Sustainable Multidimensional Performance Prediction by ANN-Based Supervised Machine Learning

Structured sustainable performance assessment helps organizations identify areas for improvement and the necessary professional development and training. This is particularly relevant for full activity integration, long-term planning, and integrated decision-making.

Implementing a sustainable strategy in the supply chain promotes the emergence of new distribution models aligned with sustainable development's three pillars. The growing concern for sustainable change has made sustainability a global trend in economic activities.

Transportation plays a significant role in logistics and the supply chain. However, product transportation has both positive and negative impacts on corporate profitability due to noise and environmental pollution. Transportation procedures have significant environmental consequences. The increase in transportation, driven by global commerce volume and fossil fuel use, has severely impacted the environment. The rise in transportation raises concerns about environmental degradation and the need for protection [1, 2]. Global transportation activity has surged due to increasing trade, urbanization, and population growth. This spike in transportation volume significantly contributes to greenhouse gas emissions, primarily from fossil fuel combustion in vehicles. Transportation accounts for a substantial portion of global emissions, notably around 28% in the United States. Urgent action is needed to implement sustainable transportation strategies to mitigate environmental degradation and combat climate change. Additionally, social factors for employees must be considered. Sustainability comprises economic, environmental, and social components [3]. In the transportation industry, decision-making should consider these three aspects. The central issue is to create an efficient and sustainable transport system that maximizes positive impacts while minimizing negative environmental consequences. Controlling and monitoring indicators through performance measurement systems (PMS) is essential.

Many works focus on the evolution of sustainable performance in the logistics sector. To compile these works, we conducted a systematic literature review using keywords such as PMS, evolution of sustainable performance in logistics, machine learning, and logistics. This paper is a continuation of our previous communication [4], providing details on article selection, inclusion and exclusion criteria, and relevant models from the literature. While most works focus on a single dimension of sustainable development, as the analyzed models fail to account for the dimensions encompassed in our performance measurement system, which typically address only one or two dimensions of sustainable development without integrating the remaining dimensions relevant to sustainability, several articles emphasize the need to integrate the operational dimension in assessing logistics performance's sustainability. Agyei-Owusu et al. [5] highlight the close relationship between supply chain sustainability and the operational aspect, while Tseng et al. [6] argue that the assessment of all three dimensions of sustainability is meaningful when the operational dimension is included. Similarly, the integration of stakeholders in assessing the sustainability of a supply chain is a research gap. Khosravi and Izbirak [7] argue that the social dimension cannot be dissociated from other dimensions independent of stakeholders, and Butzer et al. [8] emphasize the importance of integrating stakeholders as a separate dimension in evaluating a supply chain. In response to the identified challenge, a viable solution entails the development of a sophisticated performance measurement framework that integrates all pertinent dimensions of sustainability. This framework system should encompass economic, social, environmental, operational, and stakeholder dimensions, offering a comprehensive evaluation of sustainable performance. By adopting such an approach, organizations can gain deeper insights into their overall sustainability endeavors and make informed decisions to drive positive change.This paper aims to introduce a new performance model that goes beyond the conventional assessment framework based on the three fundamental dimensions of sustainable development. Our goal is to develop a comprehensive framework capable of evaluating performance across a wider spectrum of sustainability criteria. By expanding the traditional boundaries, our contribution seeks to redefine how sustainable performance is measured and understood. This effort represents a significant advancement in the field, promising to enhance the precision of performance evaluation methodologies within the context of sustainable development.

The objective of this paper is to contribute to the development of a performance measurement system for the logistics sector. The proposed system is based on supervised machine learning and aims to measure global multidimensional performance by evaluating the five dimensions: economic, social, environmental, operational, and stakeholder. We selected these dimensions through a systematic review of the literature, which justifies their inclusion in the paper. The program includes a learning phase, where the machine is trained on a significant portion of observations in the consolidated database, and a test phase, where output values are estimated based on input elements.

