ALOG is developing Autonomous or Driverless tech for indoor vehicles that can be used for transport in large facilities like warehouses, hotels and airports.

Can automation deep learning improve efficiency in the warehousing industryImage for representational purpose only
Atom Startups Wednesday, August 30, 2017 - 17:26

Ever since the ecommerce boom in India, another sector that simultaneously saw a spurt in growth was logistics. As more and more ecommerce players entered the market and home deliveries increased exponentially, not only did incumbents in the logistics space benefit, several delivery startups also emerged to ride the wave.

However, one problem that was left answered was that of intra-logistics. Not just ecommerce companies, any product related company has a warehouse, be it a fulfilment or distribution centre. And the warehousing industry employs millions of workers for material handling.

Their job involves monotonous and tiring work, be it unloading inventory, putting away in storage racks or picking, packing and shipping. They walk around 5-10 kilometres per shift, pushing heavy carts to pick / put inventory away, leading to several health and social issues.

With several years of experience in developing, implementing and selling software solutions for warehousing and transportation, Hyderabad-based Raghuram Nanduri saw first-hand, the huge value that can be delivered by these software applications.

“But I could also see a huge gap in automation of material flows, which even today relies heavily on human labour. I started studying this problem and felt that there is a need for intelligent, flexible and affordable automation solutions which can co-exist with shop floor workers,” he adds.

And the validation to his idea came when Amazon acquired Massachusetts-based order fulfilment company Kiva Systems for $775 million in 2012. Kiva makes autonomous mobile robots and a control software to improve productivity at fulfilment centres.

Raghuram then started working on the idea and founded Autonomous Logistics Technologies (ALOG Tech) in May 2016.

ALOG is developing Autonomous or Driverless technology for indoor vehicles that can be used to transport materials and people within large facilities like warehouses, factories, hotels and airports.

The startup is using computer vision, deep learning and motion control techniques to transform manually operated material handling equipment like push carts, pallet trucks and forklifts into robotic equipment supervised by intelligent software applications.

The idea is to make the intra-logistics process less labour intensive and more efficient.

For this, ALOG Tech has built Autonomous Carts for Distribution Centres (ACDC). Autonomous Carts are battery-powered robotic carts specifically designed for a range of tasks including order picking, inventory put away, shelf stock replenishment, cycle counting and as flexible conveyors in distribution centres and retail stores.

“In warehousing, this would mean adding intelligence to the Warehouse Management System (WMS) to dynamically improve picking strategies and direct a fleet of self-driving carts to pick more orders in less time and for less cost,” Raghuram says.

Raghuram Nanduri, founder of ALOG Tech

This autonomous warehouse system assigns tasks to the Carts dynamically in an intelligent manner to help workers pick more orders in less time and with less physical effort.

This, Raghuram says, will double productivity and halve the pick-pack-ship cycle time, resulting in lower order fulfilment cost and better customer service.

“Initially the idea was to develop the software and licence it to OEMs. But after much debate we decided to take the tougher route of developing the entire system including hardware and go-to-market directly. This we believe will give us a sustainable competitive advantage in the long run and deliver greater value to all stakeholders,” Raghuram says.

So currently ALOG offers a combination of autonomous equipment and autonomous systems for logistics. Raghuram says that together they address problems in logistics at the physical level as well as at a process level to enable Autonomous Logistics.

While ALOG is still in early stages of piloting its concept, for now it plans to sell its products using a licencing model.

“We are targeting large MNCs globally who prefer the regular model as the total cost of ownership is lower and they have enough capital. This also reduces our need for external funding” adds Raghuram.

While the technology can address multiple use cases across industries, ALOG’s immediate focus is on warehousing.

Raghuram’s experience in the industry is proving helpful when it comes to acquiring customers. ALOG has presented its concept and prototype to several large retailers and logistics companies in India and abroad, including Fortune 500 companies. It will soon be running pilots with several of them.

A prototype of ACDC in a warehouse

The target audience for ALOG will be developed markets with high wages and labour shortage.

And while it will face competition from a few like Fetch in the US and Grey Orange in India and also some from traditional automation heavyweights like Kuka, Raghuram says that ALOG’s strategy to create a multipurpose mobile robot platform with multiple modes of operation differentiates it from the rest.

“This allows us to address multiple use cases across industries. We also have a huge cost advantage over the global players, like how it is in the IT industry. The government’s various incentives under the Startup India scheme are also adding to this,” he adds.

While it is in the process of piloting its solution, it is also planning to raise money to scale up and serve global customers.

There is a vast untapped market and as per International Federation of Robotics (IFR), the market for mobile robots is projected to be $5.3 billion by 2019.

“There is no significant Indian player in this market. We want to change this and do what ‘Infosys’ did for India in IT,” Raghuram says.

This article has been produced with inputs from T-Hub as a part of a partner program.