3.1. Introduction

Some logistics activities contribute to congestion, pollution, and wasting of resources. These activities can affect delivery efficiency, mobility, the environment, and human health. This explains the need to develop other strategies in order to create new and more efficient logistics systems such as collaborative logistics. In this case, the shared supply chain is proving to be a useful choice for producers and logistics service providers to improve their economic and environmental performances.

In addition to the traditional objectives of efficiency and control of the supply chain, there are now challenges in terms of CSR (corporate social responsibility) and sustainable development (Chay et al. 2013). To achieve these objectives, entrepreneurs must move toward new inter-organizational configurations, such as logistics pooling, in order to optimize their pooled material resources (warehouses and means of transport) through the appropriate selection of a scenario that minimizes transport and storage costs in addition to the volume of GHG emissions. In order to consolidate the successful pooling of transport, interested parties should share and exchange data and information to facilitate the implementation of this concept (Lundqvist 2018).

In order to improve economic performance while maintaining social and environmental objectives, different forms and approaches to logistics pooling have been studied in several publications, such as collaborative urban transportation (Cleophas et al. 2019), collaborative transportation management (Okdinawati et al. 2015), collaborative transport planning (Wang and Kopfer 2014), collaborative transport between terminals (Gharehgozli et al. 2017), pooled transport for children with disabilities (Tellez et al. 2016), pooling of last-mile delivery (Liakos and Delis 2015), collaborative air traffic flow management (Murça 2018), and collaborative intermodal transport (Liu et al. 2019).

Transport pooling allows partners to benefit from several advantages that lead to green and sustainable transport, integrating sustainable development objectives (Moutaoukil et al. 2013). At the economic level, we can mention the following: the lowering of logistical costs, carbon taxes, and fuel costs (Guajardo and Rönnqvist 2016; Wang et al. 2017). At the ecological level, it is worth mentioning the following: the simultaneous lowering of GHG emissions, noise, the exploitation of natural resources (energy), the encouragement of recycling, and waste processing (Alvarez et al. 2018; Niu and Li 2019; Ntziachristos et al. 2016; Senkel et al. 2013). At the societal level, we can consider the following: the expectations of various stakeholders, establishing a culture of sharing (Wang et al. 2018), minimizing stress for drivers – less congestion (Gonzalez-Feliu and Battaia 2017; Hensher 2018) – and saving time (Ghaderi et al. 2016; Moutaoukil et al. 2015).

In this context, a literature review will be presented to highlight the techniques of collaboration, pooling, and resource sharing, in addition to their benefits for goods-producing companies. Furthermore, a suggestion of seven scenarios for pooling between a set of three companies is discussed. This is done in the urban and interurban distribution phases in order to optimize resources and minimize the distances traveled. As a result, we achieve reductions in transportation costs, congestion, and GHG emissions. In addition, the study continues through the analysis of the presented scenarios in order to choose the optimal path by showing the challenges and opportunities of this pooling.

3.2. Concepts of collaborative logistics

3.2.2. Forms of logistical collaboration

Companies are currently seeking to create synergies with other organizations and create value chains through horizontal and/or vertical collaboration with logistics partners.

3.2.2.1. Horizontal collaboration

In logistics, horizontal collaboration consists of a collaboration between operators at the same level (between suppliers, manufacturers, distributors, etc.) in a supply network. Vanovermeire et al. (2013) declared that horizontal collaboration can be defined as the grouping of transport companies which operate at the same level of the supply chain, and which have similar or complementary transport needs. It is also a collaboration between a group of stakeholders from different supply chains, acting at the same levels and with similar needs (Gonzalez-Feliu et al. 2013).

According to McKinnon and Edwards (2010), horizontal collaboration overcomes financial and commercial barriers and benefits society by reducing the number of vehicles and, therefore, CO₂ emissions and congestion. It also reduces prices due to bulk purchasing, reduces supply risks, reduces administrative costs due to centralized purchasing activities, and also reduces inventory, transport, and logistics costs and facilities. All these benefits are achieved through a rationalization of the use of equipment and better sharing of human labor and information (Bahinipati et al. 2009).

