Zheng Zhang’s research while affiliated with Zhejiang University and other places

A systematic framework of automated value stream mapping (VSM) to rejuvenate traditional lean tools is proposed. The framework enables the efficient use of VSM for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies. Also, root cause analysis based on traditional VSM requires intensive on-site investigation and experience, which hinders decision-making efficiency in dynamic and complex environments. To deal with these challenges, the proposed framework follows the Data-Information-Knowledge hierarchy model, and demonstrates how data can be collected in a production workshop, processed into information, and then interpreted into knowledge. In this paper, the necessity and limitations of VSM in automated root cause analysis are first discussed, with a literature review on lean production tools, especially VSM and VSM-based decision making in Industry 4.0. Second, the proposed framework is elaborated. Third, an implementation case involving a furniture manufacturer in China is presented, in which a decision tree (DT) algorithm was used for automated root cause analysis. The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small-batch production, with timely on-site waste identification and automated root cause analysis. The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.

COVID-19 pandemic has accelerated the reshaping of globalized manufacturing industry. Achieving a high level of resilience is thereby a recognized, essential ability of future manufacturing systems with the advances in smart manufacturing and Industry 4.0. In this work, a conceptual framework for resilient manufacturing strategy enabled by Industrial Internet is proposed. It is elaborated as a four-phase closed-loop process that centered on proactive industry assessment. Key enabling technologies for the proposed framework are outlined in data acquisition and management, big data analysis, intelligent services, and others. Industrial Internet-enabled implementations in China in response to COVID-19 pandemic have then been reviewed and discussed from 3Rs' perspective, i.e. manufacturer capacity Recovery, supply chain Resilience and emergency Response. It is suggested that an industry-specific and comprehensive selection coordinated with the guiding policy and supporting regulations should be performed at the national, at least regional level.

It has been recognized by manufacturing companies that working collaboratively is the way to advance their competiveness. Order fulfillment estimation addresses the issue of uncertainty from vendors. It is significant for collaborative manufacturing, which enhances companies’ responsiveness to market dynamics. In a data-rich scenario, order fulfillment estimation can be performed based on information extracted from data acquisition devices, such as smart sensors. The analysis result should serve the decisions-making of the production planning, and an indicator should be passed along the production chain even to its end customer for collaborative purpose. In the meanwhile, the manufacturer’s sensitive or confidential information is excluded to avoid risks. This article studies a method to effectively evaluate the order fulfillment process in an Industrial Internet of Things (IIoT) facilitated make-to-order production system. An order fulfilment progress (OFP) indicator is proposed to dynamically represent the fulfillment progress, and its estimation mathematical models are proposed. To improve the practicability of the OFP indicator in production, the influence of abnormal event scenarios are discussed to modify the OFP. A case study presented in this research demonstrates the proposed indicator with consideration of job in process (JIP) is promising comparing to conventional indicators that are represented by the proportion of finished over total products.