Research on Evaluation of Intelligent Manufacturing Capability and Layout Superiority of Supply Chain by Big Data Analysis

With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing industry, becoming the commanding point of a new round of global manufacturing competition. Based on the literature review of intelligent manufacturing and intelligent supply chain, a total factor production cost model for intelligent manufacturing and its formal expression are proposed. Based on the analysis of the model, 12 first-level indicators and 29 second-level indicators of production line, workshop/factory, enterprise and enterprise collaboration are proposed to evaluate the intelligent manufacturing capability of supply chain. This article also further studies the layout superiority and spatial agglomeration characteristics of intelligent manufacturing supply chain, providing useful reference and support for enterprises and policy makers in the decision-making.

The Key Factors of the Industrial Revolution 4.0 in the Malaysian Smart Manufacturing Context

IR 4.0 is a new phase for the current trend of automation and data exchange in manufacturing industry that focuses on cloud computing, interconnectivity, the Internet of Things, machine learning, cyber physical learning and creating smart factory. The purpose of this article was to unveil the key factors of the IR 4.0 in Malaysian smart manufacturing context. Two key data collection methods were used: (1) primary data from the face-to-face interview (2) secondary data from the previous study. Significantly, five key factors of IR 4.0 consider for this study. Autonomous production lines, smart manufacturing practices, data challenge, process flexibility, and security. As a result, IR 4.0 for quality management practices might get high impact for the best performance assessment, which addressed in various ways; there are few studies in this area have been conducted in Malaysian manufacturing sector, and to recommend the best practices implemented from the managers’ perspectives. For scholars, this enhances their understanding and highlight opportunities for further research.

MICOSE4aPS: Industrially Applicable Maturity Metric to Improve Systematic Reuse of Control Software

automated Production Systems (aPS) are highly complex, mechatronic systems that usually have to operate reliably for many decades. Standardization and reuse of control software modules is a core prerequisite to achieve the required system quality in increasingly shorter development cycles. However, industrial case studies in aPS show that many aPS companies still struggle with strategically reusing software. This paper proposes a metric-based approach to objectively measure the m aturity of i ndustrial IEC 61131-based co ntrol s oftwar e in aPS (MICOSE4aPS) to identify potential weaknesses and quality issues hampering systematic reuse. Module developers in the machine and plant manufacturing industry can directly benefit as the metric calculation is integrated into the software engineering workflow. An in-depth industrial evaluation in a top-ranked machine manufacturing company in food packaging and an expert evaluation with different companies confirmed the benefit of efficiently managing the quality of control software.

Development and Application of Multi Commodity Multi Location Integrated Model for Manufacturing Industry

This paper presented the development and application of Linear Programming to the modeling of Multi-Commodity Multi-Location production-distribution model for manufacturing industry. The Manufacturing industry has two plants, three depots and twenty retailer’s axis in Lagos. The products are based on how they are packaged; Product 1(P1), Product 2(P2), Product 3(P3) and Product 4(P4). TORA software is used in analyzing the data obtained from the company. Comparing the optimal Multi-Commodity Multi-Location transportation cost of One trillion, Five Hundred And Thirty Billion And Four Hundred And Ninety Million Naira to existing transportation cost of truckload Three Trillion, Five Hundred And Forty Four Billion Naira, the difference is Two Trillion, Thirteen Billion And Five Hundred And Ten Million Naira which is Four Hundred And Two Billion And Seven Hundred And Two Million Naira annually resulting to 56.82 percent gain in profit.