Impact of Information,Technology Infrastructure,Inventory Management and Demand on the Overall Performance of the Pharmaceutical Supply Chain

Abstract

This study was conducted primarily to investigate the impact of information, technology, inventory management and demand on the performance of the pharmaceutical supply chain (PSC). In order to realize the objectives, a structured closed-ended questionnaire was prepared, and responses were collected from retail outlets considering the respondent’s location, size of the store, type of store and association of the store with manufacturers/companies. In total, 103 complete responses were used for the study. It was found that information, technology infrastructure, inventory management and demand all have shown to have a direct positive influence on the overall performance of the PSC. The variables information systems, system software, inventory operations and promotions were found to have the highest association with information, technology infrastructure, inventory management and demand, respectively. The results highlight insights and critical factors that need to be focused on to improve the performance of supply chain operations. The results of the present study can be important for chain personnel involved in decision-making.

Keywords

Supply chain pharmaceutical supply chain (PSC)supply chain management (SCM)information sharing (IS)technology infrastructure (TI)inventory management (IM)demand

Introduction

After 1990, globalization and intense competition created the challenge of effectively conducting supply chain operations in a cost-efficient manner. Thus, recognizing and epitomizing the practice of supply chain management (SCM) has become a necessary condition to remain relevant in the competitive world and increase profitability. Organizations have felt the need to strengthen their supply chain capabilities by adding the dimension of technology to generate better responses as per their customer expectations and value (Lee, 2004). Customer value maximization is possible by integrating activities throughout the length of the supply chain. SCM practices confer certain advantages to organizations, which include improvement in operations management, improved decision-making like outsourcing, enhanced customer satisfaction and profitability, improved overall quality, edge over competitors and proliferation of online businesses giving a thrust to export businesses (Habib, 2010).

Due to the changing circumstances around the world, there is a need to understand the impact of supply chain factors that will improve the overall supply chain performance of the organization. This will happen when collaborating with transportation providers to ensure process improvement and digitalization by optimizing manufacturing, distribution and logistics, streamlining the customer journey and aligning information systems in a way that avoids supply chain disruptions (Rai et al., 2006). Changes in business dynamics create challenges that affect almost all functional areas in a pharmaceutical organization; therefore, supply chain design requires a more strategic approach to avoid challenges by increasing coordination across departments and functions so that businesses realize the competitive difference.

Li et al. (2005) identified six elements of SCM practices which improved the performance of the organization as well as conferred a specific competitive advantage to the firm. Marinagi et al. (2014) observed that by leveraging information technology (IT), business functions can be integrated on a common internet-based platform that gives the ease of digital control also regulates external as well as internal operations pertaining to SCM (Rai et al., 2006). George and Madhusudanan Pillai (2019) observed that the supply chain structure, inventory control strategy, information exchange, customer demand, forecasting method, lead time and review period duration were all found as key contributors in enhancing the performance of the supply chain.

Pharmaceutical organizations need a deep understanding of their business needs and align their supply chain in a manner that they gain an edge over their competition. Pharmaceutical companies have adopted these changes and redesigned their supply chains to serve their customers better (Naraharisetti & Karimi, 2010). The present study is undertaken primarily to understand the impact of information, technology, inventory management and demand on the performance of pharmaceutical supply chain (PSC). These factors were identified after a thorough literature search. The four factors considered are mentioned next:

Sharing of Information

The importance of information is considered vital in the current competitive scenario. Information sharing refers to the disbursement of quintessential information to customers, suppliers and other key partners involved in the supply chain (Raweewan & Ferrell, 2018). Information sharing is important as it contributes to reducing supply chain costs, improving partner relationships, increasing material flow, enabling faster delivery and improving order fulfilment rates, thereby contributing to customer satisfaction, increasing channel coordination and facilitating the achievement of competitive advantage (Koçoğlu et al., 2011) and improves operations to enhance the development of new products (Awan et al., 2021; Haque & Islam, 2018; Jayaram et al., 2000; Nazifa & Ramachandran 2018). In today’s highly competitive environment, pharmaceutical companies and healthcare supply chains use information as a tool to enhance operational performance in terms of product availability, optimal cost, quality service, delivery reliability and flexibility in providing services and to improve inventory management and capacity planning (Kochan et al., 2018).

Information Technology

(IT is incorporated as a major platform for networking, controlling and regulating external and internal operations related to SCM efficiency (Lee, 2004; Mehrjerdi & Shafiee, 2021; Mourtzis, 2011; Rai et al., 2006; Saraf et al., 2007). Even in the pharmaceutical sector the companies have adapted such changes and redesigned their supply chains to serve their customers better (Chen, 2019; Naraharisetti & Karimi, 2010) and to achieve competitive advantage (El Mokrini et al., 2018). Sutduean et al. (2019) opined that a firm that is equipped with capabilities of IT has exhibit improved performance in overall supply chain practices. The new capabilities—‘Big Data Analytics Capabilities’ (BDAC)—have a strong positive impact on the sustainability of the pharma supply chain (Shokouhyar et al., 2020). In pharmaceuticals, in addition to the flow of products from manufacturers to the patient’s bedside. IT optimizes supply chain performance and ensures sustainable availability of life-saving medicines that improve public health outcomes (Biswas & Jaydip, 2016).

