Revising history for cost-informed process improvement

Abstract

Organisations are constantly seeking new ways to improve operational efficiencies. This study investigates a novel way to identify potential efficiency gains in business operations by observing how they were carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how these trade-offs can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A number of optimisation techniques are proposed to explore and assess alternative execution scenarios. The objective function is represented by a cost structure that captures different process dimensions. An experimental evaluation is conducted to analyse the performance and scalability of the optimisation techniques: integer linear programming (ILP), hill climbing, tabu search, and our earlier proposed hybrid genetic algorithm approach. The findings demonstrate that the hybrid genetic algorithm is scalable and performs better compared to other techniques. Moreover, we argue that the use of ILP is unrealistic in this setup and cannot handle complex cost functions such as the ones we propose. Finally, we show how cost-related insights can be gained from improved execution scenarios and how these can be utilised to put forward recommendations for reducing process-related cost and overhead within organisations.

Partial-order-based process mining: a survey and outlook

“…The framework has also been extended for prediction [87,88]. In [89], the authors assume that cases describe activity instances with an associated partial order. The orders are, however, used in an implicit manner as the authors assess various optimization strategies to perform "cost-informed" process improvement.…”

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

“…There is extensive evidence of how in industry process mining has highlighted inefficiencies in existing organisational processes, for example where quieter sections of the pathway are over-resourced or pinch-points where demand exceeds capacity15–17; provided informed simulation of new scenarios, for instance how reallocating resources might affect run-time or process outcome18; or identified where tasks could be undertaken by an alternative member of staff such as those with a more appropriate level of seniority or skill set 19…”

A Multi-Criteria Process Mining Optimization Tool and Its Application in a Sepsis Clinical Pathway

“…Four publications provide insights for optimizing the resource (workers) allocation for executing a set of activities. Low et al [2014Low et al [ , 2016 and van der Aalst et al [2015] propose an approach to update an event log changing the resource allocation, and in this way, recreating an optimal execution scenario. The new event log can help users to identify process improvements.…”

“…As a simple example, we could provide as input the event log from our previous research study and define as criterion that we want to minimize the time to give antibiotics. The technique processes the event Lehto et al [2016Lehto et al [ , 2017 e Low et al [2014Low et al [ , 2016 and van der Aalst et al…”