Future-Proofing Logistics with Real-Time Analytics

Retail has long been a reactive business, struggling to meet demand with supply. But thanks to real-time data analytics, logistics is getting a long-overdue revamp.

When logistics company Hermes was forced to switch off part of its ageing technology infrastructure because it couldn’t cope with a spike in demand, CIO Chris Ashworth knew something had to change.

“Many industries have their peaks,” he explains. “In travel, the holiday push is massive; in retail it’s Christmas. The difference in logistics is that you don’t have a reset period. If you’ve got two million parcels in your hub, and your systems go down, you can’t get rid of two million parcels. And in 12 hours’ time, you’ve got another two million right behind them. Trailers in the yard are blocking those that want to get in and the parcels just come and come and come... So, if you do have a problem, it can take you weeks to fix.”

According to Ashworth, digital transformation for Hermes was a “no-brainer.” For behind every successful business is an efficient supply chain, the optimised movement of goods from supplier to warehouse to store to customer. It’s why big data analytics and cloud computing are at the core of revolutionising logistics - tracking and tracing product flow and stock levels in real-time, leveraging customer data to predict buying patterns, even using robots to tirelessly fulfil orders in vast automated warehouses.

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Done right, everybody benefits. Customers enjoy an enhanced shopping experience, with greater choice, convenience, and service. While retailers, suppliers and manufacturers gain greater visibility over their supply chain from end-to-end, allowing them to streamline operations, deal more effectively with partners and proactively manage stock or services.

Of course, you can’t optimise what you can’t see. So, monitoring solutions (from smart shelving to RFID tags) are at the heart of any data-driven logistics infrastructure, allowing businesses to capture key data and visualise stock control using intuitive web dashboards. By linking this visualisation to manufacturing, distribution and sales information, a detailed operational picture of a business can be revealed.

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For example: “Where notification of a high-selling promotion is received through an [RFID] implant,” a supply chain manager told The Chartered Institute of Procurement & Supply[1], “it allows the manufacturer to make decisions on stock much earlier in the supply chain, and this can help keep costs low.”

Supermarkets are trying to anticipate what consumers want before they know they want it.

The big UK supermarkets arguably lead the charge in this area, often simulating distribution scenarios with historical data to optimise stock levels. Analytics tools are becoming instrumental in deciding what products to carry, how many to order and how best to price them. For example, if you’ve ever shopped at Tesco, you might have noticed some products are labelled with a 'red star'. According to the data, these stars define the 1,000 most important/popular lines in that particular store.

Efficient inventory management is crucial to reducing waste, so forward-thinking businesses are increasingly using analytics to anticipate consumer demand. This requires a system that can not only pull in data from a diverse array of sources (including existing operational systems, vehicle diagnostics, advertising responses, and even social media mentions), but also make sense of it.

We’re moving towards a supply chain that will be fully trackable in real-time.

“Based on shopping data and artificial intelligence,” says an article in Retail Detail[2], “supermarkets are now trying to anticipate what consumers want even before they know it themselves.”

This is key. At the front-end, predictive analytics allows retailers to suggest products and/or services to potential buyers. Behind the scenes, it allows retailers to identify areas of high demand, quickly capitalise on sales trends and ensure that the right stock is sent to the right store.

In Japan, for example, Nippon Paint used SAP Hana in-memory analysis software to analyse trends in consumer behaviour based around colours, styles and designers[3]. On the back-end, SAP’s analytics tools connected to key data sources (sales, suppliers, and purchases) to ensure that the development, manufacturing, and delivery components of the business could meet customer demand.

Retail and manufacturing giants are also embracing robotics as part of their automation processes. Amazon has long used bright orange Kiva robots in its warehouses. They scurry around the company’s vast Orion Boulevard building in Manchester, tirelessly bringing shelves to the human employees who pick the products.

Technology is finally bringing some agility to the traditionally rigid supply chain.

Innovative online supermarket Ocado has also invested heavily in robotic warehousing. In its 18-acre customer fulfilment centre in Andover, “hundreds of cuboid robots whizz around on a vast metal grid”[4], automatically plucking shopping orders from crates in the floor. The facility can reportedly process 65,000 customer orders per week and pick a 50-item shop (from a range of 45,000 products) in only five minutes.

“For a £2.00 item you’ve got 60p to pay for picking, packing and delivery,” Paul Clarke, Ocado’s chief technology officer told The Engineer[5]. “And the only way you’re going to do that and have a profit is to use a lot of technology and automation, and that’s what we’ve done from day one.”

Technology is revolutionising the supply chain, from smart shelves to cloud-powered data analytics, capturing information from millions of data points and fitting it together into a meaningful, trackable whole. It’s a process that can not only support business strategies but offer fresh insights into new opportunities for growth and efficiency.

“If you think about retail and logistics,” says Hermes CIO Chris Ashworth, “those companies that are further along the curve are using big data to understand where products should be in the supply chain, to satisfy consumer demand faster and to identify potential problems before they become larger ones.

“With real-time data analytics, you move from being a business that focuses on 'yesterday' to a business that focuses on 'now'. The benefits are absolutely massive.”

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