Inventory Management

Effective inventory management helps to meet consumer demand, generate healthy revenue and ultimately achieve financial success. Inventory decisions are getting maximum attention in the global scenario (Ballon, 2000), especially after Industry 4.0, which has brought new approaches to manufacturing systems as well as supply chain. Effective and efficient inventory management can reduce operating cost and enhance customers’ service levels (Cetinkaya & Lee, 2000) and satisfaction (Li et al., 2006; Gabisa & Ram, 2021), thereby improving financial performance. In the pharmaceutical sector, a multi-level inventory management system ensures the availability of medicines at various establishments (Sbai et al., 2020). The new system, viz., blockchain technology, helps manage supply chain operations while ensuring confidentiality and privacy, tracking systems, transparency, demand–supply management and quality management (Umar et al., 2022). Shortages and improper management of medical supplies disrupt the pharmacy supply chain, leading to lower quality care and higher prices, which indirectly impacts patients. Inventory management of pharmaceutical products is necessary to meet patients’ above requirements (Denton, 2013; Mathur et al., 2018).

Demand

Establishing demand–SCM (DSCM) is optimal for a firm as it enhances supply chain performance, while improving demand chain performance and enhancing the competitiveness of the organization. Key aspects of DSCM include coordinated supply–demand processes, market orientation, equal importance to both supply as well as demand processes, cost-efficient operations, innovation, differentiation, accountability and creation of a sustainable relationship with consumers (Hilletofth, 2011; Shahi et al., 2021). Demand management ensures the right balance between supply and demand processes and manages customer segments with integrated processes and synergy between SCM and marketing to maximize overall output (Strandberg & Asenius, 2022). In the case of the pharmaceutical industry, there has been a significant increase in the number of generic companies due to patent expiration. And these companies are focusing on developing efficient, effective and cost-effective supply chains. The challenges posed by expiring patents have forced companies to focus their attention on the challenges of forecasting future demand and supply-side inventory management.

Research Methodology

This study is conducted primarily to investigate the impact of information, technology, inventory management and demand on the performance of PSC. The study aims to achieve the following objectives: (i) to deduce the influence of information on the performance of PSC; (ii) to explore the influence of technological infrastructure on the performance of PSC; (iii) to determine the impact of inventory management on the performance of the PSC; and (iv) to investigate the effect of demand on the performance of PSC. In order to realize the objectives a structured closed-ended questionnaire was prepared which included a total of 13 statements. While selecting the respondent sample location, size of the store, type of store and association of store with manufacturers/companies were considered. The data were collected from the retail pharmacy owners from urban as well as rural areas with the help questionnaire by random sampling technique. In total, 103 complete responses were used for analysis with the help of SPSS v 26 and SmartPLS (v.3.3.7) and ‘goodness-of-fit of the model’. The hypothesis was tested by using parametric statistics using t (equality of two group means) and f statistic (equality of more than two group means) testing procedures. All statistical tests were two-sided, and P < 0.05 was considered statistically significant, except for the Levene test used in analysis. The present study is limited to one type of supply chain partner (retailers) only.

Results

Demographic Profile of Selected Respondents

Profiling of the respondents involves segregating the respondents into specific groups on the basis of the location of their pharmacy store (rural and urban), size of the store (small retailor – 100 sq ft; large—more than 150 sq ft, normally located near to hospitals), number of companies they are associated with (less than 5; 5 to 10; more than 10) and type of store (hospital store, standalone stores and chain pharmacies store). Demographic analysis revealed that 72.8% of the pharmacy store is located in urban area while 27.2% are present in rural area; 46% of the pharmacy store are small in size and 56% are big in size; 43% of the retailers are associated with more than 10 pharmaceutical product and 31% of the retailers were selling the 5 to 10 different company’s products while 27% of the store has less than 5 types of company’s product; 61% of the pharmacy stores are a standalone type of store and 24% are hospital pharmacy while 15% are the chain pharmacy.

