Chloe Wanert

Fedoua El Louali

Sarab Al Dybiat

Karine Nguyen

Stéphane Zaffran

Caroline Ovaert

C Wanert

F El Louali

Dr Chloé Wanert

Genetic profile and genotype-phenotype correlations in childhood cardiomyopathy

Keywords: cardiomyopathy, sarcomeric cardiomyopathy, genetic, child

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Cardiomyopathy (CM) is a rare disease in childhood. Incidence is estimated to be 1 case per 100 000 person-years in children < 18 [START_REF] Lee | Pediatric Cardiomyopathies[END_REF][START_REF] Nugent | The Epidemiology of Childhood Cardiomyopathy in Australia[END_REF][START_REF] Cox | Factors Associated With Establishing a Causal Diagnosis for Children With Cardiomyopathy[END_REF]. Morbidity and mortality remain significant with over 40% children dying or requiring heart transplant within 5 years of diagnosis [START_REF] Kindel | Pediatric Cardiomyopathy: Importance of Genetic and Metabolic Evaluation[END_REF].

Cardiomyopathies are, as in adults, classified into 5 groups according to cardiac phenotype.

The most frequent ones are the dilated cardiomyopathies (DCM, 55% of the cases in children) and the hypertrophic cardiomyopathies (HCM, 25 to 45 %). Restrictive cardiomyopathies (RCM), left ventricular non compaction (LVNC) and arrhythmogenic right ventricular dysplasia (ARVD) occur less frequently in 2.5 to 9% of cases [START_REF] Cox | Factors Associated With Establishing a Causal Diagnosis for Children With Cardiomyopathy[END_REF][START_REF] Kindel | Pediatric Cardiomyopathy: Importance of Genetic and Metabolic Evaluation[END_REF][START_REF] Yuan | Cardiomyopathy in the pediatric patients[END_REF][START_REF] Lipshultz | The incidence of pediatric cardiomyopathy in two regionsof the USA[END_REF][START_REF] Towbin | Left ventricular non-compaction cardiomyopathy[END_REF].

Familial transmission is most often autosomal dominant, but can also be autosomal recessive or X-linked [START_REF] Towbin | Left ventricular non-compaction cardiomyopathy[END_REF][START_REF] Mcnally | Dilated Cardiomyopathy: Genetic Determinants and Mechanisms[END_REF][START_REF] Nguyen | Whole Exome Sequencing Reveals a Large Genetic Heterogeneity and Revisits the Causes of Hypertrophic Cardiomyopathy: Experience of a Multicentric Study of 200 French Patients[END_REF][START_REF] Richard | Hypertrophic Cardiomyopathy: Distribution of Disease Genes, Spectrum of Mutations, and Implications for a Molecular Diagnosis Strategy[END_REF]. In children, there is a higher rate of neo-mutations than in adults [START_REF] Vasilescu | Genetic Basis of Severe Childhood-Onset Cardiomyopathies[END_REF].

Advances in human genome sequencing technology have increased the panel for genetic testing in cardiomyopathies. Around 65 genes are currently being screened in patients with "idiopathic" cardiomyopathy as well as in their first-degree relatives from the age of 10-12 years old if they are asymptomatic [START_REF] Richard | Génétique des cardiomyopathies héréditaires[END_REF][START_REF]ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: The Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC)[END_REF] . Most mutations identified are in genes encoding components of the cardiac sarcomere. [START_REF] Peters | Familial dilated cardiomyopathy[END_REF][START_REF] Hershberger | Genetic evaluation of cardiomyopathy: a clinical practice resource of the American College of Medical Genetics and Genomics . ACMG Professional Practice and Guidelines Committee[END_REF][START_REF] Charron | Genetic counselling and testing in cardiomyopathies: a position statement of the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases. European Society of Cardiology Working Group on Myocardial and Pericardial Diseases[END_REF][START_REF] Forleo | Targeted next-generation sequencing detects novel gene-phenotype associations and expands the mutational spectrum in cardiomyopathies. Ai T, éditeur[END_REF][START_REF] Biswas | Next generation sequencing in cardiomyopathy: towards personalized genomics and medicine[END_REF] .

Many studies in adults aim to describe genotype-phenotype correlations and establish severity factors to improve treatment decisions [START_REF] Lopes | A systematic review and meta-analysis of genotypephenotype associations in patients with hypertrophic cardiomyopathy caused by sarcomeric protein mutations[END_REF][START_REF] Bos | Characterization of a Phenotype-Based Genetic Test Prediction Score for Unrelated Patients With Hypertrophic Cardiomyopathy[END_REF][START_REF] Kayvanpour | Genotype-phenotype associations in dilated cardiomyopathy: meta-analysis on more than 8000 individuals[END_REF] . Pediatric studies are more scarce and show, as adult studies, a higher morbi-mortality and frequency of heart transplantation in children with a genetic mutation [START_REF] Robert W Loar | Genotype-phenotype Correlations of Hypertrophic Cardiomyopathy When Diagnosed in Children, Adolescents, and Young Adults[END_REF][START_REF] Norrish | Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids)[END_REF][START_REF] Miron | A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy[END_REF] .

The aim of our study was to describe our cohort of idiopathic cardiomyopathies in a pediatric population. The secondary outcome was to establish genotype-phenotype.

MATERIALS AND METHODS

Study Population

All children less than 18 years old who were diagnosed with cardiomyopathy in our institution between January 2005 and December 2019 were considered for inclusion. Patients were identified through the institutional informatics system based on clinical codes.

Cardiomyopathies considered to be secondary to anatomical cardiac lesions, viral, toxic, inflammatory, or systemic neuro-muscular disorders and inborn errors of metabolism, were excluded. Informed consent was obtained from all subjects and/or their legal guardian(s). The study was approved by the institutional Review Board of the Faculty of medicine, Aix Marseille University (number EudraCT 2016-A00958-43).

Data collection

The medical charts of all consecutive children were analyzed. Clinical data at presentation and during follow-up were collected. Patients were classified into 6 groups of cardiomyopathies, based on echocardiographic and/or magnetic resonance published criteria (18; 25-29): dilated cardiomyopathy, restrictive cardiomyopathy, left ventricular non compaction, arrythmogenic right ventricular dysplasia and mixed cardiomyopathy. Outcomes including death or heart transplantation were recorded. Familial history was analysed. We established a composite endpoint of interest corresponding to the severity of the disease [START_REF] Vasquez | Low Left Atrial Strain Is Associated With Adverse Outcomes in Hypertrophic Cardiomyopathy Patients[END_REF][START_REF] Jansen | Blood-based biomarkers for the prediction of hypertrophic cardiomyopathy prognosis: a systematic review and meta-analysis[END_REF][START_REF] Marston | Clinical characteristics and outcomes in childhood-onset hypertrophic cardiomyopathy[END_REF]. It included death, heart transplant, critical care hospitalization and the need for implantable cardiac defibrillator (ICD).

