Big Data in Logistics | What is Predictive Analytics?

Predictive analytics in logistics is the Holy Grail of modern business and industry. At its simplest, predictive analytics provides data today, which companies can act on in anticipation of what will happen tomorrow.

What is Predictive Analytics?

Every time a company brings a new product to market, it is impossible to know for sure how popular products will become with consumers. Moreover, similar uncertainties impact every step of the manufacturing and supply chain process.

l Weather, global politics, and economic events can disrupt international shipping

l Uncertainty concerning consumer viability of products can make retailers hesitant to stock new inventory

l Manufacturing costs can fluctuate in accordance with seasonal raw material, labor, and energy costs

Predictive analytics and big data logistics is used to anticipate a variety of risks. Once risks are identified, companies can plan for events as wide-ranging as future labor market unrest and adverse weather events.

How Does Predicative Analytics Work?

Predictive analytics is essentially a form of fortune telling. However, to anticipate the likelihood of future events, businesses rely on hard math, rather than hocus pocus.

At its most basic, predictive analytics uses predictive mathematical modeling, ad-hoc analysis (which draws on up-to-the-minute business insights), real-time scoring (which takes place during real-time customer interactions), text analysis and big data mining.

When data is accurately compiled, current and historical data can be used to spot all-important new consumer trends. More importantly, data can be used to predict shipping issues, including the likelihood of inventory being damaged during transit.

Predictive Analytics in Healthcare

To demonstrate the raw power of predictive analytics, one need only look to the global healthcare industry. Each year, outbreaks of diseases like influenza can be predicted ahead of time. This is thanks to statistical understanding of historic flu season data, and real-time scientific analysis of new influenza strains.

l Doctors and pharmacists can identify new influenza strains before the flu virus reaches Western shores in autumn and winter

l Infection rates can be accurately forecasted ahead of time

l Local and national healthcare workers can prepare in advance for new outbreaks, by stockpiling vaccines and antiviral medications

Of course, in healthcare and regular commerce, supply and demand miscalculations can lead to high warehousing costs and waste. However, predictive analytics can be used to indicate what quantities of goods should be ordered, in mind of expected consumer demand.

Predictive Analytics at Work in the Real-World

Predictive analytics is integral to modern retail. Data insights allow retailers to depend on just-in-time (JIT) supply chain management. This reduces costs by reducing how much inventory businesses warehouse at any one moment. Moreover, just-in-time supply chain management is instrumental to the success of major retailers like Amazon.

The Future of Predictive Analytics

Predictive analytics and use of big data in logistics is nothing new. However, predictive analytics processes are continually evolving.

As of 2019, blockchain technology, AI, and smarter supply chain reporting tools, are already being used to increase the accuracy of companies forecasting capabilities. Many goods can subsequently be tracked throughout every step of the manufacturing and supply chain process. Moreover, sharing of marketing data and real-time sales information, currently allows many brands to avoid product failures, by ensuring consumer viability of products during initial product research and development.