# Enhancing Pharmaceutical Supply Chain Resilience: A Comprehensive Review of Visibility and Demand Forecasting

## Abir Ghannem

abirghannembapro@gmail.com

**ISITCOM:** Universite de Sousse Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse https://orcid.org/0000-0002-1967-4238

## Hajer Nabli

**ISITCOM:** Universite de Sousse Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse

## Raoudha Ben Djemaa

**ISITCOM:** Universite de Sousse Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse

## Layth Sliman

**ISITCOM:** Universite de Sousse Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse

## Research Article

**Keywords:** Drug supply chain, Supply chain visibility, Forecasting demand, Blockchain, IoT, Machine learning

**Posted Date:** February 21st, 2024

**DOI:** https://doi.org/10.21203/rs.3.rs-3932079/v1

**License:** © Ⓟ This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
# Enhancing Pharmaceutical Supply Chain Resilience: A Comprehensive Review of Visibility and Demand Forecasting

Abir Ghannem<fnref n="1" />*, Hajer Nabli<fnref n="1" />, Raoudha Ben Djemaa<fnref n="1" />, Layth Sliman<fnref n="2" />

*Corresponding author(s). E-mail(s): abirghannembapro@gmail.com;

Contributing authors: nabli.hajer@yahoo.fr; raoudhaham@yahoo.fr;

layth.sliman@efrei.fr;

## Abstract

The escalating challenge of drug shortages within the pharmaceutical supply chain has emerged as a critical global concern, posing substantial threats to public health and the integrity of healthcare systems. This paper provides a thorough examination of the origin of drug shortages, impacting a diverse range of pharmaceutical products and instigating heightened concerns among patients, pharmacists, and the healthcare community. This review takes an in-depth look at the underlying causes of drug shortages, highlighting the critical importance of supply chain visibility and demand forecasting in managing this pressing issue. By exploring the current gaps in communication between stakeholders, as well as the challenges of accurately forecasting demand, the article proposes innovative solutions. The analysis particularly focuses on the emergence of decentralized data sharing systems and the integration of advanced technologies such as blockchain, Internet of Things (IoT) and machine learning. These technological advances offer significant prospects for strengthening visibility, traceability and predictive modeling within the pharmaceutical supply chain. Highlighting the transformative potential of these solutions, this article argues for an overhaul of health systems, advocating equitable and continued access to essential treatments through collaborative and technologically advanced approaches.

**Keywords:** Drug supply chain, Supply chain visibility, Forecasting demand, Blockchain, IoT, Machine learning

<footnote n="1">¹*ISITCom of H.Sousse, University of Sousse, H. Sousse, Tunisia.</footnote>

<footnote n="2">²EFREI Paris, Paris Pantheon-Assas University, Villejuif, France.</footnote>
# 1 Introduction

Drug shortages are a growing problem globally, has become a major public health challenge. Drug shortages manifest in various forms, impacting a broad range of pharmaceutical products, from generic medications to advanced treatments [1, 2]. Causing growing concern among patients, pharmacists and the entire health care system. The United States Food and Drug Administration (FDA) defines drug shortages as “a shortage occurring when demands exceed supply at any point in the supply chain, ultimately creating a ‘stock-out’ at the point of appropriate service delivery to the patient if the cause of the shortage cannot be resolved in a timely manner relative to the clinical needs of the patient” [3].

Therefore, shortages can lead to major consequences for both patients and health-care professionals, including the possibility of death in critical patient cases, treatment changes, substandard treatment and medication errors [4]. Patients may face signif-icant repercussions due to drug shortages, particularly when medications for cancer, COVID-19, intense pain, severe allergies, mental health conditions, childhood diseases become indispensable [5, 6]. Drug shortages can present serious challenges for pharma-cists. Faced with a shortage, the pharmacist must find appropriate alternatives within the same Interchangeable Group (IG) of the drug, or alternatives within a Therapeutic Class (TC). Substituting a familiar drug with an alternative can lead to adjustments in treatment plans, potentially affecting the risk for patients [7]. Searching for alterna-tive medications can be a recurring task throughout the day, resulting in considerable labor costs. Additionally, shortages have other effects for pharmacies, including safety risks, the potential for medication errors and stress among healthcare professionals [8]. The issue of drug shortages has become a global concern, and improving access to medicines remains a complex challenge [9, 10]. In fact, the medicine crisis raises essential questions about the need to rethink health systems to ensure equitable and uninterrupted access to essential treatments.

In recent years, many researchers and stakeholders have examined the causes. Numerous factors can lead to drug shortage. In [11], the authors that emphasize price and quality regulations are both important causes of drug shortages or drug unavail-ability. The authors in [12] mentioned economic reasons. Production problems are cited as causes by [13]. While the most common reasons behind medicine shortages by [14] were the small size of the pharmaceutical market, sudden or fluctuating demand, small stock sizes, long delivery time and a long or complex production chain. Accord-ing to the FDA [3], the causes of drug shortages are a lack of incentive to produce less profitable drugs, a lack of recognition and reward for mature quality management systems, and logistical and regulatory challenges that make it difficult to market recov-ery after a disruption. The European Association of Hospital Pharmacists (EAHP) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) have published their own list of causes, most coming from supply chains [15, 16]. The American Society of Health-system Pharmacists (ASHP) states that the cause of drug shortages was due to 3% of drug raw materials, 6% production discontinuities, 23% manufacturing problems, 13% supply/demand, and the largest 55% unknown cause [17]. In the end, the approach to managing drug shortages differs in each country, influenced by the unique conditions prevalent in each [18].
However, because drug supply chains are highly interdependent and complex systems, it may not be possible to identify these “root” or initial causes. Drug shortages result from multiple decisions, events, and interactions by many distinct actors, including pharmacies, health actors, suppliers, manufacturers, regulatory action, and patients [11, 12]. Many researchers in the pharmaceutical supply chain are cooperating to develop solutions to the problems of drug shortages. The introduction of a data sharing system is emerging as a promising proposition to address drug shortage issues [19]. This system has significant advantages, notably by facilitating the authentication of medicines. Indeed, a more efficient medication process can be greatly promoted by optimized traceability and visibility systems. By implementing the sharing of logistics data between integrated players, particularly with regard to traceable items, a notable improvement in product visibility can be achieved. This increased visibility would directly help alleviate drug shortage issues by enabling more proactive inventory management, anticipation of changes in demand, and rapid response to potential supply chain disruptions. In short, a decentralized data sharing system offers considerable potential to strengthen the resilience of the health system in the face of challenges related to drug shortages. On the other hand, the importance given to demand forecasting is increasingly emphasized, and contemporary research is actively working to improve these forecasts. The implications of these efforts prove crucial, as the development of research into accurately forecasting future demand proves to be of paramount importance in the pharmaceutical industry. These advances are seen as a key way to overcome the challenges of drug shortages [20].

In this context, the integration of emerging technologies being leveraged is anticipated to mitigate shortages and enhance transparency within the pharmaceutical supply chain. Blockchain technology is distinguished as a potential remedy in the pharmaceutical supply chain due to its characteristics of transparency, immutability and decentralization [21]. By employing blockchain technology, transactions can be securely recorded, confirmed, and tracked across a network of participants on a decentralized platform. Moreover, the Internet of Things (IoT) carries significant potential for revolutionizing supply chain management by facilitating swift information transmission among stakeholders [22]. IoT technology, on the other hand, can offer valuable insights to enhance product authentication, ensure proper storage and transportation conditions, improve supply chain visibility. Consider the utilization of traceability data through analytics, which has the capability to enhance interactions among stakeholders and preserve the integrity of the chain. Predictive models, employing machine learning algorithms, are well-positioned to derive valuable insights regarding future needs or potential disruptions [23]. This article highlights a review of existing literature in this domain, analyzing drug supply chain visibility and drug demand forecasting to tackle the drug shortage problem. Specifically, it addresses the following research questions:

* How to automatically track drugs throughout the supply chain?

