Internet of Things based Decision Support System for Green Logistics

Abstract

This work proposes an IoT-based Real-Time Decision Support System for Perishable Products. The proposed system collects data during the transportation process and will interfere in the case of failure. Three different simulation models corresponding to different configurations and mitigation plans are built. The simulation models consider decisions such as stopping transportation and rerouting shipments to minimize losses in case of failure. The three different supply chain simulation models are implemented through a case study that considers transporting a perishable fruit in the intercontinental United States. A financial and environmental analysis is conducted to show the benefits of the proposed system.

1. Introduction

The Cold Supply chain plays a crucial role in supplying people around the world with their needs of food. With a growing population, climate change, depleted resources, and supply chain disturbances that may arise because of global crises (i.e., coronavirus pandemic, wars, etc.), limiting wasted food within the supply chain and increasing its shelf life is vital to help mitigate these challenges. Other than limiting waste, fuel efficiency and consumption, as well as financial benefits through cost reduction, will help achieve environmental sustainability and green logistics by feeding the world with the minimum financial and energy resources.

FAO reported that around 30% of the world’s food production is wasted, where agricultural production and fresh produce (vegetables and fruits) account for 28% and 40% of the total wasted food, respectively [1,2,3]). The US Department of Agriculture conducted a study in 2010 [1] and found that food waste accounts for up to 40% of the food supply, which is the equivalent of 133 billion pounds of produce with a total financial value of USD 161 billion worth. With this wasted food, its required production resources (water, land, bio-diversity, and financial resources), and its effects on the environment (recycling, pollution, damage to freshwater fisheries, CO₂ emissions), the US Department of Agriculture decided to work with the U.S. Environmental Protection Agency to decrease food loss by 50 percent by 2030 where 2010 is considered as a baseline at 219 pounds of food loss per capita. To reach this optimistic target (50% reduction), the supply chain (from producer to the customer) should be thoroughly investigated to identify food waste at the different supply chain stages and their corresponding levels in order to effectively address the main and most important causes of food loss.

In another study [4], the USDA estimated that 141 trillion calories per year, or 1249 calories per capita per day, was not consumed. The top three food groups in terms of share of the total value of food loss are meat, poultry, and fish (30 percent); vegetables (19 percent); and dairy products (17 percent). The same study classified food waste at four levels: farm, farm to retail, retail, and consumer levels. When farm to retail level is considered, the identified causes include (but are not limited to): spillage and damage, such as equipment malfunction (for example, faulty cold or cool storage) or inefficiencies during harvesting, drying, milling, transporting, or processing.

The paper [5] mentions that refrigerated transportation is required for 40% of all foods, which uses 15% of world fossil fuel energy. Furthermore, refrigerated transportation leads to significant CO₂ emissions (around 40%) originating from vehicle’s engine and refrigerant leakage. For example, in terms of CO₂ emissions, a 38-ton articulated vehicle class generates the equivalent of 58 kg CO₂/pallet-km while a multi-drop frozen medium rigid vehicle class generates a115 kg CO₂/pallet-km.

Circular economy emphasizes the production of products that can be reused and recycled as well as eliminating waste and considering the environment when managing operations. The paper [6] that defines circular economy CE as a closed loop economy applying a materials balance model conducted a Systematic Literature Network Analysis and found eight emerging research trends in a circular economy that include the transition to CE at the micro level, transformation of wastes into valuable resources and enabling CE throughout internet technologies. In a second paper covering CE [7], the authors conducted a systematic review of drivers and critical success factors in circular economy. Some of the drivers mentioned are economic (improving cost efficiency and profitability), environmental (climate change and global warming), and supply chain (communication and collaboration). Furthermore, another paper [8] that developed A Maturity Model for Logistics 4.0 identified the flow of material and information among its dimensions. The Flow of material covers the Degree of automation in transportation and the Internet of things, while the flow of information covers Data-driven services, RFID, RTLS (real-time locating systems), and IT systems as areas of evaluation. In light of the above papers, this work fits the already mentioned CE research trends and success factors as well as the logistics 4.0 maturity model by investing in IoT, RFID, and RTLS to reduce cost and adverse effects on the environment. Additionally, while integrating many technologies (such as RFID, WSN, Geographic Information System, IoT, etc.), this work will emphasize automation by introducing a decision-making controller capable of recommending a course of actions and mitigation decisions using the sensing and tracking data as inputs.

In fact, many works assessed the use of new technologies (IoT, RFID, WSN, GP, etc.) and their potential for helping decision makers achieve sustainability in transportation operations. Nonetheless, as far as we are aware, there is no comprehensive approach in the literature that addresses different sustainability dimensions that includes both cost efficiency and transportation effects on the environment, as well as the adoption of logistics 4.0 maturity model dimensions. In other words, current research still lacks a comprehensive framework that can be utilized by decision makers (such as producers, truckers, and retailers) to assess the benefits of investing in such technologies. Thus, the purpose of this paper is to fill the gap in the literature by introducing a real-time IoT-based monitoring and control system framework for green logistics decision-making. With this purpose, this work will help perishable products supply chain companies achieve economic and environmental sustainability through cost reduction, energy consumption minimization, and waste control. Additionally, this work targets several decision makers that include producers who control their transportation operations through their own trucks fleet and trucking companies that offer logistics services to producers and retailers. Consequently, the aforementioned decision makers will be able to assess the proposed system hardware (RFID, WSN, and GPS systems, etc.), its maintenance and cloud services, and the cost savings and environmental benefits (CO₂ emissions, reduced food waste, energy consumption). The targeted decision makers will also be able to link cost and environmental benefits to their intrinsic supply chain configurations parameters such as cost components, failure probabilities, wasted shipment, etc., which will help them decide whether to adopt the proposed system or not.

As a matter of fact, this work helps answer the following questions: what is the architecture and the different components of the proposed system? How can the benefits (financially and environmentally) of such a system be quantified in terms of the system variables and parameters? The proposed system is implemented through a case study where different scenarios are compared in terms of their financial and environmental benefits.

In the following section, the different technologies used in the proposed system, as well as cold supply chain monitoring systems in the literature, will be reviewed.

