Industrial support business processes often involve work outside core production activities, including record retrieval, spreadsheet checking, supplier communication, and follow-up of operational events. We examine these issues in a maintenance-support case where a low-code conversational Artificial Intelligence (AI) layer was connected to existing information and communication routines. Two agents were configured: ManuBot, for querying and updating maintenance-history data, and MailBot, for recurrent supplier-email handling. The empirical sequence covered baseline diagnosis, prototype testing and implementation-stage evaluation, drawing on workflow observations, user feedback, task comparisons and records from the implemented tools. The clearest measured changes were task-specific. MailBot reduced supplier-email preparation from about 12-15 min to 2-3 min per message. ManuBot reduced maintenance-data retrieval and querying time by approximately 50%. Users also reported easier access to historical malfunction records, better visibility of recurrent events, and more structured email routines. The case remained constrained by incomplete ERP (Enterprise Resources Planning) integration, data-structure quality, platform permissions and differences in user readiness. The evidence points to a task-specific use of low-code conversational AI: gains were observed when the agents were tied to specific records, supplier-email workflows and human validation points.
Paulo Peças, Diogo Pires, Diogo Jorge· International journal of mat...· 0 citations
Manufacturing companies often register process deviations in operational systems while managing continuous improvement (CI) actions through separate spreadsheets, templates and meeting records. This fragmentation weakens traceability between detection, prioritisation, execution and verification. This paper presents Digital for Continuous Improvement (D4CI), a configurable digital CI system developed from eight literature-derived requirements covering event traceability, detection and escalation rules, transparent assessment, workflow routing, planning, verification and interoperability. The architecture combines data input, relational storage, application logic and user interfaces within a shared information model. Deviations are recorded against process targets or expected conditions, while recurrence criteria consolidate related deviations into occurrences. Impact, Effort and Waste–Cost inputs are stored with the calculated scores and used to recommend an Action for Immediate Improvement, Quick Win or A3 pathway. Planning, execution and verification records remain linked to the originating problem. D4CI was deployed in a metalworking company with established Lean routines and evaluated through implementation records, observation of system use and consolidated feedback. The requirement–function mapping confirmed coverage of the eight design requirements. Deployment evidence indicated centralised problem records, traceable prioritisation criteria, shared visual follow-up of open actions and retrieval of completed CI records. Operational effects require longer observation and comparative performance data.
Paulo Peças, Jéssica Lopes, Hugo Botelho et al.· Applied System Innovation· 0 citations
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