Technology has always had a significant impact on supply chains. In 2023, it is going to be more important than ever as the "Great Supply Chain Disruption" continues to challenge organisations around the world, according to SAPICS (The Professional Body for Supply Chain Management in Southern Africa). War, raw materials shortages, rising energy costs and extreme weather conditions are just some of the factors that will disrupt global supply chains in 2023, warns the non-profit organisation that aims to elevate, educate and empower the community of supply chain professionals across Africa. "In South Africa, the electricity crisis will continue to challenge businesses across all sectors. The negative impact on energy-intensive and irrigation dependent agricultural industries in particular will resonate through the entire supply chain – from the farm to consumers, who will have to pay more and have fewer competitive options available on supermarket shelves," says SAPICS president MJ Schoemaker.

Industry (or Manufacturing) 4.0 started as a German government initiative in 2011. It refers to a Fourth Industrial Revolution characterized by smart factories using robotics, autonomous operations, the Internet of Things, big data, analytics, artificial intelligence, and a convergence of IT and OT. The goal is to create efficient, agile and intelligent manufacturing. There wasn't a prescriptive Industry 4.0 methodology for manufacturers to follow, so early adopters tried various approaches to see which worked best. "We focus on the capabilities that [Manufacturing 4.0] technology can deliver for our clients," said Stephen Laaper, principal and smart factory leader at Deloitte.

Supply Chain Digital explores the utilisation of AI and analytics with experts in the sector, particularly in regard to how it is shaping corporate attitudes to data. In an era calling for latency-sensitive applications, where the emergence of edge computing, 5G and artificial intelligence (AI) powered analytics are ushering in the possibility of real-time solutions, companies, now more than ever, are looking for the most efficient ways to make use of their data. The sheer volumes of information that can be gathered from every aspect of a business are overwhelming: with so much data available, where do you start when examining it? The challenge for modern supply chains is knowing where to place a strategic focus and not becoming paralysed by information overload, whilst also not excluding details that could increase efficiency, allow for better forecasts or enhance customer experience (CX). Grant Millard, Director of Technology at Vendigital, says that, prior to the advent of Big Data and analytics, companies struggled to deliver clear or credible data-based insights.

The retail industry in India, and globally, has been in a state of flux. With the euphoria around e-commerce having tempered, online retailers understood the importance of selling through stores even as offline players realised how significant the internet is for future growth. Yet, despite acknowledging the importance to co-exist through an "omnichannel model," most Indian retailers have not managed to successfully crack the code of offering shoppers the best of both worlds. But there's some inspiration they can take from Tesco, UK's leading supermarket. The 100-year old company operates in nine markets, including China, India, Malaysia, Poland, and Slovakia.

In 2016, the AI hype was just beginning, and many people were still cautious when mentioning the term "AI". After all, many of us have been indoctrinated for years to avoid this term, as something that had spread confusion, over-promised, and under-delivered. As it turned out, the path from big data and analytics to AI is a natural one. Not just because it helps people relate and adjust their mental models, or because big data and analytics were enjoying the kind of hype AI has now, before they were overshadowed by AI. But mostly because it takes data -- big or not-so-big -- to build AI.

BIG DATA and analytics have been around for so long, that sometimes, they sound like'technologies' from a previous generation. Especially with all the talk around using artificial intelligence (AI) and machine learning (ML) to make more sense of all the data that today's businesses collect. However, no business can implement an AI or ML solution without the right digital infrastructure in place. Big data and business analytics, therefore, are simply a part of the digital maturity curve that organizations must climb in order to leverage AI and ML -- and leapfrog ahead of the competition. But this isn't to diminish the value of big data or analytics in any way.

Digital Enterprises generate voluminous data from transactional and operational activities. Billions of connected things are also generating data at a massive scale which is harnessed for real time analytics. The future belongs to those who harness data opportunities. Growth and competitiveness depend on your ability to collect, integrate, manage and transform data into business insights and outcomes. Big Data will converge with other technologies like Artificial Intelligence technologies to support ever growing data sources and to harness raw data into real time insights.

Tom Davenport, a professor at Babson College near Boston, a Fellow of the MIT Initiative on the Digital Economy, a co-founder of the International Institute for Analytics, and a senior advisor to Deloitte's analytics practice, shares his views on AI and analytics in an interview with TCS. He is co-authored the 2016 book'Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.' Davenport, the man responsible for making big data and analytics a topic of boardroom discussions, explains his views on the connection between analytics and artificial intelligence, automation and augmentation, opportunities arising from cognitive technologies, and how companies should address AI's impact on jobs. Davenport argues that the largest and most sophisticated branch of AI today is machine learning. While asserting that AI is primarily based on big data and analytics, Davenport believes any company that would skip analytics and go straight to AI is less likely to be successful. He explains that every industry has major opportunities from cognitive technologies and AI.

Most people in the field of analytics can remember writing their own analytical code. Today, our Data Scientists in the MSiA program at Northwestern, can produce analytical models from regression, decision trees, support vector machines (and more) – all with more or less one simple execution. The manual step is minor. In fact, the manual step is being removed as analytics moves into automation and artificial intelligence. Career Take Away: Develop skills in many model types.

Machine learning has been one of the biggest advancements in the history of computing, and now it is believed to be capable of taking on significant roles in the field of big data and analytics. Big data analysis is a huge challenge from the perspective of businesses. For example, activities such as making sense of huge volumes of varied data formats, data preparation for analytics and filtering redundant data can consume a lot of resources. Hiring data scientists and specialists is an expensive proposition and not within every company's means. Experts believe that machine learning is capable of automating many tasks related to analytics – both routine and complex.