Factor Identification and Extraction of Factors Related to Overall Performance of Supply Chain Model in Pharmaceutical Sector

The exploratory factor analysis technique was used to validate the constructs related to the performance by the method of communalities and percentage of variance, and important factors were extracted by the varimax procedure. The Kaiser–Meyer–Olkin (KMO) value of sampling adequacy is greater than 0.8; hence, we can consider factor analysis for the study is appropriate. Table 1 indicates eigenvalues and extraction sum of squared loadings. The cumulative percentage of variance of 4 factors sums up to 65.314% which indicates the importance of these predictors. The loading component of 13 variables is used in factor analysis. Factor loading describes the intensity of the relation between a factor and its variables. Factor loads below 0.40 have been omitted because they were not deemed relevant. The first element of the study ‘information’ fell under component 2 with IF1 (Information system) having the highest and IF2 (Information sharing) having the lowest value. The second element ‘technology infrastructure’ falls under component 1 with the highest value of TI1 (system software) and lowest TI3 (security system). The third element ‘inventory management’ falls under component 3 with the highest value of IM1 (inventory operation) and lowest IM2 (product). The last element is ‘demand’, which falls under component 4 with DM1 (promotion) having the highest value and DM4 (disease effect) with the lowest value.

Table 1.

Supply Chain Model Validation by Principal Component Analysis.

Factor Name	Variable Name	Communality	Eigenvalue	% of Variance	Rotated Component
Factor 1 (information)	Information system (IF1)	0.768	5.087	39.13	0.841
	Information sharing (IF2)	0.535			0.783
	Information quality (IF3)	0.695			0.802
Factor 2 (technology infrastructure)	System software (TI1)	0.686	1.329	10.226	0.816
	App integration (TI2)	0.51			0.787
	Security system (TI3)	0.7			0.552
Factor 3 (inventory management)	Inventory operation (IM1)	0.535	0.916	8.122	0.82
	Product (IM2)	0.541			0.638
	Stock replenishment (IM3)	0.695			0.654
Factor 4 (demand)	Promotion (DM1)	0.762	0.716	7.836	0.827
	Price of substitute (DM2)	0.638			0.758
	Discount and schemes (DM3)	0.876			0.738
	Disease effect (DM4)	0.876			0.682
KMO measure of sampling adequacy	0.855	Bartlett’s test of sphericity	Approx. Chi- square	505.852	df (104)

Association Between the Variables

To check the association among the variables such as information, technology infrastructure, inventory management and demand with each other, the sum score of variables within the construct has been calculated. The first construct ‘information’ has shown the highest value with technology infrastructure (0.645) and the lowest value with demand management. The second construct ‘technology infrastructure’ has shown the highest association with information (0.645). The third construct ‘inventory management’ has shown the highest association with technology infrastructure (0.626). The fourth construct ‘demand’ has shown a high association with technology infrastructure (0.518) (Table 2).

Table 2.

Relationship Between the Constructs Related to Improvement in PSC.

		Information	Technology Infrastructure	Inventory Management	Demand
Information	Pearson correlation	1	0.645^**	0.488^**	0.478^**
	Sig. (2-tailed)		0.000	0.000	0.000
	N	100	100	100	100
Technology infrastructure	Pearson correlation	0.645^**	1	0.626^**	0.518^**
	Sig. (2-tailed)	0.000		0.000	0.000
	N	100	100	100	100
Inventory management	Pearson correlation	0.488^**	0.626^**	1	0.360^**
	Sig. (2-tailed)	0.000	0.000		0.000
	N	100	100	100	100
Demand	Pearson correlation	0.478^**	0.518^**	0.360^**	1
	Sig. (2-tailed)	0.000	0.000	0.000
	N	100	100	100	100

Note: ** Correlation is significant at the 0.01 level (2-tailed).

The consistency of different constructs such as information, technology infrastructure, inventory management and demand has been calculated with reliability statistics. The values suggest (Table 3) that the construct has good consistency with all the components of information, technology infrastructure, inventory management and demand. It implies that these factors could be used to predict its effect on overall SC performance.

Table 3.

Reliability Statistics of Principal Components of PSC.

		Information	Technology Infrastructure	Inventory Management	Demand
Cronbach’s alpha	Part 1	0.574	0.806	0.674	0.23
Total no. of Items		3	3	3	4
Correlation between forms		0.234
Spearman–Brown coefficient	Equal Length	0.38	0.562	0.350	0.344
	Unequal Length	0.397	0.582	0.367	0.344
Guttman split-half coefficient		0.344	0.488	0.338	0.335

Characteristic Summary of Different Variables

Mean and standard deviation are the two tools used in descriptive statistics to summarize the characteristics of the sample data. The weighted average score was calculated from the frequency and the ranking was given according to them. Thus, rank 1 would be considered more impactful among the variables in the construct. It is clear from Table 4 that IF1, which is information system, has scored rank 1 and has the highest impact among other variables within information. TI2, which is app integration, has scored rank 1 and it has the highest impact among other variables within technology infrastructure. IM1, which is product, has scored rank 1, and it has the highest impact among other variables within inventory management. DM1, which is availability, has scored rank 1, and it has the highest impact among other variables within demand. As indicated, the weighted averages were higher (approx. > 75.0%); hence, all these variables identified were contributing towards SCM.

Table 4.

Descriptive Statistics of Sub-components of Supply Chain Predictors.