3.2.2.2. Vertical collaboration

Vertical collaboration occurs between partners operating in different levels of the logistics network, where the benefits of collaboration include lower supply costs due to the synchronization effect through information sharing (Moutaoukil et al. 2012). In this regard, Fernandez et al. (2016) have stated that vertical collaboration is proposed when shippers and customers collaborate on their objectives. This concept is used to develop long-term relationships, as well as loyalty and commitment. Sharing information and technology in the vertical supply relationship is a crucial factor in supply chain efficiency.

Successful models can cross these two areas of collaboration (Figure 3.1). Cases of vertical collaboration mainly focus on the exchange of information between entities and involve members of the same value chain (industrial and distributor). However, most cases of horizontal collaboration focus on the pooling of resources or structures with the aim of expanding flows, which involves companies that can provide complementary goods or services, whether they are competing or not (among several manufacturers).

However, in practice, logistics pooling presents certain obstacles that discourage logistics service providers from applying it. Abbad (2014) and Abbad et al. (2016) have identified the obstacles to horizontal pooling, which relate to storage activities, transport activities, and the specifics of the context studied:

• – The obstacles to the pooling of goods storage: nature of products, location of operators, sharing of information, logistics services, and so on.
• – The obstacles to the pooling of transportation of goods: compatibility of goods, proximity of delivery of goods, and so on.
• – Cultural barriers related to certain practices.

However, several positive effects of a shared management of supplies, which are related to these obstacles, have been identified from three perspectives in a global manner or for distributors and manufacturers alone:

• – Global: reduction of transport costs, reduction of GHG emissions, standardization of processes and organizations, and so on.
• – Distributors: increase in delivery frequency, increase in service rate, and decrease in required stock levels.
• – Industrial: pooling of logistical costs and better management of the marketing of new products, seasonal products, and promotions.

In the same perspective, Chanut et al. (2010) presented several aspects of motivations and obstacles that are related to vertical and horizontal pooling: see Table 3.1.

	Motivations	Obstacles
Vertical sharing	– Competitive advantage – Source of power – Ownership and stock management – Number of suppliers/references	– Network size – Financial constraints – Efficient upstream logistics organization by supplier
Horizontal sharing	– Competitive advantage – Optimization of logistics platforms – Capital connections – Regulatory constraints – Societal pressure and collective objectives	– Reluctance to share data between competing companies – Reactions from independent distributors

3.3. Pooling of physical flows between organizations

Logistics pooling corresponds to co-design between partners (suppliers, customers, transporters, etc.) with the common objective of a logistics network of pooled resources (warehouses, platforms, means of transport, etc.), in order to share these networks with the data needed for management. By seeking a common objective and with the proximity of facilities, pooling is adapted for supply chains that operate in the same sectors (Pan et al. 2013). This co-design results in a shared network logistics where the different entities jointly share and manage their skills, structures (distribution warehouses, offices, etc.), and resources (handling equipment, transport vehicles, information systems, etc.). This is why Ballot and Fontane (2008) have considered pooling as a form of horizontal collaboration.

There are several types of pooling, including, for example, the pooling of resources. Thus, resource pooling is the cooperation and beneficiation between the different partners in a supply chain. It is possible to talk about the resources developed by the shared partners, the disposition of work tools, resources, collaboration tools, and so on (Hadj Taieb et al. 2014). The pooling of physical flows can be mentioned here. Indeed, the pooling of resources allows the sharing of resources and methods in terms of improving quality and reducing costs. It guarantees a better functioning of the management of different operations. In order to assess this pooling, two criteria can be used. The first is the economic criterion (costs of transport, transfer platform, storage, etc.) and the second is the environmental criterion (greenhouse gas emissions, congestion, noise, pollution, etc.) (Zouari and Cherif 2018).

3.4. Literature review

3.4.2. Analysis and discussion of the results

Logistics pooling is one of the strategies used to improve competitiveness and bring benefits to all stakeholders. Based on the results of the articles studied, we can see the rarity of articles dealing with purely vertical collaboration on one side, and on the other, we can see that the majority of articles refer either to horizontal collaboration or to integrated horizontal and vertical collaborations. Figure 3.2 shows the opportunities for transport pooling.

