Using Data in Supply Chain and Logistics

Most companies that rely on supply chain and logistics data to measure performance recognize the value of big data. While 97 percent of executives understand its benefits to their supply chain, only 17 percent are using big data analytics , according to a 2014 Accenture study.

Big data analytics allows companies to gather information that enables faster and better decision-making. However, most companies depend on simple tools that cannot process the volumes of data coming in. Investing in big data analytics generates more information and insights than the basic tools.

How Companies Use Big Data

Traditional data in supply chain companies can provide sales and order tracking data. Big data, on the other hand, can take business intelligence to another level. With the right tools and expertise, it can even monitor the weather, global events and developing news. An unexpected weather change can affect sales for a supply chain in the food industry, for example. A product appearing in the news could drive traffic to the company’s website. A transportation company can reroute drivers based on traffic accident alerts.

Manufacturers want to know how much space they have on retail shelves. Real-time data allows them to measure and monitor shelf inventory. By using big data to assess the condition of fleet, machinery and roads, for example, management can prepare for or pre-empt breakdowns.

Benefits of Using Big Data in Supply Chain

There are two components to big data analytics. The first consists of the 3 Vs: volume, variety and velocity. The analytics technology should be advanced enough to process volumes of varied data as close to real time as possible. Once the data comes in, the second component consists of gleaning insights from the data. When supply chains have both components, they can do the following:

  • Shorten order-to-delivery times.
  • Increase supply chain management efficiency.
  • Optimize inventory management.
  • Boost loyalty by acting on customer intelligence.
  • Speed reaction time to supply chain issues.

Accenture’s study found that supply chain companies are reaping the benefits of big data. Forty-six percent state that big data has improved customer service and demand fulfillment by 10 percent or more, 41 percent report that big data has shortened reaction time to supply chain issues, and 36 percent say it has increased supply chain efficiency by at least 10 percent.
By putting big data analytics to work, logistics and supply chain management companies can revolutionize the way they do business and track data.

Types of Insights

Big data can provide four types of insights:

1. Descriptive — What is happening?

Descriptive data comes from a variety of sources, including weather updates, customer relationship management, social media and past shipment reports. For instance, a company may notice a pattern of slow shipping products. It can then launch an investigation to resolve the problem, with solutions aimed at improving processes and increasing customer satisfaction.

2. Diagnostic — Why did it happen?

When a problem occurs in the supply chain, diagnostic analytics look to the past to find out what happened and why. They can identify the source or cause of the problem so that the company can take steps to keep it from happening again.

3. Predictive — What is likely to happen?

Companies use predictive analytics to forecast shipment delays and changes in demand, which helps to optimize inventory levels and provide better customer service. The supply chain and logistics segment can also use predictive analytics for risk mitigation.

4. Prescriptive — What are the next steps?

Prescriptive analytics help a business make optimal, forward-looking decisions that relate to developing opportunities or risk mitigation. They provide specific recommendations for next steps, including options for multiple courses of action with likely outcomes. Only a small fraction of organizations use prescriptive analytics.

Barriers to Adopting Big Data Analytics

According to Accenture’s study, the two biggest barriers to adopting big data are investment cost and security issues. Companies sometimes struggle with how to handle unused data and hire experts to convert it into usable insights.

Another challenge that companies face is that most of the data is not internal. In fact, 80% of it is external. As a result, supply chains may need to partner with others to take full advantage of external data.

Furthermore, the external data may not be compatible with the company’s systems. Resolving this incompatibility can increase cost.

The Payoff for Data in Supply Chain

While implementing big data analytics can have a pronounced effect on supply chain optimization, companies can dramatically increase their success rates by implementing analytics across the entire enterprise. This approach outperforms a process-focused or ad hoc approach.

Accenture found that companies with an enterprise-wide strategy shortened their order-to-delivery cycle times by 61 percent. Companies with a process-focused strategy improved it by only 14 percent. The enterprise-wide strategy notably outperformed the process-focused approach in every metric.

Businesses can also use analytics to improve forecasting accuracy, enhance planning, optimize inventory levels, increase reliability and accelerate response times, which can, in turn, have a positive effect on customer satisfaction, asset management and the company’s financial health.

Learn more about the University of Tennessee Chattanooga online MBA program.


Sources:

Big Data Analytics in Supply Chain: Hype or Here to Stay?

Five Challenges of Managing Big Data in Supply Chains

Forbes: Ten Ways Big Data Is Revolutionizing Supply Chain Management

Four Types of Big Data Analytics and Examples of Their Use


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