Genetic analysis

Before 2018, the genetic screening was performed in an outreach laboratory and included up to 49 genes. From January 2018, the genetic screening was performed in our own centre and included 60 genes (CARDIOGEN) by next-generation sequencing (NGS).

The results of genetic analyses were recorded. When genetic investigations were incomplete according to current practices, the investigations were, when possible, completed either on available blood samples stored in the genetic laboratory, or by resampling the patient.

Data included affected gene and variant pathogenicity, classified as pathogenic, likely pathogenic, variant of uncertain significance (VUS) or benign. Patients with genetic results as pathogenic variant, likely pathogenic variants or VUS were considered genotype positive.

Statistical analysis

Continuous variables were expressed as means with standard deviation (SD) or median with interquartile ranges (IQR) when appropriate. The normality of continuous data was verified by using Kolmogorov-Smirnov test. Categorical variables were presented as frequencies with percentages. Estimates of time-related survival after surgical repair were calculated using the Kaplan-Meier method.

A sub group analysis was made to explore hypertrophic cardiomyopathies (n=33). Univariate analysis was made to compare the two groups according to the presence of genetic abnormalities (HCM with genetic anomaly n=21 vs HCM without genetic anomaly n=12).

Chi square test (or Fisher's exact test when appropriate) were used to compare categorical data, and Student's tests (or Mann-Whitney U-test when appropriate) for independent continuous variables.

All analyses were performed using IBM SPSS statistics (Version 24.0. Armonk, NY: IBM Corp).A p less than 0.05 was set as the level of statistical significance.

RESULTS

Population

Eighty-three patients were included between January 2005 and December 2019. Table 1 details patients and familial characteristics. Hypertrophic cardiomyopathy (HCM) was the most prevalent disease, diagnosed in 40% (n=33), followed by dilated cardiomyopathy (DCM) diagnosed in 28% (n= 23), mixed cardiomyopathy in 13% (n=11), left ventricular non compaction (LVNC) in 12% (n=10), restrictive cardiomyopathy (RCM) in 7% (n=6) and arrythmogenic right ventricular disease (ARVD) in 1.2% (n=1).

Characteristics at presentation and clinical course

Clinical symptoms at diagnosis and during follow-up are detailed in Table 1 Survival curve is shown in Figure 1.

Family history

Twelve patients (14.5%) had consanguine parents and CM was diagnosed in other family members in 29% of the cases (24/83). Most cases were HCM (10/24) and DMC (9/24). (Table 1)

Genetic findings

Sixty-four patients had complete genetic analysis, 11 had no genetic analysis at all and in 8 patients analysis was incomplete. A genetic anomaly was found in 41 out of the 64 complete tested patients (64%), with a predominance of mutations ion MYH7 (14/41, 34%) and MYBPC3 (5/41, 12%) genes. Results are shown on Figure 2.

Genotype-phenotype correlations

The cohort of 64 patients who had complete genetic analysis, was divided in 2 groups: those with identified genetic anomaly (group 1, n=41, 64%) and those without (group 2, n=23, 36%). There were no significant differences between the 2 groups, except for the need for implantable cardiac defibrillator (ICD): 8 patients in group 1 required ICD (19.5%), versus none in group 2, p=0.025. Results are shown in table 2.

Patients with hypertrophic cardiomyopathies

Thirty-three patients of the cohort had hypertrophic cardiomyopathy. Twenty-one of them (63.6%) had genetic anomalies (group 1), with a predominance of mutations in the MYH7 gene (36%) (Figure 3). Extra-cardiac impact, including hepatic or renal dysfonction, arterial pulmonary hypertension and metabolic acidosis, was more frequent in patients of group 1 (38 % vs. 8%, p=0.009). Mean right ventricular pressure was higher in group 1 (29.7±12.8 vs. [START_REF] Biswas | Next generation sequencing in cardiomyopathy: towards personalized genomics and medicine[END_REF].7±6.1 mmHg, p=0.041). More patients in group 1 required implantable cardiac defibrillator (ICD) (5 (24%) vs. 0, p=0.025) and 4 (19%) patients in group 1 vs. none in group 2 needed heart transplantation (p=0.047). Severity, representing by composite endpoint, was higher in group 1: 10 patients (47.6%) vs. 1 (8%) of group 2 (8%), p=0.041. Other characteristics were not significant. Results are shown on Table 3.

DISCUSSION

Description of Global cohort

This study evaluates a cohort of idiopathic cardiomyopathies presenting during childhood.

The distribution of cardiac phenotypes differs slightly from what is described in literature, with a predominance of hypertrophic cardiomyopathies, when most studies report a predominance of dilated cardiomyopathies [START_REF] Nugent | The Epidemiology of Childhood Cardiomyopathy in Australia[END_REF][START_REF] Cox | Factors Associated With Establishing a Causal Diagnosis for Children With Cardiomyopathy[END_REF][START_REF] Lipshultz | The incidence of pediatric cardiomyopathy in two regionsof the USA[END_REF][START_REF] Vasilescu | Genetic Basis of Severe Childhood-Onset Cardiomyopathies[END_REF]. This may be explained by the fact that, unlike other studies, our cohort does not include inborn errors of metabolism or neuromuscular causes of CM [START_REF] Cox | Diagnostic Approaches to Pediatric Cardiomyopathy of Metabolic Genetic Etiologies and Their Relation to Therapy[END_REF]. There might also be a selection bias with some dilated cardiomyopathies, considered as post-myocarditis, not included in this study.

In our cohort, survival without transplantation, all cardiomyopathies considered, was similar to, or better than those described in the paediatric studies [START_REF] Lee | Pediatric Cardiomyopathies[END_REF][START_REF] Alexander | National Australian Childhood Cardiomyopathy Study. Long-Term Outcomes of Hypertrophic Cardiomyopathy Diagnosed During Childhood: Results From a National Population-Based Study[END_REF][START_REF] Norrish | Clinical presentation and survival of childhood hypertrophic cardiomyopathy: a retrospective study in United Kingdom[END_REF].

