Industry 4.0 is pushing companies to adopt new technologies and approaches to face the growing speed of change. One of the main aspects of this new industrial revolution is the management of data, not only at strategic levels but also at operational ones. The growing amount of available data isn’t always aligned with equal capabilities of exploiting the full potential and value from them. Especially on the shop floor level, automation and data collection from sensors clash with lack of digital tools that could allow to drive proper decisional processes. A growing phenomenon is the spreading on the market of low cost and easy-to-learn software for Business Analytics (BI). This is allowing to extend the use of these tools also to fields where some years ago the benefits of implementing third-part BI software would have not balanced the high investment costs. This new phenomenon is called “Self-BI”. Occurring to Self-BI allows to track the desired KPIs and benchmarking them with the targets defined by the company. Of course, to have a proper benchmarking it is required to set target values. In the case of loss indicators is not enough to just look for a “Goal of Zero” since the target values for KPIs should be based on an ideal but realistic scenario. This thesis aims to present a real business case, carried out together with Marelli company in the plant of Corbetta (MI-Italy). The project is about introducing Self-BI tools to develop a dashboard that tracks the operational losses that occur across the shop floor, such as scrap, labour losses and premium freights. Also, this thesis presents an example about the evaluation of target values for a peculiar KPI which is required to benchmark the current performances.
L'industria 4.0 sta spingendo le aziende ad adottare nuove tecnologie e nuovi approcci per affrontare la crescente velocità di cambiamento. Uno degli aspetti principali di questa nuova rivoluzione industriale è la gestione dei dati, non solo a livello strategico ma anche operativo. La crescente quantità di dati disponibili non è sempre allineata con le stesse capacità di sfruttarne appieno il potenziale e il valore. Soprattutto a livello di produzione, l'automazione e la raccolta dei dati dai sensori si scontrano con la mancanza di strumenti digitali che possano consentire di guidare adeguati processi decisionali. Un fenomeno in crescita è la diffusione sul mercato di software per la Business Analytics (BI) a basso costo e di facile apprendimento. Ciò consente di estendere l'utilizzo di questi strumenti anche a settori in cui alcuni anni fa i vantaggi dell'implementazione di software di BI di terze parti non avrebbero bilanciato gli elevati costi di investimento. Questo nuovo fenomeno è chiamato “Self-BI”. L'avvento di Self-BI consente di tracciare i KPI desiderati e di confrontarli con gli obiettivi definiti dall'azienda. Naturalmente, per avere un benchmarking adeguato è necessario fissare dei valori target. Nel caso degli indicatori di perdite operative non è sufficiente cercare solo un “Goal of Zero” poiché i valori target dei KPI dovrebbero essere basati su uno scenario ideale ma realistico. Questa tesi si propone di presentare un vero e proprio business case, svolto in collaborazione con l’azienda Marelli nello stabilimento di Corbetta (MI-Italia). Il progetto considera l'introduzione di strumenti di Self-BI per sviluppare una dashboard che tracci le perdite operative che si verificano a livello di produzione, come scarti, perdite di manodopera e spedizioni urgenti. Inoltre, questa tesi fornisce un esempio reale sulla valutazione del valore target per un determinato KPI, necessario per eseguire un confronto con le prestazioni attuali.
Benchmarking the shop-floor : a case study on dashboard design and target setting for KPIs
Talacchini, Alessandro
2021/2022
Abstract
Industry 4.0 is pushing companies to adopt new technologies and approaches to face the growing speed of change. One of the main aspects of this new industrial revolution is the management of data, not only at strategic levels but also at operational ones. The growing amount of available data isn’t always aligned with equal capabilities of exploiting the full potential and value from them. Especially on the shop floor level, automation and data collection from sensors clash with lack of digital tools that could allow to drive proper decisional processes. A growing phenomenon is the spreading on the market of low cost and easy-to-learn software for Business Analytics (BI). This is allowing to extend the use of these tools also to fields where some years ago the benefits of implementing third-part BI software would have not balanced the high investment costs. This new phenomenon is called “Self-BI”. Occurring to Self-BI allows to track the desired KPIs and benchmarking them with the targets defined by the company. Of course, to have a proper benchmarking it is required to set target values. In the case of loss indicators is not enough to just look for a “Goal of Zero” since the target values for KPIs should be based on an ideal but realistic scenario. This thesis aims to present a real business case, carried out together with Marelli company in the plant of Corbetta (MI-Italy). The project is about introducing Self-BI tools to develop a dashboard that tracks the operational losses that occur across the shop floor, such as scrap, labour losses and premium freights. Also, this thesis presents an example about the evaluation of target values for a peculiar KPI which is required to benchmark the current performances.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/186463