2. Literature Review

2.1. Transportation of Perishable Food

Improving supply chain metrics and operations has been addressed by research for years. For instance, [9] considers a supply chain comprised of one supplier serving retail centers and optimizes inventory, transportation, food-waste, and stock-out costs in the presence of random demand by changing inventory levels and delivery routes. The authors integrate a Monte Carlo simulation within an iterated local search, and the results show that the semiheuristic algorithm is acceptable in terms of computation times. In another work, [3] developed a multi-objective decision-making model to minimize cost, transportation time, and emissions for a perishable supply chain. A case study pertaining to a fruit importer in North-Eastern European was considered. In the study, different network configurations (that depend on the weight given to each criterion) were generated and discussed.

Because of its spoilage and perishability characteristics, cold supply chain is increasingly challenging to suppliers, shippers, and retailers alike. The authors in [10] used Literature review analytics (LRA) on sustainable cold-chain for perishable food products, including articles published during 1985–2017, and found that there should be a future emphasis on advanced technologies to ensure the quality of perishable products during transportation. Monitoring the transportation conditions of the perishable food during transportation in terms of temperature, humidity, gases levels help supply chain decision makers to interfere in the case of failures or incidents. As a matter of fact, monitoring the transportation conditions will provide the decision maker with the necessary information regarding the quality of the transported food products, such as spoilage, remaining shelf life, and degradation. Technologies used in monitoring and tracking transportation condition are widely covered in the literature. For example, [11] mentions that traceability is very important to maintain the safety, security, and integrity of the product where temperature, humidity, and environmental gases are tracked to monitor transportation conditions. Radio Frequency Identification RFID, Wireless Sensor Networks WSNs, Telematics, the Internet of Things (IoT), and cloud computing can provide visibility and intelligence, which will lead to optimized decisions based on real-time data rather than historical datasets. In another paper, [12] review the literature on the different technologies capable of improving the perishable food supply and found that technologies such as GPS (Global positioning system), GIS (Geographic information system), RS (remote sensing), IoT, RFID, and the blockchain allow real-time tracking and traceability while transporting large-scale food crates from the manufacturing firms to the distributors.

This paper introduces an IoT-based monitoring and control system for a two echelons supply chain composed of one supplier and multiple customers. The system uses WSN, RFID, and cloud computing technologies to collect shipment data (such as temperature and humidity) while en route. If the required refrigeration conditions are not maintained (due to a failure, accident, or human error), then the system controller interferes with mitigation decisions such as stopping transportation (to save transportation costs) or rerouting the shipment to another customer. In the following text, WSN, RFID, and IoT, as well as their applications, are covered. Then, the proposed IoT-based monitoring and control system, along with its physical architecture and different components, are introduced.

2.2. IoT-WSN-RFID-GPS Technologies

• WSN

Figure 1 presents the architecture of a WSN that has several sensor nodes that communicate with each other and transmit the processed data to the sink node, which is transferred to the end user then. The sensor node is composed of a microcontroller, transceiver, external memory, and a power source that enable the integrated sensor to collect data that are then communicated to the end user [13]. WSNs are used for environment monitoring, working regimes of vehicles monitoring and human health monitoring. An example of WSN application in the supply chain can be found in [14], where the authors developed a smart service provision system using WSNs for cargo transportation and management processes.

• RFID

RFID uses electromagnetic fields for the identification and tracking of tags attached to objects. An RFID system is composed of a radio transponder, a radio receiver, and a transmitter. Because of its identification and tracking capabilities using easily deployable tags, RFID applications are rapidly growing in a wide range of applications, including supply chain management. RFID tags can be deployed as standalone sensors as well as Radio Frequency front-ends for other sensors [15] Therefore, the combination of the two technologies (WSN and RFID) enables monitoring and a control system to gather sensing data for the tagged physical objects, which helps in traceability and monitoring. Figure 2 depicts different applications within the supply chain that include but are not limited to:

• Monitoring raw materials and work in process;
• Condition monitoring and identification during transportation;
• Inventory management and control in warehouses.

• IoT

The authors in [16] define The Internet of Things (IoT) as a global network of machines and devices with interaction and communication capabilities. To benefit from IoT, connected devices should be able to interact with each other and integrated with vendor-managed inventory control systems, customer support, business intelligence applications, etc. The authors in [17] mention that in the IoT paradigm, RFID and sensor network generate data that have to be stored, processed, and communicated, whereas cloud computing can offer the infrastructure for computing that integrates monitoring and storage devices, analytics tools, visualization platforms, and client delivery. For instance, to achieve smooth, accident-less oil pipeline transportation, the authors [18] built an IoT-based module that optimizes costs related to commercial data loggers and sensing modules. IoT is widely used in industrial applications such as production, inventory control, and warehouse management. For instance, the paper [19] proposes an IoT-based solution for product class-based warehousing. The IoT solution offers high responsiveness in terms of incompatible product allocation by optimizing the storage path and minimizing the storage time. In another paper [20], the authors developed a warehousing IoT-based system aimed at monitoring hazardous products. The IoT-based solution combines the ZigBee WSN platform, and LabView software, which offer real-time conditions monitoring of products, and, therefore, dynamic risk assessment and safety assurance.

2.3. Existing Systems and Their Shortcomings

During the many stages of production, transportation, and storage, the temperature distribution is variable, where temperatures in trucks, palettes, or cold stores may not be within the required ranges. Additionally, the lack of accurate real-time data leads to low responsiveness of manufacturers in the face of customer requirement changes or production failures. Consequently, the above-discussed technologies (RFID, WSN, IoT, etc.) were used in many frameworks and management systems to counter those challenges. For instance, [21] introduces an RFID-enabled social manufacturing system that tracks the material flow using RFID and global positioning systems (GPS). The system will optimally dispatch decisions of inter-enterprise production and transportation decisions with an objective function that minimizes the total cost. A prototype for the proposed system was implemented, and results show improvements in the inter-enterprise production transparency.

While the discussed technologies (RFID, WSN, GPS, IoT, etc.) earlier have significant contributions to the supply chain when deployed separately, integrating them will offer more capabilities in terms of real-time sensing, tracking, and decision making. For example; [22] proposes a framework for real-time temperature measurement protocols supported by passive RFID, IoT, and Statistical Process Control (SPC) charts in order to minimize food loss in the food cold chain industry. In another work, [23] introduced a blockchain-based shipping system that offers speed and security for local cargo networks. The proposed management network uses RFID to track shipments, Internet of Things (IoT) sensors, and blockchain-based smart contracts. The integration between the different technologies was successfully validated in the test processes.

