Speeding up Explorative BPM with Lightweight IT: the Case of Machine Learning
通过分析一家丹麦制造商的四个行动案例，研究轻量级IT如何加速嵌入机器学习的探索性业务流程管理，发现轻量级ML能大幅缩短机会评估和技术实施时间，但需要松散耦合的IT基础设施和大量使用构建块。
Abstract In the modern digital age, companies need to be able to quickly explore the process innovation affordances of digital technologies. This includes exploration of Machine Learning (ML), which when embedded in processes can augment or automate decisions. BPM research suggests using lightweight IT (Bygstad, Journal of Information Technology, 32 (2), 180–193 2017) for digital process innovation, but existing research provides conflicting views on whether ML is lightweight or heavyweight. We therefore address the research question “How can Lightweight IT contribute to explorative BPM for embedded ML?” by analyzing four action cases from a large Danish manufacturer. We contribute to explorative BPM by showing that lightweight ML considerably speeds up opportunity assessment and technical implementation in the exploration process thus reducing process innovation latency. We furthermore show that succesful lightweight ML requires the presence of two enabling factors: 1) loose coupling of the IT infrastructure, and 2) extensive use of building blocks to reduce custom development.