The word Artificial intelligence invokes images from science fiction movies. Of robot uprisings, of space missions to far fletched galaxies and of consciousness that can be transferred to clones thereby making humans immortal. Surf for a while on the subject, on the web, and you will find thought provoking talks by professors pondering over the question if Artificial Intelligence is or will ever be self-aware. The more adventurous amongst us will find themselves drawn towards theosophical discussions on creationism or maybe even towards discussions on how observation influences reality in the quantum world.
It is true that discussions about Artificial Intelligence (AI) border on ‘Science Vision’ if not Science Fiction or philosophy itself, and maybe for a good reason. Technologies that we first saw on the movie screen have now manifested themselves in real life, bringing convenience and adding value to our daily being. Gestural interfaces, machine aided purchases, facial recognition, autonomous cars, miniature drones, ubiquitous advertising, and electronic surveillance are all technologies that we first saw in the movies. Machines are already making predictions on trading stocks, customer purchases, traffic flows and crime detection, very much in the way we saw in the Tom Cruise movie, The Minority Report.
Technology leaders have placed bets on technologies like Brain-Computer interface, AI in medicine, Deep learning, and Machine learning tools. Artificial intelligence is expected to lead the new economy which has been labelled as the ‘fourth industrial revolution’ or ‘Industry 4.0’. AI is at the forefront along with other emerging technologies such as , robotics, the Internet of things, 3D-printing, quantum computing and nanotechnology ,in the emerging business landscape.
The design about how AI will fit into the normal business processes is a discussion happening at the highest echelons of the organisations today. Not long ago the question that were being discussed were whether self-learning machines will replace or assist humans in white collar jobs. Whether it would be possible for machines to learn common sense and empathy. How do you decide who owns the ‘insights’ that come out of AI and who owns the responsibility for an erroneous decision being taken by a machine? While these are questions that still need deliberation, industry also realises that the possibilities for deployment of AI in business are immense. Top ten deployment or ‘Use Cases’ as they are called, are Data Security, Personal Security, Financial Trading, Healthcare, Marketing Personalization, Fraud Detection, Recommendations, Online Search, Natural Language Processing (NLP) and Smart Cars .
Now businesses have attained some maturity in dealing with questions pertaining to AI. From trying to wrap the head around the concept of AI, business has moved to asking how the new technology can be embedded and used in normal business processes .While routine transactions had already been automated in organisations over the past few decades, many companies now use the predictive and prescriptive capabilities of analytics to guide their decision making. And the reason for this development are the many developments that have brought the technology nearer to users and has made it relatable to people who can understand and intuitively adopt it. The growing confidence on the technology has encouraged businesses to look at use cases in the core functions such as supply chain management, making it one of the most promising area of application.
Businesses can be said to be set to be transformed through a creation of an’ intelligent Supply Chain’.
At this point it is worthwhile to examine the trends and developments that are behind the rapid development and deployment of this technology. Among the many reasons, one important one is, that the eco-system of data science is now more easily available to normal businesses and not just restricted to the world of big data companies in the world.
Free and open-source AI software is providing opportunities to the companies to quickly create proof of concept, speed up research and test their use cases. Several businesses have started taking advantage of these software to study their internal data, gather insights, and predict business outcomes.
The AI effort however needs to be supported by a work force trained in data sciences. And the growing availability of a workforce equipped with machine language and coding skills has been one of the biggest contributors to this transformation. To empower and equip the workforce , the number of tools and frameworks available to data scientists and developers has also increased with the growth of AI and ML. Code writing applications are now so common that they come inbuilt with the operation systems of personal computers. Collaborative platforms further enable developers to manage, edit, version control and share their work with other developers. Think about this like a social media platform for developers. This has led to the creation of vast community of AI/ML developers and a supporting ecosystem. Coding skills in ML languages such as ‘R’ and ‘Python’ will now be as ubiquitous in 2020s, as working with spreadsheets was just a few years ago.
The core requirement for deployment of AI is management of data. Database Services allows users to set up, operate, and scale a database in the cloud. Many service providers provide cost-efficient and flexible data management capacity while automating time-consuming administration tasks such as hardware provisioning and set-up. Software as a Service (SAAS) easily integrates with enterprise applications to address common use cases. If all this looks too much then, pre-trained AI Services provide ready-made intelligence for applications and workflows
With all the above developments the stage is now set for organisations to adopt and embed Artificial Intelligence and Deep Learning into their business processes. The ecosystem of service providers, platforms, software, and manpower is already there. The only thing required is the decision to venture into making AI a part of the way business is done. Coming to the supply chain management function, people often talk about degrees of autonomy that can be incorporated in the supply chain planning process. The concept of an intelligent Supply Chain incorporates self-learning capabilities of the machine to take better supply chain decisions.
In the Logistics World the good news is that organisations are already custodians of huge amounts of historical data. Airlines, shipping lines, freight forwarders, Airport Custodians and transporters collect large amounts of demand and supply data as they transact their business, thus providing a fertile ground for multiple use cases to run analytics and predict business outcomes. The right use cases can lead to better utilisation of freight capacity, better decisions on forward freight contracts, better deployment of resources and correct pricing. Analysis of corelation between parameters that seem unrelated can throw up insights that could be missed by the human mind. The logistics industry has already taken the lead in deployment of new technologies like IOT and blockchains, which is another factor that will accelerate the industry’s adoption of the technology. IOT is the perfect technology that bridges the gap between the physical world of logistics and the digital world of analytics by providing a seamless flow of real-time data. Blockchains are further ensuring that supply chain transacts within itself in a secure and de-centralised way. The data harvested by both the technologies can be a gold mine for an Artificial Intelligence layer that can be the overarching technology between all these blocks of data, thereby creating a Digital Twin of a logistics network in the virtual space. Imagine a ‘Strategic Control Tower’ in an airline’s or a global freight management company’s corporate office, overseeing its entire global network over a Digital Twin and taking long-term capacity , as well as operational decisions ,with the use of AI powered prediction algorithms. The thought itself of having such a powerful system at your command is extremely liberating. Imagine what a real system can do to your business.
Many more of such use cases can be envisioned, deliberated upon, and tested. As the familiarity with the technology increases and organisations gain maturity in dealing with it, the turn-around time from ideation to deployment would decrease. It is said that organisations compete based on their supply chain eco-systems. Future organisations would compete based on intelligence embedded in their systems. The winner would be the supply chain that is faster to learn, the one which is more ‘Intelligent’.