While the aforementioned studies integrate different technologies for sensing and tracking, they fall short in offering a decision-making mechanism that uses the collected data as input and recommends real-time mitigation decisions in case of incidents or failures. In the literature, some works include decision-making and mitigation processes using technologies such as RFID, WSN, and GPS, for example, [24,25].

Developed mathematical models used for decision making under different scenarios and showed reduced costs for the supply chain (such as out-of-stock, lost sales costs, and penalties imposed by customers) that result from the spoilage of the transported products. In another work, the authors [26] built a monitoring and control system for perishable products transportation, where the WSNs are responsible for collecting and processing data that will be stored in RFID tags retrieved by readers at specific checkpoints. In case of failures, the monitoring and control system intervenes with mitigation decisions aimed at reducing losses.

In the previously discussed papers, mathematical programming models, as well as the monitoring and control system, did not include real-time decision making and made decisions at specific points within the supply chain, which might increase losses and make mitigation plans not effective.

Consequently, incorporating RFID, WSN, and IoT to build a real-time decision-making system will surely help the decision makers to be more responsive and the mitigation decisions to be more effective and reduce losses resulting from the spoiled products in case of deviation from the optimal transportation conditions. Additionally, a thorough economic analysis that covers the cost and savings of implementing such technologies is missing in the literature. As a matter of fact, in order to convince Supply chain Decision makers to implement such technologies, cost analysis, and financial benefits should be quantified in terms of the different system parameters. Finally, any proposed system should not only focus on operational metrics (on-time delivery, lost sales, backorders...etc.) like in the works [9,14,25] but should also consider sustainability metrics such as food waste, fuel consumption, and co2 emissions when developed.

In summary, this study bridges the gap in the literature by making the following contributions:

• The proposed IoT framework is two levels composed of the network of things and cloud computing and combines RFID, WSN, and GIS.
• Real-time decision making mitigates losses in case of failures by stopping the transportation of affected shipments or assigning them new routes (different final customers).
• Different simulation models that cover different transportation scenarios were developed. The IoT-based system controller offers real-time mitigation decisions that would not exist without the proposed technologies.
• The developed simulation models consider the major cost components of any supply chain, such as transportation, shipment value, inventory, and lost sales costs.
• For the different simulation models, the financial and environmental benefits of the developed system are quantified in terms of CO₂ emissions, saved produce, traveled distance, and cost savings.
• The financial and environmental benefits corresponding to the different simulation models are expressed in terms of cost components (transportation cost, product cost, lost sales cost...etc.), failure probabilities, and other system decision variables and parameters. This will enable the IoT-based system adopters to assess its financial and environmental benefits according to their business models and decide whether to adopt it or not.
• The rest of the paper is organized as follows:
• The second section, Methods, introduces the developed system, its physical architecture and components, and the simulation models. A case study that covers the developed simulation models will be presented in this section.
• The third section, Results and Discussions, discusses the financial and environmental benefits of the proposed system. Additionally, sensitivity analysis and the effect of the different system variables and parameters on financial benefits are discussed. On top of cost benefits, sustainability metrics (saved food and CO₂ emissions) are assessed for each supply chain configuration.
• The proposed system, the different supply chain configurations, as well as their cost and environmental benefits, are summarized at the end in the Conclusions section.

3. Methods

The methods section consists of the following three different parts:

• First, the architecture of the IoT cloud computing system is introduced, which covers its network of things and the cloud computing levels. The components of both levels, their capabilities as well as the shared data between the two levels are described.
• Second, the assumptions and annotations that are used for the different simulation models are introduced. This includes all variables ad parameters, such as the cost components, failure probabilities, different timing annotations, etc.
• Third, the three simulation models, as well as their intrinsic scenarios, are explained in this section. For each simulation model, the set of considered decisions, as well as each scenario probability and cost, is calculated.
• Fourth, at the end of the Methods part, the case study data that cover transporting produce in the intercontinental United States are introduced. Data include produce and transportation costs, distances from supplier to the different destinations, etc.

3.1. Proposed IoT Cloud Computing Architecture

Figure 3 presents the two levels of architecture of the proposed IoT cloud computing system where the network of things provides sensing, identification, and location awareness data to the cloud computing unit that is responsible for computation, visualization, analytics, and control.

• Network of things:

The things in a transportation network are the three echelons supply chain network which are the producer, the trucks, and the customers. The trucks are equipped with WSN to monitor the transportation conditions of the goods, and the transported products are equipped with RFID for identification purposes. The Geographic information system tracks the location of the traveling goods and calculates the distances and travel times between the trucks and the different destinations.

• Cloud Computing:

The cloud computing level comprises the controller responsible for decision making and interfering with mitigation plans whenever necessary. It also offers storage, computation, analytics, and visualization of real-time data received from the network of things such as transportation conditions (temperature, humidity, etc.) and real-time distances between the trucks and the different destination. The controller preloaded with decision making modules can automatically make transportation-related decisions without human interference.

3.2. Simulation Models Assumptions and Annotations

In the following built simulation models, a producer fulfilling the nTj periodic demand dj of NC customers Cj’s is considered. In case of failure, the following annotations are adopted:

• SnTj = failure time;
• FT = the time it takes for the products to perish just right after failure;
• t_nTj = SnTj + FT is the spoilage time;
• Similar to the works [24,26], and without loss of generality, the following cost components will be considered:
• Products Cost: if vj is the product unit cost, then the product cost corresponding to customer Cj is equal to vj ∗ dj.
• Transportation cost: If rj is the transportation cost per time and product unit, and the transportation time corresponding to customer Cj is tj, then the transportation cost is dj ∗ rj ∗ tj.
• Penalty: If products fail to reach customer Cj and Lj is the penalty per product unit, then the penalty incurred is equal to Lj ∗ dj.
• Inventory Cost: if a shipment aimed at customer Cj is rerouted to another customer Cj’, and stored in inventory for a period Tjj’ before being used to fulfill customer Cj’ periodic demand, then an inventory cost equal to Ij’ ∗ dj ∗ Tjj’is incurred, where Ij’ is the inventory cost per product unit per time period.
• Failure probability pntj: the Failure probability during period n corresponding to Cj.
• Spoilage probability psntj: the probability that products spoil before reaching Cj during nTj.