Positive genetic testing in our global cohort (64%) was reasonably similar to available data in the literature. Ellepola et al., found a yield of 77%, in a pediatric population, excluding however HCM [START_REF] Alexander | National Australian Childhood Cardiomyopathy Study. Long-Term Outcomes of Hypertrophic Cardiomyopathy Diagnosed During Childhood: Results From a National Population-Based Study[END_REF]. Parrott et al., related only 40% of mutations in their pediatric cohort but did not include VUS results [START_REF] Parrott | Investigation of de novo variation in pediatric cardiomyopathy[END_REF]. In our population, MYH7 and MYBPC3 were the most frequently found genes, especially in HCM patients, which is consistent with literature [START_REF] Biswas | Next generation sequencing in cardiomyopathy: towards personalized genomics and medicine[END_REF][START_REF] Ho | Genetic advances in sarcomeric cardiomyopathies, state of the art[END_REF][START_REF] Ho | New paradigms in hypertrophic cardiomyopathy: Insights from genetics[END_REF][START_REF] Norrish | Clinical presentation and survival of childhood hypertrophic cardiomyopathy: a retrospective study in United Kingdom[END_REF].

Genotype-phenotype correlations in global cohort

Only few studies examined genotype-phenotype correlations in pediatric cardiomyopathies, and tend to focus on HCM patients [START_REF] Robert W Loar | Genotype-phenotype Correlations of Hypertrophic Cardiomyopathy When Diagnosed in Children, Adolescents, and Young Adults[END_REF][START_REF] Norrish | Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids)[END_REF][START_REF] Miron | A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy[END_REF]. In adult studies, an identified mutation appears to be associated with a more severe course of the disease [START_REF] Lopes | A systematic review and meta-analysis of genotypephenotype associations in patients with hypertrophic cardiomyopathy caused by sarcomeric protein mutations[END_REF][START_REF] Bos | Characterization of a Phenotype-Based Genetic Test Prediction Score for Unrelated Patients With Hypertrophic Cardiomyopathy[END_REF][START_REF] Kayvanpour | Genotype-phenotype associations in dilated cardiomyopathy: meta-analysis on more than 8000 individuals[END_REF].

Our study did not allow us to establish genotype-phenotype correlations. Neither the age at diagnosis nor the symptoms presented by the patients, death or transplant, were different in children with a positive genetic testing compared to those without. The small number of patients may explain this. There was however a trend to have more implantable cardiac defibrillators in positive patients. This result may be biased by the important number of HCM in this group, with more genotype-positive patients [START_REF] Robert W Loar | Genotype-phenotype Correlations of Hypertrophic Cardiomyopathy When Diagnosed in Children, Adolescents, and Young Adults[END_REF][START_REF] Norrish | Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids)[END_REF][START_REF] Miron | A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy[END_REF].

Hypertrophic cardiomyopathies

The mutation rate in our study (63.7%) was higher than in previous reported studies: 43% in pediatric HCM according to Kindel et al. (4) or 41% in a general HCM population according to Waldmüller et al [START_REF] Norrish | Clinical presentation and survival of childhood hypertrophic cardiomyopathy: a retrospective study in United Kingdom[END_REF].

Several adult, but also some pediatric studies describe a worse clinical course in patients with HCM and a positive genetic test [START_REF] Robert W Loar | Genotype-phenotype Correlations of Hypertrophic Cardiomyopathy When Diagnosed in Children, Adolescents, and Young Adults[END_REF][START_REF] Norrish | Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids)[END_REF][START_REF] Miron | A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy[END_REF]. Our data are similar with more extra-cardiac complications, a higher need for ICD or heart transplantation in this group. Our composite criteria occurred most frequently in patients with positive genotype.

Interestingly, we noticed higher right ventricular pressure on ultrasound data, in group 1 when compared to group 2, despite similar posterior wall left ventricular diameter Z-values. This differs from some published adult studies reporting more severe myocardial hypertrophy in patients with a positive genetic testing [START_REF] Lopes | A systematic review and meta-analysis of genotypephenotype associations in patients with hypertrophic cardiomyopathy caused by sarcomeric protein mutations[END_REF][START_REF] Bos | Characterization of a Phenotype-Based Genetic Test Prediction Score for Unrelated Patients With Hypertrophic Cardiomyopathy[END_REF][START_REF] Kayvanpour | Genotype-phenotype associations in dilated cardiomyopathy: meta-analysis on more than 8000 individuals[END_REF]. This difference could be explained by the small size of our population but also by the age of the patients. Smaller children may have more reactive pulmonary vessels. Disease and LV hypertrophy may progress with time and become more pronounced at adulthood. Larger studies and longer follow-up are required to verify these data and analyse their clinical impact.

Limitations

Main limitations of our study are inherent to its retrospective and monocentric design and the small sample size. Moreover, several patients died or were lost before genetic analysis could be performed, decreasing the strength of the study.

  Twenty-five patients (30%) required cardiac transplant at a median age of 6.76 years [1.07 -11.49], corresponding to a median follow-up of 0.49 years [0.23 -0.92] after diagnosis. Nine patients died (11%) at a median age of 7.2 years [3.33-11.41], corresponding to a median follow-up of 1.9 years old [0.29 -3.83] after diagnosis. Survival without transplantation at 5, 10 and 20 years after diagnosis was 94.7%, 89.3% and 78% respectively.

Figure 1 :

 1 Figure 1 : Survival curve

Figure 1

 1 Figure 1

Figure 2 :

 2 Figure 2 : distribution of genetic anomalies in the whole population

Figure 2

 2 Figure 2

Figure 3 :

 3 Figure 3 : Distribution of genetic anomalies in the HCM population

Figure 3

 3 Figure 3

  . Median followup, until death, cardiac transplant or end of study, was 2.28 years [0.37 -6.73].

	At diagnosis, 40/83 (48.2%) patients were symptomatic. During follow-up, 60/83 (72%)
	patients had complications and needed hospitalization in cardiology department (30%) or in
	intensive care unit (34.6%), especially for cardiac failure or cardiogenic shock (33.7%),
	syncope (12%) or rhythm abnormalities (11%).

