Designed for Workarounds: A Qualitative Study of the Causes of Operational Failures in Hospitals

Frontline care providers in hospitals spend at least 10% of their time working around operational failures, which are situations where information, supplies, or equipment needed for patient care are insufficient. However, little is known about underlying causes of operational failures and what hospitals can do to reduce their occurrence. To address this gap, we examined the internal supply chains at two hospitals with the aim of discovering organizational factors that contribute to operational failures. We conducted in-depth qualitative research, including observations and interviews of over 80 individuals from 4 nursing units and the ancillary support departments that provide equipment and supplies needed for patient care. We found that a lack of interconnectedness among interdependent departments' routines was a major source of operational failures. The low levels of interconnectedness occurred because of how the internal supply chains were designed and managed rather than because of employee error or a shortfall in training. Thus, we propose that the time that hospital staff spend on workarounds can be reduced through deliberate efforts to increase interconnectedness among hospitals' internal supply departments. Four dimensions of interconnectedness include (1) hospital-level—rather than department-level—performance measures; (2) internal supply department routines that respond to specific patients' needs rather than to predetermined stocking routines; (3) knowledge that is necessary for efficient handoffs of materials is translated across departmental boundaries; and (4) cross-departmental collaboration mechanisms that enable improvement in the flow of materials across departmental boundaries

A maintenance prediction system using data mining techniques

In the last years we have assisted to several and deep changes in industrial companies, mainly due to market dynamics and the need to converge with a globalized and impatient world. These changes are transversal to the entire company also impacting on company maintenance function. In an attempt to eliminate faults and keep systems running without interruption, companies incorporated tools into their Information and Communication Technologies (ICT) systems. The benefits are clear in terms of resulting quality and in costs reduction, particularly those related with the data processing time and accuracy of the resulting knowledge. In their daily routine, companies produce and store endless and complex quantities of data of different nature, increasing the difficulty of use in real time. In this sense, considering the relevance of data collected on industrial plants, namely in its maintenance activities, it is intended with this paper to present a functional architecture of a predictive maintenance system, using data mining techniques on data gathered from manufacturing units globally dispersed. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in machines. The remote data collection is based on an intricate system of distributed agents, which, given its nature, will be responsible for remote data collection through the functional architecture.Fundação para a Ciência e a Tecnologia (FCT)

A mechanism to assess the relationship between socio-technical congruence and project performance in incremental model

No AbstractKeywords: coordination; software development; software project; software engineering project; socio-technical congruenc

An investigation into the improvement of maintenance quality in a production plant through the use of reliability management

Abstract: Extensive research has been conducted on plant reliability maintenance. However, getting the maintenance system in place remains the core of maintenance improvement. The purpose of this research dissertation is to investigate the impact reliability management has within production industries and its role in improving the quality of maintenance. This study will assist management to reflect on the costeffectiveness and influence that the maintenance management system has within the organisation. The study will also contribute to existing knowledge in plant reliability maintenance. It is, therefore, significant for an organisation to establish good management practices and set maintenance as an integral part of their overall plant strategy. The approach that was followed by this research is a mixed method – a combination of quantitative and qualitative. Data collection was carried out through a literature review, observations, a questionnaire, a survey, interviews and from company documentation. An employee questionnaire was prepared and distributed to 25 participants to conduct gap analysis and evaluate the maintenance practices within the observed company. A benchmark study was also performed using an online survey, based on 85 responses from employees in other processing plant industries within South Africa. The empirical study conducted with Company A’s employees (mainly from the maintenance and operation departments) identified possible blunders, which transpired during the reliability management system implementation phase. Based on this sample the results obtained indicated that maintenance is done unnecessarily on the plant on average. The study also found issues regarding maintenance financial planning ineffectiveness, unavailability of spares and lack of skillsets to perform jobs. The online survey revealed that organisations do make use of Computerised Maintenance Management System (CMMS) to facilitate their maintenance. CMMS also has a positive impact on the overall maintenance processes and productivity. The study identified the importance of planning and scheduling shutdowns in advance, as a significant part of the maintenance annual budget and cost reduction. ..M.Phil. (Engineering Management

Towards robustness of production planning and control against supply chain disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Towards Robustness Of Production Planning And Control Against Supply Chain Disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control