Eye-SCOR: A Supply Chain Operations Reference-Based Framework for Smart Eye Status Monitoring Using System Dynamics Modeling

Abstract

This work is a unique integration of three different areas, including smart eye status monitoring, supply chain operations reference (SCOR), and system dynamics, to explore the dynamics of the supply chain network of smart eye/vision monitoring systems. Chronic eye diseases such as glaucoma affect millions of individuals worldwide and, if left untreated, can lead to irreversible vision loss. Nearly half of the affected population is unaware of the condition and can be informed with frequent, accessible eye/vision tests. Tonometry is the conventional clinical method used in healthcare settings to determine the intraocular pressure (IOP) level for evaluating the risk of glaucoma. There are currently very few (under development) non-contact and non-invasive methods using smartphones to determine the risk of IOP and/or the existence of other eye-related diseases conveniently at home. With the overall goal of improving health, well-being, and sustainability, this paper proposes Eye-SCOR: a supply chain operations reference (SCOR)-based framework to evaluate the effectiveness of smartphone-based eye status monitoring apps. The proposed framework is designed using system dynamics modeling as a subset of a new causal model. The model includes interaction/activities between the main players and enablers in the supply chain network, namely suppliers/service providers, smartphone app/device factors, customers, and healthcare professionals, as well as cash and information flow. The model has been tested under various scenarios and settings. Simulation results reveal the dynamics of the model and show that improving the eye status monitoring device/app factors directly increases the efficiency/Eye-SCOR level. The proposed framework serves as an important step towards understanding and improving the overall performance of the supply chain network of smart eye/vision monitoring systems.

1. Introduction

1.1. Background Literature and Motivation

Vision, as one of the most highly regarded senses for the perception of the surrounding world, is generally taken for granted. However, it is critical to take proper preventative care to avoid loss of eye sight. Eye diseases such as age-related macular degeneration (AMD), cataract, diabetic retinopathy, and glaucoma are the leading causes of blindness and low vision across the world. These chronic eye diseases may primarily develop as a result of aging and/or genetic history [1,2,3,4,5,6]. Specifically, glaucoma, also referred to as the vision thief and silent blinding disease with no pain, is generally due to sudden or gradual elevated pressure inside the eye, known as intraocular pressure (IOP). A cataract, on the other hand, forms as a result of the clouding of the normally clear lens of the eye. Tonometry is the clinical method eye care professionals use at healthcare settings to determine the IOP level for evaluating the risk of glaucoma. Trained operators at the clinic generally perform a number of other eye examinations, such as the visual field test and the optical coherence tomography (OCT) imaging test, along with tonometry to evaluate eye vision and possible chronic eye conditions, including cataract and glaucoma. These tests require dilating the pupil, which causes discomfort for the patient for a couple of hours. Currently, there are very few (under progress) automated non-invasive and non-contact techniques where users can check their risk of IOP and other eye conditions conveniently at home [7]. Most computer-aided techniques rely on fundus images of the eye obtained in clinical settings, showing the status of the optic nerves [8]. Smartphone-based applications that can conveniently monitor the status of the eye would be very appealing to the public.

Mobile/electronic health (m-health/e-health) applications have evolved with the advent of smartphones, accommodating online communication and connectivity features. The COVID-19 pandemic has also heavily contributed to the prevalence of smartphone-based remote and telemedicine platforms. The embedded sensors and smart processor chips of smartphones allow for the acquisition of various physiological data. The acquired data are generally processed via smartphone apps and converted into meaningful information for the end user. To date, over 100,000 m-health/e-health applications have been developed and made available through online stores [9], with many more applications underway [10,11].

Automated decision support frameworks for smart healthcare monitoring rely on artificial intelligence (AI)-assisted models using machine/deep learning and/or signal/image processing, as well as data analysis techniques applied to the acquired digital health data. Examples include techniques monitoring heart status using electrocardiogram (ECG) signal [12], brain and mental status from electroencephalogram (EEG) signal [13,14], breathing and respiratory status from breathing sounds [15,16,17,18], skin lesion analysis from images [19], eye disease determination [7,20,21,22,23,24], and so on, some of which have been translated into smartphone apps [10,25].

For chronic eye diseases, intelligent frameworks can help in the initial diagnosis of conditions such as high IOP early on. Frequent screening for early diagnosis is the most effective approach to prevent full or partial IOP/glaucoma-based vision loss. Nonetheless, IOP risk assessment from purely frontal eye images that can be used on smartphones has not been sufficiently investigated. Many researchers have proposed several works for IOP and glaucoma detection and analysis of the eye from images. However, there is a lack of studies regarding IOP based on frontal eye images in the computer vision field [20,21]. Most prior related work either focuses on fundus/optic disc images or requires additional hardware sensors for eye blood vessel detection, pupil/Iris detection, and/or eye redness determination.

