outside Electronic Data Interchange (EDI), Payments made too early, and differences in payment conditions.
By implementing this solution, the pharmaceutical company could improve its payment time for more than 800,000 invoices annually in 12 countries. It could also provide the list of invoices per supplier and identify the list of late products at specific stages. Apart from saving around $300,000 over six months, the company also experienced:
- A 12% boost in the rate of invoices paid on time
- A five-point decrease in double payment rate
- Higher contactless payment rate
- Identification of areas to be automated
Harnessing AI for profitability
The pharmaceutical company’s case highlights the importance of quality data for any successful process today. However, many companies remain challenged by this factor in transforming their business.
In a recent webinar, Etienne Ouvry, Senior Principal Business Consultant at QAD, explained how companies can unlock profit margins by analyzing their existing data with patented algorithms. These algorithms are also available in solutions like QAD Process Intelligence. The AI in these solutions also helps customers identify over- and under-performing product lines and justify automation projects with quantitative analysis.
In the concluding part of this series, we will look at how pragmatic AI can help organizations find value opportunities and improve their profit margins.
