TQM implementation challenges: a case study of a building maintenance department of an institution of higher learning

Author(s):  
Mariam Akinlolu ◽  
Ruben Ndihokubwayo ◽  
Fredrick Simpeh
2018 ◽  
Vol 2 (2) ◽  
pp. 1-11
Author(s):  
Peter U. Anuforo ◽  
Hazeline Ayoup ◽  
Nasiru Saidu

The incessant decline in the performance of Malaysian public institution of higher learning since 2007 to date has brought to fore the question of its performance management process. Studies have shown that performance management is a general phenomenon in the institution of higher learning worldwide. The implementation issues of Balance scorecard (BSC) is more pronounced in the non-profit outfit and governmental organizations (NPGOs) compared to the profit-oriented outfit. Reasons being that the BSC was originally developed and (or) meant for the profit-oriented outfit and not exactly the other way round. The purpose of this paper is to provide insight into the issues of performance management in the institution of higher learning and specifically regarding the implementation issues of BSC encountered among Malaysia public institution of higher learning. This study provides a review of previous literature on the BSC implementation issues. Several implementation issues identified from the literatures are strategy misalignment, strategy communication issues, manager’s acceptance/employees buy-in, the issue of clarity of vision, mission, strategy, and outcome, and strategy map implementation issues. Findings revealed that the implementation issues of the BSC among Malaysia public institution actually helps to unravel the pitfalls of BSC project and provides a roadmap show that can improve and sustain effective performance management. The implication of this study is aimed at providing the means by which the Malaysian public institution of higher learning can identify the issues inherent in BSC implementation or strategy implementation and the way forward.  


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.


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