A computer-vision-based approach using machine/deep learning techniques for high-risk IOP determination solely from the frontal view of eye images has been introduced in [20]. Several image preprocessing steps, such as Adaboost face detection algorithm and Haar cascade eye detection, are applied to extract the eye, along with histogram equalization to enhance the contrast of the iris and pupil area, morphological reconstruction technique to remove the light reflection, adaptive thresholding to separate the foreground from the background, canny edge detection to detect the edges of the pupil and the iris, and circular Hough transform to identify the iris and pupil regions. A fully convolutional neural network (an instance of deep learning) is employed for sclera and iris segmentation [20]. The segmented areas are then further processed to extract six features for IOP risk determination. These features include the pupil/iris diameter or radius ratio, the mean redness level (which is a weighted average of the red, blue, and green pixels on the extracted portion of the sclera), red area percentage (average of the red pixels in extracted sclera region), and three features from the contour of the sclera (area A, distance D, and angle $\alpha$). Trigonometry relations of the triangle formed in the extracted region of the sclera are used to compute the sclera contour features D, A, and $\alpha$ (see Figure 1). After feature extraction, a binary classifier is applied to train and test the images and categorize the eye image into normal (healthy) IOP or high-risk (unhealthy) IOP. These ideas can be further embedded into ubiquitous hand-held devices for convenient use. Frontal eye images captured from the smartphone/tablet camera can be used as the input image to the smartphone app performing the analysis.

The major components of a thorough analysis of smart systems (which is the focus of this research) include people, practice, process, and performance. These components are the key features of the supply chain operations reference (SCOR) model. The sustainable SCOR model provides a standard process language to communicate between three pillars of sustainability (people, planet, profit) in the supply chain. These supply chain processes are plan, source, make, delivery, return, and enable [26,27]. The SCOR model is frequently used in supply chain performance measurement [28]. The SCOR model was established by the Supply Chain Council in 1996 and has been updated regularly to adapt to changes in supply chain business practices [29]. It focuses on the supply chain management function to assist firms in increasing the effectiveness of their supply chain [30,31]. In particular, the SCOR model is a tool for mapping supply chain processes and activities [32]. Moreover, SCOR utilizes a business process reference model that links process description with metrics, best practice, and technology [31,33]. It is used to describe a measure and evaluate the supply chain configuration [32]. As noted in the literature as well as the market, the SCOR model is widely recognized by industries for supply chain operations, including customer interactions, physical transactions, and market interactions, and evaluating supply chain performance [27,34]. For example, the SCOR model has been used by several companies throughout the world [29]. Companies such as Medtronics and Intel have adopted the SCOR framework [35]. These companies have experienced faster cycle time, lower inventories, improved visibility of the supply chain, better performance of their supply chain members, access to important customer information in a timely fashion, increased customer satisfaction, and improved delivery of product and service quality [29,36].

This paper is focused on Eye-SCOR: a supply-chain-operations-reference-based framework for smart eye status monitoring. As there are many players and enablers in the eye monitoring network, a systems perspective would allow a better understanding of the inter-relationships among the players and enablers in the Eye-SCOR model. System dynamics is a systems perspective method that captures the dynamic interactions within a system from a holistic perspective and studies the behavior of the system [37,38]. A causal model, which is a tool within system dynamics, would capture the concepts and the inter-relationships from a visual viewpoint. System dynamics addresses non-linear relationships and feedback scenarios simultaneously [39]. The formal steps of systems dynamics include (i) problem articulation, (ii) dynamic hypothesis, (iii) formulation, (iv) testing, and (v) policy formulation and evaluation [40]. System dynamics was first developed by Jay Forrester and was primarily utilized as a decision making and modeling tool in business and industrial management [41]. System dynamics has now been applied to a vast range of areas in healthcare [42,43,44,45,46,47,48,49,50], in addition to some areas of smart healthcare [9,51,52,53]. There have been limited studies utilizing the combination of a SCOR-based model with system dynamics for a health-based monitoring system. Most studies with the combination of SCOR-based model, system dynamics, and healthcare in general focus on the plan, make, and source processes [54,55,56]. Some have focused on supply chain and system dynamics modeling for disaster management [57] and the refugee crisis [58]. Others focused on SCOR with fuzzy Delphi methods to evaluate the supply chain performance in medical equipment companies [59]. The main reason for utilizing SCOR is the objective of this research, which aims to understand the dynamics of the supply chain network of smart eye status monitoring applications, including all players in the supply chain, to improve the performance and efficiency. Existing SCOR-based frameworks provide information on the players of the supply chain but lack information and knowledge regarding the dynamics of the components and/or players. System dynamics is a systems engineering approach that can provide detailed information regarding the dynamics of the components of a system and is thus applied to Eye-SCOR in this work.

