Next Article in Journal
Adaptive Sliding-Mode Controller for Flyback-Based PV Systems Featuring Constant Switching Frequency
Next Article in Special Issue
Barriers to Using Cloud Computing in Sustainable Construction in Nigeria: A Fuzzy Synthetic Evaluation
Previous Article in Journal
Invariant Finite-Difference Schemes for Plane One-Dimensional MHD Flows That Preserve Conservation Laws
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach

Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Government Girls College, Panchkula 134113, India
Author to whom correspondence should be addressed.
Mathematics 2022, 10(8), 1252;
Submission received: 17 February 2022 / Revised: 28 March 2022 / Accepted: 7 April 2022 / Published: 11 April 2022


Developing a promising technology that copes with the industrial warehousing environment requires special preparation. It includes infrastructure, equipment, resources, knowledge, efficiencies, and strategies for dealing with failures. This study examines Technology 4.0 driven warehouse practices and performance based on a thorough literature review. The study presents a unique proposition as it considers a two-fold fuzzy Delphi analysis to rank the Technology 4.0 driven practices using best-worst method (BWM) based on experts’ responses. Warehouse performance measures are evaluated by the Combined Compromise Solution (CoCoSo) method. The results indicate the contributions of a ‘Man-machines or robots for facilitating human’; ‘Planning system for management’; ‘Storage systems’ as as leading practices contributing to ‘improved inventory management’, ‘effective storage and distribution’, and ‘improved distribution and shipping or delivery process’. Using this study, researchers and managers will better understand how to adopt technology in warehouse management system.

1. Introduction

The success of the Kingdom of Saudi Arabia’s (KSA) National Development Logistic Program (NDLP) initiatives is contingent upon multi-layers of programs and stakeholder support from management, funding, education, and training of the workforce, development of more connected and digitally-enabled infrastructure, implementation of automated products, processes, and procedures. In today’s demanding and competitive business world, warehousing organizations continuously evaluate and change to embrace automation in manufacturing to expand and be profitable [1]. Technologies such as Big Data, IoT (Internet of Things), Cloud Computing, A.I. (Artificial Intelligence), Blockchain, and RFID (Radio Frequency Identification) are playing a vital role in business transformation at a reduced cost to achieve sustainability in warehouse operations [2].
The surge in the industrial growth of the Kingdom of Saudi Arabia is likely to touch 25 billion by 2022 (, accessed on 20 September 2021), with capital investments of more than 106 billion USD (, accessed on 23 September 2021). This is an outcome of the National Development Logistic Program (NIDLP) proposed economic diversification strategy to position Saudi Arabia as a global hub for mining, energy, and logistics sector (, accessed on 23 September 2021). The vision of the Kingdom of Saudi Arabia (KSA) 2030 has also suffered a setback impacting the country’s growth chart and progression plans. Despite this, the businesses and economy are likely to recover soon, requiring continuous and vigorous government and organizational efforts. Although developed countries have faced such challenges due to the pandemic, such issues are occurring in developing and emerging economies. Based on KSA vision 2030, the Saudi government has toiled to bring the fiscal stabilization, development of economic systems such as banking and economy, reforms in societies, culture, industry, healthcare, technology, and many more (, accessed on 23 September 2021). Transformations in their logistics industrial sector are a must for an effective contribution towards vision 2030 and to invite the interest of foreign investors. This requires organizations to improve the quality of their industrial warehousing sector [3], outputs, and performances. Supply chain efficiencies in the warehousing sector are an outcome of integrating technologies. All warehousing processes related to distribution and supply chains, right from procurement, production, distribution, logistics, and warehousing, have a high scope of improvement through automation. In a report, middle eastern retailers highlighted IoT and A.I. as crucial factors driving their growth (, accessed on 28 September 2021). The need for smart warehouses is considered one of the top drivers by 38% of retailers (, accessed on 28 September 2021). The retail sector in KSA has also shared similar views, promoting big data and IoT as essential to economic growth. In countries such as China and Saudi Arabia, companies invest more in foreign markets, but they are more restricted in the cross-border approach. The flow of cross-border data cannot be facilitated without a platform that is competitive (, accessed on 29 September 2021). The list of countries in Table 1 shows how they invest in Artificial Intelligence to achieve their goals, growth in emerging economies, and sustainability index.
Implementing operating environments is ranked number one in Saudi Arabia; therefore, technology integration in warehouses is viable. However, much work needs to be carried out to explore how difficult it will be to implement, establish measurement standards, establish benchmarking practices, train employees, and most importantly, what effect it will have on the environment. These technologies remodel warehouses into new economic rhythms, social trends, and environmental patterns. Understanding the technological integrations is necessary to understand the readiness for the required performance metrics [4]. Instead, these digital technology models or facilities must integrate technology-enabled functions along the length of warehouse operations to receive maximum benefits. To stay relevant in the competitive business environment, these techno-laden facilities must be evaluated from their reliability, scale, quality, and cost perspective [1]. This is required to cope with the complexity of warehouse operations due to globalization and outsourced manufacturing breakthrough technologies; when integrated into warehouse operations, convert these ordinary facilities into robust, transformative, well-integrated networking systems and models. These models, in turn, can translate the value potential across a chain of portals and channels to yield expected revenues [5].
Furthermore, warehouses can enhance operational performance by evaluating technologically driven systems and data-driven system practices and integrations [6]. However, the following questions arise to gain an understanding of the issue. How do warehouses remain productive and sustainable while working with ever-changing technologies? What are the pull factors or enablers that support them? Is there a need to identify enablers by the warehousing organizations to understand their capabilities and limitations for such integration [7]? This study contributes to smart warehouses from a perspective of technological development and proposes a decision-making framework [8]. It is critical to bring innovative technologies to the grass-root level of industrial warehousing setups [3] so that organizations can holistically realize the benefits of integration. For organizations to meet the industry’s needs, they need to be technologically savvy, have a technologically-enhanced workforce, and employ a labour force with the right skills. This study enriches the scientific knowledge related to smart warehouses from multiple perspectives: technology and stakeholders. Integrating innovative technologies into industrial warehouses setups will benefit from their implementation holistically. Technology-enabled warehouses, employees, and labour bases help align the organization’s goals with the industry’s needs. However, one must remember that the substitution of manual work by machines and the introduction of superior digital technology or digital initiatives at specific points will not deliver the expected returns. Therefore, researchers propose the following research questions to highlight the need for the research undertaken.
  • RQ1: How do Technology 4.0 driven warehouse practices contribute towards achieving performances?
  • RQ2: What will be the decision-making framework to help the warehousing industry achieve its operational goals?
Researchers have explored technology-driven practices based on the proposed research questions to gain more insight and clarity. The following section reviews the literature to identify prevailing Technology 4.0 driven practices, considering various aspects of warehouse operational performance. Using fuzzy Delphi, the selected Technology 4.0 driven practices and warehouse performance are re-evaluated in Section 3 for their appropriateness, based on an expert consensus. In Section 3, we discuss the proposed decision-making hybrid model based on BWM and CoCoSo, and in Section 4, we describe the case in detail. Section 5 provides results and discussions, and the final section includes the conclusion and future research.

