In a previous article, author Sibanjan Das focused on advanced analytics in procurement. In this article, I will pick up where Sibanjan left off, taking a look at the cases where advanced analytics can be implemented in an Order to Cash (O2C) process. Similar to the Procure to Pay process (P2P), O2C is also a business process which mostly concentrates on receiving, fulfilling, and managing a customer's request for goods and services. It's an inverse of the P2P process which was procuring goods and availing services from suppliers.
An order to cash cycle typically consists of multiple business offices working with each other to give customers a great experience. The series of functions and stages of a standard Order to Cash cycle is depicted in the diagram below.
The standard process almost appears to be a six step process. But, the complexity involved in each of these stages is enormous. The order management team, warehouse personnel, accounts receivable team, and customer relationship crew must work hand in hand to keep each and every order tracked from its inception until it reaches the intended consumer. There are tons of Key Performance Indicators (KPIs) used by organizations to track this complex process. I'll list a few important metrics that are necessary for each one of us managing the O2C process.
These metrics are important to all teams involved in the O2C process.
- Total order-to-cash process cost as a percentage of revenue
- Number of days between shipment or service and billing
- Order-to-cash cycle time
- Days sales outstanding (DSO)
Sales Order Creation and Booking
- Perfect Sales Orders
- Customer Backorder Rate
- Total inventory accounting cost as a percentage of revenue
- Annual inventory turnover
- Days inventory on hand
Pack and Pick Release Items
- Cases Picked and Shipped
- On-time Shipment Readiness
Create Invoice and Payment
This subprocess mostly includes the Account Receivables KPIs such as:
- Total accounts receivable cost as a percentage of revenue
- Average days unapplied cash
- Bad-debt expense as a percentage of revenue
- Average number of days until an invoice would be considered past due
Advanced Analytics in Order to Cash
Order to Cash (OTC) is often challenged by siloed operations and inefficient processes. Advanced analytics can support to break these silos by utilizing the most important data available from the enterprise business systems, social media, and IoT. The analytical application and intelligent systems can help reduce consumer fraud, identify anomalies in the processes, predict risk, and much more. We have outlined some possible use cases below to improve and optimize your order to cash process using advanced analytics.
Today most of the financial transactions are done on credit and almost every time for B2B customers. Accounts receivable is the most critical function for an organization to use well. Their competence and judgment will help the organization to receive money for the sold good and services. This requires the AR group to be pro-active in knowing whom to give credit and when to start the credit recovery process. Advanced analytics can help go beyond the standard AR aging report. Using advanced analytics, AR personnel can predict payments at risk, judge the likelihood of recovery of long overdue payments, and identify customers who are at risk of potentially not paying.
Account Receivable Frauds
Advanced analytics can build models that identify attributes or patterns that can identify fraud. For example, anomaly detection can detect a sudden drift in historical customer payment pattern. Customer profiling can assist in determining potential fraudsters by matching attributes with known fraudsters attributes. Text mining can help discover the customer sentiment's towards the product/service.
A product recommendation system provides suggestions for products to a user. The recommendations relate to various decision-making processes such as what items to buy or what service to avail. A recommendation system provides useful and practical suggestions for the particular type of product that is of benefit to a user. The goal of this system is to provide relevant product recommendations for customers so that the probability of a sales conversion is high. Product recommendation systems are widely used in e-commerce applications. However, they can also be leveraged for physical stores. There are various types of recommendation systems. Content-based and collaborative filtering are two widely used recommendation system designs in the e-commerce space. Association rules, which are widely used for market-based analysis can also be used as a recommendation engine.
Promotions and discounts are often necessary to attract customers for a new product launch, increase sales for some goods, or give away old inventory at some price. However, it is important to target the right consumers with good offers to increase the sales. Advanced analytics can come in handy to profile the customers and segment them based on the similar attributes. Also, we can predict the most likely discounts to be offered to each customer based on the past sales history.
Effective pricing decisions can make or break sales. Organizations can leverage big data, analytics, and technology to more deeply understand consumers and implement the most compelling pricing strategies. Doing so requires the evaluation of current or recent programs to model the drivers behind revenue, predict how consumers react to different pricing, and build an optimized strategy across different products, customer segments, market, and competitive situations.
Inventory Forecast and Demand Planning
Inventory forecast and demand planning have been practiced for a decade. Most of the methods for inventory optimizations and forecast remains the same. What has changed is the "data" and this has grown due to the increased adoption of digital technologies. The average time to order is declining due to digitalization. Now, consumers can order using mobile phones and various e-commerce websites, storing a list of products they want in digital wish-lists. Product recommendation engines increase the probabilities of item purchase and also tons of customer sentiments are available for people to assess a product before buying. This a small list of new factors that are to be considered in the current age of demand planning. Apart from these, cost-effective replenishment planning and transportation analytics are necessary to be operationally efficient.
Internet of Things (IoT) Analytics
IoT is changing the face of global order management. Traditionally, there were several variations in the order management and fulfillment process which had long been the enemy of efficiency. However, now with the data provided by IoT deployments, an organization can reduce these variations. With this progress, now, if you ask about the location of an item at any point in time, or about the status of a piece of processing equipment, you can know within seconds. This requires the combination of real-time data and processing known as streaming analytics. There are various designs to deploy streaming analytics solutions. Find out more the design patterns from this previous article: CEP Patterns for Stream Analytics.
Robotic Process Automation
RPA can assist account receivables and order fulfillment staff in handling manual tasks, thereby eliminating human errors and freeing them up to concentrate on better things. If we analyze common pitfalls in an order to cash process, we can find many functions such as inconsistent data and documents, process inefficiencies, duplicates, and reactive fixes of credit memos/discounts. All of these can be automated using automated assistants. Also, intelligent chatbots can be used to communicate with customers to get their queries resolved on minor and day-to-day issues like the status of orders, reasons for delivery delays, etc. in real time.
Business Process Analytics
Let's think of an organization that wants to optimize the order to cash process. The traditional approach is a standard interview-style discussion with business users, and their answers form a baseline, an 'As-Is' process, for process improvement. However, most of them provide an ideal answer about the standard process followed. This forms the average 70-80% times the process is followed in the organization. The remaining 20% are the exceptions that are not so visible and the areas that need attention for improvement. Here, the process analytics can aid to mine this information for the event logs. You can get more information about this in Sibajan Das article: Business Process Analytics.