Estimating the Intensity of Cargo Flows in Warehouses by Applying Guanxi Principles

Abstract

Proper supply chain management helps to ensure business continuity considering the increased importance of globalization. Logistics processes play an important role in keeping the supply chain running smoothly, and warehouse management is one of the most important logistics activities. The movement of freight flows in the supply chain poses many challenges to the arrangement of sustainable processes at the international level. The stopping of these flows at warehouses and/or terminals temporarily and/or for longer periods of time is yet another challenge. In this respect, a number of analyses and studies have been conducted in the scientific literature to identify the most serious and frequent problems: lack of planning of work activities, partial accounting of the work performed, lack of timely accounting of the internal movement of goods within the warehouse, absence of a spare-parts management system, etc. On the other hand, warehousing processes have been analysed to identify certain efficiency gaps in freight flows within warehouses; thus, this article addresses this problem by applying guanxi principles. Using guanxi theory and practice to test various assumptions for efficient freight flow movement in warehouses, a study was conducted using quantitative and qualitative methods and expert judgment. Based on the results of the conducted empirical studies, guanxi philosophy can be concluded to have an impact on the efficient management of warehouse processes when goods are removed from a warehouse over 365 days with an annual daily loading flow rate of 103,490 t/m and a loading density of 280 kg/m³. This indicates that the application of guanxi principles is important, which reflects the intensity of the logistics processes of cargo flows and transport dynamics between guanxi and warehouse optimization.

1. Introduction

Industrial companies use warehouses to smooth out dynamic market fluctuations and gain significant benefits such as improved service levels or faster delivery to the market, etc. [1]. Inventory management and the assignment of storage locations are typical planning issues. Smart inventory management helps to reduce warehousing costs. The principles of using data to manage warehouse flows should be systemized. Information flows speed up and refine the work of storage centres, which allows for better customer service [2]. The time spent on warehousing activities is an important factor in the total request cycle time. Therefore, it is essential to analyze viable and sustainable means to minimize this time. Different storage strategies can be used. The implementation of each storage strategy is an operational issue. Warehousing is an element of the supply chain process that is important for an optimally functioning supply chain [3]. Order picking is often considered the most critical operation in warehousing. Packaging is essential in the handling process and logistics. Guanxi philosophy has an impact on supply chain management. This article describes the importance and functions of storage, types of warehouses and different purposes of use of warehouses [4].

Guanxi philosophy impacts supply chain management, the effect of personal relationships on the selection of and cooperation with suppliers, peculiarities of the logistics outsourcing industry, and the characteristics of business relations in China. In terms of relationships, the guanxi concept is an ambiguous term that embeds a Chinese philosophy of social structure and interactions. Thus, it is important to examine the relationship between logistics service value, relationship quality, guanxi and financial performance.

As one of the supply chain mechanisms, the storage procedure currently dominates supply chains and systems. Scientific problems in warehouse management networks have been identified in applications of a specific methodology and empirical research, by defining the warehousing load flow intensity (to ensure timely delivery) and technical transportation parameters that take into consideration different geographical areas (for example, distribution centres and loading terminals located near airports, seaports and train stations facilitate multi-modal transport loading and unloading processes). A common goal of warehousing operations is to meet customer needs and utilize resources effectively to ensure the smooth functioning of a warehouse [5]. In conclusion, it is important to note that warehouses play an essential role in logistics, primarily when it comes to product distribution. A considerate decision as to whether the transportation process should include storage in warehouses should be made. Since warehousing activities are frequent and numerous, even small improvements can achieve significant savings [6]. Planning in the warehousing process is strictly based on correct calculations. Scholars in the areas of engineering, logistics, transportation and management mainly focus on the R&D and application of underlying technologies, business logic and operation frameworks, related management systems, and specific optimization problems under smart logistics [7]. Goods storage strategies must be selected with precision and accuracy, which can also affect the loading and unloading processes. Storage is one of the key factors in logistics supply chains and load transportation. The key research question involves the warehouse management network, by identifying guanxi principles and the impact of the philosophy on the effective management of the load flow intensity, to facilitate the multimodal transport loading and unloading processes. The hypothesis raised considers how the practical application of guanxi principles for different functions of storage and supply chain mechanisms affect the effective process of loading and unloading, facilitating multimodal transport mechanisms.

The problems faced include poor infrastructure, lack of adequate transportation networks and IT capacities and poor integration of transportation, warehousing and distribution facilities; this leads to higher transportation costs and a lack of reliability in pick-up and delivery time. Finding qualified staff, the lack of logistics training programs and high-quality service providers who are able to fully meet customers’ requirements (which in turn gives shippers little confidence in service levels and makes it difficult to find good providers that can deliver high-quality and consistent services across all geographical regions), the lack of a nationally integrated intermodal transport network, entrenched regulation and local protectionism, poor IT infrastructure, inability to use advanced technology and underdeveloped warehousing services are crucial issues undermining the intensity of cargo flows in warehouses applying guanxi principles. Thus, such warehouses aim to assess the impact of guanxi on a new logistics perspective based on a performance index and cost-time-reliability construct.

The aim of this paper is to analyse the intensity of cargo flows in warehouses and to identify which cargo flow processes in warehouses the elements of guanxi philosophy should be applied to. The object of this study is the intensity of cargo flows in warehouses. To achieve the aim of the article, quantitative and qualitative research, mathematical calculations and expert evaluations are used.

The structure of the article consists of an introduction; theoretical analyses of warehouse operations, storage systems and technological processes; a research methodology; results and a discussion; and conclusions and references.

2. Theoretical Analysis of Warehouse Operations, Storage Systems and Technological Processes

A warehouse is understood as a place for storing materials that do not directly affect production. Available materials are used at a later point in time. Warehousing in a narrow sense has the function of a time overlap between available materials and needs. The warehousing process begins with the reception of materials and ends with the release of products to product or forwarding warehouses. Process simulation models are an effective tool for identifying obstacles in the process and improving process parameters. The course of the simulation must be monitored at each stage. Impacts on the overall functioning of the system can be determined according to the changes that occur at the output of the simulation model. This study also reviews the related literature on smart logistics and explores future research on this topic combined with modern logistics practices. Storage is one of the most traditional aspects of logistics. A good storage system facilitates the identification of physical materials, which helps increase productivity and reduce the cost of manpower facilitating the management of inventories. The authors assume that the intensity of cargo flows in warehouses is the key factor for determining a particular warehouse allocation (Table 1).

A warehouse is one of the key components for companies performing distribution functions. Sustainability of the entire company depends largely on the sustainability of warehousing, which in turn impacts economic and social sustainability as part of the company’s way of absorbing the consequences of changes in demand [8].

Warehouses significantly contribute to greenhouse gas emissions in the supply chain, primarily as a result of energy consumption for lighting, heating, cooling, air conditioning and the handling of goods. Thus, companies invest in renewable energy sources, green technologies, recycling systems, and so on. Also, the number of articles written on the topic of sustainable warehousing has increased significantly in recent years [9].

Economic, social and environmental sustainability are equally important for achieving overall warehouse sustainability [10].