* How to predict the future need for drugs?

Also, the survey paper highlights and examines constraints from previous studies and underscores the combined potential of blockchain, IoT, and machine learning technologies to tackle the issue of drug shortages. In order to comprehensively answer our
research questions, we subdivided the areas of literature into two distinct and complementary sections: end-to-end supply chain visibility and predicting drug demand. The first section aims to provide a holistic perspective on the medicine journey and the second addresses the ability to anticipate future needs through prediction of the demand for medicines.

# 2 Research methodology

The development of our systematic review on drug shortages in the pharmaceutical supply chain followed a methodical and transparent approach, consistent with the guidance of Kitchenham and other leading experts in the field. Following these guidelines [24, 25], our process began with a careful exploration of various sources, encompassing scholarly journals and relevant sites, thereby facilitating accurate classification of articles based on the different causes of the shortage of medicines in the context of the pharmaceutical supply chain. The fundamental objective of our review was to present a clear, systematic and accessible analysis of articles, resulting from multiple repeated reviews. This helped to accurately define issues, identify opportunities to replicate studies, and highlight current gaps in research related to drug shortages within the pharmaceutical supply chain.

## 2.1 Search Strings

In the pursuit of identifying a relevant article for our research, a thorough examination of multiple sources was conducted. Among the prominent platforms explored were ACM Digital Library, IEEE, Science Direct, Elsevier, and Springer, each renowned for its wealth of scholarly content. These repositories were chosen due to their credibility and extensive coverage within the academic community. Furthermore, the exploration extended to the vast expanse of Google Scholar, a dynamic web source that facilitates a comprehensive and inclusive search for scholarly articles across various disciplines. Leveraging the capabilities of Google Scholar allowed for a broader scope, ensuring a more exhaustive survey of available literature. To enhance the precision of the search, a meticulously curated list of keywords and strings was compiled, as detailed in Table 1. These strategic terms served as navigational guides in the sea of information, steering the search towards articles directly aligned with our research objectives. The combined utilization of these reputable databases and web resources, coupled with a strategic keyword approach, lays a robust foundation for acquiring high-quality and pertinent literature in our quest for knowledge and insights.

## 2.2 Selection Criteria

In the meticulous pursuit of identifying articles that best align with the objectives of our review in the context of drug shortages and the pharmaceutical supply chain, a set of discerning selection criteria, as detailed in Table 2, was systematically applied. These criteria function as a refined filter, enabling us to sift through a vast array of literature and pinpoint the most pertinent and impactful contributions to our research.
<table><caption>**Table 1** Research Sources and Keywords</caption><tr><td>Sources</td><td>ACM Digital Library, IEEE, Elsevier, Science Direct, Springer, ResearchGate, Google Scholar</td></tr><tr><td>Keywords</td><td>Drug shortage, medication shortage, supply chain, pharmaceutical, healthcare industry, traceability, visibility, tracking, tracing, Blockchain, Internet of Things, Machine Learning, Artificial Intelligence, demand forecasting, demand prediction</td></tr><tr><td>Search Strings</td><td>("drug shortage" AND ("supply chain" OR "pharmaceutical" OR "healthcare industry") OR ("medication shortage" AND ("supply chain" OR "pharmaceutical" OR "healthcare industry") AND (("supply chain" AND ("pharmaceutical" OR "healthcare industry") AND ("visibility" OR "traceability" OR "tracking" OR "tracing") AND (("Internet Of Things" OR "IoT") OR "Blockchain" OR "("Machine learning" OR "Artificial Intelligence"))) AND (("demand forecasting" AND ("pharmaceutical" OR "healthcare industry") OR ("demand prediction" AND ("pharmaceutical" OR "healthcare industry")))</td></tr></table>

The selection criteria are strategically designed to ensure that the chosen articles precisely meet the specific requirements and focus areas of our investigation into the intersection of drug shortages and the pharmaceutical supply chain. Key factors include the publication date, relevance to contexts of drug shortages, a strong emphasis on visibility applications, and demand forecasting with our thematic keywords. This thoughtful approach aims to curate a collection of articles that not only provide depth and insight but also resonate with the core themes guiding our research on managing drug shortages through pharmaceutical supply chain dynamics.

Table 2 encapsulates these pivotal benchmarks, underscoring our commitment to a systematic and rigorous selection process. By adhering to these criteria, our goal is to produce a final compilation that significantly contributes to advancing our understanding of the intricate relationship between visibility of pharmaceutical supply chain and demand forecasting in the context of mitigating drug shortages.

## 2.3 Process Workflow

The development of the literature review involved the selection of pertinent papers, the application of inclusion or exclusion criteria, and a comprehensive review (Fig. 1). Throughout this procedure, a total of 158 articles were taken into account, with 57 papers undergoing detailed examination related to the keywords outlined in Table 1. Following the selection process, a meticulous review of the 59 articles was conducted to extract detailed information for our investigation. Among these, we carefully curated the 24 studies and contributions focusing on traceability and demand forecasting in the pharmaceutical supply chain, leveraging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI). Based on these works, we introduced
<table><caption>**Table 2** Inclusion or exclusion criteria</caption><thead><tr><td>Inclusion</td><td>Exclusion</td></tr></thead><tbody><tr><td><ul><li>Articles reviewed in journals, conference proceedings, and articles published in reputed journals.</li><li>Works authored in English and published in prominent journals.</li><li>Published between 2018 and 2023 as most of the pharmaceutical supply chain visibility applications via Blockchain and demand forecasting via AI articles were published after 2018.</li><li>Selected articles concentrate on traceability within the pharmaceutical industry's supply chain and the prediction of demand.</li></ul></td><td><ul><li>Posters, prefaces, abstracts, book reviews, editorials, correspondence from readers, panels, and conference materials.</li><li>Articles written in languages other than English.</li><li>Not pertinent to the specified area or theme of interest.</li></ul></td></tr></tbody></table>

innovative methodologies and solutions to address challenges related to drug shortages through the adoption of these emerging technologies. Ultimately, guidelines were formulated to guide future research in this direction.

# 3 End-to-end supply chain visibility

## 3.1 Background

### 3.1.1 Definition

End-to-end supply chain visibility from the capability perspective is defined by Ivanov and al. [26] as "the ability to represent a physical supply chain in a digital space with all relevant data that can be collected, processed, updated and accessed in real-time to support planning, monitoring, and control decision-making".

In the pharmaceutical supply chain, end-to-end visibility represents the strategic ability to comprehensively monitor and control the journey of pharmaceutical products from manufacturing to distribution. This approach involves the implementation of a sophisticated visibility system that makes it possible to carefully monitor each pharmaceutical product throughout its life cycle. End-to-end visibility also includes proactive management of production, storage, transportation and distribution conditions, ensuring compliance with pharmaceutical quality and safety standards. It provides a real-time understanding of inventory levels, supporting agile planning and rapid response to fluctuations in demand or emergency situations. Additionally, this approach aims to ensure rigorous compliance with pharmaceutical regulations by providing verifiable product history data. Fundamentally, end-to-end visibility in the
<figure><img src="image_1.png" /><figcaption>Fig. 1 Workflow of the search strategy</figcaption></figure>

pharmaceutical supply chain is essential to ensuring drug integrity, patient safety and
effective management in a regulated and dynamic environment.

