Retail Automation: A Do It By Itself Story
Think of the successful retailers you know and what they have in common. Whether it is Costco, which earns about US$ 1,540 per square foot of space – that’s more than twice what Walmart nets – despite its low prices, or Trader Joe’s, which sells stuff you can’t find anywhere else, or Amazon, whose virtues don’t need elaboration, the mantra is always low cost, great convenience, speedy response, and memorable experience.
While that may seem simple in theory, it is significantly difficult to implement, as the legion of fallen retailers will tell you. And technology has a big play in saving the day. It’s critical that technology – especially next-gen technologies like AI and ML - drive the core of business functions, in a culture of deep business-IT alignment, that is then the fertile ground for new experiences, and even new business models to be created and adopted. A simple but powerful first step that can greatly increase the odds of success is pervasive automation that cuts across business processes.
Speaking of processes, in the world of retail consumption, a (physical) product or service makes the journey from producer to consumer in five stages - planning, sourcing, producing, distributing and selling. As raw materials convert to physical goods, and physical goods go from factory to warehouse, to store shelf, to home, they leave a trail of data at every phase. The retail industry has largely optimized the onward flow of goods and services, and has learnt to leverage data to improve business. At the same time, there is also a flow of information in the reverse direction, from consumer to producer via intermediaries. Today the retail ecosystem is greatly invested in finding ways to gather, process, and analyze this data, to gain additional insights into things like product performance in real life, user feedback and post purchase behavior, ecosystem bottlenecks, unstated needs, competitive response, and so forth. Retailers are especially focused on getting information on point of purchase and analytics around consumer behavior and product feedback. There is huge scope to automate this insight generation with AI and ML to take the industry even closer to its end goal of “low cost, great convenience, agility of response to needs or memorable experience.”
With this as context, a comprehensive retail automation plan would look something like this:
Automate physical flow of goods and services
Here are some examples of how automation of repetitive, transactional supply chain processes is improving cost-related parameters, such as productivity, inventory level, and turnaround time in various retail organizations.
Apart from using robots in its warehouses and drones for deliveries, Amazon – the gold standard of supply chain automation – is using autonomous trucks and forklifts to cut down delivery costs. Meanwhile, Alibaba is working with a consortium on a new Blockchain-based food supply chain to reduce fraud.
Then there’s Adidas where advances in robotics and automation means that they can now afford to bring production back closer to customers to make faster delivery of new styles without the hassles of lengthy shipping times. They are also heavily invested in virtualization for product sampling to create virtual products accurate enough for decision-making before they are brought to life at scale. In fact, this wave of automation at Adidas flows right into their stores. Visualize walking into an adidas store, running briefly on a treadmill and instantly getting a 3D-printed running shoe that suits you perfectly! Such a flexible, fully breathable carbon copy of the athlete’s own footprint, matching exact contours and pressure points, is a clear possibility today.
While not quite in the league of these ecommerce giants, the traditional retail industry has also automated parts of its highly complex business over the years. The intensity of automation varies tremendously across markets, product categories and store formats. Consumer Packaged Goods (CPG) companies have been quick to adopt because they need a ruthlessly efficient supply chain to compensate for wafer thin margins. A great example is P&G, now part of Gartner’s “Masters” list of supply chain leaders, which has automated workflows to minimize exceptions and execute end-to-end planning. At the second-largest discount store retailer in the US, Target, a “goods-to-person” automated fulfillment system enables staff to pick stocks for instance, for several stores simultaneously from their workstations.
Automate data flow and decisions
Poor customer service is usually the outcome of an unfulfilled commitment (about lead time, for example), stock-out, wrong or inconsistent communication, and unreliable delivery. It is possible to dramatically improve service levels by leveraging the knowledge trapped within the supply chain.
At Walmart, the secret of supply chain success is a collaborative planning, forecasting, and replenishment system that enables vendors, distributors, and others to synchronize forecasted sales. The sharing of information between stores, warehouses, and suppliers has helped the company to optimize its supply chain and take better decisions about inventory to ensure customers find what they need. Luxury department stores chain, Nordstrom has a piece of software, which allows it to do “drop shipping”, that is, ship goods directly from the manufacturer to the consumer. When the retailer receives an order on its website, the software sends it to the manufacturer who packs and ships the products to the customer’s delivery address. This has not only helped Nordstrom to reduce inventory holding costs and risk, but also improve customer service through data and insights-driven stock allocation and assortment, not to mention fewer stock-outs.
Smart digital marketing programs, that create avenues to engage with consumers in innovative ways, help create better outcomes. Popular examples like miadidas and NIKEiD systems have already proven how ordinary consumers now have the opportunity to personalize their products, and disperse hundreds of uniquely designed products across the internet to ‘entice’ other fans. P&G’s direct to consumer subscription business is another effort to create better fulfilment. Dollar Shave Club, now a Unilever company, is a great example of great experiences born out of data monetized.
With big data and analytics technologies making great strides, several players are leverage them to automate supply chain planning and other knowledge-based processes. Several CPG companies employ predictive analytics to analyze hundreds of variables, from weather to social trends, to build a significantly more accurate demand forecast.
Automate ecosystem from end to end
Trader Joe’s key to success is its distinctiveness. From its merchandising approach (micro niche products, and a thoughtful but small assortment of items), to its lively atmosphere, to the smell of its samples, to a customer experience that tells a story, everything is uniquely Trader Joe’s.
For most brands, which don’t have the same infallible instinct about what their customers really seek, data-led insight might be the ticket to better customer experience. Here, capturing post-sale data that flows in reverse – from consumer to manufacturer via all the intermediaries – is especially important. At a minimum, this calls for close integration of all entities within a single supply chain, and eventually, similar connections between supply chains and even ecosystems where both physical goods and data flow seamlessly from end to end.
Already, there are instances of supply chain collaboration where the exchange of data has resulted in lower inventories, shorter turnaround time, and faster response to crises and other events. But to see how the automation of data across the ecosystem can elevate experience, look no further than Amazon, which years ago, patented a big data and predictive algorithm-based concept called “anticipatory package shipping” that would allow it to deliver goods even before they were ordered.
As the Internet of Things hurtles towards 50 billion connections, give or take, by 2020, it will create stronger links between longer supply chains, leading up to giant ecosystems. At that time, it is entirely possible that a chef researching a certain type of organic cereal will find a delivery drone at the door even before an order has been placed, along with a choice of companion ingredients for the recipes that have magically found their way into his mobile. Payment for the goods is adjusted against a credit the supplier needs to make for a previous order, which the customer didn’t return, but reviewed unfavorably on social media. A few days later, an automated messenger enquires about the experience of using those ingredients, and offers to feature the new dish on the restaurant menu in various online forums, as well as send out an alert to regular clientele.
With Amazon making its future plans clear (once again) with its acquisition of Whole Foods, the pressure is again on, on the global retail industry. On the marketing side, retailers need to go all out to carve an identity for themselves to keep customers coming. At the operational level, they have to raise efficiencies, service levels and quality of experience. In automation, they will find many answers.