The sustainability of a warehouse largely depends on the utilization of the warehouse’s resource. If a warehouse is considered only as a space between four walls, then its main resources are space, equipment and workforce. The scheduling of the use of space and stationary equipment is a tactical or strategic decision and generally does not require daily optimization. On the other hand, workers and mobile parts of equipment are scheduled every day. Sustainable order picking cannot be ensured without the efficient use of these resources. Since order picking is an extremely intensive process that consumes a lot of energy, capital and human resources, its impact on overall warehouse sustainability is extensive. Andriansyah et al. [11] point out that an efficient use of resources is a prerequisite for sustainable order picking and thus for sustainable warehousing.

Tan et al. [12] define a sustainable warehouse as a warehouse where all economic, environmental and social inputs are integrated, balanced and managed. In their work, they developed a model for testing warehouse sustainability.

Table 1. Main areas of research in WoS.

Ref.	Scientific Literature Analysis of Cargo Flows in Warehouses
[13]	“Therefore, the objective of finding solutions for the stock location problem is to reduce the requirement for space and to minimize the total distance travelled or travel time throughout the warehousing process.”
[14]	“The number of different products in a distribution warehouse maybe large, while the quantities per order line may be small, and this often results in a complex and relatively costly order picking process.”
[15]	“In order to meet customer needs for shorter cycle times when picking orders, warehouse managers are interested in finding the most economical way of processing orders, the one which minimizes the costs involved in terms of distance travelled or travel time.”
[16]	“Most companies are facing warehousing problems these days.”
[17]	“Successful companies are those that provide the right products to the right clients, in the right place, at the right time, and for the right price.”
[18]	“A crucial link between order picking and service level is that the faster the items on an order can be retrieved, the sooner they are available for shipping to the client.”
[19]	“The efficiency of an order picking process greatly depends on the storage policy used, i.e., on where products are located within the warehouse.”
[20]	“Warehouse storage decisions affect almost all the key performance indicators of a warehouse such as order picking time and the cost of using storage space, labor, etc.”
[21]	“Handling and storage are arranged from the lean production viewpoint.”
[22]	“Examined the main processes for determining the required number of warehouses and studied warehouse layout systems that reduce transportation costs.”
[23]	“Theoretical knowledge (simulation technique, specific simulation systems) and practical experience (description of the system, its elements and their mutual interactions and connections) are needed for the development of a correct simulation model.”
[24]	“Order picking—the retrieval of stock keeping units (SKUs) from a warehouse to process customer orders—is a vital supply chain component for many companies, both from the production system point of view (i.e., the supply of assembly stations with assembly kits) and from the point of view of physical distribution activities (i.e., processing customer’s orders).”
[25]	“Within warehouse management, the most time-consuming activities are the storage and retrieval actions, which is due to increasing warehouse floorspace, bottlenecks and congestion, making the travel time the key factor to manage.”
[26]	“Essential objective of this scientific article is to actuate the finest option for appointing a product to a warehouse stockpile area; one way to decrease handling time is by changing the operational procedures.”
[27]	“The logistics activity “receipt of goods” involves several operations, including adding a vehicle to the point of unloading.”
[28]	“Such understanding and knowledge can then prevent errors and shortcomings to avoid possible failures.”
[29,30]	“Digitalization of warehouses is currently one of the research topics of Logistics 4.0.”
[31]	“From a sustainability perspective, efficient logistics is green and energy-saving, both of which need to be systematically integrated with the logistical planning processes.”
[32]	“The increasing use of information technology (IT) in supply chain management and logistics renders a competitive advantage to companies, while enhanced competitiveness is provided by enterprise resource planning and warehouse management systems.”
[33]	“Container shipping supply chain (CSSC) from the logistics perspective covers all major value-adding segments in CSSC including freight logistics, container logistics, vessel logistics, port/terminal logistics and inland transport logistics.”
[34]	“Insufficient production inputs and perishable outputs can aggravate the impact of logistics disruptions on losses, while the purchase of agriculture insurance and higher regional GDP can mitigate this effect.”
[35]	”The movement of information and materials in the supply chain essentially link operational uncertainties and structural variations with internal and external sources.”

Table 1. Main areas of research in WoS.

Ref.	Scientific Literature Analysis of Cargo Flows in Warehouses
[13]	“Therefore, the objective of finding solutions for the stock location problem is to reduce the requirement for space and to minimize the total distance travelled or travel time throughout the warehousing process.”
[14]	“The number of different products in a distribution warehouse maybe large, while the quantities per order line may be small, and this often results in a complex and relatively costly order picking process.”
[15]	“In order to meet customer needs for shorter cycle times when picking orders, warehouse managers are interested in finding the most economical way of processing orders, the one which minimizes the costs involved in terms of distance travelled or travel time.”
[16]	“Most companies are facing warehousing problems these days.”
[17]	“Successful companies are those that provide the right products to the right clients, in the right place, at the right time, and for the right price.”
[18]	“A crucial link between order picking and service level is that the faster the items on an order can be retrieved, the sooner they are available for shipping to the client.”
[19]	“The efficiency of an order picking process greatly depends on the storage policy used, i.e., on where products are located within the warehouse.”
[20]	“Warehouse storage decisions affect almost all the key performance indicators of a warehouse such as order picking time and the cost of using storage space, labor, etc.”
[21]	“Handling and storage are arranged from the lean production viewpoint.”
[22]	“Examined the main processes for determining the required number of warehouses and studied warehouse layout systems that reduce transportation costs.”
[23]	“Theoretical knowledge (simulation technique, specific simulation systems) and practical experience (description of the system, its elements and their mutual interactions and connections) are needed for the development of a correct simulation model.”
[24]	“Order picking—the retrieval of stock keeping units (SKUs) from a warehouse to process customer orders—is a vital supply chain component for many companies, both from the production system point of view (i.e., the supply of assembly stations with assembly kits) and from the point of view of physical distribution activities (i.e., processing customer’s orders).”
[25]	“Within warehouse management, the most time-consuming activities are the storage and retrieval actions, which is due to increasing warehouse floorspace, bottlenecks and congestion, making the travel time the key factor to manage.”
[26]	“Essential objective of this scientific article is to actuate the finest option for appointing a product to a warehouse stockpile area; one way to decrease handling time is by changing the operational procedures.”
[27]	“The logistics activity “receipt of goods” involves several operations, including adding a vehicle to the point of unloading.”
[28]	“Such understanding and knowledge can then prevent errors and shortcomings to avoid possible failures.”
[29,30]	“Digitalization of warehouses is currently one of the research topics of Logistics 4.0.”
[31]	“From a sustainability perspective, efficient logistics is green and energy-saving, both of which need to be systematically integrated with the logistical planning processes.”
[32]	“The increasing use of information technology (IT) in supply chain management and logistics renders a competitive advantage to companies, while enhanced competitiveness is provided by enterprise resource planning and warehouse management systems.”
[33]	“Container shipping supply chain (CSSC) from the logistics perspective covers all major value-adding segments in CSSC including freight logistics, container logistics, vessel logistics, port/terminal logistics and inland transport logistics.”
[34]	“Insufficient production inputs and perishable outputs can aggravate the impact of logistics disruptions on losses, while the purchase of agriculture insurance and higher regional GDP can mitigate this effect.”
[35]	”The movement of information and materials in the supply chain essentially link operational uncertainties and structural variations with internal and external sources.”

