Ray Y. Zhong

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

An RFID-event driven mechanism is used to integrate planning and scheduling.RFID production shopfloor data was used to obtain APPS parameters.Release strategy is efficient to reduce the total tardiness by 44.46% averagely. Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.

RFID-enabled real-time advanced planning and scheduling shell for production decision making

In a radio frequency identification (RFID)-enabled real-time manufacturing environment, different decision makers are often confronted with the inconsistency between the highly synchronised information flow and unstandardised decision-making procedures, especially under conflicting objectives and dynamic situations. This study proposes an RFID-enabled real-time advanced production planning and scheduling shell (RAPShell, in short) to coordinate different decision makers across production processes. RAPShell has several key innovations. First, it uses RFID technology for enhancing information sharing, which provides the basis for coordinating decisions and operations of different parties involved in production planning, scheduling, execution and control. Second, it adopts Software as a Service (SaaS) model and a standard service-oriented architecture (SOA) with key modules, adaptive optimisation models, solution algorithms as well as scheduling rules developed and deployed as web services. Finally, extensible makeup language (XML)-based data models are utilised to achieve easy-to-deploy and simple-to-use system customisation and implementation. A case study demonstrates how RAPShell is customised and deployed in a small- and medium-sized company to facilitate the operations of typical production decision makers and operators. Benefits from qualitative and quantitative are discussed from the case study to show RAPShells practical effectiveness and efficiency on production decision making.

Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors

Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.

Allocation of emission permits using DEA: centralised and individual points of view

This paper discusses mechanisms for the allocation of emission permits (AEP) among a group of manufacturing companies aiming at controlling the total emission level of the group. Data envelopment analysis is adopted and extended to construct the AEP models. Proposed methods allocate the permits on the basis of the current input and output levels of individual firms, rather than historical records. Two variations of AEP mechanisms are considered. One situation is that one of the member firms dominates the responsibility of allocating emission permits on behalf of the group (individual AEP scenario). This dominating firm will maximise its own emission permit before allocating permits among other members in the group. The other situation is that a third-party central governing body is centrally responsible for coordinating the AEP among all group members (Central AEP scenario). Proposed mechanisms are applied to analyse efficiencies of a group of paper mills. The result shows that the mode of central AEP mechanism is a better choice than that of the individual AEP mechanism. Central AEP not only maximises the whole efficiency of the group but also improves efficiencies of individual firms. In contrast, with individual AEP, one firm gains efficiency at the loss of others.

A radio frequency identification-enabled real-time manufacturing execution system for one-of-a-kind production manufacturing: a case study in mould industry

One-of-a-Kind-Production (OKP) is a non-repetitive manufacturing mode that produces customised products with unique components. Due to the varying production requirements and inadequate operation experience, the unique components and related operations often causes great dynamics in the workshop execution process. Since most of the OKP companies currently adopt paper-based manual data transaction and report mechanism in workshop production process, such dynamics are hard to be timely detected and controlled, resulting in serious order delays and work-in-progress redundancies. Radio frequency identification (RFID) enables automatic and accurate object data capturing capability, and thus makes the real-time visibility and controllability possible to workshop execution process if combined with manufacturing execution system (MES). This article presents an easy-to-deploy and simple-to-use RFID-enabled MES to achieve such real-time control for typical OKP workshops. A real-life case study in a mold and die manufacturing company is presented to demonstrate how technical, social and organisational issues have been addressed in such project. A set of enabling technologies and systems that are key to the development of such RFID-enabled MES are introduced, including hardware like machine data terminal and workshop base station as well as software like scheduling and communication programmes. It is hoped that insights and lessons gained could be generalised for future efforts across small-and-medium-sized OKP manufacturers that share similar requirements.

Data-source interoperability service for heterogeneous information integration in ubiquitous enterprises

In a ubiquitous environment, various enterprise information systems (EISs) are used for supporting daily operations. Besides, with the support with Auto-ID technology, enterprises are able to collect real-time operation data but these data are continuously pushed to different EISs. Data without processing are meaningless and not able to support managerial decision-makings. The wide use of EISs and Auto-ID devices within an enterprise increases the difficulties in data interoperability among difficult data sources. Since data sources from applications and devices are characterized by multiple types of heterogeneities, such as communication protocols, blinding methods, and developing environments, the difficulty in managing heterogeneous data sources is highly raised. Information integration in ubiquitous enterprises is critical because it has a significant influence on the efficiency and effectiveness of decisions. This article presents an innovative Data-Source Interoperability Service (DSIS) that serves as a middleware for providing a querying and information integration service for heterogeneous data sources. The DSIS applies software agent technology that is capable of accomplishing tasks in an autonomous way without human intervention. Operations provided by DSIS Agent are converted into standard web services. The agent-based services are managed by DSIS Universal Description, Discovery and Integration registry (DSIS-UDDI) which facilitates collaboration among agents. Additionally, the DSIS platform also provides set of visual tools for users to manage and (re)configure different data sources within enterprises.