This work is a unique integration of three different areas, including (1) smart eye status monitoring, (2) SCOR model, and (3) system dynamics, representing the first of its kind in the literature. The technical gaps include lack of a systematic framework to understand the dynamics of the supply chain network of smart eye status monitoring systems. To explore the dynamics of the overall system, including all players in the supply chain (patients, users, app developers, smartphone app markets, physicians, policy makers, etc.), the supply chain operations reference (SCOR) framework is necessary for this work.

1.2. Contribution

To study the effectiveness of smartphone-based eye status monitoring apps, this paper proposes Eye-SCOR: a supply chain operations reference (SCOR)-based framework using system dynamics modeling. The model includes interaction and activities between the main players and enablers in the supply chain network, namely suppliers/service providers, smartphone app/device factors, customers, and healthcare professionals, as well as cash and information flow. The SCOR model is uniquely adopted in this work with a systems engineering and system dynamics perspective to capture the complex dynamics and evaluate the overall system performance.

The purpose of a SCOR model is to define a framework to describe and evaluate the processes, their interactions, and their performances, in a way that makes sense to the key players in the system. Through a systems perspective, the various factors and their inter-relationships are identified within the SCOR framework. As SCOR-based frameworks using system dynamics modeling have not been previously applied to smartphone-based eye monitoring apps, this Eye-SCOR research provides a novel insight in this respect.

The problem of this research can be articulated as advancing knowledge by combining a systems engineering perspective of SCOR modeling using system dynamics in smart eye status monitoring applications.

To deal with intrinsically complex problems, a holistic systems approach is required to understand the complicated behavior of various system factors that have linear and non-linear relationships with feedback loop interactions. With several factors, players, and their various inter-relationships involved, Eye-SCOR is considered a complex system. SCOR models are generally used in identifying, measuring, and improving system processes [60]. The type of analysis for the performance of SCOR models is referred to as root causing [27]. System dynamics uses causal models to represent cause and effect relationships between factors. Therefore, initially, a causal model is introduced to demonstrate the key factors and the relationships involved. Based on the causal model, a system dynamics model is then proposed for the Eye-SCOR framework. To test the Eye-SCOR framework, various simulations have been conducted and presented in this work.

1.3. Paper Organization

The remainder of this paper is organized as follows. In Section 2, a causal model is introduced, followed by the overall structure of our proposed system dynamics model for the Eye-SCOR framework. Section 3 presents the simulation results of the Eye-SCOR framework under various scenarios, followed by a discussion in Section 4. Finally, in Section 5, the findings of this research are summarized, and the paper is concluded.

2. Methodology

2.1. Causal Model

We propose a causal model to map the processes from the SCOR framework with our factors and inter-relationships in the Eye-SCOR model. This model captures outcomes from the players and enablers in the system in a causal map that represents the factors, inter-relationships, and feedback connections that will help with evaluating the effectiveness of the Eye-SCOR model.

Causal models provide a graphical understanding of the system process’ factor and inter-relationships [46]. These models are utilized in support of system dynamics to capture the major feedback mechanisms within a model, and they can also simplify the representation of a system. Causal model diagrams can exhibit various hypotheses about factors. The elements of a causal model represent the factors, and the signed links associated with the elements depict an increasing (+) or decreasing (−) relationship among the factors involved.

To create a causal model, we carried out a thorough review of the literature in the areas related to system dynamics models in healthcare [42,44,49,50,61] and smart healthcare monitoring [9,51,52,53] to first identify the factors that will be involved in Eye-SCOR. The proposed causal model includes a set of dynamic hypothesized relationships among factors. The causal model should cover those factors and their relationships that are essential for patients and stakeholders. In particular, we focus on eye status monitoring apps and are interested in the features and service performances of these apps. The set of factors we identified include software, app- and device-related factors, as well as social and economic factors in the supply chain process. The eye status monitoring app’s design and performance metrics, such as the ocular (eye status) medical monitoring capability, eye data acquisition feasibility, eye analysis algorithm and software management competence, cloud connectivity, reliability, security/privacy and bandwidth capacity, and location tracking ability, constitute the embedded software/hardware factors of the eye status monitoring system. Factors such as cost, rate of need, demand, patient satisfaction, and patient well-being, on the other hand, are examples of socioeconomic factors.