Bank and Murphy [36] emphasize the importance of warehouse sustainability for the sustainability of the entire supply chain. They introduced warehouse sustainability metrics, measurements and guidelines. Similarly, Torabizadeh et al. [37] provided a list of 33 performance indicators that can be used to assess the sustainability of a sustainable warehouse management system. Bartolini et al. [9] pointed out that warehouses make a major contribution to increasing greenhouse gas emissions in supply chains.

An exhaustive and systematic review of literature gaps has been presented based on the insufficient scientific information on unresolved key issues (questions) and the absence of research in specific areas of cargo flow intensity in warehouses that have not been studied before. Having observed unexplored areas in supply chain warehouse management, the aim of this scientific article is to make a solid contribution to a new concept of comprehensive research and to assess potential critical gaps in the relevant literature, providing valuable research towards the theoretical and practical implications, opportunities for improvement, and calculations of the intensity of cargo flows in warehouses.

The term guanxi is an ambiguous term that describes the Chinese philosophy of social structure and interaction [38].

Guanxi has increasingly been viewed as a core attribute of Chinese entrepreneurship, in the absence of which Chinese entrepreneurship could not exist. Guanxi stands for trustworthiness, which is the result of relatively long-term interactions and forms the basis for future exchange relations. Given the fact that few exchanges within guanxi networks have been formalized, this trust is essential. Trust is also important for reciprocal obligation, which is another key aspect of guanxi. Moreover, fulfilling one’s obligations to the guanxi group is culturally expected by both the Confucian tradition and the new ethics in contemporary China. Once guanxi is established between two individuals, each can ask a favor of the other with the expectation that the debt incurred will be repaid sometime in the future. Positive guanxi relationships can protect dignity and allow, affirm and honor the relationships of individuals involved in logistics. However, the number of people in active guanxi relationships is limited. Thus, like social capital, guanxi needs to be actively developed, succored and maintained. However, guanxi can also be construed as a linking mechanism; if guanxi is good, one single guanxi can open many doors. A single guanxi can thus provide access to a much wider network of internal connections.

Previous research has shown that guanxi is one of the most important success factors for manipulating business in China; thus, having the right guanxi can offer many benefits [39].

The business guanxi reflects the managers’ network and a social connection with their suppliers, vendors, wholesalers and competitors [40].

Previous research has shown that building positive guanxi relations when conducting business in China brings benefits and competitive advantages [41].

Guanxi practice is only identified as reasonable demands and reciprocal favors in interpersonal relations, long-term personal networks and helping one another [42].

Chen, Ellinger and Tian [43] found that supply and demand market uncertainties and the complexity of the legal environment significantly affect the manufacturer–supplier guanxi level, and that guanxi affects the use of non-coercive power by manufacturers.

Luo, Hsu and Liu [44] found that guanxi can enhance the impact of customer orientation and customer trust, as well as their effect on performance.

Further, Li and Lin [45] state that guanxi offers a moderate resource integration and manufacturing flexibility in global logistics.

Chu et al. [46] found that the impacts of guanxi on operational performance are positive and significant.

Chen, Ellinger and Tian [43] suggested that to minimize the negative effects of legal and regulatory complexity in China and ensure that products move smoothly through global supply chains, manufacturers and suppliers should maintain close relationships with each other to ensure proper coordination of operations. Many studies have improved our understanding of how companies strive to achieve a higher level of trust and commitment and further maintain a long-term relationship with each other, to better manage the uncertainties and risks inherent in any business relationship. For instance, personal relationships may encourage managers to use such a relationship in their own interest rather than in the interest of their companies. In the context of a relationship, guanxi means a personal relationship that bonds the boundary of exchange through social activities, reciprocal obligations and favors.

While increasing number of studies on logistics service providers (LSPs) in China have become available, there are few studies that examine how intangible resources such as social capital in guanxi affect the competitiveness or performance of LSPs [47].

There are several important principles underlying the application, utilization and maintenance of guanxi. Also, it should be mentioned that: (1) guanxi is transferable; (2) guanxi is reciprocal, i.e., a person who does not follow the rule of reciprocity by refusing to return a favor for a favor will lose their good reputation and be seen as untrustworthy; (3) guanxi is intangible; (4) guanxi is essentially utilitarian rather than emotional; (5) guanxi is contextual, i.e., it involves interactive conduits between people; and (6) guanxi is long-term oriented. The principles of guanxi are frequently discussed in terms of four closely related constructs: favor, face, trust [48] and emotion. These are all constructs that collectively reflect the quality of guanxi principles used in supply chain management.

Other case studies of guanxi principles in warehouse management allow us to state that trust may be important and that trust and its concomitant vulnerability are based on knowing the other. This supports the statement that guanxi is an important part of Chinese entrepreneurship. “The market has changed, guanxi seemed not that important; control of information is even better than using guanxi; use your own power”. This seems to suggest that guanxi outcomes may be changing and that—for some at least—business is not so much about personal relationships. Words such as sincere, frankness or helpful are all indicative of trust; however, “finding the right person who can provide appropriate guanxi” is necessary.

The following section of the article will evaluate how the guanxi philosophy works and how it impacts the management of cargo flows in warehouses.

3. Research Methodology

After the theoretical part of the scientific article, we present the methodological part—an important component of a scientific article research—and conclude with an empirical part. To achieve the purpose of the research, the following empirical research tasks must be accomplished: expert evaluation of the quality of warehousing processes in logistics must be obtained as part of the qualitative research, and proposals for improvement must be made. The research is conducted using qualitative and quantitative research methods. The qualitative research involves a standardized interview with experts, while the quantitative research includes the preparation of a report on the quality of the warehouse flow of goods and other indicators related to warehouse processes. Rapid qualitative research has a long-standing history in social sciences [49].

The first part of the methodology involves analysis of the working process of warehouses. When collecting data, the following points were considered to accomplish the tasks of the article: capacity, length, width, height, dimensions, etc. Quantitative research must assess the quality of warehouse logistics processes. After analyzing all the received results, we draw conclusions about the quality of the warehouses processes, and we also provide recommendations to help businesses grow. After summarizing the results of the qualitative and quantitative research, we assess indicators of warehouse processes of logistics, offering solutions for improvement. Warehouse goods flows were assessed using mathematical calculations, equations, tables and numbers. It is important to mention that the essential problem in determining different methods of warehousing in logistics and supply chain management—to improve freight traffic movement in warehouses and identify methods for its improvement—is a lack of calculations which allow us to optimize warehouse operations and expand cargo flows with the aim of optimizing warehouse operations and expanding further cargo transportation flows. Below are the main equations drawn up from mathematical calculations of the intensity of flows of goods.