Complications, n (%) Cardiology department hospitalisation Intensive care hospitalisation Cardiac failure/Cardiogenic shock

	Characteristics	n = 83
	Age at diagnosis, median [IQR]	1.28 [0.27-10.48]
	Sex (male)	39 (47%)
	Distribution of cardiomyopathies, n (%)
	HCM	33 (39.8)
	DCM	23 (27.7)
	RCM	6 (7.2)
	LVNC	10 (12)
	ARVD	1 (1.2)
	Other	11 (13.2)
	Family history
	Consanguinity	12 (14.5)
	CM in family	24 (29)
	Follow up, yr., median [IQR]	2.28 [0.37 -6.73]
	Symptoms at diagnosis, n (%)	40 (48.2)
	Dyspnea	31 (37.3)
	Feeding and growth difficulties	23 (27.7)
	Cardiac failure/cardiogenic shock, n (%)	22 (26.5)
		60 (72.2)
		25 (30.1)
		28 (34.6)
		26 (33.7)
	Rhythm abnormalities	9 (10.8)
	Cardiac transplant	25 (30.1)
	Age at transplant, median [IQR]	6.76 [1.07 -11.49]
	Time between diagnosis and transplant, yr, median [IQR]	0.49 [0.23 -0.92]
	Death	9 (10.8)
	Age at death, median [IQR]	7.2 [3.33-11.41]
	Time between diagnosis and death, yr, median [IQR]	1.9 [0.29 -3.83]

Table 1 :

 1 Patients characteristics at presentation and clinical course

	Table 1

Table 1 :

 1 Genotype-phenotype correlations

Table 2 HCM with genetic anomaly (group 1, n= 21) HCM without genetic anomaly (group 2, n=12) p Age at diagnosis, median [IQR

 2

	]	7[0-11]	6[0-13.2]	0.627
	Symptomatic, n (%)	8 (38.1)	4 (33.3)	0.117
	Ventricular rhythm abnormalities at holter ecg, n (%)	3 (14.3)	0 (0)	0.089
	Zscore IVSD, mean (SD)	4.9+/-1.5	4.3+/-1.5	0.354
	Zscore PWLVD, mean (SD)	2.9+/-1.4	2.2+/-1.3	0.356
	RVP, mmHg, mean (SD)	29.7+/-12.8	18.7+/-6.1	0.041
	Extra cardiac impact, n (%)	!	1(8.3)	0.009
	Recovered sudden death, n (%)	1 (4.7)	0 (0)	0.667
	ICD, n (%)	5 (23.8)	0 (0)	0.025
	Transplant, n (%)	4 (19)	0 (0)	0.047
	Critical care hospitalization, n (%)	4 (19)	1 (8.3)	0.244
	Death, n (%)	0 (0)	1 (8.3)	0 .149
	Composite score*, n (%)	10 (47.6)	1(8.3)	0.041

Table 3

 3 Genotype-phenotype correlations in HCM population

Table 3

 3

CONCLUSION

Only few studies have so far analysed the genotype-phenotype correlations in children with genetic cardiomyopathies. In our small study, a positive genetic test was associated with a worse clinical course in children with HCM. Those date need to be confirmed by larger studies. Adequate genetic screening is very important in children with 'idiopathic' cardiomyopathy and will hopefully allow us in the future to improve medical care.

CONFLICTS OF INTEREST

The authors have no conflicts of interest to declare.


Thi Hien Nguyen

Huu Du Nguyen

Kim Duc

Dinh Duy

Kha Nguyen

Kim Phuc Tran

Enabling Smart Supply Chain Management with Artificial Intelligence

Keywords:

It can be said that a well-functioning supply chain management (SCM) is a key to ensuring the success of any business in a competitive global economy. SCM consists of the management of the entire production flow, ranging from supplying raw materials all the way to delivering the final products to the consumer. It aims to minimize the total expenses and maximize the customer value. In recent years Artificial Intelligence (AI) has emerged as a powerful tool that allows SCM to be more effective. The applications of AI techniques have been witnessed in all the components of SCM. This chapter provides a survey on these applications, including the prominent applications of AI algorithms in demand forecasting, logistics, production, and designing a decision support system. We also discuss the use of blockchain for SCM. Finally, in the case study, we present a method for fashion retail sales forecasting using machine learning algorithms.

Introduction

Supply Chain Management (SCM) plays a very important role in collaboration among many stakeholders such as producers, suppliers, carriers, distributors, and retailers. The purpose of SCM is to create maximum benefit not as singular realities but also as the result of a sum of interactions between entities sharing common interests. As a consequence, a large number of studies have been devoted to developing effective methods for SCM. In the literature, the number of articles related to the SCM topic has increased dramatically as shown in Figure 1 (extracting from WebofScience). In the period from 2010 to 2020, the number of articles per year has doubled compared to the previous 5 years.

There are different points of view about the definition of SCM. For example, Ellram 1 defined SCM as a network of companies interacting to deliver a product or service to the end customer and involving a set of flows from raw materials to final delivery. Christopher 2 referred to SCM as "the management of upstream and downstream relationships with suppliers and customers in order to deliver superior customer value at less cost to the supply chain as a whole". Bechtel and Jayaram 3 counted more than 50 definitions of the SCM and classified them into five categories. Regardless of differences in the way to express the definition of SCM, there is a common among all these definitions about the increasing importance of SCM for any organizations or businesses in competing in the global market and networked economy (Cooper et al. [START_REF] Cooper | Supply chain management: more than a new name for logistics[END_REF] ).

Due to the growth of Internet businesses and mobile computing, there is a profound change in the way of shopping of customers and the operating of businesses. By allowing customers to contact product distributors directly and encouraging cooperation, these new advances shorten the supply chain. Internet businesses like Amazon have improved their service quality in terms of on-time delivery and convenience. Normalizing features like next-day delivery can increase order fulfillment, but also make it difficult for other parts of the chain. Because orders can be made and received faster, they need to be delivered at a faster rate. As a consequence, several disadvantages can be encountered in the supply chain: it leads to waste when businesses order too many raw materials or it does not supply enough products according to the order in the opposite case. This is only one among many challenges a business can meet in its operation and this can only be overcome by developing an efficient SCM method. The application of advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) for SCM is then encouraged. AI refers to a set of computer algorithms that allows machines to perform complex tasks "as a human" by perceiving the working environment and taking actions to maximize the possibility of success. Shortly, it is regarded as intelligence demonstrated by machines. Recent times witnessed the sharp development and the wide applications of AI in many areas of real life, and SCM is not an exception. Dash et al. [START_REF] Dash | Application of artificial intelligence in automation of supply chain management[END_REF] highlighted the recent trends and applications of AI in the retail and manufacturing industry. Oh 6 introduced an SCM solution connecting many components such as suppliers, manufacturers, customers, and other companies to enable efficient inventory management and timely product supply, based on AI and IoT technologies. A systematic review that aims to identify the contributions of AI in SCM has been conducted in Toorajipour et al. [START_REF] Toorajipour | Artificial intelligence in supply chain management: A systematic literature review[END_REF] . The authors classified the applications of AI in SCM into four main fields, including marketing, logistics, production, and supply chain. Another review on the implementation of machine learning (ML) techniques within SCM in Younis et al. [START_REF] Younis | Applications of artificial intelligence and machine learning within supply chains: systematic review and future research directions[END_REF] concluded that the ML applications are still at the infant stage and their opportunity for elevating SCM performance is very promising. In this chapter, we provide a survey on the applications and perspectives of AI that enable effective SCM approaches. We focus on the benefits of AI algorithms in the important fields in SCM like demand forecasting, logistics, production, and decision support systems. We also discuss the use of blockchain techniques for SCM. In addition, we suggest a novel method for forecasting retail sales in the fashion industry based on a random forest algorithm.