Figure 2 illustrates our proposed causal model for the Eye-SCOR framework. The factors deemed most important for Eye-SCOR have been selected as a result of comprehensively reviewing the literature related to causal models for healthcare applications [9] and specifically eye status monitoring systems [20,21]. The model carries hypothesized information among the factors and their inter-relationships. As seen from the figure, the model suggests that as various system factors (such as the eye analysis algorithm and software management competence, data acquisition feasibility, ease of use, and demand) increase, patient satisfaction and well-being, and therefore the efficiency of the Eye-SCOR framework, will also increase.

The embedded software/hardware factors of the eye status monitoring system play a major role in determining the efficiency of Eye-SCOR. Software factors such as the eye analysis algorithm refer to the specific image/signal processing or deep/machine learning algorithm used for analyzing the eye image to determine the eye status. It is clear that the more accurate the algorithm is, the better the performance will be. Other software management factors include to licensing, IT support, and upgrading features of the app. The eye status monitoring capability factor refers to the overall objective and/or stage of the eye analysis algorithm (e.g., determining the stage of the eye disease or merely the existence of an eye disease). Data acquisition refers to the method and/or device used to collect data from the eye. In this work, we mainly focus on the most feasible method of frontal eye images that can be captured using the smartphone camera. An alternative would be another high-resolution camera, whose image would be then transferred to the smartphone/app to be processed. Another option would be fundus images showing the optic nerves, which require micrometer imaging and may not be possible to acquire and process using a smartphone. Other alternatives may be air puffs that measure the air flow and pressure. In the case of using images as the acquired input data, the quality of the image would be another determining factor affecting the performance. As the main processing of the input data will be performed in the cloud, the cloud connectivity, data security and privacy, and bandwidth capacity are the main characteristic features related to the cloud/internet, which directly affect the performance. Response time refers to the time required for the eye data analysis algorithm to report the results back to the user. Faster response times (real-time or near-real-time responses) are generally desirable, especially for urgent cases that require immediate action. Ease of use refers to the user friendliness level of the app interface for convenient use. Battery life refers to the battery level of the smartphone device. On the other hand, factors such as patient well-being, # of eye diseases, and # of patients with risk factors refer to the level of health status overall or particularly with respect to eye diseases in the population. Patient satisfaction is analogous to customer satisfaction, which is an evaluation of/reaction to received services and experience [62]. Patient well-being is defined as the level of proper health, security, safety, and happiness of the patient. Moreover, factors such as actual need, demand, cost, and patient retention rate mostly relate to the economic aspects.

There is a close correspondence between SCOR model attributes and the factors of a causal model [63].

Table 1. Eye-SCOR model attributes and their corresponding metrics in the causal model.

SCOR Attributes	Definition	Causal Model Metrics/Factors
Reliability	The ability to perform tasks as expected. Reliability focuses on the predictability of the outcome of the process. Reliability is a customer-focused attribute.	Patient satisfaction, bandwidth capacity, cloud connectivity, patient well-being, patient retention rate
Responsiveness	The speed at which the tasks are performed. Responsiveness is a customer-focused attribute.	Performance, response time, ease of use
Agility	The ability to respond to external influence and the ability to change, for example, increase or decrease in demand, and cybersecurity threats. Agility is a customer-focused attribute.	Data security and privacy, eye data acquisition feasibility
Cost	The cost of operating the process. It includes engineering costs and software/app costs. Cost is an internally focused attribute.	Eye status monitoring, app cost
Asset Management	Captures the ability to efficiently utilize assets. This is an internally focused attribute.	Eye analysis algorithm and software management competence, licensing

shows the definition for Eye-SCOR model attributes and their corresponding metrics mapped in the proposed causal model. The SCOR model maximizes the supply chain visibility, efficiency, and the corresponding metrics when the dynamic interconnections throughout the supply chain are aligned with the model [64].