3.1.2 Challenge

While the benefits of end-to-end visibility in the pharmaceutical supply chain are
evident, several challenges can impede its seamless implementation. These challenges
include:

*   **Real-Time Monitoring:** The difficulty lies in the inability to instantly obtain monitoring data, given that each piece of information must be collected, recorded and made available by the stockholder information system. This is crucial to enable the various players in the pharmaceutical supply chain to retrieve the necessary data.

*   **Data centralization:** The centralization of pharmaceutical product traceability data represents a significant risk for the availability and reliability of this information in the healthcare chain. The way in which the stakeholders collect and transmit information has a considerable impact on the quality of the data provided by her information system.

*   **Data analyzing:** The difficulty of analyzing data to identify potential hazards or disruptions in the pharmaceutical supply chain can compromise the ability to implement effective contingency plans and mitigate the impacts of disruptions.
To overcome these challenges, the judicious integration of the Internet of Things (IoT), blockchain and artificial intelligence (AI) technologies is emerging as a promising solution. These technological advancements provide the ability to establish real-time monitoring, ensure secure decentralization of data and implement advanced analytics, paving the way for proactive, efficient and resilient management of the pharmaceutical supply chain.

### 3.1.3 The importance of IoT integration to address Real-Time monitoring

According to [27–29], the Internet of Things is a "paradigm where everyday objects can be equipped with identifying, sensing, networking and processing capabilities that will allow them to communicate with one another and with other devices and services over the Internet to accomplish some objective. Ultimately, IoT devices will be ubiquitous, context-aware and will enable ambient intelligence".

Using IoT in the supply chain allows stakeholders to collect and use data to improve inventory management, anomaly detection, visibility and incident response. These capabilities pave the way for using machine learning models to create advanced, responsive supply management solutions that predict bottlenecks, save time and money, and accelerate incident response. Therefore, the Internet of Things can offer evolution in the supply chain context.

**How Can IoT Improve the Supply Chain?**

* **Real-time visibility:** IoT devices enable real-time monitoring of the location, status, temperature, humidity and other parameters of goods throughout the supply chain. This provides complete and immediate visibility into operations.

* **Chain monitoring:** IoT devices, like RFID chips, make it easier to accurately monitor products throughout the supply chain. This is particularly crucial in industries requiring strict monitoring, such as food and pharmaceuticals.

* **Inventory Optimization:** Inventory tracking sensors enable more accurate management of stock levels. This helps avoid shortages or surpluses, thereby reducing costs associated with storage and stock-outs.

* **Data-Driven Decision Making:** The data collected by IoT sensors provides a solid foundation for strategic decision-making. Stakeholders can make informed decisions based on real data rather than estimates.

### 3.1.4 Blockchain Technology to address Data decentralization

Blockchain is defined as a distributed ledger technology secured by cryptographic mechanisms where transactions are added chronologically to interconnected blocks. These blocks form an immutable chain, ensuring data integrity and transparency. Satoshi Nakamoto [30], the creator of Bitcoin, introduced blockchain as a decentralized database to support cryptocurrency. Blockchain is a distributed ledger technology where data is stored securely and transparently. It uses decentralization, cryptography and consensus validation to create an immutable ledger of transactions.

In reality, full exploitation of the blockchain can only be achieved through smart contracts. Smart contracts are autonomous programs that run automatically when
they meet predefined conditions [31]. On blockchain, they are often used to automate
contractual processes, eliminating the need for intermediaries. Ethereum is recognized
as the leading smart contract execution platform [32]. It was specifically designed to
support the creation and execution of smart contracts through its blockchain and ded-
icated programming language called Solidity [33]. Since its launch in 2015, Ethereum
has played a central role in the development and adoption of smart contract technology.

Subsequently, the integration of decentralized file systems such as IPFS (Inter-Planetary File System) was explored. is not directly an integral part of blockchain but rather a peer-to-peer communications protocol and distributed file system [34]. However, it is often used in conjunction with blockchain-based applications to provide decentralized storage and file distribution. IPFS uses a hashing system to identify content, making file storage and sharing more resilient and decentralized [34]. On the blockchain, IPFS is sometimes used to store data off the main chain, thereby reducing the load on the blockchain itself while maintaining secure and full access to the stored information.

Conventional supply chains typically follow a centralized structure, connecting stakeholders through the use of cloud and database storage to oversee the delivery process. In this context, an administrator acts as an intermediary between these different actors. However, this system has major vulnerabilities. The administrator is solely responsible for tracking products and storing modifiable information, thereby exposing this data to the risk of falsification without notification to other stakeholders. This centralized dependency creates a lack of trust among stakeholders, putting the integrity of product information at risk.

Then, blockchain has colossal potential to revolutionize all phases of the supply chain, from the acquisition of raw materials to distribution to consumers [35].

What is the contribution of blockchain to the supply chain?

* **Improved traceability:** Blockchain provides transparent and immutable traceability at every stage of the supply chain. Transactions are recorded chronologically and securely, making it possible to track the origin and journey of the products.

* **Increased transparency:** The decentralized nature of blockchain makes the entire procurement process more transparent. Stakeholders can access real-time information, fostering trust and collaboration.

* **Data security:** Data stored on the blockchain is encrypted and secure, reducing the risk of tampering or manipulation. This strengthens information security throughout the supply chain.

* **Contract automation:** Smart contracts on the blockchain enable the automation of many contractual processes. This includes managing payments, deliveries and other transactions, reducing administrative time and costs.

* **Statistical information:** Data once saved cannot be modified. This creates an immutable history of transactions, ensuring the integrity of statistical data over time. Users can trust that past statistics have not been altered.
### 3.1.5 Intelligence Artificial to address Data analyzing

Big data analytics, also known as Big Data, involves the processing and interpretation of massive volumes of data from various sources. AI, and particularly machine learning, offers unique capabilities to extract meaningful insights from these complex data sets [36]. Machine learning algorithms enable AI to learn from this data, identify patterns, make predictions, and provide valuable insights. This facilitates data-driven decision-making, optimizes operational processes and opens new perspectives in various areas, including the supply chain. The supply chain, through its complexity and dynamics, benefits considerably from the integration of artificial intelligence, increasing its efficiency, transparency and responsiveness.

**How might the supply chain benefit from the artificial intelligence?**

* **Predictive Analytics:** AI can process and analyze massive streams of data from sensors, transactions and other sources in real time, providing immediate visibility into supply chain operations.

* **Identification of Anomalies:** AI algorithms can quickly detect anomalies in data, whether they are unusual behaviors in the supply chain or signals of potential problems.

* **Continuous Oversight:** Real-time monitoring by AI algorithms utilizing IoT sensor data, provides swift analysis of environmental conditions, product quality, and safety in the ever-evolving supply chain landscape.

Continuing from this section, we have reviewed various studies on the implementation of visibility in the pharmaceutical supply chain. These studies explored different approaches, technologies and methodologies aimed at increasing visibility.