The chapter is structured as follows: In Section 2, we discuss the applications of AI algorithms for demand forecasting in SCM. The benefits of AI for logistics are presented in Section 3. Section 4 and Section 5 are for the covering of AI technique in the production process and in designing a decision support system, respectively. A case study has been conducted in Section 7 where we present a method for forecasting fashion retail sales using several ML algorithms. Finally, some concluding remarks are given in Section 8.

AI for Demand Forecasting

Forecasting refers to the prediction of what is going to happen in the future by analyzing what happened in the past and what is going on currently. Demand forecasting is an essential strategy for improving customer service levels and reducing costs related to supply-demand mismatches. For businesses, is very important to keep the balance between supply and demand. Any oversupply or shortage of goods to meet customer needs leads to negative effects on the businesses, both in terms of economy and operations or even the competitiveness with others. However, the customer demand is an unpredictable quantity and this number can only be estimated us-ing the predictive methods. An accurate forecast of customer demand is able to result in the right decision or an effective policy in managing the supply chain. It facilitates principal business activities such as budgeting, financial planning, sales, marketing planning, material planning, production planning, risk assessment, formulating mitigation plans, and promotional campaigns.

In the literature, a variety of forecasting techniques has been introduced to best suit the specific business environment such as long-term forecasting, short-term forecasting, etc. In general, the core idea in forecasting is that the future needs should follow the past demand patterns, and the forecast is based on the accuracy and validity of historical data no matter the utilized methods. Among many methods applied for demand forecasting, AI algorithms have become increasingly powerful and popular. Yu et al. [START_REF] Yu | Dynamic pattern matching for demand forecasting using temporal data mining[END_REF] proposed a dynamic pattern matching process within an agent-based system framework that combines human expertise and data mining techniques to predict new product demand. Jeong et al. [START_REF] Jeong | A computerized forecasting system using genetic algorithm in supply chain management[END_REF] improved the accuracy of the prediction without relying much on historical data by introducing a genetic algorithmbased causal forecasting technique that outperformed traditional regression analysis. These studies are both useful for products that have not yet been introduced to the market because no historical data are required. Snyder and Shen 11 presented an overview for the demand forecasting approaches. The authors also made a comparison between the performance of the ML methods and the classical methods for predicting demand, leading to a conclusion that the ML algorithms significantly increase the prediction accuracy. Amirkolaii et al. [START_REF] Nemati Amirkolaii | Demand forecasting for irregular demands in business aircraft spare parts supply chains by using artificial intelligence (ai)[END_REF] suggested an AI-based method for the prediction of irregular demands in business aircraft spare parts supply chains. A decomposed deep learning (AL) approach for tourism demand forecasting has been introduced in Zhang et al. [START_REF] Zhang | Tourism demand forecasting: A decomposed deep learning approach[END_REF] . Recent studies about the benefits of an accurate prediction for customer's demand and the AI solutions for the problem can be seen in Carbonneau et al. [START_REF] Carbonneau | Application of machine learning techniques for supply chain demand forecasting[END_REF] ; Raza and Khosravi 15 ; Bousqaoui 16 ; Boone et al. [START_REF] Boone | Forecasting sales in the supply chain: Consumer analytics in the big data era[END_REF] ; Zhu et al. [START_REF] Zhu | Forecasting smes' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach[END_REF] ; Nguyen et al. [START_REF] Nguyen | Forecasting and anomaly detection approaches using lstm and lstm autoencoder techniques with the applications in supply chain management[END_REF] ; Shao et al. [START_REF] Shao | Multistage implementation framework for smart supply chain management under industry 4.0[END_REF] ; Kamble et al. [START_REF] Kamble | A machine learning based approach for predicting blockchain adoption in supply Chain[END_REF] , and Nikolopoulos et al. [START_REF] Nikolopoulos | Forecasting and planning during a pandemic: Covid-19 growth rates, supply chain disruptions, and governmental decisions[END_REF] .

AI for Logistics

An important objective in SCM is to make sure smooth and efficient operations at the production side of the chain. Once the scale of the production processes increases, several problems in logistics could be faced such as overstocking, stock-out, and delivery delays. Moreover, the logistics operations should be upgraded due to the increasing complexity and variety of customer orders. The inefficient and inaccurate order picking process may lead to negative effects on order fulfillment. That is to say, it is necessary to have good logistics management and AI algorithms are a powerful tool to per-form this task: they can lead to a revolution in transportation operations (Wang 23 ). The assessment of supply chain logistic performance using AI and simulation-based Techniques was presented in Bruzzone and Orsoni [START_REF] Bruzzone | Ai and simulation-based techniques for the assessment of supply chain logistic performance[END_REF] .

In logistics management, warehousing refers to a storage repository that allows the goods and the cargo to be stored properly. The major operation of the warehouse involves the storage and the protection of goods, risk-bearing, grading/branding, and transportations. A number of the necessity for warehousing was mentioned in Pandian [START_REF] Pandian | Artificial intelligence application in smart warehousing environment for automated logistics[END_REF] . The authors also proposed a method for automated warehouse logistics in which the sensor networks were utilized to gather the information about the number of the items entering and leaving the warehouse and the AI algorithms was applied to handle these data to perform automatically the tasks in the storehouse such as placing the items in the proper rack, picking back the items from the rack as per the order placed. Lee et al. [START_REF] Lee | Design and application of internet of things-based warehouse management system for smart logistics[END_REF] provided an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using AI techniques to enable smart logistics that improves the warehouse productivity, picking accuracy, efficiency, and robustness to order variability. Mahroof 27 discussed many perspectives for the AI adoption in a warehouse along with reviewing a number of studies on warehousing and technology in the literature. Recently, Zhang et al. [START_REF] Zhang | Artificial intelligence in e-commerce fulfillment: A case study of resource orchestration at alibaba's smart warehouse[END_REF] conducted a study to see successful applications of AI in businesses considering an in-depth case study of Alibaba's smart warehouse, a leading e-commerce fulfillment center in China.