The pooling of transport has a positive effect on:

• – Economic performance: through cost reduction, financial gains by 82%, logistics time reduction by 21%, and improved flexibility and responsiveness of transporters by 14%.
• – Environmental performance: through the reduction of GHG emissions by 57%, road congestion and occupation by 21%, and noise.
• – Social and societal responsibility: through the reduction of risks by 11%, the number of trips by 11%, the improvement of customer satisfaction by 7%, and the reduction of distances traveled by 7%.

It should be noted that the majority of articles mainly focus on economic gain and/or GHG reduction, but with less intensity on the social and societal sides. However, among these opportunities, several have a strong interaction and can have effects on all three aspects of sustainability:

• – GHG emissions can be related to economic performance (CO₂ tax, fuel costs, etc.). Similarly, reducing it is part of social responsibility (smog, health of residents, etc.).
• – Congestion increases GHG emissions, noise, time loss, fuel consumption, driver stress, customer dissatisfaction in the event of delays, accident risks, and so on.
• – Reducing distances and the number of trips leads to a reduction in fuel consumption, GHG emissions, and costs, saved time, better use of resources (less depreciation of trucks), better working conditions for drivers, and so on.

It can, therefore, be deduced that pooling and logistical collaboration make it possible to:

• – reduce logistics costs;
• – reduce GHG emissions;
• – reduce logistics times;
• – reduce congestion;
• – improve the flexibility and responsiveness of transporters;
• – reduce risks;
• – optimize the use of resources (number of trucks, distances traveled, loading, etc.).

At present, manufacturers are aware of the importance of optimizing their supply chain. This optimization is based on an economic approach, which is manifested through the integration of economic, environmental, and social concerns, which in turn are in line with the objectives of sustainable development. Thus, the environmental and societal degradation that goes hand in hand with economic development calls for serious action by all stakeholders, including regulators such as government, industry operators, and consumers.

In this context, the literature review shows that the majority of authors used scenarios as the initial source of knowledge for their analysis, with a rate of 57%. However, other sources are also used, such as hypotheses, questionnaires, and empirical data. In order to analyze the initial knowledge, the authors used a variety of tools and methods (both accurate or approximate). This majority opted for simulation in order to validate their basic proposal, with a rate of 35%. Figure 3.3 shows the methods based on mathematical models such as statistical analysis, linear programming, and heuristics/metaheuristics with a rate of 18% each. Similarly, Shapley optimization and values were also used with a rate of about 11% each.

In most of the articles on how to deal with problems, the most notable approach is to first propose scenarios and validate them, and then to use a single analytical method such as simulation (Chan and Zhang 2011; Junior et al. 2019; Thompson and Hassall 2012), linear programming (Muñoz-Villamizar et al. 2015; Pan 2010), metaheuristics (Abbassi et al. 2018), the Shapley value (Vanovermeire et al. 2013), optimization (Wang et al. 2019), and the algorithm (Perez-Bernabeu et al. 2015); or two methods, such as the mathematical model/simulation (Gonzalez-Feliu and Battaia 2017; Gonzalez-Feliu and Salanova 2012), metaheuristics/simulation (Gonzalez-Feliu 2011), linear programming/simulation (Danloup et al. 2015), and mathematical model/optimization (Silbermayr et al. 2017). On the other hand, Gonzalez-Feliu et al. (2013) used three methods to analyze the scenarios they proposed: namely, metaheuristics, clustering, and the Shapley value. In addition, some other authors adopted questionnaires and/or hypotheses and validated them with statistical analysis (Alvarez et al. 2018; Cao and Zhang 2010; Chen et al. 2013; Ghaderi et al. 2012).

3.5. Proposal of pooling scenarios for the urban distribution of goods

Pooling in the distribution phase consists of bringing together means of transport, warehouses, distribution centers, and, of course, information. Before focusing on pooling scenarios, we will emphasize on a scenario where companies do not use this technique: see Figure 3.4.