## 3.2 Related Works

### 3.2.1 Blockchain

Major research encounters show to eliminate the drug counterfeiting issue. Blockchain technology is the solution to this issue because it ensures the features of traceability, security and transparency in the pharmaceutical supply chain. In this context, in [37], authors developed a system to ensure the traceability of medication in the supply chain with blockchain and QR code encryption. The manufacturer creates an encrypted QR code for each medication and attaches the transaction to the blockchain. When participants seek drug details, they share their public key with the manufacturer. The manufacturer then encrypts the QR code and sends it back to the participant. The valid participant decrypts the QR code using their private key for access. Throughout the transaction process, participants will include the sender's public key and digital signature, as well as the receiver's public key and the information sent by the sender. The shared information is encrypted in QR code format and accessible only through the receiver's public key. Subsequently, the sender's public key undergoes verification by all participants in the medical supply chain. Upon successful verification, the committed transaction is distributed to all participants involved in the chain. In [38], the authors presented Drugledger, blockchain-based system that ensures drug traceability and regulation throughout the drug supply chain. Drugledger redesigns the service
architecture by dividing the service provider into three distinct entities: the certificate
service provider (CSP), the query service provider (QSP), and the anti-attack service
provider (ASP). In CSP, the public keys and roles of Drugledger participants are vali-
dated and stored in certificates. This service could be the drug supply chain regulator
or some other pertinent entity. Then, each node within the system has the capabil-
ity to function as a QSP, giving stakeholders within the same drug supply chain the
flexibility to register with one or more preferred QSPs for collaboration. This enables
the QSP to establish correlations between stakeholders’ public keys and their real-
world physical identities, facilitating the provision of traceability services for specific
drug packages. Also, Drugledger sets the role of an ASP to oversee unusual activi-
ties within the chain, ensuring its proper integrity and security. Drugledger finalizes
its workflow by leveraging the extended Unspent Transaction Output (UTXO) data
structure, with a specific focus on package, repackage, and unpackage transactions.
The utilization of drug expiration dates plays a crucial role in pruning blockchain stor-
age, resulting in a stable and acceptable level of storage efficiency. The authors in [39]
developed a mobile application that allows doctors, patients, and other participants
to confirm the authenticity of medications. If the medicine is genuine, the applica-
tion displays its history; otherwise no recordings will be displayed. On the backend,
there’s a blockchain that stores all necessary transactions. When a factory produces
a new product, a unique hash is generated and assigned to the product. The prod-
uct is then recorded on the blockchain using its hash (unique ID), establishing it as
a digital asset within the blockchain network. The hash serves as a means to track
the product across the network at any time. Once the manufacturer registers the
product on the blockchain, ownership can easily be transferred to another participant
through a mobile app. For instance, if a wholesaler wishes to purchase drugs from the
manufacturer, the physical transfer of the drugs occurs, and a corresponding trans-
fer transaction is simultaneously recorded on the blockchain. The wholesaler repeats
this process when transferring the drugs to a distributor, and the distributor does the
same when engaging with a pharmacy.

Blockchain technology, with all its innovative potential, reaches its zenith through
smart contracts. These are nothing short of sophisticated algorithmic codes capable
of autonomously operating within the blockchain. They function as programmable
agreements that automatically execute when certain predefined conditions are met.
This automation provides increased transparency, eliminates the need for interme-
diaries, and ensures precise and immediate transaction execution. This is the case
in [40], where the authors presented Medledger, a blockchain system aiming to con-
trol the drug supply chain and reduce counterfeit drugs. The authors chose to use
Hyperledger Fabric, a secure and transparent Byzantine-fault-tolerant (BFT) consen-
sus algorithm for ensuring secure and reliable communication. It consists of three
main components: Certificate Authority (manages certifications), Peer (Endorser node
or Committer node to endorse or commit a transaction proposal submitted to the
blockchain ledger network) and Orderer (provides the order of transaction proposals on
the blockchain in the form of blocks). The Medledger system leverages the Hyperledger
Fabric blockchain platform using chaincodes (smart contracts). These chaincodes are
used to register the drugs, their ingredients, their distribution, and tracking information. So, Medledger are created three chaincodes: Drug Registration Contract (DRC) are deployed between supplier and manufacturer to register a newly produced drug and its active ingredients, Consignment Accumulation Contract (CAC) was created and executed to add all related consignment information about the drug in the system and Transaction Update Contract (TUC) was deployed automatically when new information was added to the CAC and DRC contracts. Finally, the patient or any other stakeholder has access to view, trace, and track the desired information about any medicine from the Medledger using the distribution application (DApp) using a unique drug code and consignment number.

In [41], the authors developed an Ethereum blockchain system to ensure the efficient traceability of drugs. A smart contract is created for each drug to record all actions on the ledger, thus ensuring transparency, accountability, security, and data provenance. The authors use a decentralized storage system (IPFS) based on off-chain storage to store large-scale transaction data. On-chain resources are employed to track and trace activities, store events, and upkeep logs generated by smart contracts. The stakeholders interact with smart contracts and access the decentralized storage system and on-chain resources through software applications (DApps). The authors in [42] proposed a cloud-based blockchain system using Hyperledger Fabric. Indeed, the objective is to accelerate pharmaceutical supply chain safety by monitoring and tracing the process. This system comprises the identification of activities through tagging, information sharing within cloud-based storage utilizing an off-chain IPFS and an on-chain CouchDB and access to this information is regulated by the system's administrator. The system aids in detecting counterfeit and cross-border products by verifying information related to the manufacturer and the country of origin.

### 3.2.2 Blockchain, IoT and IA

The integration of IoT sensors into the supply chain represents a significant advancement that enhances the real-time traceability and visibility of drugs. With strategically positioned IoT sensors, it becomes possible to monitor and record crucial parameters in real time during their delivery. This is the case in [43], a designed NFT-IoT Pharma Chain solution to increase drug traceability and security, an IoT-based pharmaceutical supply chain that integrates blockchain and Non Fungible Tokens (NFT). The process involves various participants such as the manufacturer, distributor, hospital pharmacy and patients, as well as various active elements such as sensors (humidity, temperature and GPS). The architecture is structured in four layers, offering drug traceability services to various stakeholders. More specifically, an IoT system is integrated with sensors connected to the blockchain. These sensors monitor the temperature, humidity and location of the medication batch. A network layer facilitates communication between the IoT system and the blockchain platform. Then, the blockchain platform is based on NFT and IoT sensors to ensure a transparent and traceable system. Indeed, smart contracts verify the access of users or sensors in the system by checking their cryptographic keys. The transactions of the tokenized drug lot with the actual token's owner are stored in the blockchain ledger, while the license and actual data are stored in IPFS. At each step of delivery, the new participant owns the token. Finally, the
application layer includes a web application with various services allowing you to track and/or update a batch of medications. The manufacturer can create drugs and batches, select a distributor for shipping, while the distributor registers their vehicles and upload a certificate of conformity to validate the integrated IoT system. Contrary to [44], the authors develop a five-layer platform based on Hyperledger Fabric blockchain and IoT. The perception layer consists of collecting data throughout the supply chain. It is based on IoT gateways such as QR codes, RFID, GPS, etc. Off-chain layer based on blockchain gateways, that have components for device management, data validation and data cache. The input data for the blockchain gateway comes from IoT assets and enterprise information systems. The blockchain layer contains consensus, smart contracts, cryptography and a peer-to-peer (P2P) network. The application layer includes four services (user identity management service, quality regulation service, product traceability and visibility service, risk analytics and smart alert service) and a tracking and tracing explorer provides for different users to research the pertinent drug data. Finally, the user layer includes drug producers, drug logistics providers, pharmacists, and consumers. In [45], the authors provide an architecture of a software system for tracking packages during transit that provides real-time information such as temperature, light, humidity, etc., using a sensor attached to the package and the Solana blockchain. This blockchain was selected from a comparative study of several widely used types of blockchain and it was selected mainly because of its speed and the cost of transactions. Another tracking and tracing system is described in [46] for the COVID-19 vaccinations, using the Ethereum blockchain and IoT sensors. A QR code is generated for each vaccine and for each person who registers to receive the vaccine. During transportation, the vaccine's temperature is monitored to ensure that it stays within the temperature range specified by the smart contract. Any beneficiary who has received a vaccine may report any problems or symptoms encountered. Before the vaccination, the patient must verify the vaccine's condition to ensure that it is correctly transported.

