Research shows that as AI adoption continues to expand, its impact on business efficiency and competitiveness will grow, marking a pivotal shift in the technological landscape. However, AI can only yield value for organizations if it is used in tandem with business expertise.
In the concluding part of this series, we look at how Process Intelligence can help organizations find value and improve their profit margins with an example of how inventory management benefits from generative AI.
Finding value in inventory management
Businesses across diverse industries are increasingly grappling with inventory or stock loss. These go beyond the size of the product and can result in business operations and profitability getting compromised. According to Process Intelligence provider QAD, some of the most common reasons for losing inventory include:
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- Incorrect part movement
- Damage due to dropped or mishandled parts
- Shipping inaccuracies
- Manual errors in processes
- Supplier underperformance
- Inaccurate forecasting
Many times, even if the supplier delivers all the components, split shipments can complicate reconciliating the full delivery.
These factors can lead to delayed production runs, disrupting schedules and customer commitments, penalties tied to service-level agreements (SLAs) and increased costs due to expedited procurement and shipping. The company’s operating margins suffer due to these barriers.
How Process Intelligence helps
Stock loss issues can arise from outdated or inefficient business processes, which can become a significant hurdle in the long term. However, QAD’s process intelligence offers visibility into real-time process performance, enabling businesses to identify bottlenecks, anomalies, and inefficiencies.
For example, one company believed its processes adhered to the ideal path 80% of the time, but Process Intelligence revealed over 140 process variations, with only 50% following the “happy path.”
Thus, the generative AI in Process Intelligence helped the organization address the root causes for these variations like supplier delays or poor forecasting instead of merely treating symptoms.
Finally, a data-driven approach to process monitoring and improvement helps companies reduce stock losses, enhance efficiency, and improve customer satisfaction, ensuring they are creating value for their customers and the business.
What this means for ERP Insiders
Conventional continuous improvement is no longer sufficient. Visibility into an organization’s actual process is the only way to determine its efficiency, agility, and resilience. However, traditional means for continuous improvements like Lean Six Sigma and KPIs cannot visualize the actual process execution, predict the scale of potential improvements, or identify the root cause of inefficient processes. As highlighted through the case studies in the first part of this series, generative AI in solutions like QAD Process Intelligence can help companies uncover their inefficiencies and resolve these issues.
Evolving manufacturing metrics call for a change in process. What used to be an optimum process yesterday can lead to inefficiencies today in a rapidly changing industrial landscape. In this case companies must identify product lines to justify automation of processes or zero-in on the best solution for a process. The case study in the second part of this series emphasized how companies can utilize Process Intelligence to optimize their processes to gain additional value and improve their cash flow.
AI can deliver value only with business expertise. While newer technologies like AI and machine learning (ML) are the go-to solutions for manufacturers grappling with inefficiencies, these technologies cannot deliver value if they are only technology-centered experiments within an organization. The case study above on stock losses highlights why it is necessary to ensure that the AI solution being used also has business acumen like QAD Process Intelligence to help an organization get measurable results and create value.