(1)

Amount of cargo containing agricultural products arriving at a warehouse per day:

$$Q_d^{arr}=\frac{Q_y·K_{uneven}^{arr}}{T_{arr}},$$

(1)

where $K_{uneven}^{arr}$ is the arrival unevenness coefficient, which is equal to $K_{uneven}^{arr}$ = 1.3; $T_{arr}$ is the number of days the warehouse is open, i.e., 253 working days.

(2)

Amount of cargo containing agricultural products leaving a warehouse per day:

$$Q_d^{dep}=\frac{Q_y·K_{uneven}^{dep}}{T_{dep}},$$

(2)

where $K_{uneven}^{dep}$ is the departure unevenness coefficient, which is equal to $K_{net}^{dep}$ = 1.1; $T_{dep}$ is the number of days upon which cargo containing agricultural products are removed from a terminal, i.e., 365 (days).

A load unit can be defined as an element connecting a warehouse with the external environment and parts of the storage system. Since general cargo units usually load/unload from/to vehicles, they can be considered an external environment. A load unit can be divided into two components: the load and the auxiliary means for forming a load unit. The load is formed on a “goods carrier”, which may be a pallet, a container, a bag, etc. Forming a load unit requires packaging or protective measures, i.e., upholstery, edging, cladding, gaskets, metal, polymers, textile tapes, special films, etc.

Calculations related to the warehouse and loading units include the following:

(3)

Mass of the loading unit used in the warehouse:

$$M_{load}={lbh}_{load}\rho \phi ,$$

(3)

where $M_{load}$ is the mass of the load unit; l, b are the pallet dimensions, where l = 1.0 m and b = 1.2 m; $h_{load}$ is the load height, which is 1.2 m; ρ is the charge density, which is ρ = 280 kg/m³; and φ is the pallet area utilization factor, which is φ = 0.8.

$$M_{load}=322.56 \mathrm{kg}.$$

(4)

Warehouse capacity:

$${E=Q}_p^{dep}·T_{storage},$$

(4)

where $Q_p^{dep}$ is the amount of general cargo arriving at a terminal per day; T_s is the general cargo storage time, which is T_s = 6 d (days).

(5)

Number of general cargo units that must fit in the storage area:

$$R=\frac{E}{M_{load}},$$

(5)

R is the number of general cargo units that must fit in the storage area; to properly select the racks, calculations are made to determine the rack parameter:

(6)

Rack section height:

$$h_r=h_{load}+h_p+h_s$$

(6)

Here, $h_r$ is the rack section height; $h_l$ is the load height, which is equal to 1.2 m; h_p is the pallet height, which is equal to 0.166 m; and $h_s$ is the stock height (0.05–0.1 m), 0.07 m ha.

(7)

Number of rack floors:

$$Z=\frac{\left(H-0.2\right)}{QUOTE},$$

(7)

where Z is the number of rack floors; H is the lifting height of the loading equipment, which is equal to 4.50 m; and QUOTE refers to the storage quotes and costs, including some of the factors that are a part of every quotation.

(8)

Warehouse height:

$$H_S=\left(Z-1\right)·h_r+h_v,$$

(8)

where $H_s$ is the warehouse height; $h_r$ is the the distance between the upper shelf of the rack and the roof of the warehouse, which is equal to 12.00 m; and $h_v$ is the rack section height.

(9)

Number of general cargo units to be stored in the width of the storage area:

$$n_{sw}=\frac{B_s}{B_w+2(b+a_m)},$$

(9)

where $n_{sw}$ is the number of stored general cargo units in the width of the storage area; B_s is the storage area width (30 m); $B_w$ is the width of the aisles between the racks, which is equal to 1.7 m; b is the palette width, which is equal to 1.0 m; and $a_m$ is the distance between the rack and the column, which is equal 0.6 m.

(10)

Number of stored general cargo units in the length of the storage area:

$$n_{si}=\frac{R}{n_{sp}·2·Z},$$

(10)

where n_si is the number of stored general cargo units in the length of the storage area; R is the number of pallets; Z = 6 floors.

(11)

Rack length:

$$L_s=\left(l_l+b_s\right)·n_{si}+b_s,$$

(11)

where L_s is the rack length; l_l is the length of the pallet, which is equal to 1.2 m; and b_s is the width of the structure, which is equal to 0.1 m.

(12)

Length of the rack storage area:

$$L_{sz}=L_s+2a_i,$$

(12)

where $L_{sz}$ is the length of the rack storage area; $a_i$ is a reserve for manoeuvring equipment, which is equal to 2.5 m.

(13)

Working area of the warehouse:

$$S=\frac{E}{P_{average}· A_s·S_p},$$

(13)

where S is the warehouse working area width; E is the warehouse capacity; $P_{average}$ is the floor load per square meter, which is equal to 19/5000 of the load unit mass $M_{load}(=322.56 \mathrm{t}$); S_p is the pallet area and is equal to 1.2 m²; and $A_s$ is the warehouse space utilization factor, which is equal to 0.6.

All cargo units arrive stacked on a European-style pallet. This facilitates both loading and storage operations. Cargo storage time is fixed and constant (i.e., six days); there is no likelihood that the cargo will be needed at any other time. Warehouses use loading equipment that can lift goods to a height of 4.5 m. Companies choose racks to be used in their warehouses based on all the above criteria. The length of a rack can accommodate 36 to 43 cargo units.

(14)

Number of arriving vehicles per day:

$$N_{auto}^P=\frac{Q_p^{arr} ·K_{arr}^{uneven}}{q ·\gamma },$$

(14)

where $Q_p^{arr}$ is the amount of the general cargo arriving at a terminal per day; $K_{arr}^{uneven}$ is the arrival fluctuation coefficient, which is equal to 1.3; q is the vehicle capacity, which is equal to 24 t; and γ is the vehicle capacity utilization factor, which is equal to 0.8.

(15)

Performance at a station:

$$W=\frac{T_{pam}}{T_{i-p}}, T_{pam}-T_{shift}; T_i-p-T_u-l,$$

(15)

where $T_{shift}$ is the shift duration, which is 8 h; $T_u-l$ is the unloading/loading time, which is 1 h.

$$W=\frac{8}{1}=8,$$

(16)

Number of stations:

$$N_S=\frac{N_{auto}^s}{W},$$

(16)

(17)

Length of the general cargo reception front (perpendicular width of parking vehicles):

$$L_f=B_a{N}_p+b\left(N_p+1\right),$$

(17)

where $B_a$ is the width of the vehicle, which is equal to 2.48 m; b is the distance required between vehicles to open the doors, which is 2.6 m; and $L_f$ = 25 m.

(18)

Distance required for manoeuvring:

$$L_g=2L_a+2,$$

(18)

where $L_a$ is the length of the vehicle, which is 16.50 m; $L_g$ = 35 m.

(19)

Number of wagons serviced per day:

$$N_{get}^p=\frac{Q_p^{dep}· K_{uneven}^{dep}}{q·\gamma },$$

(19)

where $Q_p^{arr}$ is the amount of general cargo leaving the terminal per day; $K_{uneven}^{dep}$ is the departure fluctuation coefficient, which is equal to 1.1; q is the wagon capacity, which is 55 t; and γ is the vehicle capacity utilization factor, which is equal to 0.8.