SCOR is organized around six management processes (plan, source, make, deliver, return, and enable) [64]. These processes are further divided into tasks and activities, which also serve as inputs to the factors of the causal model, as explained hereafter. Plan is the part of processes that is responsible for matching supply and demand to meet the sourcing requirement or when the level of advertisement satisfies the demand. The supply chain planning process uses information from supply chain partners to match supply with demand. Previous studies have suggested that coordination between supply chain partners is critical for planning since the alignment between functions is necessary and information sharing in supply chains can lead to improved performance and efficiency in the system [65]. Source process is used to find and acquire the goods and services to meet the demand. Providing feedback about the suppliers’ performance is a good sourcing practice. For the sources in a smart eye monitoring system, the suppliers can be the app providers in consultation with physicians, and the buyers can be patients as well as health providers (clinics, telemedicine platforms/websites). Evaluating the supplier has a direct positive impact on the buyer–supplier relationship (in our study, patients and the app or the doctors) and also improving the system’s efficiency [66]. It includes monitoring the relationship for customer satisfaction to ensure that the customer’s needs are met [33]. Plan and source decisions have a greater positive effect on customer-focused SCOR-based factors (reliability, responsiveness, and agility). Make includes the processes that convert resources and inputs into a finished state to meet the demand. Make decisions are more influential on internally focused SCOR factors (cost and asset management). Delivery includes all processes that provide finished goods and services to meet the demand. One capability of delivery is to share real-time information with supply chain partners, which increases the visibility [67]. An internet-based delivery process improves the delivery performance [29]. Delivery decisions are more influential on customer-focused SCOR factors (reliability, responsiveness, agility) [64,68]. Return includes activities associated with the reverse flow of information and patients. Return decisions affect internally focused SCOR factors (costs) [27]. Finally, enable is associated with management of the supply chain processes. Enable includes establishing rules, assessing performance, managing the capital assets, and managing regulatory compliance [30]. Enable decisions influence internally focused SCOR factors (asset management) [29].

A careful look at the causal model reveals the existence of multiple feedback loops (see Figure 2). A reinforcing loop is seen among the patient satisfaction, efficiency, and patient well-being factors that spans the reliability SCOR attributes of Table 1. The relationship of the efficiency of several healthcare apps and patient well-being has been confirmed in the literature [69,70]. Another loop is a balancing loop that starts from patient satisfaction. As patient satisfaction increases, patient loyalty increases, which thus positively affects patient retention rates [45]. As the number of loyal customers increases, the demand is expected to increase, and more patients would return to use the app. It is noteworthy to mention that patient retention is a positive factor in this model. The eye status monitoring app measuring the risk of IOP, if fully deployed, will be just like another vital sign (similar to temperature, blood pressure, and heart rate) that any user can measure frequently, at the convenience of a smartphone app, anywhere and anytime. Therefore, customers returning and frequently using the app is an indication of their satisfaction and also of the need to take these vital measurements frequently. To this end, an increase in demand is expected to lower the cost. On the other hand, as cost rises, patient satisfaction is expected to decrease. The connection from cost to patient satisfaction would thus form the complete loop.

2.2. System Dynamics Model

Based on a subset of the proposed causal model, a system dynamics model of the Eye-SCOR model is developed. Figure 3 depicts our system dynamics model designed using Stella® Architect software v3.0.2 [71].

The system dynamics model includes SCOR attributes that represent reliability, responsiveness, agility, costs, and asset management, which correspond to the causal model factors. The dynamic behavior of the factors and their inter-relations can be observed through the system dynamics model. As shown in Figure 3, the proposed system dynamics model is comprised of a set of auxiliary variables, flows, and stocks, which together represent factors of the Eye-SCOR model. In this work, the centered Eye-SCOR level shown in Figure 3 corresponds to the efficiency factor of the causal model of Figure 2. This auxiliary variable denotes the efficiency (effectiveness) of smartphone-based eye status monitoring apps. Through a series of simulations and scenarios, the dynamics of the efficiency (or Eye-SCOR level) will be observed.

The nature of the relationship among the factors of the model impacting the Eye-SCOR level is non-linear with feedback loops involved. The variables and elements of the system dynamics model in Figure 3 are based on underlying equations presented hereafter. Equations (1)–(8) show the mathematical relationship of some of the key factors in the model. Equation (1) represents the mathematical formulation of the Eye-SCOR level.

The variable relationships have been hypothesized and the equations have been formulated based on a literature review in system dynamics modeling [72,73] and our expert judgement. We came up with non-linear relationship equations that show either directly proportional or inversely proportional relationships of factors to one another. Calibration is performed through the offset and scaling factors for a base value of 0.5 or 50% within the acceptable range of 0–1.

As can be seen, several factors with non-linear relations affect the Eye SCOR level in our model. A scaling factor and offset value have been included in the equation so that the model can run within the expected and acceptable range. As can be observed from the remaining Equations (2)–(8), some factors are directly proportional to other factors (have increasing relationships), while others are inversely related.