The article's structure is as follows: Section 2 highlights models for evaluating sustainable performance in the logistics chain. Section 3 presents our approach to predicting multidimensional sustainable performance. Section 4 provides detailed information on the main performance results obtained. Finally, in Section 5, we conclude the article with a discussion and suggestions for future work.

The paper introduces a novel performance measurement system that incorporates five distinct dimensions (social, economic, environmental, operational, and stakeholder) and three constraints (difficulty, duration, cost) to reflect the practical circumstances under which the performance levels of these dimensions are achieved as shown in Table 2. The rationale behind selecting these elements will be discussed in subsequent sections.

Our measurement system utilizes the minimum conditions algorithm to calculate the overall value of multidimensional sustainable performance. Each dimension comprises five fields, capable of accommodating an indefinite number of indicators depending on the specific case. Consequently, the system enables the determination of the comprehensive level of sustainable performance for any organization, irrespective of its industry. Its dimensional nature stems from its primary reliance on the five chosen dimensions.

To assess the robustness of this performance measurement system, further investigation will be conducted through a generalization approach. This involves developing a supervised machine learning model, utilizing an artificial neural network, to predict the overall value of multidimensional sustainable performance. The model will be trained using input data (dimensions and constraints) and output data (multidimensional sustainable performance) from the measurement system.

3.1 Development of the measurement system

3.1.1 Dimensions and constraints

The approach employed in developing the performance measurement system involved initially defining the dimensions to be evaluated prior to data collection and testing.

A systematic literature review guided us in selecting the dimensions as the primary inputs for our performance measurement system. It is crucial for a performance measurement system to rely on well-defined metrics derived from objective parameters to ensure its effectiveness.

Given the core objective of quantifying sustainability in the supply chain, the system's development necessitated the integration of the three fundamental pillars of sustainable development: economic, social, and environmental, aligning with the Triple Bottom Line approach.

Furthermore, advancing the sustainable development approach requires the engagement of all stakeholders who strive to attain specific performance levels. However, the expectations of these stakeholders regarding the supply chain are often overlooked. To address this gap, we incorporated a dedicated dimension that considers the sustainable supply chain from the stakeholders' perspective.

The importance of involving all stakeholders when assessing performance is underscored by Butzer et al. [8], who developed a novel performance evaluation system for international supply chains based on the balanced scorecard approach. Khosravi and Izbirak [7] established that the social dimension can only be comprehensively examined when linked to the dimension concerning stakeholders within the studied supply chain activity.

Agyei-Owusu et al. [5] affirmed the close relationship between the supply chain and the operational dimension, which deserves significant emphasis during performance analysis. Similarly, Tseng et al. [6] argued that sustainable supply chain management performance necessitates considering the relationships and inter-relationships among the traditional dimensions of sustainable development (economic, social, environmental) and the operational dimension.

In general, to ensure the sustainability of any activity, a vision and corporate strategy are indispensable. However, their implementation is equally critical. Concrete objectives in the long term, guided by an applied strategy, have implications for day-to-day activities managed operationally in alignment with current management practices. Recognizing this complementary aspect of strategy, we deemed it essential to integrate an operational dimension into the measurement system.

To evaluate the conditions and circumstances under which different performance levels are achieved, we enriched the system with achievement-oriented constraints. Specifically, for the case under study, the added constraints include what is shown in Table 2.

Table 2. Model constraints

3.1.2 Fields

After establishing the dimensions, the next step involved identifying the appropriate fields (sub-dimensions) for each dimension that align with their nature and purpose.

To accomplish this, we conducted an extensive review of relevant literature articles and sought expert opinions from industry professionals. This comprehensive analysis enabled us to pinpoint the most significant fields within the realms of sustainable development (economic, social, and environmental dimensions), operational activities (operational dimension), and transparency (stakeholders dimension). Our selection of fields was influenced by various considerations of importance and causality.

