Module-based quality system functionality evaluation in production logistics

Journal of Industrial Engineering and Management JIEM, 2016 – 9(2): 310-329 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 http://dx.doi.org/10.3926/jiem.1509 Module-based Quality System Functionality Evaluation in Production Logistics Mahmood Reza Khabbazi1 , Jan Wikander1 , Mauro Onori2 , Antonio Maffei2 , De-Jiu Chen1 Mechatronic, Department of Machine Design, KTH Royal Institute of Technology (Sweden) 1 2 Technologies for Adaptable Production, Department of Production Engineering, KTH Royal Institute of Technology (Sweden) , , , , Received: May 2015 Accepted: March 2016 Abstract: Purpose: This paper addresses a comprehensive modeling and functionality evaluation of a module-based quality system in production logistics at the highest domain abstract level of business processes. Design/methodology/approach: All domain quality business processes and quality data transactions are modeled using BPMN and UML tools and standards at the business process and data modeling. A modular web-based prototype is developed to evaluate the models addressing the quality information system functionality requirements and modularity in production logistics through data scenarios and data queries. Findings: Using the object-oriented technique in design at the highest domain level, the proposed models are subject further development in the lower levels for the implementing case. The models are specifically able to manipulate all quality operations including remedy and control in a lot-based make-to-order production logistics system as an individual module. Practical implications: Due to the specification of system as domain design structure, all proposed BPMs, data models, and the actual database prototype are seen referential if not a solution as a practical “to-be” quality business process re-engineering template. -310- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1509 Originality/value: this paper sets out to provide an explanatory approach using different practical technique at modeling steps as well as the prototype implementation. Keywords: business process, data modelling, production logistics, QIS, UML, BPMN, data query 1. Introduction Quality, as a definitive factor in all aspects of any organization assures a long-run success (Tari, MolinaAzorín & Heras, 2012). One of the main competitive edge in that matter is on the successful implementation of an effective Quality Information System (QIS) (Psomas, Kafetzopoulos & Fotopoulos, 2013; Wahid & Corner, 2009). Since, quality operations can no longer be carried out in paperwork as the manual quality data handling is extremely error-prone and inefficient, nor as an individual back office system, an integrated system provides desirable benefits in an automated manufacturing environment (Law & Tak, 2003). As a relatively new effort at integrating with manufacturing information system, the QIS increases the efficiency of any application of the production logistics information system as a broader and general perspective view (Anderson, Jerman & Crum, 1998; Tak & Hang, 2002). Hence, applications such as finding the quality problem root in the product lifecycle would be addressed efficiently (Ngai, Chau & Chan, 2011). In fact, QIS should ensure sending the right quality data to the right person at the right time. This in turn will highlight the important key role of data modeling based on a careful business process analysis as the backbone structure of the QIS development. Besides, the quality data should not be considered as another property of the manufacturing objects such as a lot or an item or a batch of items in manufacturing control information system (Khabbazi, Ismail, Ismail, Mousavi & Mirsanei, 2011). As such, the necessity of considering the quality system as another operational module in a modular system design and development for the production logistics is dramatically seen crucial. As a part of larger effort on conducting an extensive modeling for module-based inbound and outbound e-logistics system at the supply chain level, this paper complements the development of the quality system data modeling {see: #813} by emphasizing on the business process modeling as the perquisite step. It is then followed by the prototype implementation and functionality evaluation of the data models focusing on the quality operations at the highest domain levels. The explanatory technique used at the business process as well as data modeling provides descriptive view of structure and behavior of the system extensively. The proposed analytical BPMs and the object-oriented data models are considered referential for further development in the lower abstract levels. They are used as the roadmap for developing an actual database prototype from design steps to evaluate the functionality requirements of the quality -311- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1509 system. The quality system requirements are highlighted and functionalities of the solution based on the identified requirements are evaluated through data queries providing real-time controllability. The modular-based design is another advantage of the proposed system proving the ability for integration with other back-office system in SMEs. The reminder of this paper is constructed in four main flowing sections. This introduction is followed by quality system and requirements, methodology description by explaining the procedure adopted for the modeling development as well as functionality evaluation, modeling development, and functionality evaluation. The conclusion is presented at the last section which is followed by the references list. 2. Quality System and Requirements Quality data are scattered in different stages, individual departments and processes in various formats such as figures, reports, tables, files, and data sets. There is therefore, always a need to build an integrated quality data model to support all quality processes, sub-processes and activities throughout the whole life of a product (Tang & Yun, 2008). Base on different stages throughout a product’s lifecycle, some quality data is listed in Table 1. Quality data is the most important basis in product quality control, quality management and quality improvement, and the most crucial resources in improving enterprise business (Gerber, Dietzsch & Althaus, 2004). Quality information system (QIS) should ensure to send the right quality data to the right person at the right time and as such the business process and data modeling is a key concern in LIS development to address such needs. Practically, the Quality data at the manufacturing perspective was considered as quality characteristics of the Product data while in quality control and management domain they are as the Operation objects (Rönkkö, Kärkkäinen & Holmström, 2007). Therefore, it is necessary to differentiate the quality data from manufacturing data at modeling t (...truncated)