(1)

$$\begin{array}{cc}\hfill & \mathrm{level}\mathrm{of}\mathrm{performance}=\left(\mathit{cloud}\mathit{connectivity}\mathit{level}\right)\times \hfill \\ \hfill & \left(\mathit{eye}\mathit{analysis}\mathit{algorithm}\mathit{and}\mathit{software}\mathit{management}\mathit{competence}\mathit{level}\right)\hfill \end{array}$$

(2)

$$\begin{array}{cc}\hfill & \mathrm{response}\mathrm{time}=\left(\mathit{level}\mathit{of}\mathit{performance}\right)\times \left(\mathit{eye}\mathit{status}\mathit{monitoring}\mathit{capability}\mathit{level}\right)\hfill \end{array}$$

(3)

(4)

$$\begin{array}{cc}\hfill & \mathrm{level}\mathrm{of}\mathrm{patient}\mathrm{satisfaction}=\hfill \\ \hfill & (\mathit{patient}\mathit{well}-\mathit{being})\times \left(\mathit{reliability}\mathit{level}\right)\times (1-(\mathit{cost}\mathit{of}\mathit{service}\mathit{level}\left)\right)\hfill \end{array}$$

(5)

(6)

$$\begin{array}{cc}\hfill & \cos \mathrm{t}\mathrm{of}\mathrm{service}\mathrm{level}=\left(\mathit{cost}\mathit{to}\mathit{customers}\right)\times (1-\mathit{level}\mathit{of}\mathit{demand})\hfill \end{array}$$

(7)

$$\begin{array}{cc}\hfill & \mathrm{level}\mathrm{of}\mathrm{demand}=\left(\mathit{level}\mathit{of}\mathit{product}\mathit{advertisement}\right)\times \left(\mathit{patient}\mathit{retention}\mathit{rate}\right)\times \hfill \\ \hfill & \left(\mathit{rate}\mathit{of}\mathit{need}\right)\times (\mathit{rate}\mathit{of}\mathit{well}-\mathit{being}\mathit{and}\mathit{care})\hfill \end{array}$$

(8)

Our system dynamics model has been designed by taking into account the nature of the factor inter-relationships according to the above equations. Dynamics of the Eye-SCOR level will next be seen by testing the system under various scenarios.

3. Simulation Results

Stella Architect [71] was used as the simulation software on a Windows 10 operating system to run the model. To simulate the model, scenarios (with different input values) should be created. Then, the model should be populated with the data from different scenarios.

3.1. Testing Scenarios

The main objective is to observe the dynamics of the model that was structured based on the SCOR framework. To test the effect of the various factors of the system dynamics model on the Eye-SCOR level, a set of input variables are fed to the model, representing different scenarios. The activities and tasks in the six SCOR processes serve as initial inputs to the input variables in our model. The input variables are the design and performance metrics of the eye status monitoring app, such as eye status monitoring capability level, eye analysis algorithm and software management competence level, cloud connectivity level, ease of use level, eye data acquisition feasibility level, location tracking ability level, and reliability level, in addition to the cost to customer, rate of need, level of product advertisement, and general health rate inputs. Though many factors will affect the Eye-SCOR level, we are mostly interested in observing the impact of the factors related to the design and performance of the eye status monitoring app.

Normalized values between 0 and 1 are used for the purpose of simulation in this work, where 0 indicates the lowest extreme (worst case) and 1 represents the highest extreme (best case). To obtain normalized values of factors (inputs) denoted as $facto{r}_{Normalized}$, the relation of the raw (actual) value of the factor with respect to the minimum ($facto{r}_{min}$) and maximum value of the factor ($facto{r}_{max}$) is considered according to Equation (9).

$$facto{r}_{\mathit{Normalized}}=\frac{factor-facto{r}_{min}}{facto{r}_{max}-facto{r}_{min}}$$

(9)

The actual values of the input factors will be measured and quantified based on real-life values of the factors, such as cost of app to customers (in the market), accuracy of the eye analysis algorithm, and so on, when data is available. This, however, requires long-term clinical trials and data collection, which are beyond the scope of this work. Therefore, the simulation of synthetically created data (resembling real data) under various scenarios is shown here to study the dynamics of the model. For simplicity, values are restricted in the [0, 1] range to better understand the dynamic behavior of the system. This also allows for benchmarking and comparing the results with respect to the extreme values of the factors in the system.

In this work, the time-frame set aside for simulation is considered to be approximately three weeks, with a time grid of one day as the step size. The reasoning behind this is because of the fact that a period of a few weeks is fair to assume for the eye status monitoring app and any associated app changes to become prevalent for public use, including the time needed for patient follow-up and feedback, as this is generally the case for most health-related mobile apps [74,75,76,77] as well. The eye status monitoring app is expected to provide its responses in near real time (seconds or fraction of a second); however, the input variables (factors) that are considered for the model generally do not drastically change within the time-frame of three weeks.