(20)

Length of the cargo dispatch front (we assume that only one wagon is loaded):

$$L_w=\frac{N_{get}^P·L_{fw}}{Z_{pr}+Z_{rearr}}+A_{ext},$$

(20)

$L_{fw}$ is the length of the wagon, which is equal to 13.84 m; $Z_{pr}$ is the number of available wagons, which is 1; $Z_{rearr}$ is the number of rearrangements, which is equal to 1; $A_{ext}$ is the road extension for road works, which is equal to 40 m; and $N_{get}^P$ = 1 wagon.

Expert interviews were the second part of the research. An expert survey was conducted in accordance with the methodology from scientific literature sources and previously published studies by the authors of the article [50,51]. Eight experts from warehouse businesses took part in the survey. The experts were chosen by taking into account their long-term professional experience in warehouse businesses and in particular their experience working with the Asian market. The experts had worked in famous and widely known companies influencing global logistics and transportation chains. Experts evaluated the elements of the intensity of cargo flows in warehouses and considered which of them could be applied to the guanxi philosophy. The weight of an expert’s opinion is significant because of the large volumes of transported freight and the developed international trade network [51].

Given that the research was conducted in several directions, using more than one research method, results and their discussion will be presented in the next section in the following logical sequence: the load intensity in warehouses of the five big cities of Lithuania is evaluated; furthermore, an expert assessment is carried out on the possibilities of applying the guanxi philosophy in order to ensure load intensity when transporting agricultural products.

4. Results and Discussion

The Chinese logistics market is more mature, and guanxi philosophy helps to compensate for cargo flow inefficiencies. Efficiency will become increasingly important in establishing and maintaining guanxi philosophy in the intensity of cargo flows. In functions with a high focus on guanxi philosophy, impact is actively applied in the intensity of cargo flows in warehouses. According to guanxi philosophy, cooperation and communication become less dependent on relationships between logistics market workers. Internationalization of the Chinese logistics market will lead to an adoption of guanxi-philosophy-based mutual relations practices in building relationships. An important element of guanxi philosophy is the exchange of favors beyond contractual agreements, which will improve the cargo flows in warehouse service quality. Logistics outsourcing will be more important in the future as it allows warehouses to concentrate on core cargo flow competencies. Some guanxi philosophy practices can be considered as such; thus, it can be assumed that the impact of guanxi philosophy will be reduced with the growing importance of logistics cargo flows in warehouse functions. Guanxi philosophy may promote trust among the parties in supply chains and improve the logistics performance of cargo flows in warehouses. The guanxi philosophy network can strengthen the efficiency and effectiveness of logistics work processes in warehouses.

Taking this into account, it can be said that globalization processes are encouraging China to look for points of interaction with other countries in the supply chain. Therefore, this interaction should be based on a new, holistic and philosophical approach to cooperation.

Therefore, when working with Chinese people in another country, the philosophical principles of guanxi should be applied in order to work smoothly and holistically. After assessing that a significant portion of cargo travels from China to/through Lithuania, the possibility of applying this philosophy in Lithuania for the transportation of agricultural products from Shanghai to the major cities of Lithuania (Vilnius, Kaunas, Klaipėda, Šiauliai and Panevėžys) will be further assessed.

The intensity of the warehousing general load flow was calculated to improve the transport of agricultural product loads to warehouses (

Table 2. Intensity of agricultural product load flows in warehouses.

	Annual Cargo Volume, i.e.,	Daily Cargo Flow, i.e.,	Outbound Daily Flow, i.e.,	Direct Cargo Flow, i.e.,	Cargo Flow
					From Vehicle to Reception Area, i.e.,	From Reception Area to Long-Term Storage Area, i.e.,	From Vehicle to Long-Term Storage Area, i.e.,	From Long-Term Storage Area to Dispatch Area, i.e.,	From Shipping Area to Vehicle, i.e.,	From Long-Term Storage Area to Vehicle, i.e.,
	${\mathit{Q}}_{\mathit{m}}^{\mathit{y}}$	${\mathit{Q}}_{\mathit{p}}^{\mathit{A}\mathit{R}}$	${\mathit{Q}}_{\mathit{p}}^{\mathit{D}}$	${\mathit{Q}}_1^{\mathit{p}}$	${\mathit{Q}}_2^{\mathit{p}}$	${\mathit{Q}}_3^{\mathit{p}}$	${\mathit{Q}}_4^{\mathit{p}}$	${\mathit{Q}}_5^{\mathit{p}}$	${\mathit{Q}}_6^{\mathit{p}}$	${\mathit{Q}}_7^{\mathit{p}}$
1 Warehouse	48,929.20	251.41	147.46	50.28	25.14	25.14	175.99	29.49	29.49	67.68
2 Warehouse	39,143.40	201.13	117.97	40.23	20.11	20.11	140.79	23.59	23.59	54.15
3 Warehouse	20,049.10	103.02	60.42	20.60	10.30	10.30	72.11	12.08	12.08	27.73
4 Warehouse	12,729.60	65.41	38.36	13.08	6.54	6.54	45.79	7.67	7.67	17.61
5 Warehouse	18,298.70	94.02	55.15	18.80	9.40	9.40	65.82	11.03	11.03	25.31
Total	139,150.00	714.99	419.36	142.99	71.49	71.49	500.50	83.86	83.86	192.48

). The initial data that determine the main parameters of warehouses (capacity, length, width, height, dimensions of receiving and shipping areas and loading fronts) are the general cargo flows and mode of operation of warehouses. The daily flow intensity handled per year is Qy = 103.490 t/y (tons per year); α₁ = 0.2, α₂ = 0.1, and β₁ = 0.2.

A load unit can be defined as an element connecting a warehouse with the external environment and parts of the storage system. Since general cargo units usually load/unload from/to vehicles, they can be considered an external environment. A load unit can be divided into two components: the load itself and the auxiliary means for forming a load unit. The load is formed on a “goods carrier”, which can be pallets, containers, bags, etc. Forming a load unit requires packaging or protective measures, i.e., upholstery, edging, cladding, gaskets, metal, polymers, textile tapes, special films, etc. The table above shows that the annual volume of cargo in five warehouses was 139,150.00 t/y, based on the intensity of agricultural product load flows in warehouses.

According to equation 3 in the methodological part, $M_{load}$ = 322.56 kg.

When assessing the intensity of the flow of agricultural products in warehouses, assessing the interdependence of the calculated factors is another important step (Figure 1).

The obtained results show that the correlation reaches unity in every point according to all indicators, which are directly proportional to each other, i.e., direct dependence is achieved (according to Pearson, Spearman and Kendall values).

After carrying out mathematical calculations of cargo intensity scales and assessing the mutual interaction and dependence of these factors, an expert assessment was carried out in order to identify which cargo flow movement processes the guanxi philosophy could be applied to when working with Chinese suppliers (

Table 3. Ranking table of evaluations according to the possibility of applying the process of guanxi philosophy to the example of the cargo intensity of agricultural products.