3.3. Simulation Models

For all simulation models, P(SMi-Scj) and C(SMi-Scj) denote the probability of scenario j (under simulation model i) and its corresponding cost, respectively.

• Simulation Model 1 (SM1): SM1 represents the baseline where the RFID-WSN-GPS Decision Support System is not deployed. Actually, if a failure occurs (snTj is the failure time), then the producer will not be notified until the delivery of products. All SM1 Related scenarios are depicted in Figure 4 below.

The three different scenarios corresponding to SM1 as well as their incurred costs are described below:

• SM1-Sc1: No failure will happen, and products will reach customer j in good condition

  P(SM1-Sc1) = 1 − p_nTj

  (1)

  C(SM1-Sc1) = Products cost + Transportation cost = vj ∗ dj + dj ∗ rj ∗ tj

  (2)
• SM1-Sc2: a failure will happen; products will be spoiled before they reach customer Cj. In this case, other than Products and transportation costs, a penalty will be incurred.

  P(SM1-Sc2) = pnTj ∗ psnTj

  (3)

  C(SM1-Sc2) = Products cost + Transportation cost + Penalty = vj ∗ dj + dj ∗ rj ∗ tj + Lj ∗ dj

  (4)
• SM1-Sc3: a failure will happen; however, products will not be spoiled by the time they reach customer j, and transportation will continue.

  P(SM1-Sc3) = pnTj ∗ (1 − psnTj)

  (5)

  C(SM1-Sc3) = Products cost + Transportation cost = vj ∗ dj + dj ∗ rj ∗ tj

  (6)

As a result, the mean cost per shipment corresponding to customer Cj is equal to:

SM1Mean Cost/per Cj shipment = (1− pnTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + pnTj ∗ psnTj ∗ (vj ∗ dj + dj ∗ rj ∗ tj + Lj ∗ dj) + pnTj ∗ (1 − psnTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj)

(7)

• Simulation Model 2 (SM2):

For SM2, stopping transportation in the case of failure is considered. In fact, when the nTj shipment corresponding to customer Cj cannot be delivered before the spoilage time t_nTj, the Decision Unit will stop transportation. SM2 scenarios are depicted in Figure 5.

The three different scenarios corresponding to SM2, as well as incurred costs, are described below:

• SM2-Sc1: No failure will happen, and products will reach customer j in good condition

  P(SM2-Sc1) = 1 − pnTj

  (8)

  C(SM2-Sc1) = Products cost + Transportation cost = vj ∗ dj + dj ∗ rj ∗ tj

  (9)
• SM2-Sc2: a failure will happen; products will be spoiled before they reach customer Cj. In this case, transportation will stop, and the goods will be disposed of.

  P(SM2-Sc2) = pnTj ∗ psnTj

  (10)

  C(SM2-Sc2) = Products cost + Transportation cost (till failure) + Lost sales cost = v ∗ dj + dj ∗ rj ∗ (snTj − nTj + tj) + Lj ∗ dj

  (11)
• SM2-Sc3: a failure will happen; however, products will not be spoiled by the time they reach customer j, and transportation will continue.

  P(SM2-Sc3) = pnTj ∗ (1 − psnTj)

  (12)

  C(SM2-Sc3) = Products cost + Transportation cost = vj ∗ dj + dj ∗ rj ∗ tj

  (13)

As a result, the mean cost per shipment corresponding to customer Cj is equal to:

SM2 Mean Cost/per Cj shipment = (1 − pnTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + pnTj ∗ (1− psnTj) ∗ (v ∗ dj + dj ∗ rj ∗ (snTj − nTj+tj) + Lj ∗ dj) + pnTj ∗ psnTj ∗ (vj ∗ dj + dj ∗ rj ∗ tj)

(14)

• Simulation Model 3 (SM3):

Under Simulation Model 3 (SM3), when customer Cj periodic (nTj) shipment cannot be delivered before the spoilage time t_nTj, the following two decisions are considered by the decision unit:

🗸

Reroute the shipment to a different customer Cj’, in case nTjj’, the delivery time to customer Cj’, precedes the spoilage time tnTj = snTj + FT.

🗸

If rerouting is not possible, then the decision unit will stop transportation.

The four different scenarios corresponding to SM3 (depicted in Figure 6), as well as their corresponding incurred costs, are described below:

• SM3-Sc1: No failure will happen, and products will reach customer j in good condition

  P(SM3-Sc1) = 1 − pnTj

  (15)

  C(SM3-Sc1) = Products cost + Transportation cost = vj ∗ dj + dj ∗ rj ∗ tj

  (16)
• SM3-Sc2: a failure will happen; however, products will not be spoiled by the time they reach customer j, and transportation will continue.

  P(SM3-Sc2) = pnTj ∗ (1 − psnTj)

  (17)

  C(SM3-Sc2) = Products cost + Transportation cost = vj ∗ dj + dj ∗ rj ∗ tj

  (18)
• SM3-Sc3: a failure will happen; products will perish before they reach customer Cj. Rerouting is not possible, and the controller will order transportation to be stopped.

In this case, other than Products and transportation costs, a Lost sales cost will be incurred.

P(SM3-Sc3) = pnTj ∗ psnTj ∗ (1 − prnTj)

(19)

C(SM3-Sc3) = Products cost + Transportation cost (from shipping point till failure time) + Lost sales cost = v ∗ dj + dj ∗ rj ∗ (snTj − nTj + tj) + Lj ∗ dj

(20)

• SM3-Sc4: a failure will happen, and products will be spoiled before they reach customer j; however, the shipment can be rerouted to another customer Cj’ without being perished if the arrival time nTjj’ to customer Cj’ precedes the spoilage time t_nTj.

