Artificial Intelligence and Future Supply Chains
“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
These were the words used in 1955 to launch the very first research project that coined the term ‘artificial intelligence’.
Fast forward 60 years and artificial intelligence – or machine learning as many call it – is emerging as the next big technology. 2016 has seen a race for artificial intelligence, with a number of acquisitions and large technology vendors – of the likes of IBM, Google and Amazon – launching new artificial intelligence-enabled products.
In SCM World’s 2016 Future of Supply Chain Survey, we found big jumps in importance for a series of disruptive technologies with respect to supply chain strategies, some of which were considered largely irrelevant just a couple of years ago.
One of these is machine learning, which in 2016 cemented its place in the technology mainstream. 47% of supply chain leaders from our larger community believe that artificial intelligence is disruptive and important with respect to supply chain strategies. The technology grew so rapidly in importance over the last couple of years that in 2014 it wasn’t even included in the research!
What’s Artificial Intelligence?
Artificial intelligence can be defined as the use of computers to simulate human intelligence, specifically including learning – the acquisition and classification of information, and reasoning – finding insights into the data. At the core of artificial intelligence is the ability to recognize patterns across the 3Vs of big data (volume, velocity and variety) and find correlations among diverse data.
Today, the term artificial intelligence encompasses everything from speech recognition to machine vision and from chatbots to collaborative robotics. The benefits of this technology lie in speed and accuracy beyond the reach of human capabilities, which is also feeding a debate about its implications in the future of work.
Business activities that require to collect and analyze lots of structured and unstructured data can benefit from artificial intelligence and its ability to support faster and smarter decision making. Supply chain is therefore a natural fit for artificial intelligence.
Artificial Intelligence and Supply Chain
An interesting 2010 research paper from Dr. Hokey Min from the College of Business at Bowling Green State University, predicted a number of applications of artificial intelligence in supply chain management. These include setting inventory safety levels, transportation network design, purchasing and supply management, and demand planning and forecasting.
Today’s artificial intelligence is mature enough to make some of those applications possible:
- Capitalizing on the machine-learning capabilities of IBM’s Watson, IBM has recently launched Watson Supply Chain aimed at creating supply chain visibility and gaining supply risk insights. The system uses cognitive technology to track and predict supply chain disruptions based on gathering and correlating external data from disparate sources such as social media, newsfeeds, weather forecasts and historical data.
- ToolsGroup’s supply chain optimization software is rooted in machine-learning technology. One area of application is new product introduction. The software begins with creating a baseline forecast for the new product. As the algorithm learns from early sell-in and sell-out demand signals, it layers this output to determine more accurate demand behavior, which feeds through to optimized inventory levels and replenishment plans.
- The machine-learning technology of TransVoyant is able to collect and analyze one trillion events each day from sensors, satellites, radar, video cameras and smartphones. In logistics applications, its algorithm tracks the real-time movement of shipments and calculates their estimated time of arrival, factoring the impact of weather conditions, port congestion and natural disasters.
- The technology firm Sentient uses machine learning to deliver purchasing recommendations to e-commerce shoppers based on image recognition. Rather than only using text searches and attributes like color or brand, the software find visual correlations with the items that the shopper is currently browsing through visual pattern matching.
- At the core of Rethink Robotics’ collaborative robots is an artificial intelligent software that allows the robot to perceive the environment around it and behave in a way that’s safe, smart and collaborative for humans working alongside production lines.
The awareness and ability to make fact-based decisions that artificial intelligence makes possible is completely new to supply chain management. This technology is expected to create the sentient supply chain of the future – able to feel, perceive and react to situations at an extraordinarily granular level.
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.