	Information			Tech. Infra.			Inventory Mgt.			Demand
Variable	IF1	IF2	IF3	TI1	TI2	TI3	IM1	IM2	IM3	DM1	DM2	DM3	DM4
Mean	4.19	4.13	3.85	4.34	4.41	4.37	4.48	3.82	3.67	4.35	3.68	4.32	4.21
Std. dev.	1.01	0.61	0.82	0.85	0.76	0.78	0.81	1.09	1.24	0.95	1.07	0.76	0.85
Wt. avg.	83.8	82.6	77	86.8	88.2	87.4	89.6	76.4	73.4	87	73.6	86.4	84.2
Rank	1	2	3	3	1	2	1	2	3	1	4	2	3

Effect of Predictors with Respect to Location and Size in PSC Units

In order to test the homogeneity of variance between location and size with different variables was justified with p-values greater than 0.05; hence, the assumption was met and equal variances were assumed in the research sample. Then, an independent sample t-test was calculated by comparing the means of two independent groups in order to determine whether there is statistical evidence with information systems as per the location factors differ or not (Table 5).

Table 5.

Equality of Mean of Predictors with Respect to Location in Rural and Urban Areas Determined by Independent Sample t-Test Procedure.

		Sig.	t Value	Hypothesis	Remarks
Levene’s t-test for information:Sum IF	Equal variances assumed	0.03	−1.979	H₀: There exists no noteworthy difference that links location and information in the supply chain.H₁: There exists noteworthy difference that links location to information in supply chain.	H₀ rejected
Levene’s t-test for technology infrastructure:Sum TI21	Equal variances assumed	0.28	−1.11	H₀: There exists no noteworthy difference that links location and technology infrastructure in the supply chain.H₁: There exists a noteworthy difference that links location to technology infrastructure in supply chain.	H₀ accepted
Levene’s t-test for inventory management:Sum IM	Equal variances assumed	0.21	−1.055	H₀: There exists no noteworthy difference that links location and inventory management and in supply chain.H₁: There exists noteworthy difference that links location to inventory management in supply chain.	H₀ accepted
Levene’s t-test for demand:SUM DM	Equal variances assumed	0.709	−0.367	H₀: There exists no noteworthy difference that links location and demand in supply chain.H₁: There exists noteworthy difference that links location to demand to in supply chain.	H₀ accepted

In the case of location, significant differences were observed with information on the SCM. Due to the lack of information and technology infrastructure in the rural areas, there is a higher concentration of staff in the urban areas as compared to rural areas. The poor infrastructure is the main cause of lower presence and availability of medicines as compared to urban areas.

In the case of size as shown in Table 6, significant differences were observed with inventory management on SCM. Standardized protocols should be in place to ensure order placement, tracking the movement of medicines, ensuring availability in optimum quantity and proper stacking, dispensing and demand evaluation by the staff. Optimum size and staffing can significantly improve inventory management and, hence, the overall SCM in the pharmaceutical industry.

Table 6.

Equality of Mean of Predictors with Respect to Size in the Pharmaceutical Units Determined by the Independent Sample t-Test Procedure.

		Sig.	t Value	Hypothesis	Remarks
Levene’s t-test for information	Equal variances assumed	0.301	−0.650	H₀: There is no significant difference between the size with respect to information in the supply chain.H₁: There is a significant difference between the size with respect to the information in the supply chain.	H₀ accepted
Levene’s t-test for inventory management	Equal variances assumed	0.005	2.850	H₀: There is no significant difference between the size with respect to inventory management in the supply chain.H₁: There is a significant difference between the size with respect to the inventory management in the supply chain.	H₀ rejected
Levene’s t-test for demand	Equal variances assumed	0.184	−1.20	H₀: There is no significant difference between the size with respect to demand in the supply chain.H₁: There is a significant difference between the size with respect to the demand in the supply chain.	H₀ accepted

Impact of All Four Components on Supply Chain Related to Number of Companies Products and the Type of Pharmacy Stores

The impact was determined by using t-tests for understanding the significant changes. It was derived that the information systems have a significant ability to improve the number of companies’ products and the type of pharmacy stores (Table 7). Hence, it can be concluded that it would be enhanced availability of medicines of different therapeutic categories as well as a higher number of stock-keeping units from the customer perspective can boost the PSC systems.

Table 7.

Equality of Means of Predictors with Respect to Number of Companies Products and the Type of Pharmacy Stores Determined by Analysis of Variance.

