A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0
Abstract
:1. Introduction
2. Related Work
2.1. Literature Review on Sustainability
2.1.1. Decarbonizing Transport
- GDP. This is related to economic production. Pope Francis [20] argues that a decrease in the pace of production and consumption can lead to another mode of progress and development where reuse, recovery, and recycling are encouraged. In addition, increasing the sharing economy and servitization trends, including access rather than ownership, can contribute to reducing the need for new primary materials.
- Ton-km/GDP. It refers to the intensity with which the economy moves goods. In order to reduce this factor, freight transport must be decoupled from economic growth. Therefore, the distance traveled by vehicles that are empty or not completely full must be reduced. In order to achieve this goal, weight limitations for each type of vehicle should be relaxed, vehicle load factors should be raised, and empty running should be avoided.
- Energy/tone-km. It is related to the energy intensity of freight transport. This element is related to energy efficiency, which means improving the amount of freight moved per energy consumed.
- GHG/Energy. It refers to the carbon content of energy used in freight transport. Therefore, it involves shifting to lower-carbon energy sources, such as natural gas, hydrogen, biofuels, or electricity generated by renewables.
2.1.2. Circular Economy
2.2. Literature Review on Optimization
2.2.1. Enterprise Modeling Methods
- The GRAI [36] methodology is based on a reference architecture describing the production manufacturing structure and how it can be managed. It also uses concepts and models to improve business performance through an efficient general approach. The GRAI methodology integrates the concepts of both previous methods by studying the company in detail during the transformation process [36]. Its originality is the analysis of the company through decisional, informational, process, functional, and physical views, studying in detail the enterprise.
2.2.2. Methods and Tools Based on Operational Research Theories
2.2.3. Methods and Tools Based on Artificial Intelligence
2.2.4. Supply Chain Sustainable Optimization Methods
2.3. Literature Review on the Digital Twin
- Digital model: this is a static, digitized version of a physical object, planned or existing.
- Digital shadow: this represents a digital version of an object with a one-way data flow.
- Digital twin (DT): this is the most advanced representation, featuring a two-way, real-time data exchange between the physical object and its digital counterpart. Changes in one are automatically reflected in the other.
- Distinguish between valuable information and noise: Not all the data collected is relevant. A DT needs to filter out irrelevant data (“pollution”) to focus on what matters.
- Incorporate rules and behavior models: a DT should represent the object’s behavior through a set of rules, enabling simulations of various scenarios.
- Perform simulations and optimization: By simulating different scenarios, DTs can answer “what-if” questions and identify optimal configurations based on defined objectives. This functionality makes DTs invaluable decision-making tools.
3. The Conceptual Framework
3.1. The Three-Dimensional Representation
3.2. The Mathematical Model
3.3. The Structure of the Performance Assessment Tool
3.4. Intelligent Support System Tool Architecture
4. Practices and Use Cases
4.1. Sustainability Practices
4.1.1. Good Practices in Decarbonizing Transport
4.1.2. Good Practices in Circular Economy
- Prevent. This business model corresponds to the prevention activity and entails the avoidance of waste. One well-known and remarkable example of this model is Xerox, which has pioneered this practice by designing products, packaging, and accessories that use resources efficiently, enhance durability, and reduce parts [85]. Another company worth mentioning is the renowned outdoor wear manufacturer Patagonia, which offers a lifetime guarantee and repairs items upon request [86].
- Reuse. It is the reuse of the product for the same or a different application without significant further treatment. It mainly addresses specific types of products, such as vehicles, clothing, books, electronic equipment, or packaging. A widespread practice in supply chain management is pallet pooling, where pallets are shared and reused within a network of producers, manufacturers, distributors, retailers, transporters, and logistics service providers (LSP) [87].
- Remanufacture. This means that the products or their components will be ready for use again after a period of refurbishment or repair. Dyson or Apple have expanded their market share by offering their refurbished products to people who are unwilling or unable to purchase their new products [88].
- Recycle. This business model requires more additional treatment than the previous ones since products must be broken down into parts or subassemblies that can sometimes be reused or simply used as raw materials for similar or lower-quality products. Renault, a major French car manufacturer, has significantly reduced its consumption of raw materials by recycling its waste (steel, leather, polypropylene, or textile) [89].