Some aspects of the project have expanded beyond ensuring the traceability of drugs in the supply chain to incorporate a machine learning module that recommends the most suitable drug for the user. According to [47], a blockchain system based on Hyperledger and machine learning is designed for drug supply chain management and recommendation. This proposed system empowers end users, including suppliers, distributors, pharmacies, doctors, manufacturers, and patients, to engage in various transactions and activities through web applications. User validation and enrollment occur through a user administrator, who provides them with confidential credentials and certificates of enrollment. Subsequently, users gain access to the secure blockchain network. A consensus algorithm facilitates the connection of enrolled users to the private network, where they can execute transactions and manage transaction orders. Each peer node within this blockchain network possesses a smart contract and a distributed ledger in simulated environments, enabling them to store data and execute transactions. Smart contracts empower these nodes to validate transactions post-execution and record the transaction blocks in the ledger. The consensus algorithm ensures the maintenance of a singular transaction history and safeguards against the inclusion of invalid transactions. Patients have the ability to verify the authenticity of
drugs before purchase by scanning the barcode on the medicine packet. Additionally, the system incorporates a dataset comprising public comments, reviews, and ratings from various pharmaceutical websites about medicines, whether positive or negative. Prediction algorithms, such as N-gram and LightGBM, are trained on this dataset to recommend the best medications to consumers within the system. In [48], the authors proposed a system based on blockchain and artificial intelligence that ensures the secure distribution of medical drugs. During the process, each node has the capability to possess attributes related to both demand and supply. To facilitate the formation of product transactions, an event response mechanism is devised to ensure mutual agreement between the transaction parties regarding the receipt and delivery of medical drugs. The transaction data on then securely stored in the Ethereum blockchain through the implementation of smart contracts. Additionally, a mobile application is developed and seamlessly integrated with the Rasa chatbot to enhance customer service functionalities (place orders, track their orders, find the source of their order). Thus, pharmacies and customers can scan the ID (address) of the purchased drugs to verify their authenticity and view their supply chain transaction history, which includes the source of even the component raw materials used in their production.

### 3.2.3 Discussions

The literature summary that is presented in Table 1 highlights that there are various works in the literature that have studied drug supply chain visibility to enhance drug counterfeiting problems, but none of them have studied drug shortage objectives.

The literature suggests a rising trend in the healthcare sector: the adoption of blockchain for drug traceability. This approach aims to establish decentralized, secure, and transparent track-and-trace systems to combat the proliferation of counterfeit drugs. However, a significant portion of the literature treats this issue primarily as a proof of concept, lacking a concrete framework or approach for implementing blockchain to address the challenge. The literature reveals that the use of blockchain in drug traceability meets a crucial need: facilitating the secure sharing of data between the different players in the pharmaceutical supply chain [37–48]. This approach aims to improve communication and collaboration between the stakeholders involved, from production to distribution to points of sale. Integrating smart contracts into the blockchain framework ensures that processes are not only transparent and traceable but also automated and enforceable. This not only reduces the administrative burden on participants but also enhances the overall reliability and integrity of the pharmaceutical supply chain. As the industry moves towards greater automation and digitization, the role of smart contracts within blockchain solutions becomes increasingly critical for ensuring a seamless, secure, and trustworthy supply chain ecosystem [40, 41, 43].

Furthermore, the integration of IoT and blockchain not only enhances the traceability of pharmaceutical products but also facilitates a more comprehensive verification process. With real-time updates on the movement and conditions of drugs, stakeholders can confidently verify the authenticity of products at any point in the supply chain, as highlighted in studies [44, 45]. Blockchain and IoT technologies address a critical need in the pharmaceutical supply chain, the ability to not only trace the movement of products but also to gather and securely store real-time data on their environmental
<table><caption>**Table 3** Overview of Pharmaceutical Visibility Strategies</caption><thead><tr><td>Authors / Year</td><td>Objectives</td><td>Solutions</td><td>Technologies</td><td>Limitations</td></tr></thead><tbody><tr><td>[37] 2019</td><td>Traceability and security medication to eliminate the drug counterfeiting.</td><td>Blockchain-based system and QR code security for medication track and security. Public keys and digital signatures for stakeholders authenticity and autorisation to access the supply chain.</td><td>Blockchain</td><td>Contact automation/ Real-Time visibility/ Chain traceability/ Data-driven decision making/ Predictive analytics/ Identification of anomalies</td></tr><tr><td>[38] 2018</td><td>Ensures the authenticity and privacy of traceability data. The drug track and trace physical workflow (Drug package, repackage, repackage)</td><td>An architecture that separates the service provider into three parts: CSP, QSP and ASP. UTXO transaction model to execute the system's workflow.</td><td>Blockchain</td><td>Contact automation/ Real-Time visibility/ Chain traceability/ Data-driven decision making/ Predictive analytics/ Identification of anomalies</td></tr><tr><td>[39] 2018</td><td>Drug authenticity and security</td><td>Product registered on the blockchain using unique ID. Mobile app for doctor, patients to verify drug authenticity</td><td>Blockchain</td><td>Contact automation/ Real-Time visibility/ Chain traceability/ Data-driven decision making/ Predictive analytics/ Identification of anomalies</td></tr><tr><td>[40] 2021</td><td>Efficient and transparency drug traceability. Delete the requirement of a centralized authority.</td><td>Track and trace system based on Hyperledger Fabric blockchain platform using chaincodes (smart contracts).</td><td>Blockchain, Smart Contract</td><td>Real-Time visibility/ Chain traceability/ Data-driven decision making/ Predictive analytics/ Identification of anomalies</td></tr><tr><td>[41] 2021</td><td>Traceability and data security of medicines</td><td>Ethereum blockchain-based solution. Smart contracts used assuring transparency, security, and data provenance. IPFS-based off-chain storage to store large-scale content.</td><td>Blockchain, Smart Contract</td><td>Real-Time visibility/ Chain traceability/ Data-driven decision making/ Predictive analytics/ Identification of anomalies</td></tr><tr><td>[42] 2022</td><td>Pharma-material traceability, data storage and privacy</td><td>Cloud-based Blockchain-powered system. Cloud storage of-chain and on-chain</td><td>Blockchain, Smart Contract</td><td>Real-Time visibility/ Chain traceability/ Data-driven decision making/ Predictive analytics/ Identification of anomalies</td></tr></tbody></table>
<table><caption>**Table 4** Overview of Pharmaceutical Visibility Strategies</caption><thead><tr><td>Authors / Year</td><td>Objectives</td><td>Solutions</td><td>Technologies</td><td>Limitations</td></tr></thead><tbody><tr><td>[43] 2023</td><td>Drug supply chain traceability. Real-time drug control</td><td>Blockchain system based on Non Fungible Tokens. Sensors are used to collect temperature, humidity and position of the drug lot during shipment.</td><td>Blockchain, Smart Contract, IoT</td><td>Predictive analytics/ Identification of anomalies</td></tr><tr><td>[44] 2021</td><td>Traceability and visibility drug supply chain. Drug identity management</td><td>QR codes and RFID sensors to manage drug identity. Hyperledger Fabric system to traceability and visibility drug.</td><td>Blockchain, Smart Contract, IoT</td><td>Predictive analytics/ Identification of anomalies</td></tr><tr><td>[45] 2023</td><td>Real-time visialisation. Real-time data management. Package Traceability</td><td>Solution based on IoT sensors attached to the package and solana blockchain</td><td>Blockchain, Smart Contract, IoT</td><td>Predictive analytics/ Identification of anomalies</td></tr><tr><td>[46] 2021</td><td>Monitoring, surveillance of distribution chain of COVID-19 vaccinations</td><td>Ethereum blockchain-based method for tracking. IoT sensors to control a temperature during the distribution vaccinations</td><td>Blockchain, Smart Contract, IoT</td><td>Predictive analytics/ Identification of anomalies</td></tr><tr><td>[47] 2020</td><td>Management and authenticity drugs. Drugs recommendation</td><td>Hyperledger Blockchain-based system to verify authenticity drugs. Prediction algorithm (N-Gram and Light-Gram model) to recommend top-rated drugs</td><td>Blockchain, Smart Contract, Machine learning</td><td>Real-Time visibility/ Chain traceability/ Data-driven decision making</td></tr><tr><td>[48] 2021</td><td>Drug Traceability and source verification. Customer service</td><td>Drug traceability based on Ethereum blockchain and smart contracts. Chatbot platform based on artificial intelligence to enhance customer service.</td><td>Blockchain, Smart Contract, IA</td><td>Real-Time visibility/ Chain traceability/ Data-driven decision making</td></tr></tbody></table>
conditions. For instance, temperature-sensitive medications require stringent control of storage conditions [43, 46]. IoT devices can continuously monitor and transmit data about the temperature of these products, ensuring that they are stored within the specified range to maintain their efficacy.