In Table 3, we present the fields chosen for this study along with their respective designations.

Table 3. Model Fields

3.2 Minimum condition algorithm

As explained above, our performance measurement system calculates the overall multidimensional sustainable performance value through the application of the minimum conditions algorithm which assigns to each dimension the minimum value of the fields contained in the same dimension (Figure 1). This value represents the performance level of the given dimension as shown in Figure 2.

The steps through which the algorithm passes can be summarized as follows:

(1) Determination of field performance: Within each dimension, assign a score ranging from 1 to 9 to each field until all five fields are scanned.

(2) Scoring the dimensions: For each dimension, assign a score from 1 to 9 based on the minimum scores of the fields contained in each dimension. The dimensional performance (the performance of the dimension under study) will be equal to the minimum value of the fields contained in the same given dimension.

(3) Comparison of dimensional performance:

(a) Sustainable development performance D.sd: Minimum dimensional performance value.

D.sd=Min (economic dimensional performance, environmental dimensional performance, social dimensional performance)

(b) Operational performance+stakeholders D.os: Minimum dimensional performance value.

D.os=Min (operational dimensional performance, stakeholder dimensional performance)

(4) Determination of levels performance D.sd & D.os.

(5) Determining the sustainable multidimensional performance D.mp:

D.mp= Min performance level (D.sd, D.os).

The quantification of the constraints is done in the same way and using the same minimum conditions algorithm.

D.mp’= Min performance level (D.sd, D.os, D.c).

The output values result from the direct application of the minimum algorithm condition, postulating that a global level of performance (output) is only reached when all the inputs have validated it.

The minimum condition algorithm includes three main performance levels where a scale from 1 to 9 is adopted Figure 3. As a result, the output performance level is equal to the level of the smallest score allocated to the dimensions.

For a better understanding of the algorithm and its application, Table 4 includes a set of scenarios explaining its use.

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Figure 1. Performance calculation levels

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Figure 2. Assessment scale adopted

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Figure 3. Flowchart on the methodology followed in the model development

Table 4. Fields scoring

Table 5. Multidimensional sustainable performance calculation

The last row of Table 5 gives us the performance level of the seven fictitious scenarios. Different dimensional performances can lead to the same level of multidimensional sustainable performance which corresponds to the lowest dimensional level. This implies that a dimension can only achieve a level of performance if all its fields are at or above that level of performance.

3.3 Generalization of the performance measurement system

To study the robustness of our performance measurement system and to examine its ability to remain unperturbed by small changes in the data or the parameters, we generalized the system into a supervised machine learning model and analyzed its performance. This generalization was the subject of the development of a multidimensional sustainable performance value prediction model based on neural networks. The purpose of this section is to design, if the model is sufficiently robust, an application that will directly return the multidimensional sustainable performance value that can define the level of performance of a company.

The prediction of output values (multidimensional sustainable performance) results from training the model on the data that emanates from the application of the minimum conditions algorithm. The methodology of this part is based on supervised machine learning techniques for the assessment of sustainability through a set of fields that have been grouped by fields forming the dimensions. Once the database has been consolidated, its content has been processed before applying the methods, training the model, and testing it. The flowchart below shows in detail the approach followed throughout the development of the model and thoroughly explains the taken steps.

3.3.1 Data selection and preparation

(1) Primary database

The database was meticulously compiled after conducting a comprehensive survey in collaboration with a prominent Moroccan freight forwarder situated in Tangier. Through this collaborative effort, data from a total of 43 road journeys, spanning various cities across the kingdom, was systematically collected and integrated into the database., with varying routes, durations, and transportation conditions. Affiliated experts have rated on a scale of 1 to 9 the estimated and projected values that relate to the eight inputs of our multidimensional performance measurement system applied to logistics for road freight transport in the current study.