A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process

– As the modern manufacturing twining seamlessly with logistics operations for value adding services, logistics service is becoming more and more significant. Under this research background, the purpose of this paper is to introduce an innovative evaluation model for customer satisfaction using fuzzy analytic hierarchy process (FAHP). , – This model uses triangular fuzzy concept to determine the weight of each index so that subjective or objective weighting is addressed. A case study from two large express companies in China is used to demonstrate the feasibility and practicality of the proposed model for examining customer satisfaction. , – One of the key findings is that Company B has higher customer satisfaction than Company A due to its quick response and flexible logistics strategy. This paper has several contributions. First, A FAHP-based customer satisfaction evaluation model is proposed for the logistics service. Second, the triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method. Third, a case study demonstrates the implementation of the model. , – First, this paper considers the fuzzy AHP for the customer satisfaction evaluation. Comparing with other multi-criteria decision-making methods like data envelopment analysis, evidential reasoning approach, and multi-attribute value theory will be carried out in the near future. Second, the manufacturing modes like make-to-order, make-to-stock, and mass-customized production may have different logistics support so that the final products may reach the final targets quickly. How to evaluate various mode-based logistics and their customer satisfactions have great significance. Finally, Big Data-enabled customer satisfaction evaluation approaches may be a possible solution. , – Based on the data from questionnaire, it is found that, in practical applications, manufacturing enterprises should amend the index system according to the specific business scope and the production characteristics. Manufacturing enterprises need to collect large amounts of data through market research and conduct the measurement on the related coefficient between the measurement indicators and customer satisfaction degree. After that, they can make sorting and filtering on the measurement index according to the measurement results. , – Customer satisfaction is very important to manufacturing and logistics enterprises due to its time constraints. The physical products with services like logistics are paid close attention to by the final customers. , – The contribution of this paper is as follows: a FAHP-based customer satisfaction evaluation model is proposed for the logistics service; triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method; a case study was used to demonstrate the implementation of the model. One of the key findings is that Company B has higher customer satisfaction than Company B due to its quick response and flexible logistics strategy.

Estimation of Lead Time in the RFID-Enabled Real-Time Shopfloor Production with a Data Mining Model

Lead time estimation (LTE) is difficult to carry out, especially within the RFID-enabled real-time manufacturing shopfloor environment since large number of factors may greatly affect its precision. This paper proposes a data mining approach with four steps each of which is equipped with suitable mathematical models to analysis the LTE from a real-life case and then to quantitatively examine its key impact factors such as processing routine, batching strategy, scheduling rules and critical parameters of specification. Experiments are carried out for this purpose and results imply that batching strategy, scheduling rules and two specification parameters largely influence the LTE, while, processing routine has less impact in this case.

A RFID-Enabled Real-Time Manufacturing Hardware Platform for Discrete Industry

Discrete industry (DI) concentrates on a class of business where the production process is basically no material change except the shape and compositions. In such DI workshop, productivity is greatly obstructed by tedious information transferring and management by paper work. This paper proposes a RFID-enabled real-time paperless hardware platform for DI. First, wireless manufacturing devices such as RFID readers are deployed in DI workshop. Then, a wireless communication network is set up. Finally, real-time manufacturing could be achieved according to the reorganization of production. A case study is also introduced in this paper to illustrate how real-time manufacturing works through this platform in DI workshop. Limitations such as channel seeking and universal work flows configuration should be improved if this platform of great merits to extend to other industry.

A big data cleansing approach for n-dimensional RFID-Cuboids

Radio Frequency Identification (RFID) technology has been widely used in manufacturing sites for supporting the shopfloor management. Huge amount of RFID-enabled production data has been generated. In order to discover invaluable information and knowledge from the RFID big data, it is necessary to cleanse such dataset since there is large number of noises. This paper uses n-dimensional RFID-Cuboids to establish the data warehouse. A big data cleansing approach is proposed to detect, remove and tidy the RFID-Cuboids so that the reliability and quality of dataset could be ensured before knowledge discovery. Experiments and discussions are carried out for validating the proposed approach. It is observed that the proposed big data cleansing approach outperforms other methods like statistics analysis in terms of finding incomplete and missing cuboids.