2. Literature Review

Automation orchestrates a gradient shift in warehouse setups by bringing down concrete walls and shifting the siloed and isolated mechanical functions to a centralized, transparent, technology-enabled, and integrated ecosystem [9]. It also offers a plethora of disruptive solutions capable of optimizing operations, streamlined logistics, and visibility across the value chain by leveraging the potential of machine learning and the integration of intelligence into the DNA of warehouse functions [2]. Furthermore, software-enabled digital processes allow prescriptive and predictive analytics for proactive forecasting and planning in logistics functions [10].
The inclination of organizations towards increased human-machine interactions transforms its configuration, which substantially influences its economic and environmental incentivization. Integrating innovative technologies in organizations usually brings a social change that can either alter their structures and operations to offer opportunities or pose challenges [2]. Hence, organizational readiness is vital to potentiate its interpretation of prevalent market trends, business, and environment to maximize returns [11]. Innovative technologies improve warehouse operations and allow regulated resource utilization [12] and inventory turnovers. It helps organizations manage the challenge related to managing delivery deadlines [13] according to significant fluctuation in customer order volume [14] and product returns. As a result, organizations tend to gain competitive advantage and customer satisfaction [15] and comply with their need to manage their resources.
Technology becomes integrated into a system based on its compatibility and characteristics, which could be routine, advanced, or breakthrough. Regular, sustainable performance in terms of effective resource utilization (reduced cost, operational efficiency, reliability, responsiveness, and flexibility) also defines its integration parameters [2]. If technology is expected to change the overall system completely and promises to deliver value, its integration becomes more accessible. Breakthrough technologies such as IoT or CPS (Cyber-Physical-System) [16,17] convert the manual operations of the warehouse of picking, deliveries, accounts into automated, well distributed, and paperless processes. Atzori et al. [3] argue that this saves resources, energy, and time and offers higher flexibility [18] in computation and energy capacity resource efficiency. Ready et al. [19] identified that role of IoT in warehouse operations is discussed in the context of inventory tracking, information sharing, and joint ordering; dispatching operations [20]; reducing TAT (turnaround time) [21]. Qiu et al. [22] find that IoT enables controlled manageability of inventory, handles data storage and management and security issues. Functions such as current inventory management, the anticipation of future orders, product safety, and durability by measuring the atmospheric conditions are managed using RIFD (radio frequency identification) and sensors [23]. Inter-machine co-operation between robotic systems reduces the burden of manual work by performing heavy and dangerous activities, thus reducing the risk of injuries [24]. The use of Robots for performing manual operations of lifting, organizing, and order picking [25] is often seen [26]. The use of A.I. offers voice recognition allowing machines to follow orders with minimal effort. The use of A.I. [27] and cloud computing allows automated storage and retrieval for easy access to stock availability in the warehouse [28]. Use of blockchain [29], big data analytics [30,31], and A.I. [20] is commonly seen in warehouse operations related to receiving [6,32], storage [6,33], more robust offering products such as raw materials, goods-in-process, finished products inventory holding, order picking [12,34], delivery, value-added-processing such as kitting, pricing, labelling, and product customization [35]. The integration of breakthrough and advanced technologies in the warehouse strengthens its ability to meet market challenges, respond to demand variations [36], staying flexible to handle peak throughputs at short notice during staff shortage [37]. Technical integrations in warehouse operations eventually lead to sustainability in terms of minimum errors, effective utilization of space [26], energy conservation, and reduced operational cost [38]. Due to increased demand, volume, velocity, and variety of data have multiplied; hence intelligent applications in the warehouse-like advanced analytics provide decisions [26,39] for competitive advantage [40].
On the contrary, social aspects of technology integration have not been discussed. It is mentioned in the works of some authors [4], but rarely has been discussed in detail. Nathaniel et al. [8] argue that technological integration complicates the equation of man and machine in warehouse operations. It causes stress because of fear of job insecurities; hence social aspects of technology integration must address the welfare of employees to provide them with a sense of job security through training. The following sub-section includes the discussions related to identified research gaps.

