As an entrepreneur I’ve been reflecting on this a lot: The current milestone in logistics and fulfillment is using emerging technologies to capture and leverage exponentially growing data sets in warehouses and throughout the entire fulfillment network. Data sets have grown quickly in the cloud paradigm – and they exploded in 2020. Perhaps nobody realized how quickly or how extensive that growth would be, but all of that data has value. How are we as supply chain leaders and businesses tapping into that data and using it to make our supply chains more efficient?
This intelligence wasn’t broadly available to us ten years ago. Cloud technology uncovered it, Covid-19 accelerated it, and today, right now, businesses need to tap it to become competitive. The challenge is that these data sets are growing too quickly, and businesses are challenged with the task of capturing, managing, understanding, and finally acting on it without having an assist – and that’s where emerging technology comes in. Automation and autonomous technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) can help supply chains harness the abundance of data to gain predictive insights into their business to increase their value and efficiencies. Leveraging different models of thinking, representing key metrics using data aggregation, exposing real anomalies, and recommending a course of action based on operating models will make businesses smarter.
The rise of autonomous technologies
There has never been a doubt in my mind that automation and autonomous technologies were the next evolution of our industry after cloud technology. Now that the world is finally understanding how fragile supply chains and businesses are in a demand driven model, businesses and consumers want supply chain leaders to leverage these advanced technologies as soon as possible. Business leaders have learned how expensive it is to manage this data manually, and they are aware their businesses cannot scale by simply adding more resources at the pace supply chains are moving. They are aware of the untapped competitive value and competitive advantage of managing data automatically. However, they are frustrated that their organizations are not quickly pivoting to adopt these technologies to extract and leverage this data, get faster insights requiring less resources, and reduce overall IT costs.
As the years pass, many supply chain vendors have decided to add intermediate layers to their enterprise resource planning (ERP) systems or warehouse management systems (WMS) as a strategy for adding automation and low-level emerging technologies. For example, Warehouse Execution Systems have been labeled as having the ability to provide unprecedented, automated business intelligence, but remember, this is a whole layer of functionality that sits on top of a WMS or ERP. A whole layer of technology had to be invented to solve this problem, and forward-thinking companies – not having the solution already available in their WMS platforms — are investing in middleware applications in order to automate and ultimately tap that reservoir of information. In my head there is no need for another layer of integration. The solution should be as simple as having one true SaaS Cloud WMS, and bring that automation and autonomous technology layer into the WMS itself. What I wanted to solve was bringing an automation layer in without creating a layer of complexity – everything managed within one application – without worrying about integrations and third-party, bolt-on, software.
Harnessing the power of managing data within a single database
One year ago, we moved in that direction by migrating our cloud native, mission-critical SaaS application to Oracle Cloud Infrastructure (OCI – Gen2) and moved all our Rackspace and AWS PostgreSQL databases to Oracle Autonomous Transaction Processing (which also runs on OCI-Gen2) for better performance, higher elasticity, improved security and reduced TCO. To fully understand our strategy, it is important to understand the differentiation between autonomous and automated. For example, a warehouse process that can be accomplished automatically is still not autonomous if a warehouse user needs to respond to an alert, make decisions, or synchronize different systems to initiate the automated activity. In contrast, an autonomous system combines the power of managing data within one single database in the cloud while also applying advanced algorithms to process KPIs, detect anomalies, predict future scenarios, offer prescriptive courses of action, and provide forecasts based on multi-dimensional trends.
Oracle Warehouse Management does this! We are able to differentiate and provide unique value to our customers by leveraging the in-database machine learning algorithms available within Autonomous Transaction Processing. We deployed more than 40 different AI/ML algorithms. This approach has further helped our customers by improving their logistics network capabilities and efficiencies not just by generating automated tasks but by helping them predict future target values, calculating expected profit from each order fulfilled, or rerouting orders within the warehouse to improve overall warehouse flow to optimize space and make better of use of robotics and other material handling components. Business executives want to go to Mars, but a good CIO knows that first she/he has to get to the launching pad. Operations teams want practical applications for their emerging technology plays — practical use cases for automation and autonomous technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT).
Next generation cloud platform
As a next generation cloud platform and leveraging our modern cloud native REST services-based architecture we rely on API calls to connect to any material handling equipment (MHE), robots, or smart machines in real-time. We orchestrate all these different systems, robots, or smart machines by constantly making API calls. We offer our customers limitless performance by instantly and transparently scaling up as demand increases making it easy to accommodate peak processing workloads in zero downtime. The elastic, independent, and autonomous scaling of our computing enables our true cloud capabilities.
As a platform that was built in the cloud, for the cloud, our integration capabilities are seamless. That ease of connectivity means we are naturally positioned to meet that next milestone of using emerging technologies to capture and leverage data sets throughout the entire fulfillment network. We are fully capable of helping the enterprise level business speed up their innovation and maybe even get ahead of their industry. Furthermore, we can do it without adding complexity to the system and without having to worry about integrating bolt-on technology.
The wave is here, and a race is on to see who can catch it first. It’s too much intelligence to let go to waste, and this intelligence was always the ultimate promise of cloud technology and we are ready today to help you realize it.
Diego Pantoja-Navajas is vice president of WMS development at Oracle and former founder and CEO of LogFire. He is an industry thought leader in SCM who enjoys exploring the impact of advanced and emerging tech on supply chains.