I got an email recently from Amazon, that as a Prime member, I was now eligible for free grocery delivery, which previously cost $14.99/month with the AmazonFresh grocery delivery service. Amazon is rolling out this free service in phases. But soon, a large fraction of Prime members will have free grocery delivery services. This will put tremendous pressure on very large brick-and-mortar grocery retailers that have a growing online presence, as well as e-commerce-only grocers like Instacart and Peapod, as this online behemoth encroaches into their turf. This most recent development highlights that omnichannel wars continue at a blistering pace. Even consumer packaged goods (CPG) manufacturers are investing heavily in the online sales channels. Boston Consulting Group (BCG) reported that CPG online sales grew rapidly from 2013 to 2018 – 19% annually versus only a tiny 1% growth rate of in-store sales. And e-commerce now accounts for 40% of the growth in CPG retail sales. One of the biggest risks with online channels is the inability to meet sudden surges in demand.
For example, if an item is out of stock, it can be quickly dropped from Amazon search results and the CPG company can lose sales. There is an emerging need for forecasting systems that can leverage machine learning (ML) and ingest Amazon ordering patterns, inventory in the channel and consumer promotions to predict the sequence of orders so that the products are in stock and the orders can be fulfilled.
In such a dynamic environment with shrinking windows to make decisions, artificial intelligence (AI) and ML techniques can enhance the forecasting of omnichannel consumer demand, shape the demand through tailored merchandising, pricing and promotions and reliably fulfill the orders for profitable growth. It is imperative for retail and CPG companies to adapt their supply chains, which were designed for a brick-and-mortar world and now need to address a wide variety of challenges introduced by e-commerce (including a CPG company’s own online channels). They continue to face challenges as network design – location and capacity of distribution centers (DC), transportation lanes and flows, SKU assortments, pricing strategies, promotion calendars that span brick-and-mortar and e-commerce, packaging well suited to e-commerce and (as mentioned above) high service expectations from retailers such as Amazon.
To succeed and thrive in such consumer-oriented markets, businesses need to become more demand-driven and orchestrate the supply chain to fulfill that demand.
Retailers and CPG companies need to predict where demand will occur, across brick-and-mortar and online channels, and efficiently supply the right quantity of products to thousands and even millions of locations.
In this journey of anticipating, shaping and responding to consumer demand, there are four core processes that that can benefit from AI/ML.
1) Forecast Demand
Forecasting the actual sell-through at the point of consumption (“Moment of Truth”, per Proctor & Gamble), whether it’s at a physical store or an e-commerce channel, drives the entire supply chain to respond. In order for the right product to be at the right location, at the right time and in the right quantity requires a complex orchestration of the end-to-end supply chain. The more accurate the sell-through forecast, the more efficient the supply chain can respond to ensure the highest customer service with the lowest investment in working capital.
AI/ML techniques can improve the accuracy of the sell-through forecast in a number of ways.
Practitioners in the consumer demand forecasting discipline have found that leveraging numerous internal and external causal factors allows the ML engine to learn continuously and greatly improve forecast accuracy over time.
2) Pricing, Promotions and Markdowns
Demand shaping “what-if” scenarios can greatly benefit from ML techniques.
The merchandise planner can now experiment with “what-ifs” – look at the impact of changing the timing or duration of the markdown or combining that markdown with an end-aisle display. On the online channel, the planner can try different product placement strategies on the web site, discounts or free shipping and review the impact on forecasted orders.
3) Assortments and the Merchandising Calendar
Planning assortments across departments/store clusters and setting the seasonal merchandising calendar can be enhanced with ML algorithms.
4) Customer Order Fulfilment
The business-to-consumer response would be ineffective if the item the customer wants is not available or if an order promised for store pickup (BOPIS) is not ready in time. This is where the application of AI/ML techniques can be very valuable.
There are an estimated 1.5 million packages delivered per day in New York City. In the city that never sleeps, about 15% of NYC households receive a package a day. A comprehensive model of the supply chain, with predictive and prescriptive analytics powered by AI/ML, is what is emerging as the best practice in responding to that business-to-consumer challenge.
This blog was written and contributed by:
Vice President, Industry Strategy