  P(SM3-Sc4) = pnTj ∗ psnTj ∗ prnTj

  (21)

  C(SM3-Sc4) = Products cost + Transportation cost (from producer till failure + from failure time to customer Cj’) + Lost sales cost corresponding to customer j + Holding cost corresponding to customer j’ = vj ∗ dj + dj ∗ rj ∗ (snTj − nTj+tj) + dj ∗ rj ∗ (nTjj’ − snTj) + Lj ∗ dj+ Ij’ ∗ ((n + 1)Tj’ − nTjj’)

  (22)

As a result, the mean cost per shipment corresponding to customer Cj is equal to:

SM3 Mean Cost/per Cj shipment = (1 − pnTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + pnTj ∗ (1− psnTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + pnTj ∗ psnTj ∗ (1− prnTj) ∗ (v ∗ dj + dj ∗ rj ∗ (snTj − nTj + tj)+ Lj ∗ dj) + pnTj ∗ psnTj ∗ prnTj ∗ (vj ∗ dj + dj ∗ rj ∗ (snTj − nTj + tj) + dj ∗ rj ∗ (nTjj’ − snTj) + Lj ∗ dj+ Ij’ ∗ ((n+1)Tj’ − nTjj’))

(23)

3.4. Case Study

Because of the stochastic environment originating from the failure probability and the failure time, the case study consists of a Monte Carlo simulation corresponding to the three Models introduced. Actually, Monte Carlo simulation selects the value of the considered random variable from the defined probability distribution and uses it in the computer trials. Some simulation parameters (such as distances, prices, trucking costs, and product costs) are real data retrieved from different sources such as databases and reports. On the other side, some other parameters that include confidential data cannot be accessed through public databases, or they are product and equipment related, assumptions were made. For example, the penalty imposed by a customer for not receiving their shipment is usually specified in a supplier contract. The list of assumptions includes the failure probability distribution, failure time, spoilage time, penalty, and inventory-related cost (as percentages of the product’s value). However, the assumptions made do not result in a loss of generality as many discussed results (financial benefits, savings, environmental benefits, etc.) are expressed analytically in terms of the simulation parameters.

The two random variables considered in this Monte Carlo simulation are:

• The failure probability p_nTj: for all shipments, a fixed failure probability p_nTj will be considered. Then the Monte Carlo process will consist of generating a random value (for example R), by sampling from the uniform distribution UNIFORM [0,1]. If 0 ≤ R ≤ pnTj, then a failure will occur (the failure Variable F = 1); otherwise, there will be no failure (F = 0). For example, if p_nTj = 0.1 and R = 0.05, then there will be a failure and F = 1. However, if p_nTj = 0.1 and R = 0.2, then there will be no failure, and F = 0.
• The Failure Time: for each shipment, values for the random variable, which is the failure time, will be generated according to the assumed probability distribution. From a real-life perspective, the probability distribution can be estimated from historical failure data retrieved from the transportation company. In our case, for each shipment, a Uniform Distribution [0, Arrival Time to Destination = nTj] will be considered.

In the conducted case study, a fresh produce (grapes) supply chain consisting of transporting a full truckload FTL shipment of 40,000lb from California to several destinations across the US is considered. Actually, two valleys in California (San Joaquin and Coachella valleys) are the biggest grape growers in the US, according to the statistics company Statista [27]. For example, in 2021, 5.76 million tons of grapes were grown in San Joaquin and Coachella valleys compared to only 325 thousand tons by the second biggest producer, Washington. The considered transportation destinations are several Terminal Markets across the US where the selling price of grapes is tracked by the Agricultural Marketing Service, US Department of Agriculture [28]. For the discussed case study in the next section, the following data and assumptions are considered:

• Google Maps [29] is used to calculate the distance (in miles) from the San Joaquin Valley to each one of the terminal markets (depicted in

  Table 1. Distance (miles) from S. J. valley to each Destination.

  Destination	Distance (Miles)
  Atlanta	2449
  Chicago	2116
  Dallas	1709
  New York	2895
  Miami	3019
  Toronto	2612
  Los Angeles	357
  San Francisco	45.4

  ).
• The trucking cost per mile is calculated using data from ATRI, the American Transportation Research Institute [30]. The trucking cost per mile from San Joaquin valley to each one of the terminal markets is calculated based on the operational trucking cost per region (depicted in

  Table 2. Destinations trucking cost USD/mile.

  Destination	Region	Cost USD/Mile
  Atlanta	west-southeast	1.886
  Chicago	west-Midwest	1.77
  Dallas	west-southwest	1.69
  New York	west-southwest-Northeast	1.78
  Miami	west-southeast	1.886
  Toronto	west-Midwest	1.77
  Los Angeles	west	1.81
  San francisco	west	1.81

  ). For instance, if the Dallas terminal market is considered, then the transportation route falls in the west-southwest regions, and the transportation cost per mile will be the average of the two regions’ costs. The different terminal markets’ transportation costs per mile are depicted in Table 2.
• The different terminal markets produce (grapes) costs per lb. considered are retrieved from the Agricultural Marketing Service, US Department of Agriculture [28]. A yearly average price is calculated based on monthly prices of 18lb containers corresponding to the different markets. Average prices corresponding to the different terminal markets are presented in

  Table 3. Terminal markets prices/18 lb containers.

  Destination	Rerouting Options	Range
  Atlanta	NA
  Chicago	NA
  Dallas	Los Angeles	[156, 471]
  New York	Chicago	[1918, 2293]
  Miami	Los Angeles	[163, 480]
  Miami	Dallas	[1509, 1909]
  Toronto	Chicago	[1908, 2308]
  Los Angeles	San Francisco	[0, 170]
  San Francisco	NA

  .
• For each shipment, there will be a fixed failure rate p_nTj, and a Monte Carlo simulation about failure/No failure will be run.
• For each shipment, if a failure happens, the spoilage time tnTj will depend on the remaining traveling time. If the remaining traveling time is greater than a certain timing FT in hours in the distance (which is the amount of time it takes for the products to spoil after failure), then products will spoil. Then, the spoilage time tnTj is equal to the failure time plus FT, tnTj= s_nTj+ FT.
• In our case study, the spoilage will happen within 200 miles of failure time.
• Rerouting will only happen if the arrival time to the new destination tnTjj’ precedes the spoilage time.
• For each of the destinations, the rerouting will only happen if the new destination is within a 200miles distance from the failure location.
• Unless mentioned otherwise, the considered failure probability is 0.01 for all discussed results.
• Google Maps [29] is used to calculate the traveled distance and possible rerouting options (terminal markets) whenever a failure happens, and rerouting is considered. For example, Figure 7 presents the route from the source (San Joaquin valley) to the destination New York, which passes by Chicago. In fact, for each destination, the route was checked on Google Maps, and a failure range representing the range at which a failure should happen in order to reroute the shipment to the new destination was built. Actually, two different types of destinations (

  Table 4. Rerouting Options per Destination.