2.1. Article Selection

A comprehensive review of the literature on warehouses was undertaken to establish the scenario of 4.0 technologies in warehouses for smarter conversions. Initially, many articles were scanned, with papers in other languages being excluded. The expanding tendency of academics focused on smart warehouses and technological integrations demonstrate its significance in the growth and success of logistics 4.0. These tendencies pointed to the future direction of research and the current research work of the vast majority of scholars worldwide. This helps the researcher propose and identify the practices or enablers required to pursue the research directions. The next section of the paper will discuss the theoretical framework for the research undertaken.

2.2. Theoretical Foundation of Building Initiatives of Technology 4.0 Practices for Warehousing Performance

Wernerfelt [41] explains internal and external firm resources in his Resource-Based View (RBV). Firms control their internal resources, such as their financial, human, and technological infrastructure, while their customers, competitors, and suppliers are determined by industry attractiveness and structural autonomy. As a result of their internal resources, these companies have a competitive advantage and can better drive to attract their customers. In light of this, the author suggests that the current RBV contributes significantly to the firm performance that operates in a relatively dynamic and agile environment [40], as in the case of current research problems.

2.3. Research Gap

Researchers have made significant contributions to the warehousing management literature from various angles [42], but literature on warehouse sustainability concepts requires more attention [43]. Work on warehouse literature has been covered from technology adoption [44], relative advantage, financial rewards [5,45], cost reduction, and dealing with complexities [6,46], and from the human perspective [47]. However, these proposed results have only been theoretically presented and have not been empirically tested. Many authors have explored the research literature on sustainable performance in the warehouse context [48,49,50,51].
All works named herein shared theoretical discussions, but analytical implications of individual aspects of warehousing technology-driven practices along with expected outcomes need further exploration. Existing literature does not empirically verify if, how and for which types of warehouses technological integrations provide further improvement and opportunities. Hence this research intends to identify the decision-making framework between the technological practice and warehouse expected performance, please refer to Section 4. A detailed discussion of the hybrid methodology to answer the proposed research questions are given in the next section.

3. Research Framework for Hybrid Model

The three-phased methodology framework is used to achieve the objectives proposed, as shown in Figure 1. Using an integrated approach of literature survey and fuzzy Delphi, the first phase identified Technology 4.0 driven warehouses practices and performance measures for operational performance. In the second phase, Technology 4.0 driven practices are compared and ranked according to pairwise comparisons based on BWM. In the third phase, a hybrid method is used to evaluate the performance of warehouses through the adoption of technology-driven practices using CoCoSo.

