Horizon CDT Research Highlights

Research Highlights

Use big data to build data based supply chain strategy and decisions

  Shuojiang Xu (2015 cohort)   www.nottingham.ac.uk/~psxsx2


Nowadays big data has been used in supply chain management to improve customer experience, even create customer needs in various industries. In China healthcare industry, business is highly impacted by changing policies and volatile economy. As the strong growth engine within Johnson & Johnson, China market has tremendous business opportunities, however available information for supply chain management in the ecosystem is in an unstructured manner, which has potential risk to support high growth. Due to environment complexity and dynamics in emerging market, current supply chain processes to capture market intelligence is more or less rely on individual’s interpretation, supply chain decision requires data outside Johnson & Johnson systems is facing challenges, the consequence is service and cost implication to end to end supply chain. So using big data to build supply chain strategies and improve supply chain processes are the key focus in 2015 & 2016 in Johnson & Johnson Medical (Shanghai) Ltd. To facilitate the supply chain decision-making process, inventory management, distribution network, etc. are all important parameters in successful supply chain management. Traditional operations research-based models, such as forecasting models, are useful but are static in nature. In addition, they are unable to capture information from unstructured data as these models are based on a prior in nature. Those models are not flexible enough in current data-driven research era. After all, marketing intelligence, for example, is customer-focused and the available data are linked to actual consumption. Therefore, this research will lead to an innovative data analysis approach from the big data for supply chain decisions. The approach can not only be applied to the supply chain decisions of Johnson & Johnson Medical (Shanghai) Ltd, but also be suitable for other companies’ decisions.

Research questions, aims and objectives

Research questions:

  • What kinds of output are desired?
  • What kinds of data will be captured?
  • How to analyze the data?
  • How to build the model to support the supply chain decision?

Aims and objectives

The aim of this project is to integrate SC internal and external mass data, develop a novel innovative data analysis approach from the big data for supply chain decisions to enhance CN SC demand forecast.The project mainly consists of two parts (1) forecasting model to capture and validate marketing intelligence from big data and (2) data filter and mining model for supply chain decisions

The objectives are:

  • Understand business strategies, models and outcomes through the use of big data
  • Collect information from current policies, volatile economy, customer behaviour, international relation, social media, technologies and etc. (Data mining)
  • Converter the information to appropriate forecasting business model to support new product commercialization in China market
  • Use big data for supply chain data based decision


  1. Chan, H. K., Lacka, E., Yee, R. W.Y., and Lim, M. K., A Case Study on Mining Social Media Data. In Proceedings of the 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 9-12 December, 2014, Kuala Lumpur, Malaysia.
  2. Chan, H. K., and Wang, X., A Multi-disciplinary Approach to Quantify Social media Data. The 2014 INFORMS Annual Meeting, 9-12 November, 2014, San Francisco, USA.
  3. Chan, H. K., and Lacka, E., Using social media data for operations management research: an example of product development. The 2013 International Conference on Social Science and Education, Hong Kong, 24-25 December, 2013 (published in Advances in Education Research, Vol. 26, pp. 522-525).
  4. ?Simchi-Levi, D. (2013). OM Forum-OM Research: From Problem-Driven to Data-Driven Research. Manufacturing & Service Operations Management, in press.
  5. ?Delage, E., & Ye, Y. (2010). Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research, 58(3), 595-612.

This work was carried out at the International Doctoral Innovation Centre (IDIC). The authors acknowledge the financial support from Ningbo Education Bureau, Ningbo Science and Technology Bureau, China's MOST, the University of Nottingham, and johnson and johnson shanghai. The work is also partially supported by EPSRC grant no EP/L015463/1.