Formula *	Annual Cargo Volume	Daily Cargo Flow	Outbound Daily Flow	Direct Cargo Flow
${\displaystyle \sum _{i=1}^n}{R}_{ij}=R_{ij}$	23	18	22	17
${\overline{R}}_j=\frac{{\sum }_{i=1}^n{R}_{ij}}{n}$	2.875	2.250	2.750	2.125
${\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)$	3	−2	2	−3
${\left[{\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)\right]}^2$	9	4	4	9

* [50,51].

). During the expert evaluation, we asked the experts to evaluate the criteria according to importance. To take into account the fact that different answers were received, an evaluation of the compatibility of the experts’ opinions was carried out (

Table 4. Determining the priority order of the possibility of applying the process of guanxi philosophy to the example of the cargo intensity of agricultural products.

Indicator Value *	Annual Cargo Volume	Daily Cargo Flow	Outbound Daily Flow	Direct Cargo Flow
$\overline{q}=\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.2875	0.2250	0.2750	0.2125
$d_j=1-{\overline{q}}_j=1-\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.7125	0.7750	0.7250	0.7875
$Q_j=\frac{d_j}{{\sum }_{j=1}^m{d}_j}=\frac{d_j}{m-1}$	0.2375	0.2583	0.2417	0.2625
${\overline{Q}}_j=\frac{{\sum }_{i=1}^n{B}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^m{B}_{ij}}$	0.2125	0.2750	0.2250	0.2875

* [50,51].

).

Considering the fact that the opinions of the experts were different, an assessment of the compatibility of opinions was carried out and obtained $W$ = 0.0542, $\mathsf{\chi }2=$1.3000 and $W_{min}$ = 0.0243. It was concluded that the opinions of the experts were compatible, and further evaluations were carried out.

The obtained results show that the importance of the criteria to which the guanxi philosophy could be applied is as follows: (1) direct cargo flow, (2) daily cargo flow, (3) outbound daily flow, and (4) annual cargo volume.

Considering that direct cargo flow was first, it is important to evaluate which processes are the most important at this stage and where the guanxi philosophy can be applied (

Table 5. Ranking table according to the applicability of guanxi philosophy process to the example of the flow directions of agricultural products.

Formula *	From Vehicle to Reception Area	From Reception Area to Long-Term Storage Area	From Vehicle to Long-Term Storage Area	From Long-Term Storage Area to Dispatch Area	From Shipping Area to Vehicle	From Long-Term Storage Area to Vehicle
${\displaystyle \sum _{i=1}^n}{R}_{ij}=R_{ij}$	21	17	19	40	42	29
${\overline{R}}_j=\frac{{\sum }_{i=1}^n{R}_{ij}}{n}$	2.625	2.125	2.375	5.000	5.250	3.625
${\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)$	−7	−11	−9	12	14	1
${\left[{\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)\right]}^2$	49	121	81	144	196	1

* [50,51].

).

Considering the fact that the opinions of the experts were different, an assessment of the compatibility of the opinions was carried out and obtained $W$ = 0.3524, $\mathsf{\chi }2$= 14.0952 and $W_{min}$ = 0.0266. It was concluded that the opinions of the experts were compatible, and further evaluations were carried out (

Table 6. Determining the priority order of the applicability of guanxi philosophy process on the example of the flow directions of agricultural products.

Indicator Value *	From Vehicle to Reception Area	From Reception Area to Long-Term Storage Area	From Vehicle to Long-Term Storage Area	From Long-Term Storage Area to Dispatch Area	From Shipping Area to Vehicle	From Long-Term Storage Area to Vehicle
$\overline{q}=\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.1250	0.1012	0.1131	0.2381	0.2500	0.1726
$d_j=1-{\overline{q}}_j=1-\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.8750	0.8988	0.8869	0.7619	0.7500	0.8274
$Q_j=\frac{d_j}{{\sum }_{j=1}^m{d}_j}=\frac{d_j}{m-1}$	0.1750	0.1798	0.1774	0.1524	0.1500	0.1655
${\overline{Q}}_j=\frac{{\sum }_{i=1}^n{B}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^m{B}_{ij}}$	0.2083	0.232142857	0.2202	0.0952	0.0833	0.1607

* [50,51].

).

The obtained results show that the importance of the criteria to which the guanxi philosophy can be applied is as follows: (1) from the reception area to the long-term storage area, (2) from the vehicle to the long-term storage area, (3) from the vehicle to the reception area, (4) from the long-term storage area to the vehicle, (5) from the long-term storage area to the dispatch area, (6) from the shipping area to the vehicle.

The next step was to evaluate agricultural product cargo units in warehouses (

Table 7. Agricultural product cargo units in warehouses.

	Warehouse Volume, t	Number of Cargo Units That Must Fit in the Storage Area, Pieces
Warehouse 1	1508.49	4677
Warehouse 2	1206.79	3741
Warehouse 3	618.11	1916
Warehouse 4	392.45	1217
Warehouse 5	564.15	1749
Total	4289.99	13,300

).

The existing warehouses can accommodate 4289.99 t, i.e., 13,300 pallets. Agricultural products and goods in warehouses are stored under appropriate conditions and in a certain order. There are various storage methods, and the efficiency of general goods collection depends on the warehouse equipment used and the warehouse architecture. The warehouse architecture depends on the costs of collecting agricultural product cargo, so it is very important to optimize the collection of agricultural product cargo according to the orders placed as storage areas increase. To properly select the racks, calculations were performed to determine the rack parameters. The table above shows that the volume at the five warehouses, based on agricultural product cargo units in warehouses, is 4289.5 tonnes per year.

According to Equation (6) in the methodological part, $h_r$ = 1.44 m.

According to Equation (7) in the methodological part, ${ H}_w$ ≈ 4.87 m.

According to Equation (13) in the methodological part, $P_{average}=\frac{M_{kr}}{S_p}·P_{average}\approx 0.2688$.

When assessing agricultural product cargo units in warehouses, it is also important to assess the interdependence of the calculated factors (Figure 2).

The obtained results, as well as the previous calculations, show that the correlation at every point reaches unity according to all indicators, which are directly proportional to each other, i.e., direct dependence is achieved (according to Pearson, Spearman and Kendall values).

Since “Direct cargo flow” and “From the reception area to the long-term storage area” are the most important aspects of cargo flows and “Warehouses“ and “Daily cargo flow“ are directly proportional to each other, it can be stated—according to experts—that the guanxi philosophy should be applied, and it can be assumed that the application of this philosophy has a direct impact on cargo flow intensity in warehouses.

The next step is to calculate the rack storage of five warehouses (

Table 8. Rack storage in 5 warehouses.

	Number of Stored Cargo Units in Width of Storage Area, Pieces	Width of Storage Area, m	Number of Stored Cargo Units in Length of Storage Area, Pieces	Rack Length, m	Length of Rack Storage Area, m	Warehouse Working Area, m2
	nsp	Bs	nsi	Ls	Lsz	S
Warehouse 1	22	123	36	47	52	1558.87
Warehouse 2	14	82	43	57	62	1247.10
Warehouse 3	7	42	43	57	62	638.76
Warehouse 4	6	32	36	47	52	405.56
Warehouse 5	8	46	36	47	52	582.99

).