However, [47] have adopted artificial intelligence (AI) algorithms for recommending the most suitable medications for patients. This use of AI makes it possible to analyze large sets of medical data and provide personalized recommendations based on the specific needs of each individual. Indeed, this approach can significantly improve the accuracy of medical prescriptions, thus providing more individualized care. In the work of [48], an integration of chatbots based on AI is implemented. These conversational agents are deployed to facilitate interactions and provide assistance to the different actors in the chain. Chatbots can quickly answer common questions, provide real-time updates on shipment statuses, and even guide users through complex supply chain procedures.

The integration of blockchain ensuring end-to-end visibility, IoT enabling real-time data collection on product conditions, and AI providing advanced analytics creates a comprehensive pharmaceutical supply chain system. This system not only addresses drug traceability but also proactively manages drug shortages, enhances product authenticity, prioritizes patient safety through optimal storage conditions, and facilitates efficient recall management. With an emphasis on continuous improvement, these technologies collectively empower stakeholders to navigate challenges, optimize processes, and ensure the delivery of genuine, high-quality medications in a responsive and technologically advanced supply chain ecosystem.

# 4 Drug Demand Forecast

## 4.1 Background

### 4.1.1 Definition

The pharmaceutical supply chain focuses on a variety of pharmaceutical products, healthcare services, and crucial information from the initial production or sourcing of pharmaceuticals to their delivery to end users (such as pharmacies, hospitals, and ultimately, the patients who rely on these vital medications) through a chain of entities and activities that are connected to one another[49]. In the pharmaceutical supply chain, typical problems often assume that key factors are capacity, demand, and cost[50]. However, the reality is more complex, given the inherent uncertainties stemming from fluctuations in customer demand, challenges in transporting supplies, organizational risks, and unpredictable lead times. The pharmaceutical supply chain must grapple with these uncertainties and dynamically adapt to the ever-changing landscape to ensure a reliable and responsive flow of essential medications[51]. In this context, utilizing demand forecasting stands out as a crucial strategy for managing uncertainties within supply chains[52, 54]. In fact, the term 'demand forecast' denotes an anticipation of future demand from customers for a particular product[60]. However, accurate demand forecasting is a fundamental element to ensure sufficient supply
of drugs. It helps to minimize costs related to stock-outs or shortages, improve opera-
tional efficiency, ensure a constant supply of essential medicines, and respond agilely to
fluctuations in demand. Ultimately, accurate forecasting helps ensure continuous and
reliable access to medications for patients while maximizing the efficiency of pharmacy
operations.

4.1.2 Challenge

Demand forecasts are usually generated by extrapolating historical sales data. Such problems exist even when a long history of demand data is available because historical data from the distant past may have little value as the supply chain situation changes. Concurrently, demand is shaped by a multitude of concealed factors, requiring an abundance of data and more advanced models to achieve precise capture.

What essential components are necessary for creating an advanced forecast?

The drug demand forecasting challenge requires an adequate combination of reli-
able data and advanced analytical approaches to obtain quality predictions. The
reliability of the data is of crucial importance, as it serves as the foundation of the
entire forecasting process. Accurate data on past sales, prescription trends, and other
key settings is essential to understanding demand patterns, as shown in Figure 1. More
thorough descriptions of the factors contributing to accurate demand forecasting in
the pharmaceutical supply chain are provided below:

<figure><img src="image_2.png" /><figcaption>**Fig. 2** The essential settings for increasing forecast accuracy</figcaption></figure>

* **Collaboration with stakeholders:** Collaboration with stakeholders represents an essential element for accuracy in forecasting drug demand within the pharmaceutical supply chain. By fostering close partnerships with healthcare professionals, pharmacists and other integral stakeholders, the supply chain benefits from invaluable access to a wealth of accurate, real-time information. This collaborative exchange
not only improves the depth and breadth of available data but also creates a feedback
loop in which field observations can inform and refine forecast models. Additionally,
involving stakeholders in the forecasting process instills a sense of shared responsi-
bility, more closely aligning the supply chain with the evolving needs of healthcare
providers and patients.

* **Prescription data:** By analyzing prescription patterns, healthcare professionals, pharmacists, and other stakeholders can gain insights into the ebb and flow of medication requirements. Understanding the trends in prescription volumes, seasonal variations, and demographic considerations enables a more accurate forecast of future demand for specific medications. This information not only aids in predicting overall demand but also allows for targeted forecasting based on therapeutic categories, helping to align the supply chain with the evolving needs of patients. Moreover, prescription data provides a nuanced understanding of medication preferences, allowing for the identification of critical drugs that may be prone to shortages. As technology continues to advance, the integration of prescription data into sophisticated predictive analytics models holds the promise of refining demand forecasts and mitigating the challenges posed by drug shortages.

* **Medical Events:** Medical events, encompassing epidemics, disease outbreaks, and groundbreaking medical discoveries, wield significant influence over the demand for specific medications. Epidemics and outbreaks, such as the COVID-19 pandemic, can trigger sudden and heightened demands for treatments related to the prevalent health crisis. These events, characterized by their unpredictability and urgency, underscore the critical need for a flexible and adaptive pharmaceutical supply chain. Stakeholders must be poised to respond swiftly to the evolving landscape of healthcare demands triggered by such medical events. As technology and research progress, leveraging real-time data and advanced analytics becomes increasingly vital in anticipating, preparing for, and meeting the dynamic demands imposed by medical breakthroughs and health crises.

* **Historical data:** Thorough analysis of past trends unveils valuable insights into seasonal patterns, fluctuations, and overarching demand trends, forming a historical context for forecasting models.

* **Seasonality:** Recognizing the seasonality of drug demand is crucial, as certain medications may experience heightened demand during specific times of the year due to distinct weather conditions or seasonal factors.