3.1.1. Baseline

A baseline is considered for the simulation that can serve as an initial reference (base) scenario. This scenario is constructed with the assumption that all input variable factors are at their midpoint level of 0.5. The dynamics of the Eye-SCOR level (efficiency) under this baseline scenario can be seen from Figure 4. As can be observed, the system does not go through much dynamic variance in the baseline scenario, and the Eye-SCOR level pretty much remains in the 0.5 range. The results of the next scenarios can be compared against the baseline scenario response.

3.1.2. Scenario 1

Scenario 1 is constructed with the assumption that one of the eye status monitoring app’s design or performance factors is at a high level. In this scenario, the eye analysis algorithm and software management competence level is set to 0.9, while the other input variables are kept at the midpoint (baseline) level. The dynamic response of the system in this scenario can be seen in Figure 5. It is clear that the Eye-SCOR level is increased in Scenario 1 compared to the baseline. In this scenario, because of the gradual increase of the Eye-SCOR level, the supply chain players such as service providers and customers (doctors, insurance companies, and patient end users) are seen to gradually benefit over time.

3.1.3. Scenario 2

Scenario 2 is constructed with the assumption that one of the eye status monitoring app’s design or performance factors is at a low level. In this scenario, the eye status monitoring capability level is set to 0.3, while the other input variables are remained at the midpoint (baseline) level. The dynamic response of the system in the scenario can be seen from Figure 5. As anticipated, the Eye-SCOR level is decreased in Scenario 2 compared to the baseline, and so the supply chain network players (app developers, patients, insurance companies, and doctors, etc.) will not see much benefit. This scenario is low on two customer-focused attributes; therefore, patients and doctors are among players that will not see much benefit.

3.1.4. Scenario 3

Scenario 3 is constructed to test the effect of social factors on the dynamics of the Eye-SCOR level. In this scenario, the cost to customer is set to 0.8, while the other input variables are kept at the midpoint (baseline) level. The dynamic responses are shown in Figure 5. Initially, a degraded response is seen, but because of the non-linear dynamics of the system, eventually, an improved response is observed compared to the baseline scenario after several days. Initially, since the cost is high, the end user patients may feel hesitant to purchase the app, showing a slight decline in their share of the supply chain network function. During this time, the app developers and app distributors (iOS and Android providers) see a high rise in their benefits. This affects the makers/providers by developing better-performing apps, raising patient satisfaction, eventually leading to a point where all the supply chain players see benefits. In other words, in this scenario, when the cost to the customer is high, the cost to design, make, develop, and distribute the app is high, which makes the supply chain cost high. This high cost will reduce the efficiency of the whole supply chain system. As time passes and the app is used more (word of mouth), more patients and doctors will recommend and use the app, and thus the efficiency of the supply chain is increased.

3.1.5. Scenario 4

Scenario 4 is constructed with the assumption that the eye status monitoring app’s design or performance factors are at reasonably high levels. In this scenario, the ease of use level, the eye status monitoring capability level, the eye analysis algorithm and software management competence level, and the eye data acquisition feasibility level input factors are set to 0.8, while the other input variables are kept at the midpoint (baseline) level. The dynamic response of the system in Scenario 4 can be seen in Figure 5. As observed, the Eye-SCOR level dynamics significantly improves compared to the baseline response. All the supply chain players and attributes benefit highly from this scenario in a short time-frame. When the app’s performance factors such as ease of use, eye status monitoring capability, and eye data acquisition feasibility are high, the attributes such as reliability, responsiveness, and agility are high in the supply chain network. These three attributes are more customer-focused, and when they are higher, they can increase the efficiency of the Eye-SCOR of the model, benefiting the patients, the doctors, insurance companies, and eventually the app developers.

3.1.6. Scenario 5

Scenario 5 is constructed to better realize the dynamics of the Eye-SCOR level under a different set of inputs for the eye status monitoring app’s design or performance factors. In this scenario, the ease of use level is set at 0.3, the eye status monitoring capability level is set at 0.4, the eye analysis algorithm and software management competence level is set at 0.7, and the eye data acquisition feasibility level is set at 0.9. The dynamic responses of the Eye-SCOR level in Scenario 5 is seen along with the other scenarios in Figure 5. Because of the dynamics of the system, the supply chain players, such as physicians, insurance companies, patients, app developers, and online app stores, see very slight benefits over time in this scenario. This scenario has low levels of responsiveness and reliability, while the supply chain agility and asset management are high. Since the customer-focused attributes are lower, patients and doctors will not benefit much in this case.

