Visibility into an organization’s actual process is the only way to determine its efficiency, agility, and resilience. Some organizations try to achieve this visibility through traditional continuous improvement means like Lean Six Sigma, while others use KPIs. However, these cannot visualize the actual process execution, predict the scale of potential improvements, nor identify the root cause of an inefficient process.
So, how can a company gain greater visibility into and optimize its processes? This is a question recently explored in two case studies, one involving a French automotive manufacturer and the other a multinational retailer and wholesaler.
The automaker discovered its procure-to-pay (P2P) process was flawed after its year-to-year costs skyrocketed. The defective process reduced the company’s cash flow, resulting in late payment penalties and RIB changes not being considered.
Meanwhile, the multinational retailer and wholesaler was experiencing an inefficient order preparation process due to supply chain challenges. This resulted in partial orders being delivered, higher labor costs, and KPIs that were unable to identify how the company could decrease the non-compliant journeys and save costs in the process.
An AI-based solution
Both companies turned to QAD’s Process Intelligence solution to help them uncover their inefficiencies and resolve the issue.
The solution can visualize an organization’s processes through a three-dimensional and time-inclusive viewer that identifies the precise root cause, measures workflow health and compliance, and discovers areas that can create value. It can also enhance and monitor processes with algorithm-enhanced simulation and a control tower that enables active deviation alerts.
Moreover, QAD Process Intelligence uses Generative AI (Gen AI) to help organizations access the power of process mining through clearly summarized insights, guided navigation, and conversational learning from a private and secure model.
Thus, the automaker could utilize the platform’s real-time and dynamic vision of actual business flows to dissect its processes at each step and isolate variables like suppliers, countries, or invoice amounts. The QAD solution highlighted all non-compliances and automation opportunities while providing active alerts to prevent future deviations from cascading into larger issues. The result? Real-time alerts that enabled staff to adjust automated steps, saving over $12m and reducing purchase order processing time.
QAD Process Intelligence worked slightly differently for the retailer. It showed the logistics process in the warehouse and during delivery as it really happened by utilizing the data from the retailer’s Warehouse Management System, Order Management System and Transportation Management System. The solution could find all the deviations of the original processes during the “peak” periods and identified the most expensive ones by selecting key KPIs. This helped the client take preemptive action, resulting in $540,000 in savings within six months and time saved in order preparation.
In the next part of this series, we will explore how manufacturing metrics have evolved and how AI impacts them.