The scenarios are based on the following assumptions:

• – The trucks used have a maximum load of 3 and 9 tons.
• – The demand is equal to the supply. The three manufacturers each supply 1 ton of their product to each of the three customers.
• – The trajectories are recorded for the round trip (loaded truck)/(empty truck).

In scenario 0 (Figure 3.4), each manufacturer distributes 3 tons of its product with its own 3-ton truck. They make a trip to deliver a ton of product to each customer.

In scenario 1 (Figure 3.5), each manufacturer uses its own vehicle to transport the products to the transfer hub, where the three manufacturers exchange their products so that each truck is loaded with one ton of each type of product. A forklift truck ensures the transfer of products between the three trucks in seven operations, as shown in Figure 3.6. Each truck delivers its load to one of the three customers and returns to the owner company, either through the hub (S1) or through the shortest route (S1a).

In scenario 2 (Figure 3.7), as in scenario 1, each manufacturer uses its own 3-ton truck to transport its product to the warehouse. Subsequently, the distribution of different types of products is done in the form of a tour. The tour can either be done with a single rented 9-ton truck (S2a) or with three trucks, after having exchanged products with each other (S2).

Scenario 3 (Figure 3.8) relates to the pooling of information and physical flows. Transport to the warehouse is done with a single vehicle, in the form of a tour. The warehouse then supplies each of the customers using a different vehicle. Therefore, in order to supply the hub with 3 tons of each product, either a 3-ton truck is used in three tours (S3) or a 9-ton truck is used in a single tour (S3a).

In scenario 4 (S4), as shown in Figure 3.9, company B is used as a consolidation center: all products are gathered in this center. A forklift performs the transfer (Figure 3.10), loading the three trucks with 1 ton of each product. Each truck loaded with the three types of product serves a different customer and returns empty to the owner company.

As in scenario 4, Company B is used as a consolidation center for scenario 5: see Figure 3.11.

In this case, the first part is similar to scenario 4, but the distribution of the different products to customers’ stores is done in the form of tours, either using three 3-ton trucks, one from each company, (S5) or a 9-ton truck from company B (S5a). In the first case, the loading/unloading is similar to scenario 4, whereas in the second case, the forklift unloads the two trucks from A and C into the 9-ton truck and completes the load with 3 tons of product B.

Scenario 6 (Figure 3.12) involves making tours between manufacturers and customers. A 3-ton truck is supplied with one-third of its capacity each time it passes through a manufacturer. This truck continues its tour to the customers in order to deliver one-third of its load of A, B, and C products to each customer. In this case, either three tours are made with a single 3-ton truck or three 3-ton trucks (S6) are used. The tour can also be done with a 9-ton truck (S6a). Like in scenario 3, this scenario uses information pooling.

In scenario 7 (S7), as shown in Figure 3.13, the three 3-ton trucks leave from company A to C while passing through company B. At each company, they supply themselves with one-third of their loads with a type of product. Each truck then supplies a customer and returns to company A.

3.6. Comparison of scenarios

Now that these scenarios have been discussed, we can choose the best one based on the distance traveled by the vehicles. The shorter the distance, the lower the cost of transportation and GHG emissions. In addition, the lower the number of trucks used, the lower the congestion and the higher the truck operating rate will be.

3.6.1. Distances traveled

Figure 3.14 shows the distances between different organizations.

For the rest of the study, we can consider the following assumptions:

• – The companies and the warehouse use trucks with a capacity of 3 or 9 tons.
• – Each manufacturer, A, B, and C, supplies 3 tons of its product (P_A, P_B, and P_C).
• – Each customer, M₁, M₂, and M₃, receives 1 ton of each product type.
• – D_i (i = A, B, C) is the distance between company i and the hub.
• – D_Mj (j = 1, 2, 3) is the distance between the hub and customer M_j.
• – D_AB is the distance between manufacturer A and manufacturer B.
• – D_BC is the distance between manufacturer B and manufacturer C.
• – D_M1M2 is the distance between client M₁ and client M₂.
• – D_M2M3 is the distance between client M₂ and client M₃.
• – D_iMj is the distance between company i and customer M_j.