  Terminal Markets	Price
  Atlanta	28.31
  Chicago	27.55
  Dallas	28.33
  New York	29.44
  Miami	32.14
  Toronto	30.09
  Los Angeles	21.65
  San Francisco	28.58

  ) emerged as the following:

A Destination with No Rerouting option: Atlanta, Chicago, and San Francisco: even if a failure happens in these destination routes, the rerouting option is not possible because the shipment cannot make it to any of the possible destinations before the spoilage time.

A Destination with a Rerouting option: Dallas, New York (Figure 4), Miami, Toronto, and Los Angeles. If a failure happens during the shipment transportation to one of these destinations, then the shipment can be rerouted to at least another destination. However, this will depend on the failure time and FT. Therefore, for each potential rerouting option, a failure range was built: if the failure happens within that range, then the shipment can be rerouted to that new destination because the arrival time nTjj’ precedes the spoilage time, which is tnTj = snTj + FT; otherwise, the shipment cannot be rerouted.

• Lj is the penalty charged by the customer in case of non-delivery (based on supplier-customer contract) or can be calculated based on the shipping time and cost in addition to the cost of the lost product and may also include reputation and sales opportunity costs. In the simulation model, it is equal to 1% of the production value.
• Inventory cost: this includes refrigeration, handling and storage, and inventory management-related costs. In the simulation model, it is equal to 1% of the production value.
• A 95% confidence interval is built for each configuration’s total supply chain cost as well as each destination-related cost. Therefore, we can be 95% confident that the mean cost is in the confidence interval. If s is the standard deviation and n is the sample size, then the 95% confidence limits formulas are as follows:
• Upper limit = mean cost + 1.96(s/$\surd n)$
• Lower limit = mean cost − 1.96(s/$\surd n)$
• To narrow the confidence interval for the supply chain mean cost, n = 20,000 trials (shipments) are considered for each customer.

4. Results and Discussion

4.1. Implemented Simulation Models

The following three simulation models were implemented using the Monte Carlo simulation approach:

• SM1: SM1 is the baseline where the IoT system is not deployed.
• SM2: in SM2, stopping transportation decision will be considered
• SM3: in SM3, stopping transportation and rerouting decisions are considered.

Statistical analysis is conducted for the total supply chain cost per period (pertaining to all destinations) to build its confidence interval.

Table 5. Data Descriptive for the SM1, SM2, and SM3.

	SM1	SM2	SM3
Mean	542,788.86	541,437.317	534,555.583
Standard Error	318.59	306.813024	340.245718
Median	506,650.41	506,650.409	506,650.409
Mode	506,650.41	506,650.409	506,650.409
Standard Deviation	45,054.96	43,389.914	48,118.0109
Sample Variance	2,029,949,786.39	1,882,684,634	2,315,342,976
Kurtosis	1.18	1.19043967	1.03851355
Skewness	1.16	1.16037684	0.6331316
Range	322,585.08	313,749.573	492,815.427
Minimum	506,650.41	506,650.409	328,455.095
Maximum	829,235.49	820,399.982	821,270.523
Sum	10,855,777,149.78	1.0829 ∗ 1010	1.0691 ∗ 1010
Count	20,000.00	20,000	20,000
Confidence Level (95.0%)	624.46	601.378873	666.909716

depicts the descriptive statistics for the total supply chain cost per period for a failure probability p_nTj = 0.1.

In Table 5, the mean shows a significant reduction under SM2 (E[Cost]_SM2 = USD 541,437) and SM3 (E[Cost]_SM3 = USD 534,555) compared to the baseline SM1(E[Cost]_SP1 = USD 542,788). SM2 cost reduction is attributed to the possibility of stopping transportation once a failure is detected, which saves the transportation cost corresponding to the distance from the failure location to the customer. On top of stopping transportation, SM3 offers the rerouting option, which increases cost reduction compared to SM2. Additionally, it can be noted from the data described in Table 5 that the confidence intervals for SM2 (+/− USD 601) are wider compared to SM1 (+/− USD 624) because of the standard deviation effect. Actually, the option of stopping transportation will result in cheaper shipment costs whenever a failure is detected, which results in a smaller SD (SD_SM2 = 43,389 and SD_SM1 = 45,054). Moreover, the same SD effect can be seen for SM3 since savings are even higher than SM2 because of the rerouting option (SD_SM2 = 43,389 and SD_SM3 = 48,118).

The chart depicted in Figure 8 shows the effect of the failure probability on total supply chain cost per period for the different simulated supply chains. The different failure probabilities considered are 0.01, 0.05, and 0.1. As expected, the mean cost increases with the failure probability for the different simulated supply chains SM1, SM2, and SM3. For example, under SM1, the mean cost per shipment is equal to USD 542,789 and USD 510,110 for failure probabilities pnTj equal to 0.1 and 0.01, respectively.

Additionally, under the same probability, E[Cost]_SM3 < E[Cost]_SM2 < E[Cost]_SM1 due to savings attributed to stopping transportation under SM2 and savings attributed to stopping transportation and rerouting under SM3. In the following sections, the savings under the different supply chain models SM2 and SM3 are investigated further by linking them to the different system probabilities and expressing them analytically. Furthermore, managerial implications drawn from the simulated models are discussed.

4.2. Simulation Model 2-SM2

• Economic and Financial benefits:

Figure 6 shows Atlanta’s mean cost per shipment under SM1 and SM2 for different p_nTj failure probabilities. As expected, savings will increase when the failure probability increases. For instance, the mean cost will decrease by 0.04% and 0.34% for failure probabilities pnTj equal to 0.01 and 0.1, respectively.