3.1.7. Scenario 6

Scenario 6 is constructed to realize the dynamics of the Eye-SCOR level when the eye status monitoring app’s design factors are low. In this scenario, the input factors related to the eye status monitoring app’s design are set to 0.4 (below the midpoint), while the other input variables are kept at the baseline (midpoint) level. Figure 5 illustrates the dynamics of the system response in Scenario 6, which is, as expected, reduced compared to the baseline response. As a result, the supply chain network players do not see any noticeable benefits when the supply chain network reliability is low. For better comparison, Figure 5 also collectively depicts the dynamic responses of the Eye-SCOR level in all the scenarios.

As each factor of the overall Eye-SCOR model is related to certain players in the supply chain network through the SCOR attributes expressed in Section 2, the simulation scenarios also indicate the dynamics of these players over (the simulation) time. For example, the cost of service factor affects the supply chain players, such as insurance companies and app developers, and can inversely affect the patients. The Eye-SCOR level is influenced by several factors (Equation (1)) and also affects many factors as well (through the feedback loop), which are inherently related to various supply chain players such as the patients (patient satisfaction, patient well-being), app developers, physicians, and so on.

4. Discussion

Validity is a process described in [78,79,80] that includes, structural, behavioral, and policy tests. The validity and credibility of the model has been confirmed using the varying structure, behavior, and implications of the user’s policy tests. The Stella Architect software, by design, has the capability of validating structural tests, such as checking for units, boundaries, and so on. The model was running as expected (and without errors), consistent with the results we obtained from the scenarios; therefore, it satisfies the structural validity and builds confidence in the model. Extreme cases have been applied to validate the behavior tests. We provided low extremes (0%) as the input variables and observed that the model’s dynamics consistently remain at 0%. When providing high extremes (100%) as the input variables, the model’s dynamics consistently remain at 100%. In addition to the six scenarios, our model reproduced these normal results under extreme values, which satisfies the behavioral tests. As user policy is directly related to the influence of other supply chain parties in the model, policy validity test is deferred to when actual data is collected.

As this work is the first of its kind to explore the dynamics of the supply chain network of smart eye monitoring systems using system dynamics modeling, there is no base of comparison to evaluate against.

The simulation results showed the dynamics of the Eye-SCOR level under various scenarios. We observed that the eye status monitoring app factors including the software, design acquisition, hardware (device), and performance factors more significantly affected the Eye-SCOR level. In this study, synthetic data has been provided to the system by considering normalized values, as long-term data collection and evaluations are beyond the scope of this paper. To observe the actual dynamics, real data should be acquired over the course of months and years. Future directions of this research include long-term data collection and analysis for sustainability and better decision support in smart eye care.

5. Conclusions

In this paper, a sustainable supply chain operations reference (SCOR)-based framework was proposed to evaluate the effectiveness of smartphone-based eye status monitoring apps using system dynamics modeling. System dynamics modeling has been deployed in various healthcare settings. However, to the best of our knowledge, system dynamics modeling has not been studied earlier in the literature for smartphone-based eye status monitoring. Moreover, through Eye-SCOR, we introduced the unique mapping of elements and factors of the model with the main game players of the supply chain using a system dynamics perspective and analytically studied their correspondence using simulations. The proposed work offers a novel contribution integrating cross-disciplinary domains (engineering, AI, healthcare informatics, supply chain, and systems of systems) along with design, modeling, simulation, and analysis.

The Eye-SCOR model provides a novel insight on various factors affecting the efficiency of smartphone-based eye status monitoring. The findings of this research could help managers and decision/policymakers in supply chain management (e.g., healthcare providers, insurance companies, doctors, patients/users, designers and app builders, etc.) to maintain and continually improve this transformative technology, allowing individuals to track and monitor the health status of their eyes, and specifically the risk of high eye pressure and IOP, conveniently and frequently using the smartphone app. These recordings could be performed along with other regularly monitored vital signs, such as blood pressure, temperature, heart rate, blood sugar (glucose) level, and oxygen saturation level. The proposed technology offers much higher convenience and accessibility through non-invasive and non-contact smartphone-based eye status monitoring compared to wearable device technologies. Immediate benefits of this research could be seen if the technology was adopted by all players in the system, as well as managers/policymakers, as a routine vital sign check to help prevent the onset of several eye diseases, such as glaucoma and irreversible blindness.