To calculate the travel distances of the different scenarios, a Microsoft Excel spreadsheet is used, as shown in Figure 3.15. We simply enter the distances between organizations (Table 3.3) in order to get all the travel distances for the different scenarios, based on the shortest route, which can be determined by Google Maps. The results showed that each scenario could be the shortest route based on the distances between organizations.

DA	DB	DC	DM1	DM2	DM3	DAB	DBC	DM1M2	DM2M3	DAMI	DAM3	DBM1	DBM3	DCM1	DCM3	DBM2	DCM2	DAM2
10	12	8	15	10	14	7	4	8	6	18	20	15	14	21	19	12	16	20

It is worth mentioning that in Figure 3.15, scenario S2 has the shortest route (96 km) when using 3-ton trucks. In addition, it should be noted that with 9-ton trucks, all scenarios (S2a, S3a, S5a, and S6a) are slower than that with 3-ton, and hence, scenario S6a is the shortest route.

3.6.2. Greenhouse gas emissions

To calculate the CO₂ equivalent (EqCO₂) of exhaust emissions from trucks (CH4, Cox, etc.), the following formula is used:

[3.1]

where Fern is the mean EqCO₂ emission factor for a diesel engine (kg/L) and Conso is the mean fuel consumption (L).

To calculate consumption (Conso), a recognized modeling that meets the standard BP X 30-323 (2015) is used, as shown by equation [3.2]¹.

[3.2]

where (x) is the consumption of the full truck expressed in L/100 km, 2/3 of the consumption is related to the empty weight, and 1/3 of the consumption is related to the loaded weight.

The consumption (x) of a 3-ton truck is 15 L/100 km and that of the 9-ton truck is 25 L/100 km.

The Fern amounts to 2.662 EqCO₂/L of fuel (diesel)².

As for the calculation of the travel distances of the different scenarios, a Microsoft Excel spreadsheet is used to calculate the EqCO₂ for each scenario, keeping the same inter-organizational distances, as shown in Figure 3.16. The results show that scenario S4 is the least expensive despite the fact that it is neither the shortest nor the least polluting scenario.

3.6.3. Distribution cost

The distribution cost (Ct Dist) is determined on the basis of the transport cost and the handling cost (loading and unloading), as shown by equation [3.3]:

[3.3]

The transport cost (Ct Tr) is calculated according to the distance of the journey traveled and the unit cost of the means of transport (3-ton or 9-ton truck, rented or not), as shown by equation [3.4].

[3.4]

where journey C3T is the course traveled by a 3-ton truck (km), journey C9T is the course traveled by a 9-ton truck (km), journey Dist C3T is the course traveled by a 3-ton truck from the distributor, Ctu C3T is the unit cost of transport by a 3-ton truck (DT/km), Ctu C9T is the unit cost of transport by a 9-ton truck (DT/km), and Ctu Dist C3T is the unit cost of transport by a 3-ton truck from the distributor (DT/km).

The cost of loading and unloading is a based on the amount that is loaded/unloaded and the relative time for loading and unloading a ton of product, in addition to the hourly cost of the handling equipment. The loading time (t Ch) is calculated by the following formula [3.5]:

[3.5]

where Charge C is the number of tons loaded/unloaded, Nb Ch is the number of times that Charge C has been repeated, and time Ch/T is the loading time for 1 ton of product.

The loading cost (Ct Ch) is the product of the loading time and the hourly cost of the handling equipment (equation [3.6]).

[3.6]

The equations are processed by an Excel sheet for the different scenarios, and the results are shown in Figure 3.17.

3.6.4. Delivery time

The delivery time (t Liv) is the sum of the transport times (ttr) and the loading time (t Ch), as shown by equation [3.7].

[3.7]

where (t Ch) is the total loading/unloading time, as shown by equation [3.5], and ∑ ttr is the sum of transport times (ttr) without counting the returns, as shown by equation [3.8].

[3.8]

where journey is the maximum distance of a 3-ton and/or 9-ton truck from the manufacturer to the customer without counting the return, and Vmoy is the mean speed (day or night) of a 3-ton and/or 9-ton truck.