Variable	F	Sig.	Hypothesis	Remark
IF	8.295	0.000	H₀: There exists no notable difference between the number of companies’ products and type of store with respect to information in the supply chain.H₁: There exists a notable difference between the no. of companies’ products and the type of store with respect to the information in the supply chain.	H₀ rejected
TI	0.575	0.565	H₀: There exists no notable difference between the number of companies’ products and the type of store with respect to technology infrastructure in the supply chain.H₁: There exists a notable difference between the number of companies’ products and the type of store with respect to technology infrastructure in supply chain.	H₀ accepted
IM	0.248	0.781	H₀: There exists no notable difference between the number of companies’ products and type of store with respect to inventory management in the supply chain.H₁: There exists a notable difference between the no. of companies’ products and the type of store with respect to inventory management in the supply chain.	H₀ accepted
DM	1.101	0.337	H₀: There exists no notable difference between the number of companies’ products and the type of store with respect to demand in the supply chain.H₁: There exists a notable difference between the number of companies’ products and the type of store with respect to demand in the supply chain.	H₀ accepted

By personal interviewing with experts from the firm’s perspective, the overall cost of the supply chain would be minimized as they can leverage their already established supply chain for more products as well as act as a strategic supply partner to other organizations that do not have a well-established supply chain.

Contribution of Predictors on the Overall Performance of the PSC

Linear regression analysis was run independently on all four parameters (as they showed association) taking the performance of the PSC as the dependent component. R-square indicates the coefficient of determination and helps to measure the strength of the model (Table 8).

Table 8.

Simple Linear Regression Model of Individual Predictors on Overall Performance.

		Regression Analysis
Variable	R-square	Interpretation
Information	0.719	Information is a good predictor to determine overall PSC performance
Technology infrastructure	0.773	Technology infrastructure is a good predictor to determine overall PSC performance
Inventory management	0.721	Inventory management is a good predictor to determine overall PSC performance
Demand	0.785	Demand is a good predictor to determine overall PSC performance

Linear regression analysis was run independently on all four parameters (as they showed association) taking performance of the PSC as the dependent component. All the components showed a significant and positive impact. So, to study the overall model, the path network was created by the structural equation modelling (SEM) technique. Approximately 70%–80% change can be observed if these constructs are dealt with individually, but the cumulative construct contribution was observed by SEM technique.

Impact of Information, Technology Infrastructure, Inventory Management and Demand on the Overall Performance of the PSC by SEM Techniques

Confirmatory factor analysis was used to structure the proposed overall performance of the supply chain model graphically with advanced tools. SEM is a multivariate statistical technique used to analyse the structural relationships between variables. Four variables were used in the present study: information (IF), technology infrastructure (TI), inventory management (IM) and demand (DM). The sub-factors under information (IF) included information system (IF1), information sharing (IF2) and information quality (IF3). The sub-factors under technology infrastructure (TI) included system software (TI1), app integration (TI2) and security system (TI3), while inventory management (IM) also has three subconstructs: inventory operations (IM1), product (IM2) and stock replenishment (IM3). The final construct ‘demand (DM)’ has four subconstructs: promotion (DM1), price of substitute (DM2), discounts and schemes (DM3) and disease outbreak (DM4). Proposed model for measurement of overall performance of PSC is given in Figure 1.

Composite Reliability and Validity and Discriminant Validity

The Cronbach’s alpha represents the ‘internal consistency reliability’ and the value for all the constructs is greater than 0.6, which shows good reliability. The composite reliability value was found to be more than 0.7, which means that the composite reliability among the construct was good to accept and the average variance extracted means the degree to which items shared between the construct and the acceptable value is more than 0.5. In this study, the information is showing less than 0.5, which means that the factor has less correlation with the latent construct. The HTMT ratio of all the variables in the study was found to be less than 0.9, which shows that there is a discriminant validity among the variables within the construct.

Multicollinearity in the Study

The value of VIF in both the order is less than 5, which means that they have no similarity among them, and there is no correlation between the independent variable.

In Table 9, we found that the value of most of the variables is more than 0.5; hence, they are highly correlated with each other. But IF2 (information sharing) and IM3 (stock replenishment) on inventory management had a moderate correlation with the performance of the PSC. Also, P value (0.000) indicates the significance of the variables. These sub-factors identified should be improved in their respective areas as discussed. The observations as per the previous literature inputs were that price of substitutes, which controls the demand for the generic products, was higher than the branded products owing to price-sensitive Indian markets. Branded generics companies usually invest heavy amounts into marketing, promotion and other value-added services, which are made available for customers, which enhance their knowledge and awareness about the condition and treatments. The second factor was information sharing, which was ineffective in order to improve the association with SCM the companies should share business information with the supply chain partners that would increase the chances of eliminating any glitches; on the other hand, the channel partners should ensure that they provide complete, reliable and accurate information to the pharmaceutical organizations. The third factor identified was stock replenishment using artificial intelligence (AI) algorithms to increase supply chain efficiency. Organizations should leverage AI and further increase its penetration in the supply chain ecosystems.

Table 9.

Factor Loadings of Factors Affecting Overall Performance of Supply Chain in the Pharmaceutical Sector.

