A bioprocess is a series of biological, chemical, and physical operations used to produce a product using living cells or their components. Bioprocesses are often used for the production of monoclonal antibodies (mAbs). The first step of the mAb production bioprocess is to take a vial containing a small amount of the selected cell line and grow those cells until they are of sufficient quantity. This step is known as the seed train in bioprocess development. During the seed train phase, it is essential to monitor the stability of the cells and their growth due to challenges such as variations in cell behaviour, batch-to-batch differences, and potential changes in cultivation conditions. In this paper, we present a case study where process mining is used to analyse the stability of cell lines during the seed train phase at a large pharmaceutical company in Australia. In order to do so, first it was necessary to transform the collected seed train data into an event log. Next, process models were discovered for high- and low-growth seed trains. We then derived insights into the performance of the seed train growth rate whereby characteristics of cell cultures in early stages can be associated with growth rate performance in later stages. Finally, we showed how the discovered models can be used to predict the growth performance of new seed trains.

Abstract Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.

Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or calling a customer to obtain missing information rather than waiting passively. However, each of these interventions comes with a cost. This paper tackles the problem of determining if and when to trigger a time-reducing intervention in a way that maximizes a net gain function. The paper proposes a prescriptive monitoring method that uses orthogonal random forests to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process. Based on this estimate, the method triggers interventions according to a user-defined policy. The method is evaluated on two real-life datasets.

This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an execution of the process (the possible treatments). We use an action rule mining technique to identify treatments that co-occur with the outcome under some conditions. Since action rules are generated based on correlation rather than causation, we then use a causal machine learning technique, specifically uplift trees, to discover subgroups of cases for which a treatment has a high causal effect on the outcome after adjusting for confounding variables. We test the relevance of this approach using an event log of a loan application process and compare our findings with recommendations manually produced by process mining experts.