As supply chains become more intelligence-driven and begin to utilise insights to serve customers in new and innovative ways, traditional transaction-led models are no longer cutting it.
65% of shopping begins at the search, according to Gartner. Instead of focusing efforts on converting people at the buy, retailers should look to convert at the point of search. Abandoned shopping carts offer insights into lost sales. Often, the reasons are related to shipping and return policies. This offers an opportunity to directly impact revenue and sales by making more attractive offers that convert abandoned carts into sales.
Amazon has led the way toward two-day and same day shipping, a chase that retailers cannot win if they hope to protect their margins. The irony lies in the fact that fulfilment speed isn’t necessarily what each order requires to satisfy the end consumer. Both B2C and B2Bcustomers frequently make purchases they don’t need to have delivered in one day, and pulling levers between price, shipping time and mode of final delivery can take advantage of this fact to generate new value. Sometimes, it might be preferred to have goods delivered at a later date, or in a particular time window. If my wife’s birthday is a month away, I don’t need her gift delivered tomorrow. In fact, I would prefer to not have it come until a Friday afternoon when I’m working from home and she’s in the office, so that she’s not there when it arrives. If the retailer knows this and offers this among my purchase and delivery options, I am likely to select that later delivery – especially if there’s a price break.
Having access to customer information and POS data is essential in knowing what options might be attractive and determining which deal to serve up as the customer initiates its search. And on the back-end, there’s a complex hairball of specific details that need to be orchestrated to make this possible. To start with, the retailer needs to know the status of all inventory available to ship. With that data in-hand, it can begin to run analytics to predict the arrival of goods based on the customer, inventory, order details, carrier capacity, and other what-if scenario simulations. The retailer can begin to run inventory and route optimisation analysis and match it with consumer demand at the point of purchase or at consumer search, to offer specific options that appeal to the consumer while eliminating the costs and burdens associated with an artificial drive toward next day shipping. The strategy shifts from being all about speed to customer-centric precision – driven by supply chain data and insights.
Each company in a supply chain tends to operate its own enterprise systems, making it a challenge to see and collaborate from one business to the next. Silos often exist not only from company to company, but from system to system. Multiple ERP, TMS, WMS systems, for example, create layers of hurdles and gaps that hinder the flow of data and visibility. Traditional thinking has been to plug these gaps with portals where suppliers and trading partners can exchange documents and collaborate. This approach has enabled commerce to “get-by” in many ways, making up for data latency and lack of true visibility by padding inventory and adding buffer stock in key locations to fulfil demand. But as supply chains aim to wring out buffer stock and become more efficient, we’ve hit a wall in terms of execution capabilities. The vision of optimising inventory and transportation routes to make customer-specific offers based on confident predictive intelligence simply cannot exist when relying on an enterprise-centric technology backbone. The hub and spoke ecosystem was built for yesterday’s environment. In today’s world supply chains must operate as a single cohesive network that lives, breaths and executes around the customer.
To successfully deliver on this vision, trading partners have to be connected on a single network where all parties are plugged into the same system, interacting and executing on the same information. There is no duplication or passing information from one system or party to another; there’s only one set of data that everyone sees. AI and machine learning offer a world of opportunities to enhance business and optimise performance. But the value is limited without access to quality data that can be acted upon. A network of clean and accurate data becomes a digital foundation for predictive and prescriptive analytics.
In this single-instance network model, visibility, connectivity and collaboration are delivered to everyone in the supply chain. So, when AI and machine learning are applied, data analytics such as predictive time of arrival can be applied to each node in the network. With this in hand, new opportunities arise to deliver customers multiple options for service based on reliable intelligence. When these analytics are reliable enough to allow retailers to build customer offers around them with confidence, the supply chain becomes a competitive advantage that creates new value streams. Through network connectivity, accurate data and cross-party visibility, a digital foundation is formed that serves as a catalyst to innovation that is built around and tailored to the end customer. This is the very essence of a customer-centric supply chain.