Factor Loadings	Original Sample (O)	P Values
Promotion < – Demand	0.879	0
DM1 < – Overall performance of supply chain	0.824	0
Price of substitute < – Demand	0.336	0.473
DM2 < – Overall performance of supply chain	0.365	0.626
Discounts and schemes < – Demand	0.855	0
DM3 < – Overall performance of supply chain	0.737	0
Information system < – Information	0.944	0
IF1 < – Overall performance of supply chain	0.819	0
Information sharing < – Information	0.422	0.929
IF2 < – Overall performance of supply chain	0.205	0.974
Information quality < – Information	0.57	0
IF3 < – Overall performance of supply chain	0.328	0
Inventory Operations < – Inventory management	0.887	0
IM1 < – Overall performance of supply chain	0.822	0
Product < – Inventory management	0.803	0
IM2 < – Overall performance of supply chain	0.628	0
Stock Replenishment < – Inventory management	0.456	0
IM3 < – Overall performance of supply chain	0.351	0
System Software < – Technology infrastructure	0.897	0
TI1 < – Overall performance of supply chain	0.824	0
App integration < – Technology infrastructure	0.857	0
TI2 < – Overall performance of supply chain	0.798	0
Security system < – Technology infrastructure	0.639	0
TI3 < – Overall performance of supply chain	0.559	0

Predictive Relevance Value (Q-square)

To predict the predictive relevance (Q²), the path model used for the study can very well predict the original observed values when you remove each construct by using the blindfolding algorithm. The Q-square value for the study is 0.276, which is more than 0; hence, the values are reconstructed, and the model has predictive relevance. Each of the variables has predictive relevance over the performance of PSC.

A positive coefficient implies that a rise of a single unit in one structure activity measure results in directly increasing activity measure and is proportional to the size of the coefficient whereas, for a coefficient that is negative, a single unit of increase in the activity measure of one structure corresponds to a proportionally direct decrease in the activity measure of performance of supply chain. In the above Figure 2, the value of all the variables was positive, which indicates that a single unit rise in the activity measures of all the structures (information, technology infrastructure, inventory management and demand) results in a directly proportional rise in the activity measure of overall performance of the PSC. For overall improvement in performance, it was observed that the demands of the product related to marketing activities are capable of increasing performance by 28.2%, whereas information sharing and transparency contribute 19.9%. The physical verification of inventory management may improve the performance of the supply chain by 28.9%, whereas the most important factor was technology infrastructure, which includes automation, integration and security by 36.9%, so the performance of the PSC should be empirically tested and resources should be induced respectively. The R-square value of technology infrastructure was highest, and the strengths were TI1 (system software) and TI2 (app integration) as these were given equal weightage by the beta coefficients, whereas the weakness was TI3 (security systems) that can be bridged out by technical advancements. The second factor with an R-square value (28.9%) was inventory management, which was led by IM1 (effective inventory operations) and IM2 (product portfolio), while stock replenishment needs rigorous work on automation activities using intelligent algorithms. The third factor was demand with R-square value 28.2%, and the positives were DM1 (promotion) and DM3 (discounts and schemes) with the major pullback by prices of substitutes. In case of any disease outbreaks, there should be an increase in the demand for effective supply chain systems under the conditions of uncertainty with proper decision-making activities. In the case of information, the R-square value was 19.9%, of which the major contribution came from IF1 (information systems) while IF2 (information sharing) and IF3 (information quality).

Goodness of Fit of Supply Chain Model

The value of the SRMR was found to be 0.092, which is less than 0.1; thus, the model is a good fitting model. The value of Chi-square ranges from 1 to infinite and the value is 505.34 (Table 10), which reduces the impact of sample size; the value is, thus, divided by the degree of freedom 505.34/104, which is approximately 5. Hence, the value falls in the acceptable ratio. The RMS theta value of the model is 0.117 and is inappropriate for this model.

Table 10.

Model Fit Statistics.

	Saturated Model	Estimated Model
SRMR	0.092	0.092
d_ULS	0.385	0.385
Chi-square	505.34	505.34
Df	104	104
NFI	n/a	n/a
RMS theta	0.117	0.117

First-order Model with Reduction in the Errors

Latent variable values are considered to reduce the errors in measuring the performance of the PSC. Results show that information had an impact of 25%, technology infrastructure impacted 31%, inventory management with 28% and demand impacted 29% on the overall performance of PSC (Figure 3).

Key Findings and Conclusion

The study was aimed at finding answers to four research objectives. The first objective was to deduce the impact of information on the performance of the PSC. Three sub-variables supported the information variable: information system (IF1), information sharing (IF2) and information quality (IF3). After running factor analysis for three attributes, the ‘Kaiser–Meyer–Olkin measure of sampling adequacy’ (KMO) was found to be 0.763, while Bartlett’s test of sphericity under Chi-square was 18.35, with 3 degrees of freedom and a significance value of p = 0.000, indicating that the PSC’s performance is directly influenced by information.