In fact, the savings/customer Cj under SM2 can be expressed (Equation (24)) as follows:

Savings/customer Cj under SM2 = SM1TotalCost-SM2TotalCost = (1 − p_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + p_{nTj ∗} ps_{nTj ∗} (vj ∗ dj + dj ∗ rj ∗ tj + Lj ∗ dj) + p_{nTj ∗} (1 − ps_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) − (1 − p_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) − p_{nTj ∗} ps_{nTj ∗} (v ∗ dj + d ∗ rj ∗ (s_nTj − nTj + tj) + Lj ∗ dj) − p_{nTj ∗} (1 − ps_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) = p_{nTj ∗} ps_{nTj ∗} (dj ∗ rj ∗ (nTj − s_nTj))

(24)

In our case, $\mathrm{snTj}$ follows a uniform distribution U[0, delivered time], then:

$$E\left[snTj\right]=nTj/2$$

(25)

and

$$psnTj=Probability\left(snTj<nTj-FT\right)=\frac{nTj-Ft-nTj+tj}{tj}=\frac{tj-Ft}{tj}$$

(26)

If Atlanta is taken as an example (Tj = 2449 miles, FT = 200, d ∗ rj = 1.886 per mile) and under a failure probability $\mathrm{pnTj}$ = 0.1, then Atlanta E[savings]_SM2 = $(\mathrm{pnTj}$ = 0.1) ∗ (Tj = 2449-FT = 200)/2 ∗ (d ∗ rj = 1.886) = USD 212.08. The Monte Carlo simulation result (which is equal to USD 227.26) validates our built simulation model.

The same conclusions are applicable for the other destinations, and the total expected savings corresponding to all customers is equal to the sum across all customers as follows (Equation (27)):

$${E[\mathit{Total} \mathit{Savings}]}_{\mathit{SM}2}{{\displaystyle \sum }}_{j=1}^{NC}pnTj\ast psnTj\ast (dj\ast rj\ast \left(nTj-snTj\right)$$

(27)

It can be concluded that SM2 adoption results in savings corresponding to SM3 scenario two, which is saving the transportation cost in the case of stopping transportation (SM3-Sc2). These savings are translated into a decrease in the total supply chain cost per period for each customer, such as the destination Atlanta, which is depicted in Figure 9.

Furthermore, its mean varies with the failure probability. For instance, the mean total supply chain cost per period under SM2 decreases by 0.03% and 0.25% for failure probabilities pnTj equal to 0.01 and 0.1, respectively.

• Environmental Benefits:

SM2 shows a decrease in the mean traveled distance per period (for all shipments), as in Figure 10. Actually, the average traveled distance per period decreased by 2.5% and 5% for failure probabilities pnTj equal to 0.05 and 0.1, respectively. According to (Environmental Defense Fund, 2022), the average freight truck Emission Factor EF is equal to 161.8 g/ton-mile.

This decrease in the total traveled distance per period translates in a decrease in the CO₂ emissions which account for 2.49% (−1,227,056.78 g of CO₂) and 4.94% (−2,428,731.54) for failure probabilities p_nTj equal to 0.05 and 0.1, respectively (Figure 11).

4.3. Simulation Model 3-SM3

• Economic and Financial benefits:

Figure 12 shows Chicago (that can receive rerouted shipments with New York and Toronto as original destination) mean cost per shipment under SM1 and SM3 for different p_nTj failure probabilities. SM3 shows a cost reduction equal to 2.7% and 3% for failure probabilities p_nTj = 0.01 and p_nTj = 0.1, respectively.

In fact, the savings/customer Cj under SM3 can be expressed as follows (Equation (28)):

Savings/customer Cj under SM2 = SM1TotalCost-SM3TotalCost = (1 − p_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + p_{nTj ∗} ps_{nTj ∗} (vj ∗ dj + dj ∗ rj ∗ tj + Lj ∗ dj) + p_{nTj ∗} (1 − ps_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj)} − {(1 − p_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + p_{nTj ∗} (1 − ps_nTj) ∗ (vj ∗ dj + dj ∗ rj ∗ tj) + p_{nTj ∗} ps_{nTj ∗} (1 − pr_nTj) ∗ (v ∗ dj + dj ∗ rj ∗ (s_nTj − nTj + tj) + Lj ∗ dj) + p_{nTj ∗} ps_{nTj ∗} pr_{nTj ∗} (vj ∗ dj + dj ∗ rj ∗ (s_nTj − nTj + tj) + dj ∗ rj ∗ (nTjj’ − s_nTj) + Lj ∗ dj + Ij’ ∗ ((n + 1)Tj’ − nTjj’))} = p_{nTj ∗} ps_{nTj ∗} dj ∗ rj ∗ [tj− (1− pr_nTj) ∗ (s_nTj − nTj+tj) − pr_{nTj ∗} ((s_nTj − nTj+tj) + (nTjj’ − s_nTj))] − p_{nTj ∗} ps_{nTj ∗} pr_{nTj ∗} (Ij’ ∗ ((n + 1)Tj’ − nTjj’))

(28)

For Simulation Model 3-SM3, $\mathrm{psnTj}$ and $\mathrm{prnTj}$ are expressed as follows:

$$psnTj=Probability\left(snTj<nTj-FT\right)=\frac{nTj-Ft-nTj+tj}{tj}=\frac{tj-Ft}{tj}$$

(29)

$$\begin{array}{c}prnTj=Probability\left(nTj{j}^{\prime }<snTj+FT\right)=Probability\left(R1j{j}^{\prime }\le snTj\le R2j{j}^{\prime }\right)\\ =\frac{R2j{j}^{\prime }-R1j{j}^{\prime }}{tj}\end{array}$$

(30)

The total SM3 expected savings corresponding to all customers is equal to the sum across all customers; as follows (Equation (31)):

$$\begin{gathered} E{[\mathit{Total} \mathit{Savings}]}_{\mathit{SM}3}{{\displaystyle \sum }}_{j=1}^{NC}pnTj\ast psnTj\ast dj\ast rj\ast [tj-\left(1-prnTj\right)\ast \left(snTj -nTj+tj\right) \\ -prnTj\ast \left(\left(snTj -nTj+tj\right)+\left(nTjj’-snTj\right)\right)]-pnTj\ast psnTj\ast prnTj\ast \\ \left(Ij’\ast \left(\left(n+1\right)Tj’-nTjj’\right)\right) \end{gathered}$$

(31)

It can be concluded that SM3 adoption will result in savings corresponding to SM3 scenarios; three and four:

• The saved transportation cost in the case of a failure and no possibility of rerouting (SM3-Sc3).
• The saved product cost in the case of failure and possibility of rerouting, minus the extra transportation and inventory costs after rerouting (SM3-Sc4).