Furthermore, forecasting in an uncertain and complex context requires a robust scenario analysis tool capable of responding to the challenges posed by uncertain parameters. Integrating advanced techniques such as artificial intelligence and machine learning becomes imperative to strengthen analysis and facilitate decision-making when predicting requirements [53]. These technologies enable dynamic adaptation to changes, providing a more agile approach to anticipate and respond to variations in demand in the pharmaceutical supply chain. Next, we concentrate on research that clarifies the approaches, tools, and techniques utilized to predict the demand for pharmaceuticals.
## 4.2 Related Works

### 4.2.1 Historical data

In this context, [54] proposed an intelligent pharmaceutical demand forecasting algorithm with the aim of improving forecast accuracy. The main objective of this work is to improve data pre-processing to enhance model output quality and accuracy and to advocate for the incorporation of decision-making and selection techniques to identify the best network structure. The proposed approach integrates clustering (K-Means algorithm and the elbow method) and classification (Classification and Regression Trees (CART) method) techniques with neural network (Multi-layer Perceptron (MLP), Radial Basis Function (RBF), and Group Method of Data Handling (GMDH)) methodologies. First, the data are clustered before entering the neural network models. For each cluster, diverse structures are formulated to select the best one. Finally, to execute the forecasting task, the classification technique is employed to determine the cluster to which the given test data belongs. Following this, the optimal structure of that cluster is utilized for accurate forecasting. In [55], the authors created a demand forecasting methodology aimed at assisting pharmaceutical companies in determining the optimal quantity for manufacturing or stocking pharmaceutical products. This methodology is based on ARIMA, shallow neural network (SNN) variants such as GR-NN, RBF-NN, P-NN and Deep Neural Network such as LSTM, and Stacked LSTM models for a time-series sales database. The proposed methodology is composed of five steps: (1) Division data such as splitting the dataset into train, validation and test datasets, (2) Hyperparameter Tuning such as training SNN models with diverse hyperparameter values and the training dataset, and then assessing the performance of these models using the validation dataset, (3) Identification of Best Model such as selecting the lowest RMSE (Root Mean Squared Error) value model and sending parameter values for the best neural network model, (4) Development of the best NN model such as performing simulations and training for the top-performing SNN model utilizing the optimal parameter configuration and assessing the prediction accuracy by using test data and, (5) Forecasting such as predicting the upcoming timestamp values and assessing the accuracy of the forecasting results. In [56], the authors focusing on the supply chain from wholesaler to distribution company in an emerging market, experimentally explore three prediction approaches for demand forecasting: a Simple Moving Average (SMA) model, Multiple Linear Regressions (MLR), and a Symbolic Regression (SR). During this experiment, SR identified the best curve with the lowest margin of error based on historical data and incorporated price as a prediction element. In the study outlined in [57], the authors explore the forecasting of product purchases for a pharmaceutical distributor. The aim was to predict future purchases for each product, addressing the unique demands and uncertainties posed by the pandemic era. The authors delved into the challenges and methodologies associated with data preprocessing, the creation of more comprehensive features, and the nuanced analysis and manipulation of data during the COVID period. Their experimentation encompassed a variety of machine learning and deep learning neural network models, such as the classical Seasonal Autoregressive Integrated Moving Average (SARIMA), Facebook's Prophet, linear regression, Random
Forest, XGBoost, and Long Short-Term Memory (LSTM). In [58], the authors aim to demonstrate the practicality of demand forecasting and capacity management for emergency healthcare services, utilizing advanced operations research tools across three hospitals. Through rigorous testing of various forecasting methods, it was determined that neural networks and support vector regression yielded the most favorable results in terms of variance and accuracy. Building on these forecasting outcomes, a comprehensive logic for hospital capacity management was conceived and implemented. This approach involves a systematic comparison between forecasted demand and available medical resources, complemented by a stochastic simulation model evaluating the performance of different configurations of facilities and resources. In the study presented in [59], the authors utilized four different models—Holt Winters, Ridge Regression, Random Forest, and Xgboost—to elevate forecasting accuracy through an ensemble approach, synergizing the strengths of these models. Notably, the authors introduced novel variables tailored for the pharmaceutical industry, including promotional activities, holiday statements, price fluctuations, stock availability, and date-time features such as weekdays and months. The objective outlined in [61] is to offer a comprehensive overview of three sales time-series forecasting methods: Moving Average, Exponential Smoothing, and Regression Analysis. The paper aims to introduce an approach that guides the selection of the most suitable forecasting method for a given scenario.

### 4.2.2 Historical data and Others

The authors in [62] proposed a forecasting framework that includes three features in training the forecasting model, such as historical demand, downstream inventory information and the supply chain structure information. The proposed framework uses cross-series training, drug grouping schemes for enhanced demand forecasting performance and a Recurrent Neural Network (RNN) machine learning model for its superior performance using domain knowledge and numerical analysis. In [63], the authors introduce a hybrid forecasting model to increase demand forecasting accuracy. In this model, they combined two statistical methods: the Auto-Regressive Integrated Moving Average (ARIMA), which gathers relevant factors from historical data and the Holt's Winter, which includes three components such as level, trend and seasonality to enable the forecasting model. The authors in [64] identify a forecasting method based on data mining for drug consumption and production planning. Numerous variables are employed for predicting drug consumption levels, including the purchasing specifications of the drug (year, season, month, customer ID, company ID, disease, price and area number) and the demographic characteristics of the consumption area (population, men ratio, educated women, educated men, marriage ratio and divorce ratio). Therefore, prediction techniques contain machine learning algorithms like Artificial Neural Network (ANN) and Decision Tree (DT), both classified as data mining techniques. In the research presented in [65], the primary objective is to enhance decision-making quality within the pharmaceutical industry through the application of data mining techniques. The authors specifically employ two classification methods, namely decision trees and neural networks, aiming to elevate accuracy in decision-making processes. The investigation is rooted in the intriguing proposition
that a correlation exists between doctors' prescriptions and pharmacy sales, particu-
larly concerning drug supply. Through meticulous analysis using classification models,
the study delves into uncovering and leveraging this relationship for more informed
and effective decision-making in the pharmaceutical domain. The authors of [66] intro-
duce predictive models tailored for psychiatric drug sales data. Their approach involves
employing seasonal trend decomposition to reveal inherent seasonality and trends in
the data. Subsequently, the study utilizes a range of statistical and machine learning
techniques, including Triple Exponential Smoothing, seasonal ARIMA (SARIMA),
SARIMAX enhanced with the integration of exogenous variables, and Long Short-
Term Memory (LSTM). The paper concludes with a comprehensive comparison of the
accuracy of these prediction models, aiding in the identification of the most effective
and relevant model for forecasting psychiatric drug sales and determining its optimal
parameters.

4.2.3 Discussions

This section highlights the techniques and data used to develop a forecasting model. Table 2 lists the common forecasting demand issues discussed in the related works, along with suggestions for data and techniques that may be used to address them. The majority of studies focus only on internal factors specific to each establishment, such as sales and demand history to develop demand forecasts [55, 56, 58, 61–63]. However, for more accurate demand forecasting, researchers often expand their scope of analysis to include other factors, such as demographic conditions [64], market trends [63, 66], seasonal influences [63, 66]. This holistic approach aims to capture the complexity of the context in which the establishment operates, thus offering a more complete and precise vision of demand dynamics. By integrating these various elements, demand forecasting models can be more resilient and better suited to anticipate future market fluctuations. A notable trend is the increasing use of machine learning algorithms and advanced statistical techniques to refine forecasting models. Machine learning algorithms can analyze large data sets, identify complex patterns, and automatically adjust models based on dynamic market changes [54, 56, 62, 64, 65]. Likewise, statistical algorithms, when applied with more sophisticated methods, provide a deeper understanding of relationships between variables, thereby enabling more accurate predictions [55, 63].