All cargo units arrive stacked on a European-style pallet. This facilitates both loading and storage operations. Cargo storage time is fixed and constant (i.e., six days); thus, there is no likelihood that the cargo will be needed at any other time. In addition, the warehouse uses loading equipment that can lift goods to a height of 4.5 m. All these circumstances determine the company’s choice of racks used in the warehouse. The length of the rack can accommodate 36 to 43 cargo units, and the width accommodates −7 to 22 units. Table 8 makes important distinctions based on stand storage in five warehouses: the number of stored cargo units in the length of the storage area (pcs), the rack length (m), and the length of the rack storage area (m) are almost the same.

According to Equation (20) in the methodological part,

$$L_w=\frac{1\ast 13.84}{1+1}+40=46.92 \mathrm{m},$$

When assessing rack storage in five warehouses, it is also important to assess the interdependence of the calculated factors (Figure 3).

The performed calculations show that the correlated parameters are very well distributed:

• The number of stored cargo units in the width of the storage area (in pieces) and the warehouse working area (in m²) demonstrates a very strong correlation according to all three correlation types.
• The width of the storage area (in m) and the warehouse working area (in m²) have a very strong correlation according to all three correlation types.
• The number of stored cargo units in the length of the storage area (in pieces) and the rack length (in m) have a direct dependency.
• The rack length (in m) and the length of the rack storage area (in m) have a direct dependency.
• All other indicators have similar correlations of about 0.8 and are described as strong.
• Given that the results obtained show that guanxi philosophy can be applied in combination with “Direct cargo flow” and “From the reception area to the long-term storage area”, it is important to assess what elements in warehouses affect the intensity of cargo (

  Table 9. Ranking table of evaluations regarding the impact of rack storage directions on the intensity of cargo flows.

  Formula *	Number of Stored Cargo Units in Width of Storage Area, Pieces	Width of Storage Area	Number of Stored Cargo Units in Length of Storage Area, Pieces	Rack Length, m	Length of Rack Storage Area, m	Warehouse Working Area, m2
  ${\displaystyle \sum _{i=1}^n}{R}_{ij}=R_{ij}$	15	34	33	30	39	17
  ${\overline{R}}_j=\frac{{\sum }_{i=1}^n{R}_{ij}}{n}$	1.875	4.25	4.125	3.750	4.875	2.125
  ${\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)$	−13	6	5	2	11	−11
  ${\left[{\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)\right]}^2$	169	36	25	4	121	121

  * [50,51].

  ).

Considering the fact that the opinions of the experts were different, an assessment of the compatibility of opinions was carried out and obtained $W$ = 0.2833, $\mathsf{\chi }2$= 11.3333, and $W_{min}$ = 0.0266. It was concluded that the opinions of the experts were compatible, and further evaluations were carried out (

Table 10. Determining the priority order of the evaluations regarding the impact of rack storage directions on the intensity of cargo flows.

Indicator Value *	Number of Stored Cargo Units in Width of Storage Area, Pieces	Width of Storage Area	Number of Stored Cargo Units in Length of Storage Area, Pieces	Rack Length, m	Length of Rack Storage Area, m	Warehouse Working Area, m2
$\overline{q}=\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.0893	0.2024	0.1964	0.1786	0.2321	0.1012
$d_j=1-{\overline{q}}_j=1-\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.9107	0.7976	0.8036	0.8214	0.7679	0.8988
$Q_j=\frac{d_j}{{\sum }_{j=1}^m{d}_j}=\frac{d_j}{m-1}$	0.1821	0.1595	0.1607	0.1643	0.1536	0.1798
${\overline{Q}}_j=\frac{{\sum }_{i=1}^n{B}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^m{B}_{ij}}$	0.2440	0.130952381	0.1369	0.1548	0.1012	0.2321

* [50,51].

).

The obtained results show that the elements that affect the load intensity in warehouses are arranged in the following order: (1) number of stored cargo units in the width of the storage area, pieces; (2) warehouse working area, m²; (3) rack length, m; (4) number of stored cargo units in the length of the storage area, pieces; (5) width of the storage area; and (6) length of the rack storage area, m.

The next step was to calculate the loading front data (

Table 11. Loading front data in 5 warehouses.

	Number of Arriving Cars, pcs t	Number of Stations, pcs	Length of Cargo Reception Front, m	Manoeuvring Distance Required, m	Number of Serviced Wagons per Day, pcs.	Length of Cargo Dispatch Front, m
	${\mathit{N}}_{\mathit{a}\mathit{u}\mathit{t}\mathit{o}}^{\mathit{P}}$	${\mathit{N}}_{\mathit{p}-\mathit{s}}$	${\mathit{L}}_{\mathit{f}}$	${\mathit{L}}_{\mathit{g}}$	${\mathit{N}}_{\mathit{g}\mathit{e}\mathit{l}}^{\mathit{P}}$	${\mathit{L}}_{\mathit{w}}$
Warehouse 1	17	2	13	35	4	46.92
Warehouse 2	14	2	11	35	3	46.92
Warehouse 3	7	1	7	35	2	46.92
Warehouse 4	4	1	5	35	1	46.92
Warehouse 5	6	1	7	35	1	46.92

).

The loading front of the five warehouses can service 4 to 17 road vehicles and 1 to 4 wagons per day. The length of the road transport loading front is 25 m and the manoeuvring length is 35 m. The length of the railway branch dispatch front is 46.92 m when one wagon is loaded. The table above shows loading front data from five warehouses, illustrating that the manoeuvring distance required (m) and the length of the cargo dispatch front (m) are the same.

When assessing the loading front, it was also important to assess the interdependence of the calculated factors (Figure 4).

The performed calculations show that the manoeuvring distance required (m) and the length of the cargo dispatch front (m) were not evaluated because they were not variable and did not form correlation relationships. All other correlations are described as very strong.

In summary, it can be concluded that direct dependencies are very strong or strong in most cases, so the entire evaluation of the process is based on the evaluation of a difference in correlation coefficients (identifying the parameter and the extent to which the correlation coefficients change, in %). An interesting new combined parameter—the correlation sensitivity—was developed (as the change in parameters is very similar, and correlations show how the selection of their control factors (how some factors control others) can adjust their correlations to direct dependence).

Given the fact that the most important item is the number of stored cargo units in the width of the storage area (pieces), it is important to assess which factors can influence these items the most (

Table 12. Table of ranking evaluations according to factors which can influence the number of stored cargo units in the width of the storage area.

