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The reluctance or incapability of further increasing production resources has made many enterprises suffering from high resource-workload situation. Production dynamics thus cannot be resolved by single resource independently, yet have to make an integral use of the adjustable capability of multiple resources of the whole system in a synchronized way. This paper considers a dynamic production logistics (PL) process comprising multiple independently operated PL stages, which adopts Internet of Things to capture real-time execution dynamics and rely on plan (re)scheduling and cloud-based resources re-configuration to cope with dynamics. A generic dynamic production logistics synchronization (PLS) solution is proposed. Qualitatively, a multi-phase multi-stage multi-degree synchronization control mechanism is put forward toward a PL system with generic structure and typical execution dynamics. Quantitatively, collaborative optimization is applied to assist the PLS to obtain the synchronization results. With a real-life case study, the effectiveness of the proposed mechanism and method has been verified. A set of sensitivity analysis is also conducted toward dynamics of different degree and different time, which provides significant managerial implication to PL managers to better deal with dynamics.

The emergence and subsequent popularization of lean has been one of the most significant developments in the history of operations management. However, there is a lack of systematic theory on the control framework underlying lean production. It is therefore difficult to conduct more in-depth research on Lean theory, specifically in the context of emerging technologies as smart manufacturing or Industry 4.0. In this study, process control theory is used to re-define several major lean methods and tools. Then a Lean-Oriented Optimum-State Control Theory (L-OSCT) is proposed that integrates these lean methods and tools into optimum-state control theory. On the level of method and mechanism, we adopt a recently emerged synchronization approach to obtain global-wide leanness of a large-scale system. L-OSCT provides dynamic process control in industrial networking systems. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach. Funding Agencies|National Natural Science Foundation of China [51475095, 51875251]; Natural Science Foundation of Guangdong Province [2016A030311041, 2017A030313401]; National Ministry of Education "Blue Fire Plan" (Huizhou) Industry-Academia-Research Joint Innovation Fund (2018-2021); Fundamental Research Funds for the Central Universities [11618401]; 2015 Guangdong Special Support Scheme [2014TQ01X706]