By: Sinan AlKhatib
Even though big data analytics is rapidly changing supply-chain management, some companies in this field still lag behind in utilizing these strategies to their full potential.
Meanwhile, professionals in other fields such as healthcare and finance have been faster at applying these new concepts to their businesses. But what supply chain firms lack is the sophisticated analytics that are transforming industries today – instant insights into huge and unstructured datasets.
Big organizations like Amazon, Uber, and AirBnB have created their own success stories because they were among those who first rode the peak of the wave by applying data science methodologies that gave them an edge over the competition.
Many of these companies use big-data analytics and machine learning algorithms as part of their pricing strategy. For instance, Amazon makes more than 2.5 million price changes each day. The company also provides you with product recommendations that are personalized based on machine learning capabilities.
Walmart uses something called a Retail Link 2.0 system, which utilizes “social listening” to mine unstructured data about brand mentions throughout the web and social media. “Trends” and other actionable information can then be extracted using machine learning.
Meanwhile, logistics company, UPS, is overhauling its entire data analytics strategy to boost efficiency in nearly every aspect of its business. Beginning in 2018, UPS plans to roll out its Network Planning Tools which will utilize big data and artificial intelligence to help employees make better decisions. For example, the current version of its Orion fleet management system uses telematics and advanced algorithms to design optimal routes for delivery drivers before they leave the lot. In the future, the firm plans to take what it has learned and utilize real-time data to dynamically update and optimize these routes as drivers take them. The initiative aims to reduce delivery spans by 100 million delivery miles and cut carbon emissions by 100,000 metric tons. At the same time, the UPS chatbot feature is being enhanced using artificial intelligence and increasingly sophisticated data from customers to help them find rates and tracking information throughout multiple platforms including Facebook and Amazon Echo.
Of course, these firms have come a long way in the past few years. Back in 2013, I noticed that supply-chain logistics websites and magazines were covering big data analytics and conceptualizing where it could be applied. Since then, supply chain leaders have made significant steps. Applications for analyzing messy data are being applied to virtually everything including forecasting, demand planning, and transportation. For example, many companies are now utilizing cameras to monitor their stock levels and setting up alerts that are automated whenever stock levels dip below a certain threshold.
So why do some supply chain companies still hesitate to apply big data analytics? For many firms, the size of the investment is the main culprit. But companies must understand that not applying big data analytics now can have severe consequences on their business performance and competitive capabilities. We noticed how many companies went out of business in no time because they were too scared or stubborn to make changes in their core functions. I’m not saying that companies should apply big data analytics because we say so, but the benefits are limitless and case studies are all over the place.
But diving into big data analytics needs to be a process as sophisticated as the technology itself. One data consultant who works with UPS and other large firms said in an interview with Zdnet.com that companies make the mistake of “drilling down on a single data set in isolation and fail to consider what different data sets mean for other parts of the business.”
As UPS looks to see where big data can enhance multiple parts of the business, other logistics firms can learn a lot from their own data.
Companies should also study the maturity level of their current analytics and align it to the company’s own data strategy. A solid business case should be made on how big data analytics can generate business value and where.
Of course, it's difficult to surpass a maturity level as current capability can limit such an approach. And many companies have a shortage of available analytical talents or tools that have the potential to stay valid for a long time. They prefer to outsource this hectic transformation.
The dynamics of complex supply chains can be difficult. But while computers can handle many of the details, the insights from supply chain data professionals will be vital to the leading organizations of the future.