Potential of big data

It’s all about the right approach: Big data in SCM

Despite much hype over recent years, big data remains an abstract topic for many businesses. What’s the right approach for supply chain optimization? Industry examples show the way.

Right now, it is rather simple to set up a supply chain to ensure that T-shirts are available in greater quantities at the start of the season, for example. However, it takes the analysis of latest online consumer search queries, sales figures, social media activities, etc. to quickly identify local trends such as an imminent surge in demand for a T-shirt with a particular design that perhaps drew attention when a celebrity was spotted wearing it in public.

This approach highlights quite well what is so crucial – and sometimes annoying – about big data. Is it really possible to predict anything by simply analyzing enough of the right data? Does everything really follow dependencies and patterns, or – to put it philosophically – is “predetermination” overrated? Is it even possible to get your hands on data of this quality and process it reliably? Are misinterpretations the rule or the exception?

Experts still argue these points and big data will definitely have to wade through these murky waters.

Do we really need big data in logistics?

Review my first post for an introduction to the hype around big data

Let’s start with an example: A company that ships a rather impressive amount of, say, a million packages a month could collect compressed package data for over 400 years before accumulating 500 GB of data. Four hundred years! Reducing this time span to a more realistic and business-relevant period of 5 years, resulting data volumes could still be easily analyzed with a simple personal computer running standard software. This even applies if the data is generously complimented with additional shipment information.

So, do supply chains really generate “big data” that needs to be analyzed?

The answer to this question is clear: well, it depends ;). For projects where the objective is to analyze the performance, costs, and vulnerabilities of even large-scale logistics operations and assess the effects of optimizations, traditional data analysis known as “business intelligence” is adequate. The goal here is to obtain a view based primarily on past data, though in some cases this data can certainly allow extrapolations into the future.

For many companies, even such relatively simple analyses harbor a great potential for improving workflows and saving costs. The “Internet of Things (IoT)” may someday yield much greater opportunities – and challenges – here. AEB’s managing director Markus Meissner explained more about the impacts of IoT in his recent post on digitization.

Not ready for dismissal at all: discovering its potential

Big data in the supply chain makes sense when you want your analysis to include dynamic factors outside your own sphere of influence. An example specific to the supply chain would be advance knowledge of supply chain risks – such as strikes or political unrest. At this point, the boundaries between strict logistics and the planning, control, and implementation of procurement, production, sales, and any after-sales services already begin to blur.

If it was possible to predict sudden surges in demand, natural disasters, or strikes with sufficient accuracy, what would this knowledge be good for at the end of the day if you lacked the capacity to respond appropriately?

The conclusion to be drawn from this strictly rhetorical question is that if you want to benefit from big data over the long term, you also need to build up a highly agile and flexible supply chain in order to act and react dynamically. Then big data has the potential to introduce a whole new generation of risk management.

Future developments and competitive edge

The question that remains, of course, is where one can obtain the necessary data of sufficient quality. It is likely we will see further market developments for service providers that deliver “bite-size” data sets on the political or meteorological climate, areas of turmoil, commodity prices, trends, and the like.

And we can continue to hope that the technology will produce data processing systems of increasing intelligence that are more resistant to singular misinterpretations.

What has become clear, however, is that advance knowledge of these types of business-relevant developments promises an invaluable competitive advantage. It is precisely this vision – and especially the nearly endless potential uses beyond the supply chain – that feeds the hype surrounding big data.

It’s your choice. But take a look around – don’t miss out.

And that’s exactly why the full idea behind big data will indeed materialize. Though in the end, it’s a tool and not a neatly packaged solution: We all need to decide for ourselves if, when, and how we use it. But make no mistake, companies are missing out on great benefits if they do not work with – or do not even consider – broader-based conventional analysis of data that is already available to them now. And more importantly, they miss laying the right foundations for future success and for leveraging big data down the road.

This topic affects all industry sectors and all businesses that are looking to future-proof their supply chains for long-term success and a sustainable competitive advantage. There are many stories in the media about businesses leading the way here.

For example, I have recently read an interesting article about Raytheon and their approach and achievements in the area of big data. With $23 billion sales in 2014 and 61,000 employees worldwide, Raytheon describes itself as “a technology and innovation leader specializing in defense, security, and civil markets throughout the world.”

On the news platform “SupplyChain247”, Raytheon detailed in July how they successfully managed new technologies and big data to establish comprehensive transparency over entire supplier programs, significantly reduce costs, and streamline all workflows in their supply chain. Take a look: How Raytheon Uses Data Visualization, Predictive Analytics, and Big Data as a Competitive Advantage.

Let me know what your thoughts on this are on LinkedIn. I’m looking forward to hearing from you!