Besides the warehouse-related problems, the management of logistics also faces difficulty in transportation. The rapid development of the economy, as well as the expansion of the scale of production, also mean the increasing requirements of the efficiency of the transporting system, i.e. the ability to transport goods from the factories to the warehouses, from the warehouses to the supermarkets or stores without breaks in the travel chain regardless of functional ability. The advanced technologies, including AI, serve to reduce difficulty in traveling and tailor solutions to the accessibility of a transportation system. By combining many algorithms like the Back Propagation neural network, the Earliest Deadline First dynamic scheduling algorithm and the Controller Area Network communication network to build an AI-empowered communication system for intelligent transportation Systems, Lv et al. [START_REF] Lv | Ai empowered communication systems for intelligent transportation systems[END_REF] concluded that the application of AI technology can improve significantly the efficiency of vehicle scheduling and the efficiency of the communication system. A large number of studies on the applications of innovative AI algorithms in decision-making, planning, modeling, estimating, and controlling that facilitate the process of transportation planning and operations has been provided in Sohn [START_REF] Sohn | Ai-based transportation planning and operation[END_REF] . These studies covered many topics in a transportation system like traffic surveillance, vehicle emission reduction, traffic safety, congestion management, traffic speed forecasting, and ride-sharing strategy. A summary of the potential application of AI in transportation has recently been presented in Vasudevan et al. [START_REF] Vasudevan | Summary of potential application of ai in transportation[END_REF] .

AI for production

Thanks to the development of the new advanced technologies in IoT and cloud computing, the use of AI in production is becoming more and more popular. One can mention many applications of AI for production such as detecting abnormality products, planning production, monitoring the production process in real-time, performing predictive maintenance, and optimizing the manufacturing process.

The production scale in modern industrial production is increasingly expanding with large production lines operating continuously. In that process, the problem of detecting defective products plays an important role, both helping to eliminate these defective products to ensure the quality of factory products and, at the same time, allowing to detect the cause of defects in the line. The machine learning algorithms like support vector machine, Extra Tree, or the deep learning methods like Recurrent Neural Network (RNN), and the Conventional Neural Network (CNN) are the effective tools in AI to perform the classification mission. In the literature, many studies have been devoted to this problem. Graß et al. [START_REF] Graß | Unsupervised anomaly detection in production lines[END_REF] discussed an approach for anomaly detection regarding predictive maintenance in an industrial data-intensive environment. Nguyen et al. [START_REF] Nguyen | Forecasting and anomaly detection approaches using lstm and lstm autoencoder techniques with the applications in supply chain management[END_REF] using a one-class support vector machine algorithm to separate anomalies from multivariate time series data outputted based on the LSTM Autoencoder network. A comprehensive survey on deep learning approaches for anomaly detection was conducted in Chalapathy and Chawla [START_REF] Chalapathy | Deep learning for anomaly detection: A survey[END_REF] .

The unqualified products maybe because of the degradation of machines in the production lines after a certain time of working. Bevilacqua and Braglia [START_REF] Bevilacqua | The analytic hierarchy process applied to maintenance strategy selection[END_REF] pointed out that maintenance actions involve high costs, ranging from 15% to 70% of total production costs Therefore, it is necessary to have a good strategy for maintenance. Predictive maintenance refers to a wellknown approach that allows determining the right time for maintenance activities. It benefits the production process by minimizing unplanned downtimes, reducing system faults, increasing efficiency in the use of resources, and planning maintenance interventions. Based on the data collected from many sensors appended to the machines, AI algorithms can predict, diagnose, and analyze future maintenance needs. In predictive maintenance, a key idea is to estimate the remaining useful life (RUL) -the remaining time before a machine requires a repair or a replacement when it no longer performs well its function. Several machine learning methods like Linear Regression, Bayesian Linear Regression, Poisson Regression, Neural Network Regression, Boosted Decision Tree Regression and Decision Forest Regression have been applied for RUL prediction in Yurek and Birant [START_REF] Yurek | Remaining useful life estimation for predictive maintenance using feature engineering[END_REF] . A hybrid model combining support vector machines for the prediction of the RUL of aircraft engines was presented in Nieto et al. [START_REF] Nieto | Hybrid pso-svm-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability[END_REF] . The deep learning algorithms like CNN, RNN, and Long-Short Term Memory (LSTM) network for estimating RUL can be seen in many studies like Li et al. [START_REF] Li | Remaining useful life estimation in prognostics using deep convolution neural networks[END_REF] ; Savargaonkar et al. [START_REF] Savargaonkar | A cycle-based recurrent neural network for state-of-charge estimation of li-ion battery cells[END_REF] ; Savargaonkar and Chehade 40 and Wu et al. [START_REF] Wu | Remaining useful life estimation of engineered systems using vanilla lstm neural networks[END_REF] .

Another important application of AI that makes the manufacturing process smart is virtual reality (VR). VR refers to an artificial environment that provides users with three-dimensional images and some effects so that they can feel like being in a real space. In smart manufacturing, VR technology supports engineers in evaluating the performance of an object via virtual prototypes, which saves a lot of expense compared to building physical prototypes. That is, it can allow the implementation of complex engineering theory from industrial real-life practice in a virtual 3D model. Several applications of VR in industrial manufacturing have been discussed in Hamid et al. [START_REF] Hamid | Virtual reality applications in manufacturing system[END_REF] , consisting of Design (design and prototyping), Manufacturing Processes (machining, assembly, and inspection), and Operation Management (planning, simulation, and training). Gong et al. [START_REF] Gong | Development of virtual reality support to factory layout planning[END_REF] proposed a point cloudbased virtual factory modeling approach for factory layout planning applied in many industries such as aerospace and truck manufacturing. The benefit of the introduction of Additive Manufacturing and Augmented Reality in aeronautical maintenance has been analyzed in Ceruti et al. [START_REF] Ceruti | Maintenance in aeronautics in an industry 4.0 context: The role of augmented reality and additive manufacturing[END_REF] . Dobrescu et al. [START_REF] Dobrescu | Process simulation platform for virtual manufacturing systems evaluation[END_REF] provided a review of the studies related to virtual manufacturing systems.

All the specific applications discussed above are ultimately for the purpose of optimizing the production process. This is a highly complex task to make the process as good, functional, and effective as possible and it requires the best combination of many controllable parameters. The AI optimization algorithms enable processing data related to products from raw materials, manufacturing, traveling, and storing, to the end of life-cycle and then refining this process which can maximize the yield in a shorter time and at reduced costs. For example, Ardabili et al. [START_REF] Ardabili | Using svm-rsm and elm-rsm approaches for optimizing the production process of methyl and ethyl esters[END_REF] suggested using several machine learning methods for optimizing the production process of methyl and ethyl esters. Oroojlooyjadid et al. [START_REF] Oroojlooyjadid | A deep qnetwork for the beer game: Deep reinforcement learning for inventory optimization[END_REF] solved inventory optimization problems using a reinforcement learning algorithm. A review of machine learning for the optimization of production processes can be seen in Weichert et al. [START_REF] Weichert | A review of machine learning for the optimization of production processes[END_REF] .

