A big data approach for logistics trajectory discovery from RFID-enabled production data Ray Y. Zhong a,b,n , George Q. Huang a , Shulin Lan a , Q.Y. Dai c , Chen Xu d , T. Zhang e a HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China b College of Information Engineering, Shenzhen University, China c Guangdong Polytechnic Normal University, Guangzhou, China d Institute of Intelligent Computing Science, Shenzhen University, Shenzhen, China e Huaiji Dengyun Auto-parts (Holding) Co., Ltd., Huaiji, Zhaoqing, Guangdong, China article info Article history: Received 18 November 2013 Accepted 17 February 2015 Available online 23 February 2015 Keywords: RFID Big data Logistics control Trajectory pattern Shopfloor manufacturing abstract Radio frequency identification (RFID) has been widely used in supporting the logistics management on manufacturing shopfloors where production resources attached with RFID facilities are converted into smart manufacturing objects (SMOs) which are able to sense, interact, and reason to create a ubiquitous environment. Within such environment, enormous data could be collected and used for supporting further decision-makings such as logistics planning and scheduling. This paper proposes a holistic Big Data approach to excavate frequent trajectory from massive RFID-enabled shopfloor logistics data with several innovations highlighted. Firstly, RFID-Cuboids are creatively introduced to establish a data warehouse so that the RFID- enabled logistics data could be highly integrated in terms of tuples, logic, and operations. Secondly, a Map Table is used for linking various cuboids so that information granularity could be enhanced and dataset volume could be reduced. Thirdly, spatio-temporal sequential logistics trajectory is defined and excavated so that the logistics operators and machines could be evaluated quantitatively. Finally, key findings from the experimental results and insights from the observations are summarized as managerial implications, which are able to guide end-users to carry out associated decisions. & 2015 Elsevier B.V. All rights reserved. 1. Introduction Big Data refers to a data set which collects large and complex data that is hard to process using traditional applications (Jacobs, 2009). With the increasing usage of electronic devices, our daily life is facing Big Data. For instance, taking a flight journey with A380, each engine generates 10 TB data every 30 min; more than 12 TB Twitter data are created daily and Facebook generates over 25 TB log data every day. It was reported that the per-capita capacity to store such data has approximately doubled every 40 months since 1980s (Manyika et al., 2011). Manufacturing and service industry largely involve in a range of human activities from high-tech products such as space craft to daily necessities like toothbrush. Manufacturing is regarded as the “hard” parts of economy using labors, machines, tools, and raw materials to produce finished goods for different purposes; while service sector is the “soft” part that includes activities where people supply their knowledge and time to improve productivity, performance, potential, and sustainability (Eichengreen and Gupta, 2013; Hill and Hill, 2009; Terziovski, 2010). This paper is motivated by a real-life automotive part manufacturer which has used RFID technology for facilitating its shopfloor manage- ment over 10 years. Logistics within manufacturing sites like ware- house and shopfloors are rationalized by RFID so that materials' movements could be real-time visualized and tracked (Dai et al., 2012). The primary application of RFID for item visibility and trace- ability is rudimentary. First of all, estimation of delivery time on manufacturing shopfloor is basic for the sales department when getting a customer order. That helps to ensure the delivery date, which has been estimated from past experiences and time studies. Such estimation is not reasonable and practical given the difference of individual operators and seasonal fluctuation (e.g. peak and off seasons). Secondly, RFID-enabled real-time manufacturing, planning and scheduling on shopfloors heavily relie on the arrival of materials, thus, the decisions on logistics trajectory are critical. This company carries the decision using paper sheets manually which always make the material delay. That causes many replanning and rescheduling, which greatly affect the production efficiency. Finally, the space on the manufacturing shopfloor is limited. As a result, the logistics trajec- tories of materials should be optimized. Currently, the logistics is not Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ijpe Int. J. Production Economics http://dx.doi.org/10.1016/j.ijpe.2015.02.014 0925-5273/& 2015 Elsevier B.V. All rights reserved. n Correspondence to: 8-23 Haking Wong Building, Pokfulam Road, Hong Kong, Tel.: þ852 22194298; fax: þ852 28586535. E-mail address: zhongzry@gmail.com (R.-n. Zhong). Int. J. Production Economics 165 (2015) 260–272