Big Data and Logistics

Big Data and Logistics

We would like to thank Dr. Yingli Wang and Dr. Stephen Pettit for providing us with this article from their book E-Logistics: Managing Your Digital Supply Chains for Competitive Advantage.


 
The increasing volume and detail of information generated by organisations, social media and the Internet of Things (IoT) has led to an ‘explosion’ in the amount of data captured. This has led to significant challenges in terms of the information processing capability of traditional database software tools. It is within this context the concept of Big Data emerged. Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyse. IBM define Big Data as having four key attributes:

  • Volume: the scale of data
  • Velocity: analysis of streamed data, that is, the rate at which data arrives at the enterprise and the time that it then takes to process and understand that data
  • Variety: the different forms of data: structured and unstructured
  • Veracity: uncertainty of data, refers to the quality or trustworthiness of the data1Zikopoulos, deRoos et al. 2015;

Big Data changed the traditional view of how information is generated and travels. Proprietary information is structured data, lodged in databases, analysed in reports and presented in familiar formats such as spreadsheets. Big Data often contains both structured and unstructured data (such as social data). Insights gained through advanced predictive analytics, particularly by analysing unstructured data, are believed by many to be the new frontier for innovation and productivity2McAfee, A. and E. Brynjolfsson (2012), “Big Data: The Management Revolution. (cover story).” Harvard Business Review, Vol.  90, No. 10, pp. 60-68.. For example, Google monitors billions of search terms (“best cough medicine,” for example) and adds location details in order to track health issues. Google is therefore better than the Centers for Disease Control (US) at identifying flu outbreaks3Crovitz 2013.

But what does this mean for the transport and logistics sector? As long ago as 2010 Atzori et al were arguing that IoT and Big Data would have important implications for transport and logistics. Later, Waller and Fawcett4Waller, M. A. and S. E. Fawcett (2013), “Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management.” Journal of Business Logistics, Vol.  34, No. 2, pp. 77-84.0 suggested that potential applications of Big Data in the logistics field were likely to be in the the areas of forecasting, inventory management, transport management and human resources.  Examples include:

  • Assisting driving by integrating on-board vehicle systems with intelligent traffic systems to allow better navigation and safety;
  • Mobile ticketing via the use of Near Field Communication (NFC) tags;
  • Measuring environmental parameters (temperature, humidity and shock) monitoring for perishable goods such as meat and fruits;  
  • Augmented maps: Physical mobile interaction techniques being employed to augment various information of the map such as nearby hotels, restaurants, monuments and events related to the area of interest for the user.

Arthur5Arthur, W. B. (2011) “The second economy.” Mckinsey Quarterly DOI: http://bit.ly/2xIxiM0 offered an illustrative example emphasising why Big Data matters using the example of RFID where manual processes are now processed through an RFID portal which “is in conversation digitally with the originating shipper, other depots, other suppliers, and destinations along the route, all keeping track, keeping control, and reconfiguring routing if necessary to optimize things along the way. What used to be done by humans is now executed as a series of conversations among remotely located servers”. Further the rapid speed of technology has meant that we are yet to fully embrace developments such as social media. It is even less clear what the link is between the data that those technologies generate and the overall value to logistics. At present most attempts are speculative. Some argue that Big Data has probably always been around in logistics– it was just in spreadsheets and emails6Field, A. M. (2014), “TOO BIG TO FAIL?” Journal of Commerce (15307557), Vol.  15, No. 12, pp. 46-51..

What actually matters is how to make sense out of those Big Data. In reality, our logistics practitioners and researchers are often overwhelmed by Big Data. To gain the most out of Big Data (in combination with a firm’s existing traditional data), advanced predictive analytics tools are needed. Common tools such as data mining, case-based reasoning, exploratory data analysis, business intelligence and machine learning could all help firms to mine the unstructured data, although each has limitations7Tan, K. H., Y. Zhan, G. Ji, F. Ye and C. Chang (2015), “Harvesting Big Data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph.” International Journal of Production Economics, Vol.  165, No.  pp. 223-233. Companies such as IBM are exploring new techniques such as cognitive computing systems which claim to help human experts to make better decisions by penetrating the complexity of Big Data8Zikopoulos, deRoos et al. 2015. Another challenge is the lack of analytical and managerial talents who are able to bring structure to large quantities of formless data and make analysis possible9Davenport and Patil 2012. People with these skills are hard to find and in great demand. The exploration of Big Data also poses challenges to organisational structure, culture, leadership and requires a new mindset of decision making.


If you are interested in how the transport and logistics industry can benefit from information and communications technology (ICT) today and how to utilize ICT to create competitive advantage, you can purchase their book here and get 20% off your purchase when quoting BIGDATA20 at check-out.

In case you are interested in practical applications of big data/predictive analytics in cargo industry (e.g. for network optimization, asset management, warehouse staff optimization etc.), you can contact Transmetrics for more information.


 

References:

Arthur, W. B. (2011) “The second economy.” Mckinsey Quarterly DOI: http://bit.ly/2xIxiM0.

Atzori, L., A. Iera and G. Morabito (2010), “The Internet of Things: A survey.” Computer Networks, Vol.  54, No. 15, pp. 2787-2805.

Crovitz, G. (2013) “Why ‘Big data’ is a big deal.” The Wall Street Journal DOI: http://on.wsj.com/2kjlJWY.

Field, A. M. (2014), “TOO BIG TO FAIL?” Journal of Commerce (15307557), Vol.  15, No. 12, pp. 46-51.

McAfee, A. and E. Brynjolfsson (2012), “Big Data: The Management Revolution. (cover story).” Harvard Business Review, Vol.  90, No. 10, pp. 60-68.

Tan, K. H., Y. Zhan, G. Ji, F. Ye and C. Chang (2015), “Harvesting Big Data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph.” International Journal of Production Economics, Vol.  165, No.  pp. 223-233

Waller, M. A. and S. E. Fawcett (2013), “Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management.” Journal of Business Logistics, Vol.  34, No. 2, pp. 77-84.

Zikopoulos, P., D. deRoos, C. Bienko, R. Buglio and M. Andrews (2015). Big Data Beyond the Hype: A Guide to Conversations for Today’s Data Center, McGraw-Hill Education.

About the authors:

 

Dr. Yingli Wang is a Lecturer in Logistics and Operations management at Cardiff Business School. Her research on e-logistics dates back to early 2000’s and has attracted funding from various funding bodies such as Engineering and Physical Sciences Research Council, European Regional Development Funding, Welsh Government, Highways England, and Department for Transport UK. She is one of the few experts who specialise in technological developments in the field of transport and logistics.

 

First selection Cardiff Business School Staff Portraits 17th April 2015.

Stephen Pettit is a Reader in Logistics and Operations Management at Cardiff Business School. He has been involved in a range of transport related research, for the UK Department of Transport analysing the UK economy’s requirements for people with seafaring experience, and EU DGTREN including the ‘Economic Value of Shipping to the UK Economy’. Stephen co-edited E-Logistics: managing your digital supply chains for competitive advantage with Yingli Wang.