The second objective was to explore the influence of technological infrastructure on the overall performance of PSC. Three sub-variables supported the technological infrastructure variable: system software (TI1), app integration (TI2) and security system (TI3). After running factor analysis for three attributes, the ‘Kaiser–Meyer–Olkin measure of sampling adequacy’ (KMO) was 0.591, while ‘Bartlett’s test of sphericity’ under Chi-square was 78.09, with 3 degrees of freedom and a significance value of p = 0.000, indicating that the PSC’s performance is directly influenced by technology infrastructure.

The third objective was to determine the influence of inventory management on the performance of the PSC. Three sub-variables supported the inventory management variables: inventory operations (IM1), product (IM2) and stock replenishment (IM3). After running factor analysis for three attributes, the ‘Kaiser–Meyer–Olkin measure of sampling adequacy’ (KMO) was 0.564, while ‘Bartlett’s test of sphericity under Chi-square’ was 37.55, with 3 degrees of freedom and a significance value of p = 0.000, indicating that the PSC’s performance is directly influenced by inventory management.

The last objective of this study was to investigate the effect of demand on the performance of the PSC. Four sub-variables supported the demand variables: promotion (DM1), price of substitute (DM2), discounts and schemes (DM3) and disease impact (DM4). After running factor analysis for three attributes, the ‘Kaiser–Meyer–Olkin measure of sampling adequacy’ (KMO) was 0.484, while ‘Bartlett’s test of sphericity under Chi-square’ was 35.172, with 6 degrees of freedom and a significance value of p = 0.000, indicating that the PSC’s performance is directly influenced by demand.

Recommendations

Based on the research, these are the following recommendations have to be considered by the supply channel partners to improve the overall performance of their supply chain:

In this advanced era of technology, the supply chain in the organization should be integrated with the advanced tools to make the supply chain effective and efficient as evident in the above results. The information sharing from one channel partner to another should be confidential and secure so that no one can misuse the information and make changes to the actual offers provided by the manufacturer to the supply chain partners.

On the basis of the above observations, it is recommended that all three components, i.e., system software (TI1), app integration (TI2) and security system (TI3), should be effectively used to improve the supply chain performance.

It has been observed that three sub-variables supported the inventory management variables: inventory operations (IM1), product (IM2) and stock replenishment (IM3) in the supply chain. So, it is essential for companies to use these to enhance the performance of the supply chain.

It is observed that demand is affected by promotion (DM1), price of substitute (DM2), discounts and schemes (DM3) and disease impact (DM4). Therefore, proper attention should be given to factors affecting the demand for improving the performance.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Anand Sharma

References

Awan

, Sroufe

, Shahbaz

(2021). Industry 4.0 and the circular economy: A literature review and recommendations for future research. Business Strategy and the Environment , 30, 2038–2060. https://doi.org/10.1002/bse.2731

Ballon

R. H.

(2000). Business logistics/supply chain management. Planning, organizing, and controlling the supply chain (5th.). Pearsons-Prentice Hall.

Biswas

, & Jaydip

(2016). A proposed architecture for big data-driven supply chain analytics. Journal of Supply Chain Management , 8(3), 7–34.

Cetinkaya

, Lee

C-Y.

(2000). Stock replenishment and shipment scheduling for vendor-managed inventory systems. Management Science , 46(2),217–232.

Chen

, Gu

, Cai

, & Yang

(2019). Impact of supply chain information sharing on performance of fashion enterprises: An empirical study using SEM. Journal of Enterprise Information Management , 32, 913–935. https://doi.org/10.1108/JEIM-04-2019-0104

Denton

B. T.

(2013). Handbook of healthcare operations management , (Vol. 10). Springer

El Mokrini

, Benabbou

, & Berrado

(2018). Multi-criteria distribution network redesign: Case of the public sector pharmaceutical supply chain in Morocco. Supply Chain Forum , 19, 42–54. https://doi.org/10.1080/16258312.2018.1433436

Fred

E. J.

(2018). Factors influencing Performance in Pharmaceutical Supply Chain - A Case Of Medical Stores Department (MSD) in Dar Es Salaam Reg. A Dissertation report submitted to Mzumbe University Tanzania.

Gabisa & Ram

(2021). The Effect of Information sharing and Inventory Management in the Supply Chain Practices on Firms’ Performance: Empirical Evidence from Some Selected Companies of Ethiopia, International Journal of Industrial Engineering and Operations Management (IJIEOM) , 3(1), 1–15

10.

George

& Madhusudanan Pillai

(2019). A study of factors affecting supply chain performance. Journal of Physics Conference Series , 1355(1),012018. https://doi.org/10.1088/1742-6596/1355/1/012018

11.

Habib

(2010). Supply chain management: Theory and its future perspectives. International Journal of Business, Management and Social Sciences , 1(1), 79–87.

12.