The company operating in the transport sector with which we conducted this study has a dashboard containing several fields to which it assigns different scores. Our primary work has been to collect the data, prepare it, and clean it before starting any processing or calculation. Once this database was ready, we applied the minimum condition algorithm and arrived at the values for each dimension in the various studied scenarios. The system admits as main inputs 5 dimensions: economic, social, environmental, operational, and stakeholder. which have three implementation constraints whose difficulty, duration, and cost guarantee a certain level of performance. The census of all these data made it possible to establish a solid database containing more than 27,000 observations.

(2) Data processing

The database was therefore subjected to processing to eliminate any outliers as shown in Figure 4, which are defined as data points that lie significantly away from the rest of the distribution, that could potentially distort the machine learning. To check the normality of the data, we used the boxplot to visualize the centers and identify the general profiles of the statistical distributions of each parameter contained in the system.

An outlier in distribution is a number that is more than 1.5 times the length of the box away from the lower or upper quartile. Specifically, if a number is less than Q1-1.5×IQR or greater than Q3+1.5×IQR, then it is an outlier. The interquartile range (IQR) is a measure of statistical dispersion, being equal to the difference between the third quartile (Q3) and the first quartile (Q1).

Once the general distribution profile was identified, we proceeded to eliminate all outliers that could distort or bias the performance of the model. They also set aside observations whose fields are not filled in. This allows the model to train under the most realistic circumstances.

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Figure 4. Boxplot example (1^st input)

3.3.2 Model’s development

The learning algorithm predominantly relies on an artificial neural network of the back-propagation multilayer perceptron architecture, characterized by interconnected layers of neurons. Neural networks stand out as one of the most extensively employed supervised techniques across diverse domains, with particular prominence in forecasting variables pertinent to the supply chain, as evidenced by the findings outlined in Table 1. The multilayer perceptron structure facilitates the network's ability to learn complex patterns and relationships within the data, rendering it highly suitable for predictive modeling tasks within the realm of supply chain management.

Artificial neural networks [100] are biologically-inspired computational models formed from hundreds of individual units (artificial neurons) connected to coefficients (weights) that form a neural structure. They are also called processing elements (PEs) because they process information. Each PE has weighted inputs, a transfer function, and an output. PE is basically an equation that balances inputs and outputs. ANNs are also called connectionist models because the connection weights represent the memory of the system.

In this study, multiple trials (different artificial networks) leading to different global multidimensional performances allowed the final choice of the type of neural network as well as the number of its hidden layers by retaining the networks with the best accuracy. We finally built a neural network comprising six hidden layers, each of which includes a certain number of neurons. Figure 5 below illustrates the final network based on the model of supervised machine learning for multidimensional global performance prediction applied to logistics.

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Figure 5. Neural network adopted

(1) Forward propagation

Along with this propagation, the input data is routed forward through the network until it reaches the output. Each hidden layer accepts input data, processes it according to the activation function, and passes it to the next layer.

To create an output, the input data must only be entered directly. The data must not circulate in the other way while the output is being formed, or else a cycle will occur and the output will never be generated. These network setups are known as network feed-forward configurations. Forward propagation is aided by the feed-forward network. Table 6 below details the accuracy values returned by applying a forward propagation neural network algorithm.

Executing the algorithm working with only the forward propagation returns average precisions oscillating between 49.31% and 52.81% which are not very strong to be able to validate the model completely.

Table 6. Mean accuracies per test

(2) Backward propagation

Backward propagation is a method generally used to adjust or correct weights and biases to achieve a minimized cost or loss function. The algorithm thus converges better by going from the output to the hidden layer.

Mathematically, the weights of the hidden layer, which is closest to the output layer, are modified, and the loss is then recalculated. If the error is still to be reduced, the process repeats in the same order, getting closer and closer to the input layer. This is done using "gradient descent," which is an optimization algorithm that calculates the local minimum of a function as the iterations follow one another. In machine learning, gradient descent is most often used to minimize a cost function. Indeed, a cost function, or "lost function," is a function that determines whether a model can make good predictions for a data set or not. At the end of the forward propagation, the model is also trained in the direction of the forward propagation to recalculate the accuracy. The results are shown in Table 7.