- Share. The collaborative economy, or sharing economy, consists of peer-to-peer (P2P) online platforms powered by digital technologies that provide temporary use of assets [90]. This business model is very resource-efficient, reduces the environmental impact of products, and has proven to be very successful. The main sectors that offer services via online collaborative platforms are transport (e.g., BlaBlaCar), accommodation (e.g., Airbnb), logistics (e.g., Saloodo), food (e.g., Blendhub), or anything you do not need (e.g., eBay).
- Product as a Service (PaaS). In a servitized economy, there is a shift from selling products to providing services. This is the case of the pay-per-use model, where producers own products forever and charge customers for the use and maintenance of them. This model provides motivation for companies to design for the longevity of their products and, therefore, to reuse, remanufacture, or recycle them, whichever is more economical, thus enabling a circular economy [91]. Rolls-Royce, which was a pioneer of the “power-by-the-hour” model in the 1960s, or HP are manufacturing companies that are working with this business case [92].
4.2. Use Case for Optimization
- Predict the results of a more reliable installation by implementing RPA (Robotic Process Automation) for the iterative processes of managing or processing information.
- Improve the productivity of equipment manufacturing processes through the introduction of cobots and mobile robots for non-value-added operations and the concentration of human skills for value-added operations.
- Reduce project margin drifts through the use of information systems and intelligent forecasting devices.
- Streamline the operational set-up through the use of support devices such as immersive realities.
- Optimize the logistics service both in terms of the management of the preceding and subsequent phases as well as that of the transport phase.
- Improve the productivity of administrative and financial processes by implementing RPA and using big data, analytics, and cloud computing (SaaS information systems) to manage the company’s structured and unstructured data.
- Optimize stocks by deploying an intelligent tool for managing store entries and exits.
- Optimize the real-time management of information and decision-making through the implementation of an IoT network and an effective supervision system.
- Improve the management of innovations and technical tools by focusing on a human-centered approach.
4.3. Use Case on the Digital Twin
- Causing a decrease in the value of inventory on each side, from sub-products to final products;
- Improvement of cash flows within the company’s operations;
- Reducing obsolete inventory;
- Reducing the risk of shortages of materials and components in the End2End approach;
- Optimize warehouse space utilization across the entire supply chain, freeing up valuable floor space for other purposes.
- Preparation of several scenarios showing the possibility of reducing the value of inventories in the End2End approach using the digital twin solution;
- Ensuring 100% availability of materials and components on both sides of the supply chain (wires, coils, torches);
- Calculation and proposal of the EOQ—Economy Order Quantity indicator.
- The main objective was defined for the conducted experiments—the preparation of a scenario showing the maximum possible reduction of inventories in the entire supply chain—to focus on semi-final products in Piła (Poland) while ensuring 100% availability of materials and components on both sides of the supply chain.
- Safety stock level at our side.
- Safety stock level at the supplier and customer sides.
- Number of orders that must be placed per week/month/quarter.
- Amount of material, which must be taken per order.
- Exact time when the order must be placed.
- A quick analysis of stock values in the End2End approach (supplier—customer)
- Easy recalculation of new stock levels at different sites based on new demand from customers.
- A very good control of stock level value.
- The possibility of reducing the stock value.
