A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)

Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.

Diffusion of Spatial Data Infrastructures： Private More Inspired than the Public Sector---A Case of Tanzania

Spatial data is a key resource for national development. There is a lot of potential locked in spatial data and this potential may be realized by making spatial data readily available for various applications. SD1 （Spatial Data Infrastructures） provides a platform for the data users, producers and so on to generate and share spatial data effectively. Though efforts to develop spatial data infrastructures started worldwide in the late 1970s, SDIs are still perceived by many institutions as new innovation; as such, they have not penetrated to all institutions to bring about effective management and development changes. This paper is reporting on a study conducted to assess SDI Readiness Index for Tanzania. The study aimed at identifying problems undermining SD1 implementation in Tanzania, despite its potential in bringing fast socio-economic development elsewhere in the world. This paper is based on a research based on views from stakeholders of geospatial technology industry in Municipal Councils, Private Companies and Government Departments in Tanzania. Results indicated that Private Companies are more inspired than Government institutions towards implementation of SDIs. And those problems affecting implementation of SDIs are lack of National SDI Policy, lack of awareness and knowledge about SDIs, limited funding to operationalise SDI, lack of institutional leadership to coordinate SDI development activities, lack of political commitment from the Government. It is recommended that delibate efforts be devised to raise awareness of SDI amongst the Tanzanian community.