Table 7. Mean Accuracies per test

The combination of backward propagation with forward propagation allows a considerable increase in terms of model accuracy. The machine learns noticeably better by going in both directions of propagation

(3) Activation Function

The activation function is a function that decides whether or not a neuron is activated by calculating the weighted sum and adding a bias. The purpose of the activation function is to introduce nonlinearity into the output of a neuron.

Without an activation function, a neural network is just a linear regression model. The activation function transforms the input in a nonlinear manner, allowing it to learn and accomplish more difficult tasks. Many activation functions exist; the most commonly used are:

1) Linear function is a straight-line function where the activation is proportional to the input which translates into a situation of proportionality. It can be written in the form of the following equation.

$f\left( x \right)=ax+c$               (1)

where, a is the slope of the line and y-intercept is held by the variable b.

2) Sigmoid function plotted in the form of an “S” shaped graph, it is differentiable and represents the distribution function of the logistic law.

$f\left( x \right)=\frac{1}{1+{{e}^{-x}}}$               (2)

3) Tanh function or hyperbolic tangent results in higher values of gradient during training and higher updates in the weights of the network.

$tanh:~\mathbb{C}\backslash ~i\pi ~($ℤ+$\frac{1}{2}$)               (3)

$Z\to \frac{\sinh (z)}{cosh\left( z \right)}$               (4)

where, sinh is the hyperbolic sine function and cosh is the hyperbolic cosine function.

Generally, the hyperbolic tangent function can be expressed using the exponential function:

$Tanh~\left( z \right)=\frac{{{e}^{z}}-{{e}^{-z}}}{{{e}^{z}}+{{e}^{-z}}}$               (5)

4) RELU or Rectified Linear Unit function that directly returns the input when positive. Otherwise, zero.

For any real x:

$f\left( x \right)=max\left( 0,x \right)$               (6)

5) Softmax function which transforms a real vector into a probability vector. It is most often used in the final layer of a classification model, especially for multiclass problems. In the Softmax function, each vector is treated independently.

$\sigma :~\mathbb{R}\to {{\left( 0,1 \right)}^{K}}$               (7)

Is defined when {\displaystyle K} KKKNKHKK, the number of classes in the multi-class classifier, is greater than one by the formula:

$\sigma {{\left( {\vec{z}} \right)}_{i}}=\frac{{{e}^{zi}}}{\mathop{\sum }_{j=1}^{K}{{e}^{zj}}}$               (8)

$\vec{z}$ being the input vector to the Softmax function, ${{e}^{zi}}$ the elements of the input vector, and $\underset{j=1}{\overset{K}{\mathop \sum }}\,{{e}^{zj}}$ the normalization term.

Taking into account the distribution and the values of the outputs contained in the dataset, the sigmoid function constitutes the best choice of the activation function as represents Table 8. To avoid biasing the model, all the functions mentioned above have been tested with different accuracy levels. These results, confirming our expectations, have reinforced our choice of function.

Table 8. Average accuracies depending on the activation functions

Based on the returned values, the activation function resulting in the best performance score is the sigmoid function.

(4) Cross-validation

Cross-validation is, in machine learning, a technique for evaluating and estimating the reliability and predictive accuracy of models. It aids in the creation of folds by dividing the data into multiple training and test groups, as well as in the comparison and selection of models in applied machine learning. Therefore, when a model is trained on labeled data, it is hypothesized that it must also work on new data.

In the algorithm, we split the data into five folds. after having tried several possible associations among the variables of the parameters until retaining the best combination. In general, the settings adopted were in Table 9 as follows:

Table 9. Values setting

The raised impact of the number of hidden neuron layers and epochs on the overall performance of the model remains the most important.