We focus on ways of combining simple database models with managerial intuition. We present a model and method for isolating managerial intuition. For five different business forecasting situations, our results indicate that a combination of model and manager always outperforms either of these decision inputs in isolation, an average R2 increase of 0.09 (16%) above the best single decision input in cross-validated model analyses. We assess the validity of an equal weighting heuristic, 50% model + 50% manager, and then discuss why our results might differ from previous research on expert judgment.

Database Models And Managerial Intuition: 50% Model + 50% Manager

Digital twins-based smart manufacturing system design in Industry 4.0: A review

On the evolution of “Cleaner Production” as a concept and a practice

Industrial information integration engineering (IIIE) is a set of foundational concepts and techniques that facilitate the industrial information integration process. In recent years, many applicat...

A Survey on Industrial Information Integration 2016–2019

The transformation from traditional manufacturing to intelligent manufacturing intrigues the profound and lasting effect on the future manufacturing worldwide Industry 40 was proposed for advancing manufacturing to realize short product life cycles and extreme mass customization in a cost‐efficient way As the heart of Industry 40, smart factory integrates physical technologies and cyber technologies and makes the involved technologies more complex and precise in order to improve performance, quality, controllability, management, and transparency of manufacturing processes So far, leading manufacturers have begun the journey toward implementing smart factory However, most firms still lack insight into the challenges and resources for implementing smart factory As such, this paper identifies the requirements and key challenges, investigates available new technologies, reviews existing studies that have been done for smart factory, and further provides guidance for manufacturers to implementing smart factory in the context of Industry 40

Smart factory in Industry 4.0

A structured approach for facilitating the implementation of ISO 50001 standard in the manufacturing sector

Standards-based integration of advanced process control and optimization.

A Simulation Algorithm of a Digital Twin for Manual Assembly Process

Power demand forecasting is a significant factor in the planning and economic and secure operation of modern power system. This research work has compared different forecasting techniques and opted to find out better technique in context of power generation, which varies rapidly from time to time. The dataset has been generated from yearly demand of electricity of Bangladesh for last five years. Year, irrigation season, temperature and rainfall amount have been considered as input parameters where as single output is demand of load in adaptive neuro-fuzzy inference system ANFIS. Another artificial intelligence technique, artificial neural network ANN has been used to validate the output results. The best suited traditional technique for forecasting power generation is seasonal forecasting. Seasonal forecasting is also used to compare with ANFIS and ANN to find out better technique. The result of experiment indicates that ANFIS is superior method to tackle forecasting of power generation from different error measures.

Hasan Latif

Papers

A structured approach for facilitating the implementation of ISO 50001 standard in the manufacturing sector

Standards-based integration of advanced process control and optimization.

A Simulation Algorithm of a Digital Twin for Manual Assembly Process

A comparative analysis of power demand forecasting with artificial intelligence and traditional approach

Integrating A Dynamic Simulator and Advanced Process Control using the OPC-UA Standard