A neural approach to O2C
To manage data, systems, and processes better, companies can adopt a neural system, which involves three steps. In the first stage, manufacturers can introduce connectedness in the O2C process by consolidating information from disparate systems and ensuring collaboration within the teams; enterprises can build intelligence through machine learning capabilities, which can predict order failure; and automation can reduce manual tasks in the process. To embed neural elements in the O2C process, manufacturers would benefit from a centralized command center or team, where all stakeholders are responsible for creating the perfect order fulfillment.
The next stage is about introducing visibility in systems and data for organizations. This step involves creating dashboards that can provide near real-time monitoring on the demand, supply, and supply-demand balance. Lack of visibility in an organization’s supply and demand leads to sub-optimal decision-making and incorrect commitment to the customer, thus impacting customer experience.
On the demand front, businesses can enable real-time visibility by tracking open orders based on their statuses such as material and shipping data and points of failures (for example, blocks). On the supply side, monitoring inventory and tracking the status by age and shelf life will help prioritize and streamline inventory consumption.
Organizations will be able to consolidate materials data by accounting for outgoing and incoming materials and make projections for net inventory availability. With visibility into these issues, the centralized team can address challenges in a timely manner.
The final step is to bring intelligence in the system through machine learning capabilities involving algorithms, which can predict order failure. Such algorithms can help firms identify which orders missed the on-time delivery date by analyzing patterns across the points of failure and static order data. This crucial step introduces predictive capabilities into the system.