As with other areas of business, such as marketing and manufacturing, the management of the supply chain for products is benefiting from big data analytics.  The use of data analysis itself is not new to supply chain management; it is the use of big data that has become more common in the past few years.  This includes analyzing data on inventory, transportation, and forecasting.One application of data analysis, as described in a 2016 Forbes article is use inventory data taken from cameras in a warehouse to forecast when a product needs to be resupplied.  

With such analysis and technology, warehouses and distribution centers may be run without human intervention. Another aspect of data analysis that manufacturers might use is information on how their products are allocated shelf space in stores and how well different products are selling.  Using sensors on the shelves, real-time data can show shelf inventory of products.  Using such data, manufacturers can make quicker decisions on how to market products and negotiate with retailers for shelf space.

New Types of Data

With today’s data-collection methods, manufacturers have much more information available to them to make production, marketing, and other decisions.  A 2014 study, Big Data Analytics in Supply Chain Management: Trends and Related Research, presented over 50 data types that can be collected and used in the decision-making process. These include structured data such as transaction data (point-of-sale records) and transportation costs; semi-structured data such as competitor pricing, customer surveys, and loyalty programs; and unstructured data such as call logs and email records.  These types of data include social media, such as Facebook status and Twitter feeds, as well as weather, traffic, and insurance claims records.  Even blogs and news events are part of the big data that can be analyzed to determine their effects on product sales.

Diagram showing the types of big data that can be collected on products that influence supply chain management decisions.


These new data can help companies in ways that they have not experienced before. For example, the data can tell manufacturers how their supply chain strategies and operations influence their financial objectives. Another example of how improved data systems can aid manufacturing is when a company has to recall or retrofit a product, which is a major area of loss to a company.  With new data systems, manufacturers will be able to trace performance of a product better and “reduce the thousands of hours lost just trying to access, integrate, and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted” (Forbes, 2015).

New Types of Job Opportunities

These changes in the way data is collected and used in supply chain management mean that manufacturers are in need of employees who can understand, analyze, and use the data to benefit their companies.  Supply Chain Talent of the Future:  Findings from the Third Annual Supply Chain Survey, points out the need for talent with different skill sets than what was needed in the past.  

A majority of supply chain executives as reported by Forbes consider big data analytics a “disruptive and important technology, setting the foundation for long-term change management in their organizations.”  

The top three areas that employers see as becoming more important with the influx of big data are 1) technical competency in analytics, 2) compliance and regulatory expertise, and 3) process engineering/design. Other areas of employment in manufacturing that will need growing talent are in sourcing and procurement, logistics and distribution, planning and scheduling, and product development.  

The need for creative thinking in all these fields will remain important to make the best use of the data and the technology that is currently available and that will continue to be developed. To find out how to start your career in data analytics and make a difference in supply chain management, click here.

Author: Neil Starr