As the digitisation of supply chains becomes ever more the norm, we asked the experts how to manage big data and deliver actionable insights across all sectors
The tsunami of big data created by the Internet of Things (IoT) demands that companies employ intelligent data management techniques to separate the wheat from the chaff, or find the ‘good data’. Adding to the fact that 90% of the world’s data has been created in the last few years alone, it’s vital that businesses grasp meaningful insight from data and analytics – but what key areas should be focusses on for best results, and how could this enhance the backbone of your company, the supply chain?
With IoT set to exponentially increase the amount of available data as billions of devices activate and connect online, companies will need to turn to machine learning. Daniel Newman, founding partner of Futurum Research and CEO of Broadsuite Media Group, warns it will be simply too much for humans to handle alone. Machine learning can eliminate data junk and “keep data lakes clean and consistent”, even when it comes to unstructured historical data, he says.
Newman also notes that the technology’s ability to recognise patterns can help users to better understand customers and their decision-making, ultimately helping you to gain more of their business. He adds: “Machine learning helps take most of the bias out of decision making by presenting information based on factual data trends”, but warns that Artificial Intelligence is not itself bias free as it was created by humans whose judgement and logic are still important parts of data processing. The overall goal should be to improve decision making with the real-time harvesting of valuable data through machine learning to allowing companies to make instant changes based on harvesting real insight from big data to enable actionable supply chain visibility. Can a business see into its supply chain and predict what’s going to happen tomorrow, next month or next year? Harnessing this vision can help companies predict where goods are going to be, compress cycle times and get smart about their business. Newman concludes: “Companies that fail to adopt a smarter IoT by processing data automatically are likely to get swallowed up by the data monster that is surely to come.”
A recent report by Accenture stresses the importance of having the right people involved in supply chain management in your organisation to deliver the data driven decision-making Newman describes.
“Supply chains can essentially jump the digital evolution curve by adopting a networked supply model of operation enabled with advanced analytics instead of following a more conventional progression based on legacy enterprise resource planning and supply chain management systems. As organisations integrate big data analytics strategies into their operations, they will need to update their talent Strategy, including upskilling and hiring, or contracting, talent to leverage the power of analytics,” asserts the report.
Accenture stresses that using analytics to automate more routine supply chain decision-making related tasks will help free up existing resources in house to upskill talent to “focus on higher value-added business tasks”. Additionally, automating the ingestion of massive amounts of data from myriad sources across the supply chain will lead to increased operational efficiency.
Extending out from just mining existing data, predictive analytics seeks to extract information from current data sets in order to forecast future probabilities with an acceptable level of reliability, including a few alternative scenarios and risk assessment. “Being in logistics, it’s crucial to help upstream and downstream partners grow,” maintains Danny Halim, Vice President of Global Wholesale-Distribution and 3PL Industry Strategy at JDA.