AI for Decision Support Systems in SCM

In today's global scale of industrial production, trade, and services, every important decision of managers has a great influence on the benefit and the long-term development of the whole enterprise. The experience-based method is no longer suitable for them to rely on. That is to say, designing an effective Decision Support Systems (DSS) is a must for any business to assist 2. Namely, the references about the methods to design a DSS and its applications in the literature are abundant. Tawfik et al. [START_REF] Tawfik | Decision support system in supply chain: A systematic literature review[END_REF] highlighted the decision-making process, components in a DSS process, and types of DSS in supply chain management. A comprehensive overview of DSS in SCM was made by Teniwut and Hasyim 50 providing information on the trends, methodologies, and applications on different sectors and platforms used by scientists for building their decision support systems in the supply chain. The author also pointed in some research questions about the most effective models used on DSS in the supply chain, the use of DSS covers various problems in the supply chain, the advantages of DSS, etc. Govindan et al. [START_REF] Govindan | A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)[END_REF] developed a practical DSS with the demand management in the health-care supply chain to fight the pandemic Coronavirus disease 2019 (COVID-19) by reducing stress in the community, breaking down the COVID-19 propagation chain, and, generally, mitigating the epidemic outbreaks for health-care supply chain disruption.

As discussed in several studies in SCM, there are five main sectors that affect the capacity of a supply chain, including production, inventory, warehouse, location, transportation, and information. An efficient SCM firstly requires an understanding of each sector. The next step is to extend the evaluation of the results achieved by the various combinations of these sectors. Therefore, a supply chain generates a lot of data, its consequence is that there are many systems with inputs and outputs, which are often too complex to model and optimize analytically. Due to its power in analyzing Figure 2: Times Cited and Publications over time data and the ability to extract important features from data, AI algorithms have been incorporated into the methods to design a DSS in SCM. Chi et al. [START_REF] Hm Chi | Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms[END_REF] proposed a machine learning algorithm as a potential methodology for system modeling and optimization vis-à-vis traditional design of experiment (DOE) methods. The result shows that these algorithms can obviate the need to perform more complex, expensive, and time-consuming DOE, which usually disrupts system operations. The models resulting from the proposed algorithms had strong explanatory and predictive power, comparable to that of DOE. A new methodology was proposed by Rodriguez et al. [START_REF] Rodriguez | An intelligent decision support system for production planning based on machine learning[END_REF] to solve a Closed-Loop Supply Chain (CLSC) management problem through a decision-making system based on fuzzy logic built on machine learning decision support tools, more specifically, decision trees. Very recently, Kamble et al. [START_REF] Kamble | A machine learning based approach for predicting blockchain adoption in supply Chain[END_REF] developed a predictive decision support system using a Bayesian network analysis featuring the significant factors. It aims to predict an organization's probability of successful blockchain adoption. Other publications about the application of ML technique on DSS can be found in, for example, Medina-Gonzalez et al. [START_REF] Medina-Gonzalez | Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework[END_REF] ; Ni and Xiao 55 ; Alavi et al. [START_REF] Alavi | A Dynamic Decision Support System for Sustainable Supplier Selection in Circular Economy[END_REF] .

Blockchain technique for SCM

It would be remiss not to mention the contribution of Blockchain to developing effective methods for SCM. Blockchain today is gradually becoming an efficient technology to enhance customer service and enhance company performance. It distributes a database that maintains a continuously growing list of data records that is secure from tampering and modification. So even if the stakeholders are not trusting or unfamiliar, they can share records of data together but still maintain confidentiality. In addition, Blockchain Figure 3: Tree-map chart for number of publications in each the Sciences Categories using Blockchain in SCM also promotes transaction activities, improves transparency while reducing unnecessary costs. It is showing huge potential for growth in a secure and efficient future economic and social system. The supply chain shows many positives today, but there are also unavoidable challenges such as Lack of traceability, Limited transparency, Outdated means of data sharing. However, with the application of the Blockchain technique, the supply chain will become more efficient, improving efficiency, increasing transparency, and reducing costs. With two keywords "Supply chain management" and "Blockchain", WebofScience shows us a tree-map chart that describes the areas where scientific publications use blockchain technique (BT) in SCM, (see Fig 3). In the sequel, we discuss the main benefits Blockchain to SCM.

Advanced traceability. Blockchain creates a checkable trace of all items that travel through the network. With the help of IoT technology, it can collect a large amount of data of items in real-time along with timestamps and collection location. Thanks to that, Blockchain has created a complete, accurate, and easy to access inspection trail, from the origin of the product to the customer. Moreover, the data in Blockchain is immutable and always requires an electronic signature to confirm ownership of information, so the technique always provides a reliable history of any product and gives provides faster traceability in the event of a compromise. Advanced traceability helps to solve problems faster, reduces product recall costs, and improves product authenticity and quality. For example, Kamble et al. [START_REF] Kamble | Modeling the blockchain enabled traceability in agriculture supply chain[END_REF] found that among the identified enablers, traceability was the most significant reason for blockchain technology implementation in agriculture supply chains (ASC) followed by suitability, immutability, and provenance. Some examples of traceability are mentioned in the following articles Caro et al. [START_REF] Caro | Blockchain-based traceability in agri-food supply chain management: A practical implementation[END_REF] ; Hastig and Sodhi 59 ; Wamba and Queiroz 60 ; J.Sunny et al. [START_REF] Sunny | Supply chain transparency through blockchain-based traceability: An overview with demonstration[END_REF] ; T.K.Agrawal et al. [START_REF] Agrawal | Blockchainbased framework for supply chain traceability: A case example of textile and clothing industry[END_REF] .

Improved transparency Thanks to the transparency and completeness of product information, companies will make better forecasts and decisions against the existence of fake goods. Blockchain technology transparency has become a technological answer to address some sustainability problems, it helps us in improving the processes involved in production. For example, Madumidha et al. [START_REF] Madumidha | A theoretical implementation: Agriculture-food supply chain management using blockchain technology[END_REF] [START_REF] Kamble | Modeling the blockchain enabled traceability in agriculture supply chain[END_REF] , the results confirmed that the power of blockchain to enable more transparency in supply chains is real and verifiable.

