In 2020 the COVID-19 pandemic swept across the globe in a matter of months, abruptly reminding companies the vulnerability of their supply chains. Its effects were suffered by almost every business, posing new challenges for many industries, including grocery supply chains. Panic buying was one of the most significant issues managers had to face. Panic buying concerns the main consequences of a large scale, severe disaster, where consumers tend to stockpile staples (e.g., rice, pasta, flour) and other essential items that they perceive may help them sustain themselves through the crisis period and in anticipation of supply shortages. Panic hugely increases the demand for some products, and it can hurt the entire supply chain, forcing companies to implement mitigation strategies to guarantee the continuity of business operations. Despite the relevance of the topic, in the context of supply chain risk management (SCRM) few studies explored panic buying’s consequences and how companies could reshape strategies to manage the related issues. This thesis aims at contributing to the SCRM research field by developing an agent-based simulation model to investigate the response of a grocery supply chain to consumer panic buying triggered by a large-scale disaster. The model analyses the performances of a supply chain that is disrupted by a sudden demand increase, and explores the strategies applied to mitigate the disruption’s effect. Two more detailed research objectives were shaped: i) to analyse the impact of panic buying on a grocery supply chain that operates with different inventory levels, and ii) to understand how two mitigation strategies, i.e., order allocation and reorder frequency can reduce the consequences of such a disaster. The simulation was designed using the Mesa library of Python to model the consumers and the other supply chain stakeholders as autonomous agents. The quantitative results showed that the greatest beneficial effects originate from implementing additional mitigation strategies at the tactical level to the sole management of stocks at the strategic level. Future studies could then leverage the results obtained and build upon the research limitations, extending the simulation to broaden the investigation of further mitigation strategies or to include more general supply chain configurations.
Il COVID-19 ha posto nuove sfide per le catene di distribuzione di generi alimentari, ed il “panic buying” è stato uno dei problemi più significativi che i manager hanno dovuto affrontare. Gli acquisti di panico sono infatti una delle principali conseguenze di un disastro su larga scala (come una pandemia, un terremoto, ecc.), in cui i consumatori tendono a fare scorta di generi alimentari di base (ad esempio riso, pasta, farina) ed altri articoli essenziali che percepiscono possano aiutarli a sostenere il periodo di crisi o in previsione ad una futura carenza di fornitura. Il panico aumenta enormemente la domanda di alcuni prodotti, e può provocare danni all'intera filiera, costringendo le aziende ad implementare strategie di mitigazione per garantire la continuità delle operazioni. Nonostante la rilevanza dell'argomento, finora, nel contesto del supply chain risk management, pochi studi hanno analizzato le conseguenze del “panic buying” e come le aziende possano intervenire con strategie di mitigazione per gestire tale problema. Questa tesi presenta lo sviluppo di una simulazione ad agenti per studiare la risposta di una filiera di generi alimentari all'acquisto di panico da parte dei consumatori. Il modello ne analizza le prestazioni e le strategie da applicare per mitigarne le conseguenze. Nello specifico, i due obiettivi principali sono legati all'impatto degli acquisti di panico su una filiera che opera con diversi livelli di inventario, e l'analisi di come l'allocazione degli ordini e la frequenza di riordino possano ridurre gli effetti della “disruption”. La simulazione è stata sviluppata utilizzando la libreria Mesa di Python, al fine di modellare i consumatori e i vari stakeholder della supply chain come agenti autonomi. I risultati dimostrano che i maggiori benefici derivano dall'attuazione di strategie di mitigazione a livello tattico, rispetto che alla sola gestione delle scorte a livello strategico. Le future ricerche in questo ambito potrebbero basarsi sui risultati ottenuti e sui limiti di questa tesi, estendendo la simulazione per includere configurazioni di filiera più generali per lo studio dei vantaggi di ulteriori strategie di mitigazione.
An agent based simulation model to study the impact of panic buying on grocery supply chains
Plotti, Filippo;Gelussi, Giulia
2020/2021
Abstract
In 2020 the COVID-19 pandemic swept across the globe in a matter of months, abruptly reminding companies the vulnerability of their supply chains. Its effects were suffered by almost every business, posing new challenges for many industries, including grocery supply chains. Panic buying was one of the most significant issues managers had to face. Panic buying concerns the main consequences of a large scale, severe disaster, where consumers tend to stockpile staples (e.g., rice, pasta, flour) and other essential items that they perceive may help them sustain themselves through the crisis period and in anticipation of supply shortages. Panic hugely increases the demand for some products, and it can hurt the entire supply chain, forcing companies to implement mitigation strategies to guarantee the continuity of business operations. Despite the relevance of the topic, in the context of supply chain risk management (SCRM) few studies explored panic buying’s consequences and how companies could reshape strategies to manage the related issues. This thesis aims at contributing to the SCRM research field by developing an agent-based simulation model to investigate the response of a grocery supply chain to consumer panic buying triggered by a large-scale disaster. The model analyses the performances of a supply chain that is disrupted by a sudden demand increase, and explores the strategies applied to mitigate the disruption’s effect. Two more detailed research objectives were shaped: i) to analyse the impact of panic buying on a grocery supply chain that operates with different inventory levels, and ii) to understand how two mitigation strategies, i.e., order allocation and reorder frequency can reduce the consequences of such a disaster. The simulation was designed using the Mesa library of Python to model the consumers and the other supply chain stakeholders as autonomous agents. The quantitative results showed that the greatest beneficial effects originate from implementing additional mitigation strategies at the tactical level to the sole management of stocks at the strategic level. Future studies could then leverage the results obtained and build upon the research limitations, extending the simulation to broaden the investigation of further mitigation strategies or to include more general supply chain configurations.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/174059