Haque

, & Islam

(2018). Impact of supply chain collaboration and knowledge sharing on organizational outcomes in pharmaceutical industry of Bangladesh. Journal of Global Operations and Strategic Sourcing , 11, 301–320. https://doi.org/10.1108/JGOSS-02-2018-0007

13.

Hilletofth

(2011). Demand-supply chain management: Industrial survival recipe for new decade. Industrial Management & Data Systems , 111, 184–211. https://doi.org/10.1108/02635571111115137

14.

Jayaram

, Vickery

S.K.

, & Droge

(2000). The effects of information system infrastructure and process improvements on supply-chain time performance. International Journal of Physical Distribution & Logistics Management , 30, 314–330. https://doi.org/10.1108/09600030010326082

15.

Kochan

, Nowicki

, Sauser

, & Randall

(2018). Impact of cloud-based information sharing on hospital supply chain performance: A system dynamics framework. International Journal of Production Economics , 195, 168–185.

16.

Koçoğlu

, İmamoğlu

S. Z.

, İnce

, & Keskin

(2011). The effect of supply chain integration on information sharing: Enhancing the supply chain performance. Procedia Social and Behavioral Sciences , 24, 1630–1649.

17.

Lee

H. L.

(2004). The triple-A supply chain. Harvard Business Review , 82(10), 102–113.

18.

, Rao

S.S.

, Ragu-Nathan

T.S.

, & Ragu-Nathan

(2005). Development and validation of a measurement instrument for studying supply chain management practices. Journal of Operations Management 23, 618–641. https://doi.org/10.1016/j.jom.2005.01.002

19.

Marinagi

, Trivellas

, & Reklitis

(2014). Information quality and supply chain performance: The mediating role of information sharing. Presented at the Procedia–Social and Behavioral Sciences . https://doi.org/10.1016/j.sbspro.2015.01.1225

20.

Mathur

, Gupta

, Meena

M.L.

, & Dangayach

G.S.

(2018). Healthcare supply chain management: Literature review and some issues. Journal of Advances in Management Research , 15(3), 265–287.

21.

Mehrjerdi

Y.Z.

, & Shafiee

(2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information-sharing strategies. Journal of Cleaner Production , 289, 125141. https://doi.org/10.1016/j.jclepro.2020.125141

22.

Mourtzis

(2011). Internet-based collaboration in the manufacturing supply chain. CIRP Journal of Manufacturing Science and Technology, Production Networks Sustainability , 4, 296–304. https://doi.org/10.1016/j.cirpj.2011.06.005

23.

Naraharisetti

P.K.

, & Karimi

I.A.

(2010). Supply chain redesign and new process introduction in multipurpose plants. Chemical Engineering Science , 65, 2596–2607. https://doi.org/10.1016/j.ces.2009.12.036

24.

Nazifa

T.H.

, & Ramachandran

K.K.

(2018). Exploring the role of information sharing in supply chain management: A case study. Journal of System and Management Sciences , 8, 25.

25.

Pharmaceutical Companies in India, Indian Pharma Industry (IBEF). (n.d.). India Brand Equity Foundation. https://www.ibef.org/industry/pharmaceutical-india

26.

Rai

, Patnayakuni

, & Seth

(2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly , 30, 225–246. https://doi.org/10.2307/25148729

27.

Raweewan

, & Ferrell

W.G.

(2018). Information sharing in supply chain collaboration. Computers & Industrial Engineering , 126, 269–281. https://doi.org/10.1016/j.cie.2018.09.042

28.

Saraf

, Langdon

, & Gosain

(2007). IS Application capabilities and relational value in interfirm partnerships. Information Systems Research , 18(3), 320–339.

29.

Sbai

, Benabbou

, & Berrado

(2020). An AHP-based approach for multi-echelon inventory system selection: Case of distribution systems. In 2020 5th International Conference on Logistics Operations Management (GOL), 1–8. https://doi.org/10.1109/GOL49479.2020.9314711

30.

Shahi

S.K.

, Shiva

, & Dia

(2021). Integrated sustainable supply chain management and firm performance in the Indian textile industry. QROM , 16, 614–635. https://doi.org/10.1108/QROM-03-2020-1904

31.

Shokouhyar

, Seddigh

M.R.

, & Panahifar

(2020). Impact of big data analytics capabilities on supply chain sustainability: A case study of Iran. World Journal of Science, Technology and Sustainable Development , 17, 33–57. https://doi.org/10.1108/WJSTSD-06-2019-0031

32.

Strandberg

, & Åsenius

(2022). Managing demand variability by distinguishing between internal and external variability: Investigating the requirements for managing demand through demand shaping in B2B companies. Master of Science Thesis, KTH Industrial Engineering and Management Industrial Economics and Management.

33.

Sutduean

, Joemsittiprasert

, & Jermsittiparsert

(2019). Exploring the nexus between information technology: Supply chain and organizational performance: A supply chain integration approach. International Journal of Innovation , 5, 17.