Greater security Supply chain security has become a subject of massive interest in both academia and industry. Several recent studies discuss the importance of security in supply chains and the measures required to achieve that, for instance, see Salman et al. [START_REF] Salman | Probabilistic blockchains: A blockchain paradigm for collaborative decision-making[END_REF] ; Tukur et al. [START_REF] Tukur | Edge-based blockchain enabled anomaly detection for insider attack prevention in internet of things[END_REF] ; W.Viriyasitavat et al. [START_REF] Viriyasitavat | When blockchain meets internet of things: Characteristics, challenges, and business opportunities[END_REF] . With the structure of Blockchain (BC), to be able to penetrate a particular loot, all previous blocks in the entire history must be modified. Therefore, hacking to threaten a centralized database in BC is almost impossible. BC technology-powered business and transaction log storage becomes much more secure. With the research in Lemieux [START_REF] Vl | Trusting records: Is blockchain technology the answer?[END_REF] , we can understand more about Blockchain technology as a solution to creating and preserving trustworthy digital records, presenting some of the limitations, risks, and opportunities of the approach.

Enhanced trust From the research of trusted protocol in Alam [START_REF] Alam | Why the auto industry should embrace blockchain[END_REF] , Blockchain proved better security when it always requires relevant electronic signatures to perform transactions on the supply chain. At the same time, Blockchain also records the time, location, and other data in the Supply chain system and synchronizes all of that data in real-time. This is called a reliable identity management mechanism. As a result, the trust of stakeholders in the Supply chain network has been enhanced. Blockchain technology promises overpowering trust issues and allows a trustless, secure, and authenticated system of logistics (see D.Dujak and D.Sajter 71 ; Oh et al. [START_REF] Oh | Enhancing trust of supply chain using blockchain platform with robust data model and verification mechanisms[END_REF] ; P.Dutta et al. [START_REF] Dutta | Blockchain technology in supply chain operations: Applications, challenges and research opportunities[END_REF] ; Alazab et al. [START_REF] Alazab | Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance[END_REF] ).

Boosted efficiency The main purpose of BC when it comes to the Supply chain is to replace outdated processes today. The data will be aggregated, backed up, and updated for relevant parties. Blockchain helps minimize human error and minimizes the need for third parties because transaction information requires instant commitment and authentication from related parties, and Blockchain synchronize also all of this data automatically with local copies.

Case study

In this section, we demonstrate a specific application of a machine learning method in SCM. In particular, we compare the performance of several ML algorithms in forecasting retail sales in the fashion industry.

Fashion has been determined as a factor in the creation of value on a global scale. The business of buying clothes from manufacturers and selling them to customers is known as retail. Retailers make initial purchases for resale three to six months before the customer is able to buy the clothes in-store. For this reason and the "sensibility" of mode, sales forecasting plays a very important task in fashion retailing. A review of several AI approaches for sales forecasting in the fashion retailing service industry can be seen in Liu et al. [START_REF] Liu | Sales forecasting for fashion retailing service industry: A review[END_REF] . In this study, we apply many ML algorithms, including Random Forest (RF), Support Vector Machine (SVM), Linear Regression (LR), and Elastics Net CV to predict the fashion retail sales based on the data set introduced in a part of Kaggle competition (https://www.kaggle.com/c/retail-case-study-batch14/data). This sales data set contains the temporal data like Month, Year, and Product category and Sales, the macroeconomic data like GDP and unemployment rate, the weather data, and the events and holiday data. Based on this information, the goal of applied models is to forecast the retail sales that can help the retailer to plan their budgets or investments in a period (monthly, yearly) among different product categories like "WomenClothing", "MenClothing" and "OtherClothing".

The performance of the proposed methods is evaluated using the measure of Mean Absolute Percentage Error (MAPE)-a measure of prediction accuracy of a forecasting method. Whilst the Mean Absolute Deviation (MAD) calculate the disparity between the predictions and the reality of bookings, the MAPE put a percentage value to that calculation. Namely, it provides the information about how many percentage points a forecasting method are off on average. The formula for MAPE is

MAPE = 1 n n t=1 A t -F t A t × 100%
where A t is the actual value and F t is the forecast value, and n is the number of fitted points. The value of MAPE for each method is displayed in Table 2. As can be seen from this table, the RF method is superior to the rest, corresponding to the smallest value of MAPE, which is 13.7328%. The LR model is not effective for the data set as the corresponding value of MAPE is highest: 37.9%. For ElasticNet CV and SVM approaches, the values of MAPE indicate similar accuracy, both are about 20%. That is to say, among all the applied methods, the RF leads to the best performance.

Conclusion

In this chapter, we have conducted a survey on the applications of AI algorithms in SCM, aiming to highlight the potential of these advanced techniques to enable a smart SCM. We have focused on several important sectors in SCM, including Demand Forecasting, Logistic, Production, and Decision Support Systems. We have also discussed the benefits of the Blockchain technique for SCM. In addition, a case study where we demonstrated the performance of several ML models to forecast retail sales in the fashion industry has been provided. Since the last two years, the Covid-19 pandemic has disrupted the global supply chains due to months of lock-down. However, this is also a motivation for the need to develop applications of advanced technologies such as IoT and AI as the recovery of the global economy requires a more effective SCM to easily adapt to day-to-day changes in current conditions

Figure 1 :

 1 Figure 1: Number of publications on SCM since 1989.

Table 1 :

 1 Top 10 domains that have the most publications on topic DSS and SCM their managers in making the most informed decisions. In fact, the concept of DSS covers not only SCM but also a wide field of applications. Applications of DSS in conjunction with several areas such as database management systems, operations research, AI, involve different levels of DSS complexities in businesses and industry. Table1reports the top ten categories that have the most publications on the topic of DSS extracted from WebOfScience. Another report in the same source about the Times Cited and Publications Overtime is reproduced in Figure

	Categories	Record Count % of 1.967 publications
	Operations Research Management Science	581	29.537
	Engineering Industrial	389	19.776
	Management	361	18.353
	Engineering Manufacturing	310	15.760
	Computer Science Interdisciplinary Applications	255	12.964
	Computer Science Artificial Intelligence	220	11.185
	Computer Science Information Systems	203	10.320
	Environmental Sciences	165	8.388
	Green Sustainable Science Technology	144	7.321
	Engineering Electrical Electronic	141	7.168

  explained how blockchain technology (transparency) helps in the food supply chain management. The work of C.Bai and J.Sarkis 64 build an appraisal model of blockchain technology supporting supply chain transparency to mitigate sustainability risks and build global supply chain competitiveness. Like in F.Wamba et al. 65 ; J.Sunny et al. 61 ; Kamble et al.

Table 2 :

 2 The Value of Mean Absolute Percentage Error (MAPE) for each method

	Method	The Value of Mean Absolute Percentage Error
	Linear Regression	37.9000
	ElasticNet CV	20.0387
	Support Vector Machine	20.0481
	Random Forest	13.7325