Supply chain visibility stands out as a critical factor in mitigating complexity and inaccuracies, exerting a profound influence on overall supply chain performance. Leveraging the Internet of Things (IoT) can significantly enhance this visibility, offering valuable insights into the movement and conditions of goods. Additionally, the exchange of information between suppliers and manufacturers plays a pivotal role in improving forecast accuracy and aligning production with customer demand. For a supply chain to execute efficiently, it necessitates producers sharing essential information, fostering a collaborative environment that contributes to the effective execution of supply chain processes. Intriguingly, it is noteworthy that, as revealed by the literature review, none of the existing studies incorporate stakeholder collaboration as an integral component in their approach to demand forecasting.
<table><caption>Table 5 Overview of Demand forecasting</caption><thead><tr><td>Authors / Year</td><td>Algorithms Type</td><td>Algorithms</td><td>Utilized data</td><td>Solutions</td><td>Limitations</td></tr></thead><tbody><tr><td>[54] 2020</td><td>Machine Learning</td><td>K-means, CART, MLP, RBF and GMDH</td><td>13 features on products (type, code, production month, age range, target gender, target disease...)</td><td>Integrate clustering and classification method for the data before entered into neural network models to perform the accurate forecasting.</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[55] 2023</td><td>Statistical and neural network</td><td>ARIMA, GR-NN, RBF-NN, P-NN, LSTM, and Stacked LSTM</td><td>Historical sales data</td><td>Methodology for forecast the quantity of pharm products to manufacture or stock.</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[56] 2019</td><td>Machine Learning</td><td>Symbolic regression (SR)</td><td>Historical sales, Price</td><td>Demand forecasting model for supply chain from wholesaler to distribution company.</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[57] 2021</td><td>Machine Learning</td><td>SARIMA, Prophet from Facebook, linear regression, Random Forest, XGBoost and LSTM</td><td>Inventory sale histories and data during the COVID period</td><td>Forecasting future purchases for each product, addressing the unique demands and uncertainties posed by the pandemic covid.</td><td>Collaboration with stakeholders, Prescription data, Seasonality.</td></tr><tr><td>[58] 2021</td><td>Machine Learning</td><td>Neural networks and support vector regression</td><td>Historical data</td><td>Demand forecast for hospital emergency services based on neural networks and support vector regression</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[59] 2022</td><td>Machine Learning</td><td>Holt Winters, Ridge Regression, Random Forest et Xgboost</td><td>Historical sales data, Promos, holiday statements, price changes, stock availability and date-time features</td><td>Introduced novel variables tailored for the pharmaceutical industry to elevate forecasting accuracy</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr></tbody></table>
<table><caption>**Table 6** Overview of Demand forecasting</caption><thead><tr><td>Authors / Year</td><td>Algorithms Type</td><td>Algorithms</td><td>Utilized data</td><td>Solutions</td><td>Limitations</td></tr></thead><tbody><tr><td>[61] 2018</td><td>Statistical methods</td><td>Moving Average, Exponential Smoothing and Simple Regression</td><td>Annual sales</td><td>Three sales time-series forecasting methods: moving average, exponential smoothing, and regression analysis</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[62] 2021</td><td>Machine Learning</td><td>Recurrent Neural Network (RNN)</td><td>Historical demand, downstream inventory and supply chain structure information.</td><td>Forecasting framework includes a cross-series training, groupins schemes and RNN models.</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[63] 2022</td><td>Statistical methods</td><td>ARIMA and Holt's Winter Model</td><td>Historical data, level, trends and seasonality</td><td>Forecasting method combine two statistical method: ARIMA and Holt's Winter.</td><td>Collaboration with stakeholders, Prescription data, Medical Events.</td></tr><tr><td>[64] 2019</td><td>Machine Learning</td><td>ANN and DT</td><td>Drug purchasing specifications and demographic specifications of the consumption area</td><td>Drug forecasting method for the consumption and production planning</td><td>Collaboration with stakeholders, Prescription data, Medical Events, Seasonality.</td></tr><tr><td>[65] 2019</td><td>Machine Learning</td><td>Decision tree and neural network methods</td><td>Doctors Prescription and Pharmaceuticals Sales</td><td>Analyzing the correlation between doctors' prescriptions and pharmacy sales using data mining</td><td>Collaboration with stakeholders, Medical Events, Seasonality.</td></tr><tr><td>[66] 2021</td><td>Statistical and machine learning techniques.</td><td>ETS, ARIMA, SARIMA et LSTM.</td><td>Historical data, seasonality and trends.</td><td>Develop predictive models for psychiatric drug sales by employing seasonal trend</td><td>Collaboration with stakeholders, Prescription data, Medical Events.</td></tr></tbody></table>
# 5 Synthesis

The pharmaceutical supply chain faces intricate challenges necessitating a dynamic and integrated approach for seamless operations. The synthesis of end-to-end visibility and demand forecasting emerges as a strategic solution, offering not only traceability and authenticity but also proactive management of drug shortages. Here's a breakdown of the key components:

* **Blockchain for Secure and Transparent Traceability:**

- Implement blockchain technology to establish decentralized, secure, and transparent track-and-trace systems.

- Ensure the secure sharing of data across the supply chain, fostering transparency and traceability from production to distribution and points of sale.

* **IoT for Real-Time Monitoring and Verification:**

- Integrate IoT devices to monitor and transmit real-time data on drug movement and environmental conditions.

- Address critical needs in the pharmaceutical supply chain by securely storing real-time data on temperature-sensitive medications and ensuring adherence to specified storage conditions.

* **Machine Learning Models for Demand Forecasting:**

- Integrate machine learning models into the demand forecasting process for more accurate predictions.

- Combine clustering and classification techniques to enhance model output quality and accuracy, addressing uncertainties and fluctuations in demand.

* **Collaboration with Stakeholders:**

- Foster collaboration with healthcare professionals, pharmacists, and stakeholders for real-time information.

- Create a collaborative environment to share responsibility, align the supply chain with evolving healthcare needs, and refine forecast models based on field observations.

* **Continuous Improvement and Scenario Analysis:**

- Focus on continuous improvement by regularly updating and refining the integrated system based on feedback and technological advancements.

- Develop a robust scenario analysis tool using AI and machine learning for dynamic adaptation to changes, ensuring an agile approach to variations in demand.

The synthesis of end-to-end visibility and demand forecasting creates a comprehensive pharmaceutical supply chain system. This system not only ensures traceability, authenticity, and proactive management of drug shortages but also optimizes processes, prioritizes patient safety, and navigates challenges in a responsive and technologically advanced ecosystem. Through the seamless integration of these components, stakeholders are empowered to deliver genuine, high-quality medications efficiently.
# 6 Conclusion

Our analysis highlights two promising strategic avenues to proactively counter the drug shortage. On the one hand, establishing complete visibility within the pharmaceutical supply chain requires, leveraging innovative technologies such as blockchain, Internet of Things (IoT) and machine learning. These tools offer opportunities to improve traceability, inventory management and collaboration between stakeholders. On the other hand, our review highlights the critical importance of drug demand forecasting, primarily guided by advanced machine learning models. By integrating these predictive methods into strategic planning, we can anticipate variations in demand, adjust production accordingly and thus prevent possible shortages. These two approaches, although distinct, converge towards an integrated vision to overcome the persistent challenge of medicine shortages. However, it is imperative to emphasize that the successful implementation of these solutions will require close collaboration between pharmaceutical industry stakeholders, policymakers and technology experts. By embracing these advances with commitment, we can envision a future where drug shortages become a rarity of the past.

# 7 Author Contributions

"Not applicable"

# 8 Funding

"Not applicable"

# 9 Data Availability

"Not applicable"

# 10 Conflict of interest

"Not applicable"

# 11 Human Participants and/or Animals

"Not applicable"

# 12 Ethical approval

"Not applicable"

# 13 Acknowledgment

"Not applicable"
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