Developing countries are facing challenges to adopt the Industry 4.0 in their manufacturing sector. One of the reasons is lacking technological and maturity models’ compatibility to assess the current state of maturity and readiness level. This empirical research has presented a maturity model based on the IMPULS models developed by the German Association of Mechanical Engineers and the Singapore Smart Industry Readiness Index with some modifications. A comprehensive and compatible lean modified manufacturing maturity model for Industry 4.0 (LM4I4.0) has been developed and validated. The proposed model has add-on dimension of the lean philosophy. This modification provides the facility to integrate the two main manufacturing paradigms of lean and Industry 4.0. The developed LM4I4.0 has been validated in fifty manufacturing industries of Pakistan and results have been presented in the data analysis format. The data mining, dimension cluster analysis, and computation of data shows that the proposed model is compatible for the developing country. Moreover, the results and analysis show that the maturity level of Pakistani manufacturing is 3.39, which means companies are immature in context of Industry 4.0. The proposed methodology may be used to conduct self-evaluations and enable businesses to undertake self-assessments in order to align themselves with Industry 4.0 in a systematic way. It will, therefore, provide a direction to formulate the enterprise transformation strategies in Industry 4.0 paradigm.

Globalization breeds increasing competition. In today’s dynamic climate, lean thinking has been found a promising business continuous improvement strategy for improving quality while reducing product cost and delivery time. However, its implementation has dynamic nature of challenges that varies from industry to industry and country to country, necessitating a specific framework by taking all stakeholders onboard. This study aims to propose a lean implementation framework to reduce defects and waste to improve the performance of the metal casting industry. The structure of the framework has been divided into three phases namely the lean conception phase, lean implementation phase, and lean sustainability phase. The proposed framework integrates the six sigma DMAIC methodology with lean tools and techniques to reduce defects and achieve performance improvement. A solid cast software has been used as a computer-assisted casting simulation tool to perform the analysis of defects within the casting. Further, the proposed framework is demonstrated and validated by employing a real-time case study that was manufactured using the sand casting process. The obtained results show remarkable improvements in poured metal weight (33.3%), mold weight including gating system (40%), casting yield (24.56%, rejection rate (90%), and financial saving (24.63%). As a result of analysis of percentage improvements data, the proposed framework can provide the practitioners with a standard roadmap and motivate the casting industries to implement lean for performance improvement through organizational change. Through the effective application of the lean implementation framework, quality enhancement has been demonstrated.

Humanity has faced unprecedented chaos in the education sector due to the inevitable sudden adoption of online mode of learning during the pandemic. The complexities associated with technology-enabled learning and assessment have different connotations in developing countries due to a lack of infrastructure and awareness. Such countries can switch over to an online mode of education more frequently in the future due to highly volatile local political and cultural situations on top of the pandemic. This study evaluates the complexities associated with technology-enabled online assessment methods in Pakistan. Technology readiness and performance for the learning assessment of students are appraised through approaching approximately one thousand students from more than one hundred public and private sector engineering universities. A screened list of assessment alternatives and their influencing factors are then prioritized using the multi-actor multi-criteria analysis (MAMCA) by considering the perceptions of national policymakers, faculty members and students. The aggregate results reveal that, among the influencing factors, ‘mental health’ received the highest weightage, and stakeholders are indifferent to associated costs despite financial challenges. Automated MCQs secured the top position in the ranking list. Sensitivity analysis incorporates some disagreements among the stakeholders, which makes this study highly beneficial for policy modeling.

One of the main challenges for leading manufacturing industries to retain their competitiveness is selection of appropriate suppliers. In developing countries like Pakistan, industries need reinforcement of the supplier selection decisions using scientific techniques. This study utilizes conjoint analysis (CA), a mathematical and statistical modeling based multi-criteria decision making (MCDM) methodology, for evaluation of supplier efficiency in a real world manufacturing organization. Six attributes are extracted from respective literature with the help of expert opinions. The developed mathematical models are solved in respective CA software by using actual data. Results reveal that product performance is the most important attribute in supplier evaluation for the concerned industry followed by costs, quality, financial stability, delivery time and technical ability respectively. The findings are beneficial for both the selected manufacturing industry and the market suppliers.

The role of Information Technology (IT) solutions is becoming prominent in organizations to achieve efficiency and competitive advantages. One of such solutions is office automation (OA). It is reasonable to judge the OA impact by comparative evaluation of pre and post implementation scenarios. In this study, five key performance indicators (KPIs) affecting the OA decision were identified. Three of them namely human resource, time, and cost were quantitative KPIs, whereas two KPIs, quality of work and control over processes were qualitative. Analytical Hierarchy Process (AHP) was applied to prioritize the KPI‟s and to quantify their impact on OA decision. Both the quantitative and qualitative analysis demonstrated that there was remarkable improvement in office operations, once automation decision had been implemented. Overall weight obtained for automated office alternative was more than double as compared to the non-automated offices. The findings were highly motivating for public sector organizations of Pakistan to adopt automated processes.