Formula *	Number of Arriving Cars, pcs t	Number of Stations, pcs.	Length of Cargo Reception Front, m	Manoeuvring Distance Required, m	Number of Serviced Wagons per Day, pcs.	Length of Cargo Dispatch Front, m
${\displaystyle \sum _{i=1}^n}{R}_{ij}=R_{ij}$	20	25	39	30	26	28
${\overline{R}}_j=\frac{{\sum }_{i=1}^n{R}_{ij}}{n}$	2.5	3.125	4.875	3.750	3.250	3.500
${\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)$	−8	−3	11	2	−2	0
${\left[{\displaystyle \sum _{i=1}^n}{R}_{ij}-\frac{1}{2}n\left(m+1\right)\right]}^2$	64	9	121	4	4	0

* [50,51].

).

Considering the fact that the opinions of the experts were different, an assessment of the compatibility of opinions was carried out and obtained $W=$0.1202, $\mathsf{\chi }2$= 11.3333 and $W_{min}$ = 4.8095. It was concluded that the opinions of the experts were compatible, and further evaluations were carried out (

Table 13. Determining the priority order of the factors which can influence the number of stored cargo units in the width of the storage area.

Indicator Value *	Number of Arriving Cars, pcs t	Number of Stations, pcs.	Length of Cargo Reception Front, m	Manoeuvring Distance Required, m	Number of Serviced Wagons per Day, pcs.	Length of Cargo Dispatch Front, m
$\overline{q}=\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.1190	0.1488	0.2321	0.1786	0.1548	0.1667
$d_j=1-{\overline{q}}_j=1-\frac{{\overline{R}}_j}{{\sum }_{j=1}^m{R}_j}$	0.8810	0.8512	0.7679	0.8214	0.8452	0.8333
$Q_j=\frac{d_j}{{\sum }_{j=1}^m{d}_j}=\frac{d_j}{m-1}$	0.1762	0.1702	0.1536	0.1643	0.1690	0.1667
${\overline{Q}}_j=\frac{{\sum }_{i=1}^n{B}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^m{B}_{ij}}$	0.2143	0.1845	0.1012	0.1548	0.1786	0.1667

* [50,51].

).

The obtained results show that the factors for the intensity of cargo flows in warehouses are distributed in the following order: (1) number of arriving cars, pcs t; (2) number of stations, pcs; (3) number of serviced wagons per day, pcs; (4) length of the cargo dispatch front, m; (5) manoeuvring distance required, m; and (6) length of the cargo reception front, m.

The scientific problem of the articles was revealed by mathematical calculations, assuming that general cargo is sent from terminals over 365 days, with the daily flow intensity handled per year being 103.490 t/y and the loading density being 280 kg/m³. The load unit mass $M_{load}$ ≈ 322.56 kg, the rack section height $h_r$ = 1.44 m, the distance between the upper shelf of the rack $H_w$ ≈ 4.87 m, the number of rack floors $H_w$ ≈ 4.87 m, and the length of the cargo reception front (perpendicular width of parking vehicles) L_f = 25 m. The work intensity at a warehouse and the efficiency of utilization of warehouse spaces were used as indicators to evaluate warehouse operations. The conducted analysis showed that information technologies are of great importance in warehousing activities. Automating existing warehouses and using information systems in company operations is essential to increasing the efficiency of warehouse operations. Producers usually take the issue of packaging into consideration and see it as relatively pricy. In the face of rapidly developing information technology, the process of product shipment and processing must become more advanced.

Taking into account the obtained results, it can be said that when evaluating the intensity of cargo flows, guanxi philosophy can be applied to the amount of cargo and the intensity of flows in warehouses. Therefore, when applying this philosophy in its four main elements, trust between partners must come first. Taking these aspects into account, experts were asked to evaluate the methods by which the guanxi philosophy should be applied in the management of cargo flows. The obtained results show that in order to apply this philosophy to manage the intensity of freight flows, the following steps must be taken: (1) it is essential to build personal guanxi/relationships before actually starting negotiations on the intensity of cargo flows in warehouses; (2) it is better to build guanxi with counterparts before the actual negotiation on the intensity of cargo flows in warehouses; (3) it is better to be interested in knowing counterparts as well as possible (even their personal information) before the negotiation on logistics regarding the intensity of cargo flows in warehouses; (4) at the initial meeting, it is better to spend a long time building guanxi with the counterpart negotiator after basic greetings and introductions, rather than starting the negotiation on the intensity of cargo flows in warehouses straight away; (5) understanding the people who it is better to negotiate with is as important as understanding the business deal regarding the intensity of cargo flows in warehouses; (6) it is better to attempt to use a relationship and friendship (guanxi) with a counterpart to obtain a better price and more concessions regarding the intensity of cargo flows in warehouses; (7) building guanxi and trust are necessary bases for making business deals and signing business agreements in value chain management regarding the intensity of cargo flows in warehouses; and (8) it is important to pay attention to maintaining a person’s own guanxi face regarding the intensity of cargo flows in warehouses.

In summary, it can be said that guanxi and relationship logistics marketing has a prominent impact on cargo flows procedures, by developing relationships. In order to achieve positive logistics relationship performances, traditional measurement systems should be used to capture information ranging from manufacturing, distribution, warehousing, inventory management and administration based on guanxi philosophical and methodological principles. The management of logistics activities in warehouses has become a common way of maintaining competitive advantages and enhancing the overall organizational performance of philosophical guanxi. The guanxi philosophical principle indexes include the following logistics cargo flows activities: customer service and support, demand forecasting and planning, purchasing and procurement, inventory management, order processing and logistics communications, material handling and packaging, transportation, facilities site selection, warehousing and storage and return goods handling and reverse logistics. Philosophical guanxi elements can be a considerable benefit to cargo flow tools for networks, as long as they follow legitimate personal or logistics affairs, whereas guanxi would be corrupted in the case of exchanging or transacting outside of the law. Guanxi practice is only identified as reasonable demands and reciprocal favors among interpersonal connections, long-term personal networks, and helping one another in logistics warehouses cargo flows processes. Positive guanxi philosophical communication may assist warehouses operators to obtain valuable knowledge and information, to gain faster access to new potential markets, as shown in the case of Lithuania in this research.

5. Conclusions

The study showed the importance of the storage function in the general context of the logistics supply chain and its relation not only to technical and technological issues but also to social and cultural aspects that shape the relevance of sustainability issues. The analysis of the scientific literature revealed the most important problem points of the storage function: the importance of storage for production and operation of the distribution system, the time and efficiency of order processing, communication with customers, efficient use of warehouse volume, and the importance of environmental impact. It has also been assumed that China’s successful involvement in global logistics is linked to values or philosophies such as guanxi. When considering the abundance of logistics operations carried out in China, warehousing requires a complex perspective for the organisation of these activities. When it comes to the relation between logistics activities and the sustainability and precision of operations, it is necessary to understand that humanitarian principles such as respect and trust must be seen as the foundation on which the entire logistics system is built. A multidisciplinary approach to the organisation and management of warehouse processes is necessary to form a sustainability approach to ongoing logistics processes, because sustainability inevitably includes both technological processes and human factors and behaviours driven by values and general philosophies. This study revealed a new direction for possible multidisciplinary scientific research that can be successfully developed in the future. It shows that when analysing storage and cargo movement processes, not only technical, technological or managerial issues must be considered: the impact of values, lifestyle, philosophy and other humanitarian issues must be treated as equally important.