In recent years, the increasingly complex and uncertain environment has driven companies to become more responsive, competitive and attractive, and to achieve this goal, an accurate forecasting process is essential. This thesis aims to develop a forecasting process for high-cost, low-volume products in the health care industry. The proposed process is intended to help managers and companies that are in a very uncertain environment, where a continuous exchange of information with the external context is necessary. Since these are very expensive and low-volume products, it is necessary to be able to plan them accurately to avoid incurring excessive inventory costs that adversely affect companies' revenues. However, having the product when the end customer requires it is essential in order to provide the necessary patient care. This process recommends the use of a dashboard to quickly and easily show only the data needed for forecasting purposes. Unifying data from different databases is essential to have both information on sales orders and data on tender opportunities and products involved. The forecast considers the quantity of products from awarded tenders and the quantity of products from ongoing tenders mitigated with the probability of winning the tender. This probability is defined by taking into consideration the characteristics of the tender opportunity and the business with respect to the customer and the external market. Guidelines from past tenders are used to define installation dates, but interaction groups are needed as the installation date becomes more truthful and more concrete information is obtained from customers and business. A system based on collaboration and sharing among all stakeholders involved is needed for this process to have greater visualization of available data and to allow each group to share knowledge with the rest of the team.
Negli ultimi anni, l'ambiente sempre più complesso e incerto ha spinto le aziende a diventare più reattive, competitive e attraenti e, per raggiungere questo obiettivo, è essenziale un processo di previsione accurato. Questa tesi si propone di sviluppare un processo di previsione per prodotti ad alto costo e basso volume nel settore sanitario. Il processo proposto intende aiutare i manager e le aziende che si trovano in un ambiente molto incerto, dove è necessario un continuo scambio di informazioni con il contesto esterno. Trattandosi di prodotti molto costosi e a basso volume, è necessario poterli pianificare con precisione per evitare di incorrere in costi di magazzino eccessivi che incidono negativamente sui ricavi delle aziende. Tuttavia, disporre del prodotto quando il cliente finale lo richiede è fondamentale per poter garantire la necessaria assistenza ai pazienti. Questo processo raccomanda l'uso di una dashboard per mostrare in modo rapido e semplice solo i dati necessari ai fini della previsione. L'unificazione dei dati provenienti dai diversi database è essenziale per avere sia le informazioni sugli ordini di vendita sia i dati relativi alle opportunità di gara e ai prodotti coinvolti. La previsione considera la quantità di prodotti provenienti da gare d'appalto aggiudicate e la quantità di prodotti provenienti da gare d'appalto in corso mitigata con la probabilità di vincere la gara. Questa probabilità viene definita prendendo in considerazione le caratteristiche dell'opportunità di gara e del business rispetto al cliente e al mercato esterno. Per definire le date di installazione si utilizzano le linee guida delle gare passate, ma sono necessari gruppi di interazione man mano che la data di installazione diventa più veritiera e si ottengono informazioni sempre più concrete dai clienti e dal business. Per questo processo è necessario un sistema basato sulla collaborazione e sulla condivisione tra tutti gli stakeholder coinvolti, per avere una maggiore visualizzazione dei dati disponibili e per consentire a ciascun gruppo di condividere le conoscenze con il resto del team.
Demand forecasting for high-cost and low-volume products in the healthcare sector
Bernardi, Chiara
2021/2022
Abstract
In recent years, the increasingly complex and uncertain environment has driven companies to become more responsive, competitive and attractive, and to achieve this goal, an accurate forecasting process is essential. This thesis aims to develop a forecasting process for high-cost, low-volume products in the health care industry. The proposed process is intended to help managers and companies that are in a very uncertain environment, where a continuous exchange of information with the external context is necessary. Since these are very expensive and low-volume products, it is necessary to be able to plan them accurately to avoid incurring excessive inventory costs that adversely affect companies' revenues. However, having the product when the end customer requires it is essential in order to provide the necessary patient care. This process recommends the use of a dashboard to quickly and easily show only the data needed for forecasting purposes. Unifying data from different databases is essential to have both information on sales orders and data on tender opportunities and products involved. The forecast considers the quantity of products from awarded tenders and the quantity of products from ongoing tenders mitigated with the probability of winning the tender. This probability is defined by taking into consideration the characteristics of the tender opportunity and the business with respect to the customer and the external market. Guidelines from past tenders are used to define installation dates, but interaction groups are needed as the installation date becomes more truthful and more concrete information is obtained from customers and business. A system based on collaboration and sharing among all stakeholders involved is needed for this process to have greater visualization of available data and to allow each group to share knowledge with the rest of the team.File | Dimensione | Formato | |
---|---|---|---|
Articles.xlsx
non accessibile
Dimensione
782.96 kB
Formato
Microsoft Excel XML
|
782.96 kB | Microsoft Excel XML | Visualizza/Apri |
Demand_Instruments.xlsx
non accessibile
Descrizione: Demand categorization scheme for the analyzed products
Dimensione
239.94 kB
Formato
Microsoft Excel XML
|
239.94 kB | Microsoft Excel XML | Visualizza/Apri |
ERD Guidelines.xlsx
non accessibile
Dimensione
336.88 kB
Formato
Microsoft Excel XML
|
336.88 kB | Microsoft Excel XML | Visualizza/Apri |
2023_04_11 Bernardi thesis 2023_05_04.pdf
accessibile in internet solo dagli utenti autorizzati
Descrizione: Thesis Chiara Bernardi
Dimensione
2.06 MB
Formato
Adobe PDF
|
2.06 MB | Adobe PDF | Visualizza/Apri |
I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/10589/204095