On the Excel sheet, the equations are processed for the different scenarios. The results, which are presented in Figure 3.18, show that for the same inter-organizational distance setup as in the previous section, scenario S7 has the shortest delivery time with a duration of 4.28 h.

3.6.5. Best scenario

With different Excel sheets, we have built a spreadsheet to calculate the values of different variables for all scenarios (EqCO₂), distribution cost, and delivery time). However, the variables have different units, which makes it quite difficult to choose the optimal scenario. In order to overcome this problem, we have opted to calculate ratios relating to each criterion. The ratio (Rat) is calculated using the relationship between the value found and the maximum value of the studied variable (in other words, for (EqCO₂, Rat Eq COe = EQ CO2i / Max (EQ CO₂1,. EQ CO₂n) and i = 1 to n). To this extent, the optimal scenario will be the one that has the minimum sum of the ratios of different variables, given that all variables are looking to be minimized. The results of the Excel sheet (as shown in Figure 3.19), which make it possible to calculate the ratios of various criteria and help choose the optimal scenario, show that scenario S4 is the best choice.

3.7. Proposal for a shared long-distance distribution model

The scenarios for pooling urban distribution, which have already been mentioned, have shown the role of pooling in reducing congestion and GHG emissions. The combination of urban and interurban distributions can generate more significant improvements. Therefore, the model we are going to propose is four cities. In each city, there are manufacturers and a consolidation warehouse that manages urban and/or interurban distribution to customers.

In scenario 1 (Figure 3.20), manufacturers in the same city transport their shipments to a warehouse that will then deliver individual orders for each customer in the city. In addition, the warehouse transports collective orders from customers in other cities through the relative warehouses. When returning, the trucks bring back collective orders from customers in the warehouse of departure from the first warehouse destination.

For example, manufacturers in City A (F_Ai) transport their products to the EA warehouse, which distributes them to customers C_Ai and F_Aj, and to the EC warehouse which in turn distributes them to customers in the city (C_Ci and F_Ci) and to other corresponding EB and ED warehouses. When returning to the EC warehouse, the trucks transport the goods to the EA warehouse. Therefore, each customer/manufacturer in each city receives all products from all manufacturers located in their city or elsewhere.

In scenario 2 (Figure 3.21), F_Ai manufacturers transport their products to the EA warehouse for distribution to C_Ai customers and to the EB warehouse, which in turn distributes them to C_Bi customers and the EC warehouse. Upon return, trucks from the EA warehouse will be loaded with products from the EB warehouse network (F_Bi manufacturers and the EC warehouse can receive products from F_Ci and F_Di manufacturers) to be delivered to customers in the EA warehouse. Therefore, customers in City A can receive products from all manufacturers (F_Ai, F_Bi, F_Ci, and F_Di), and in addition to this, F_Ai manufacturers receive their supplies that have been manufactured in other cities.

The combination of the two scenarios allows us to implement our model presented in Figure 3.22.

This model can generate benefits for all stakeholders. The most significant advantage is that vehicles do not make empty returns, which leads to improved service efficiency, reduced GHG emissions, reduced road occupancy rates, better resource optimization (inventory, trucks, etc.), and reduced logistics costs.

In addition, this model clearly shows the role that pooling has in reducing GHG emissions – by reducing the number of vehicles used – and also in reducing congestion, given that pooling warehouses only allow small vehicles to be used in the city center.

3.8. Conclusion

The scenarios which we have suggested make it possible to identify some opportunities for transport pooling, such as the reduction of congestion and GHG emissions, while also considering the economic and social opportunities and gains. The comparison between the pooling scenarios and the scenario in which pooling is absent makes it possible to clearly show the improvements made by pooling transport in terms of distribution. The spreadsheet allows employees to choose the optimal scenario by considering different sustainability variables that lead to more efficient guidelines.

In conclusion, this is a multi-criteria decision support study using the following criteria: the cost of transportation (based on distance traveled), GHG emissions (based on distance traveled and truck characteristics), congestion (based on the number of trucks used), use of resources (based on the number of trucks used), and delivery time (based on distance, number of trucks used, and congestion). In addition, we plan discrete event simulations in order to consider the random concept of transport times in any distribution function.

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