5. Results and Discussion
- The planning and control, the global organization, the supply chain coordination, and its monitoring for the basics of supply chain management;
- Justice, social responsibility exemplarity, mutual trust, and respect for others in the frame of ethics;
- Social equity, preservation of the environment, and economic efficiency for sustainable management;
- New technologies, agility, structural minding, and AI tool exploitation for digital supply chain management.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Waste Stream | Recycling Rate | Strategy/Directive |
---|---|---|
Plastics | 32.5% | EU Strategy for Plastics in the Circular Economy |
Textiles | 1% | EU Strategy for Textiles |
E-waste | <40% | WEEE Directive |
Food | 20% 1 | EU Farm-to-Fork Strategy |
Water and nutrients | 90% 2 | Water Framework Directive |
Packaging | 64% | Packaging and Packaging Waste Directive |
Batteries and vehicles | 47% 90.5% | Batteries and Accumulators Directive End-of-Life Vehicles Directive |
Buildings and construction | 50% | Energy Performance of Buildings Directive |
Index | Description |
---|---|
i | Index of the procurement processes, i varying from 1 to I |
j | Index of the manufacturing processes, j varying from 1 to J |
k | Index of the distribution processes, k varying from 1 to K |
l | Index of the cost criterion |
m | Index of the quality criterion |
n | Index of the lead time criterion |
w | Index of the digital maturity degree criterion |
o | Index of well-being criterion |
p | Index of health criterion |
q | Index of transport criterion |
r | Index of waste criterion |
s | Index of water criterion |
t | Index of air criterion |
u | Index of energy criterion |
v | Index of environmental management criterion |
Parameters | Description |
---|---|
Ni | Number of procurement processes |
Nj | Number of manufacturing processes |
Nk | Number of distribution processes |
Cl | Cost minimization limit |
Qm | Quality maximization limit |
LDn | Lead time minimization limit |
DMw | Digital maturity degree maximization limit |
WBo | Well-being maximization limit |
Hp | Health maximization limit |
Trq | Transport minimization limit |
Wstr | Waste minimization limit |
Ws | Water minimization limit |
At | Air pollution minimization limit |
Enu | Energy minimization limit |
EMv | Environmental management maximization limit |
Decision Variables | Description |
---|---|
Xli | Unit Procurement cost |
Xlj | Unit Manufacturing cost |
Xlk | Unit Distribution cost |
Xmi | Unit Procurement quality |
Xmj | Unit Manufacturing quality |
Xmk | Unit Distribution quality |
Xni | Unit Procurement lead time |
Xnj | Unit Manufacturing lead time |
Xnk | Unit Distribution lead time |
Xwi | Unit Procurement digital maturity degree |
Xwj | Unit Manufacturing digital maturity degree |
Xwk | Unit Distribution digital maturity degree |
Yoi | Unit Procurement well-being |
Yoj | Unit Manufacturing well-being |
Yok | Unit Distribution well-being |
Ypi | Unit Procurement health |
Ypj | Unit Manufacturing health |
Ypk | Unit Distribution health |
Yqi | Unit Procurement transport |
Yqj | Unit Manufacturing transport |
Yqk | Unit Distribution transport |
Dri | Unit Procurement waste |
Drj | Unit Manufacturing waste |
Drk | Unit Distribution waste |
Dsi | Unit Procurement water |
Dsj | Unit Manufacturing water |
Dsk | Unit Distribution water |
Dti | Unit Procurement air |
Dtj | Unit Manufacturing air |
Dtk | Unit Distribution air |
Dui | Unit Procurement energy |
Duj | Unit Manufacturing energy |
Duk | Unit Distribution energy |
Dvi | Unit Procurement environmental management |
Dvj | Unit Manufacturing environmental management |
Dvk | Unit Distribution environmental management |
Semi-Finished Product | Min stock Level (Meters) | Real Average Stock | Simulated Average Stock Level (Meters) | Exceeded Stock Value kEuro |
---|---|---|---|---|
A1 | 270,000 | 7550,000 | 4200,000 | 4824.00 |
E1 | 70,000 | 878,000 | 330,000 | 2220.00 |
A2 | 12,600 | 80,000 | 39,000 | 1110.00 |
E2 | 50,000 | 191,500 | 108,500 | 3170.00 |
Total | 11,324.00 |
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Dossou, P.-E.; Alvarez-de-los-Mozos, E.; Pawlewski, P. A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0. Mathematics 2024, 12, 2737. https://doi.org/10.3390/math12172737
Dossou P-E, Alvarez-de-los-Mozos E, Pawlewski P. A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0. Mathematics. 2024; 12(17):2737. https://doi.org/10.3390/math12172737
Chicago/Turabian StyleDossou, Paul-Eric, Esther Alvarez-de-los-Mozos, and Pawel Pawlewski. 2024. "A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0" Mathematics 12, no. 17: 2737. https://doi.org/10.3390/math12172737
APA StyleDossou, P. -E., Alvarez-de-los-Mozos, E., & Pawlewski, P. (2024). A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0. Mathematics, 12(17), 2737. https://doi.org/10.3390/math12172737