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.
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:
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.