The savings per each customer Cj will affect the total supply chain cost per period depicted in Figure 1. Furthermore, its mean will vary with the failure probability. According to Figure 1, the mean total supply chain cost per period under SM3 will decrease by 0.16% and 1.52% for failure probabilities pnTj equal to 0.01 and 0.1, respectively.

• Environmental benefits:

SM3 shows a decrease in the average traveled distance per period (for all shipments), as in Figure 13. Actually, the average traveled distance per shipment decreased by 2.5% and 5% for failure probabilities p_nTj equal to 0.05 and 0.1, respectively. This decrease in the total traveled distance per period results in a decrease in the CO₂ emissions; accounting for 2.47% (1,216,259.46 grams of CO₂) and 4.9% (2,408,372.91 grams of CO₂) corresponding to failure probabilities pn_Tj equal to 0.05 and 0.1, respectively (Figure 14).

Other than CO₂ emission reduction, significant quantities of produce were saved thanks to the rerouting option. In fact, for a failure probability p_nTj equal to 0.1, the mean total quantity of rerouted produce (that would be disposed of without the rerouting option) is equal to 4162 lb per period, which represents 1.3% of the total shipped produce (to all destinations) per period. The breakdown of the average rerouted produce per period per original destination is depicted in Figure 15, which shows only the original destinations with rerouting options. The average rerouted quantities per period range from 201 lb (Miami) to 1898 lb (Los Angeles), which represent 1% and 4.75% of the shipped quantities to those destinations, respectively.

In summary, through the three developed simulation models, cost savings were linked to the different system variables and parameters and expressed analytically, unlike some of the works covered in the literature [22,23] that do not discuss their proposed systems financial benefits. This will offer decision makers such as truckers, suppliers, and customers the to assess the financial benefits of the proposed system according to their intrinsic parameters (i.e., failure probability) and costs (Inventory, penalties imposed by customers, produce value...etc.).

Furthermore, other works that covered financial benefits analysis of their proposed systems [24,26] considered decision making at specific checkpoints and not in real time as in this work. In fact, the developed simulation models in this paper consider stopping transportation and rerouting shipments in real time which makes cost savings higher compared to making decisions only at certain checkpoints.

Moreover, this study serves as a case study of the already mentioned research trends and success factors in the Circular Economy [6,7] as well as the logistics 4.0 maturity model [8] by investing in IoT, RFID and RTLS to reduce cost and adverse effects on the environment. In fact, this paper considers the environmental benefits of the IoT cloud computing system in terms of traveled distance, CO₂ emissions, and saved produce, unlike other papers that focused only on financial benefits like in [25].

5. Conclusions

This work introduces an IoT Real-Time Decision Support System for a Green supply chain. The system will interfere in case of failure during the shipment route with mitigation plans such as stopping transportation and rerouting to minimize financial losses. The proposed IoT framework is two levels composed of the network of things and cloud computing and combines RFID, WSN, and GIS.

Three different supply chain simulation models that consider the major cost components of any supply chain, such as transportation, shipment value, lost sales, and inventory cost, were built:

• A baseline denoted as SM1 where the system is not deployed;
• SM2, where stopping transportation is considered;
• SM3, where stopping transportation and rerouting decisions are considered.

The financial and environmental benefits corresponding to the different simulation models are expressed in terms of cost components (transportation cost, product cost, lost sales cost, etc.), failure probabilities, and other system decision variables and parameters. This will enable the IoT-based system adopters to assess its financial and environmental benefits according to their business models and decide whether to adopt it or not. In fact, the proposed system considers stopping transportation and rerouting shipments in real time, which makes cost savings higher compared to a monitoring and control system with data checked only at certain checkpoints.

The deployed decision support system shows cost reductions under SM2 and SM3 compared to the baseline SM1. For instance, under SM2, the mean cost per shipment corresponding to Atlanta is equal to 0.34%. The savings under SM3 exceed savings under SM2 because of the rerouting option. For example, Chicago-related mean cost per shipment decreased by 3% under SM3.

In addition, the effect of failure probability on cost savings was investigated. As expected, under SM2, the mean cost will decrease by 0.04% and 0.34% for failure probabilities pnTj equal to 0.01 and 0.1, respectively.

Other than financial benefits, environmental benefits were quantified for the different supply chain models. For example, SM3 shows a decrease in the average traveled distance per period (for all shipments). Actually, the average traveled distance per shipment decreased by 2.5% and 5% for failure probabilities p_nTj equal to 0.05 and 0.1, respectively. This decrease in the total traveled distance per period results in a decrease in the CO₂ emissions; accounting for 2.47% (1,216,259 grams of CO₂) and 4.9% (2,408,372 grams of CO₂) corresponding to failure probabilities pn_Tj equal to 0.05 and 0.1, respectively.

Also, significant quantities of produce were saved thanks to the rerouting option. In fact, for a failure probability p_nTj equal to 0.1, the mean total quantity of rerouted produce (that would be disposed of without the rerouting option) is equal to 4162 lb per period, which represents 1.3% of the total shipped produce (all destinations) per period. For instance, 4.75% of the shipped product to Los Angeles was saved.

For future work, the following should be considered:

• Expand the list of decisions that the controller can take.
• Consider simulation models with different supply chain configurations, such as considering many suppliers or considering a three-echelon supply chain. In this case, other decision variables, such as the optimal quantities that should be transported from each supplier to each destination, should be considered. In addition, supplier capacity, as well as customer demand, should be included.
• Investigate the implementation cost of the suggested system.
• In addition to cost efficiency and sustainability metrics (such as CO₂ emissions and saved produce), other supply chain indicators, such as on-time delivery and truck usage, must be considered in the analysis to better show the utility of the proposed IoT monitoring and control system.