A neural view to enhancing order management and fulfilment

Introduction

The world and virtually every macro- and micro-economic trend today is volatile, uncertain, complex, and ambiguous (VUCA). Adapting to this hypercompetitive situation of constant, unpredictable change is quite a herculean task for organizations. Unprecedented situations like COVID-19 have made manufacturers realize the pitfalls and problems of not having efficient processes in place. Over time, as companies grow further, through mergers and acquisitions or business expansion, leaders might face several challenges arising from various business and technological factors, in streamlining their business processes. 

One process that manufacturers must focus on to realign themselves is order-to-cash (O2C). It typically involves multiple hand-offs and approvals among various teams across the value chain. What makes O2C complex are various factors like products sold, global distribution, different customer segments, and more, which must fit into existing processes that might not be clearly designed and defined. Adding to this complexity, is the inherent variation in the O2C process, such as multiple decision-making levels within a manufacturing organization and a limited, non-integrated view of life cycle data

Challenge

The O2C process is far from ideal in most organizations, and they face major challenges such as siloed operations—which make data handling difficult—legacy systems, and multiple systems and applications. Apart from this, a typical sales order has many points of failure across its entire journey owing to numerous order exceptions and other documentation errors that arise in the system due to manual interventions. 

Order exceptions can happen during order booking, such as incomplete order details, material availability issues, and credit blocks while exceptions during order fulfillment can be due to delivery and billing block, late goods issue, missed orders, and shipped but not billed orders. Such issues can distort the standard way of order fulfilment and introduce operational inefficiencies. 

Additionally, frequent changes in an order due to modifications in product offerings or altered price lists, make the order flow complex. Any delay across any point of failure can result in missing the on time in full (OTIF) delivery for customers.

Maintaining the order and material flow throughout the order life cycle is no easy task. The problem is magnified to a greater extent when companies deal with hundreds of orders daily and do not know where the problem exists in the value chain.

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.