The Impact of Big Data on Logistics


Posted by katy on
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Almost 85 percent of organisations using big data expect it to dramatically alter the way they do business. Let’s look at the impact of big data on logistics.

Data is all around us. When we leave the house in the morning, our mobile devices can tell us our ETA to work—without us even telling them we’re going there.

Our music streaming accounts can give us suggested songs to add to our playlists based on our recent listening habits, and electronic personal assistants (such as Amazon’s Alexa) can complete a wide variety of home-help tasks using data that they continuously collect. Data has always existed, but it is only recently that we have started to collect it automatically and use it consistently.

It is when we start to look at extremely large data sets—large enough that they can be analysed in order to establish trends, patterns and linkages—that the idea of ‘big data’ comes in.

Sources of big data in the supply chain

In the supply chain, sources of big data include back-end systems, trading partners, Internet-of-Things enabled devices, connected devices such as machine sensors and RFID tags, and publicly available structured and unstructured data.

According to an Accenture survey, close to 90 percent of those organisations that are already using big data believe it will revolutionise business operations in the same way that the internet did. Almost as many (85 percent) expect big data to dramatically alter the way they do business.

Close to 90 percent of those organisations that are already using big data believe it will revolutionise business operations in the same way the internet did.

This article explores some of the potential impacts of big data on logistics, including in the field of predictive analytics. It also takes the discussion back a step, by examining some of the basics around effective data creation and management. After all, the quantity of data alone does not necessarily make it useful, or guarantee that it can contribute to organisational success.

In fact, the danger posed by big data is that an ever expanding quantity of data (from an ever expanding number of sources) can make it harder to see the wood from the trees.

The impact of big data on logistics

The aforementioned Accenture survey found that companies embedding big data analytics in their operations are far more likely to generate a range of important supply chain benefits.

Of companies who had embedded the use of big data in their operations, around 50 to 60 percent of them experienced improvements in shortened order-to-delivery times, increase in supply chain efficiency, improvement in demand driven operations, improved cost to serve and better customer and supplier relationships. Of those companies who used big data on an ad hoc basis, only 10 to 20 percent had experienced the same improvements.

…around 50 to 60 percent of [companies making use of big data] experienced improvements in shortened order-to-delivery times, increase in supply chain efficiency, improvement in demand driven operations, improved cost to serve and better customer and supplier relationships.

Big data also can help you answer probably the most fundamental question in supply chain management: are you serving the customer? It can enable you to react quicker to changes in customer behaviour, stock availability and transportation, based on data trends. In industries such as retail, customer behaviour data can be analysed and fed into product development. It can also be used to create targeted pricing.

Big data is just one way supply chains are innovating to guarantee ROI.

Predictive analytics in logistics

An important use of big data is in the field of predictive analytics. This area has the potential to bring significant advantages compared to supply chain management models that only draw on previous demand, supply and business cycles.

The idea is that only drawing on the past no longer provides a comprehensive model of future needs. Instead, it uses techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to make predictions about unknown future events.

The idea is that only drawing on the past no longer provides a comprehensive model of future needs.

Data is pulled together from different sources and correlated in order to produce a series of assumptions. These are then overlaid on future events and plans in order to help inform decision making. It is also possible to model a variety of scenarios and see what the impact would be on the entire supply chain. For example, you could compare the difference between taking short-term decisions and long-term decisions.

Learn how to future-proof automotive supply chains.

Next thoughts: big data supply chain risks

A fundamental point is that data analytics is a highly specialised field and should be treated as such. The Accenture survey found that companies which employ a dedicated team of data scientists are far more likely to generate a range of important supply chain benefits from their use of big data analytics. But putting such a team together may not be straightforward, due to the shortage of available analysts, particularly those with experience in specific sectors.

Those with big data experience that is directly relevant to your organisation can be hard to find but worth seeking out, as they are able to pick out which data is relevant and important, and how to modify processes or analytics in order to get it.

The other key challenge lies in having clear objectives around what you are trying to achieve from the use of big data in the first place. This is no different from other IT implementation projects. You need to have clearly defined the issues and opportunities facing your supply chain, and to have developed a strong analytics strategy, before getting involved in the use of big data.