3.1. Fuzzy Delphi

Delphi is a traditional method for determining a consensus among experts’ opinions, but this consisted of several rounds of surveys, resulting in longer execution times and higher costs [52]. As a result of the experts’ responses having various meanings, it is impossible to express their feedback in quantitative terms. As a result, the fuzzy Delphi method was developed to overcome these disadvantages by combining the fuzzy set theory with the traditional Delphi methodology [52]. Tseng et al. [53] have determined that using this method has benefited over Delphi methods as it reduces the number of survey rounds and saves time. Below are the detailed information about the steps involved in implementing the fuzzy Delphi method.
Step 1. Identify the factors/criterion
This step starts with a literature survey and expert interviews to identify the reasonable factors/criteria related to the problem of the study.
Step 2. Collecting the opinions of expert group
A questionnaire survey is conducted to collect expert opinions about Technology 4.0 driven warehouse practices and warehousing performance. A five-point Likert scale is used to gather expert opinions, which is given in tabular in the paper by Ishikawa et al. [52] (See Table 2).
Step 3. Setting up of the triangular fuzzy numbers
A linguistic scale is used to transform the experts’ inputs into TFNs. The observations in the inputs are used to find maximum and minimum by using the TFNs. The consensus of the group of the experts is calculated by geometric mean ( M A ), by using the following procedure:
Let the value of evaluation for the significance of jth element given by ith expert from the ‘n’ expert is; w ˜ i j = l i j , m i j , u i j , i = 1, 2, … n and j = 1, 2, … m. Then fuzzy weighting w ˜ j of jth element is:
w ˜ j = l j , m j , u j l j   = m i n i ( l i j   ) m j   = i n m i j   n   u j   = m a x i ( u i j   )
where w i j signifies that ith expert’s evaluation for Technology 4.0 driven warehouse practices j, lj characterize the lowest appraisal values of Technology 4.0 driven warehouse practices j, mj indicate the geometric mean of all the expert assessment values for element j, and uj is experts’ highest assessment value for criterion j. Same process is repeated for the warehousing performance indicators.
Step 4. Defuzzification of the TFNs
TFNs, in this step, are converted into crisp number (Si) of Technology 4.0 driven warehouse practices and warehousing performance using Equation (2) based on centre of gravity method.
S j = l j + m j + u j 3
Step 5. Finalisation of theTechnology 4.0 driven warehouse practices and warehousing performance
Lastly, Technology 4.0 driven warehouse practices and warehousing performance are finalized using the fuzzy Delphi method. The obtained weights’ significance of Technology 4.0 driven warehouse practices and warehousing performance are compared with a threshold value (λ) as follows:
The practice/performance i is considered, if Siλ, else i is not considered.

3.2. BWM Method

The Best Worst Method in decision-making frameworks is used to determine the prioritizing factors. Given the relatively low number of pairwise comparisons among the factors (in this study, Technology 4.0-driven warehousing practices) and less mathematical complexity, the academic community has widely applied the BWM method. Ali et al. [54] applied it to find out the decision-making framework for Drone integration in various companies by using the opinion of eight experts; Chen and Ming [55] have used it as a method of development to select smart product-service modules with six experts responses. A further advantage of the BWM is that it effectively handles inconsistencies that may arise from pairwise comparisons. The purpose of this method is to evaluate the effectiveness of Technology 4.0 driven practices by comparing them to the best and worst Technology 4.0 driven practices. As a result, the best TDP practices are preferred over the other TDP practices, and the worst TDP practices are preferred over the other TDP practices when a comparison is made, usually using a 9-point scale (1–9). The description of the stepwise procedure for applying the BWM method is given below:
Step 1. Identification of Technology 4.0 driven warehouse practices
This step identifies the major Technology 4.0 driven warehouse practices (“n” number of Technology 4.0 driven warehouse practices: TDP1, TDP2, TDP3, … TDPn) by examining the literature and applying the fuzzy Delphi.
Step 2. Determine the best and worst Technology 4.0 driven warehouse practices
The experts will select the best and worst from the finalized Technology 4.0 driven warehouse practices. The best and worst Technology 4.0 driven warehouse practices are denoted as cB, and cW, respectively.
Step 3. Perform the reference comparison with Technology 4.0 driven warehouse practices
Expert input is used to determine the best Technology 4.0 driven warehouse practices based on a 9-point scale, and it is represented by the AB vector as follows:
AB = (aB1, aB2, …, aBn)
where AB the Best-to-Others (BO) vectors, aBj denotes the preference of the best Technology 4.0 driven warehouse practices B over the best Technology 4.0 driven warehouse practices j and aBB = 1.
Step 4. Perform the reference comparisons with worst Technology 4.0 driven warehouse practices
The predominance of the other Technology 4.0 driven warehouse practices is calculated through expert input using a 9-point scale and represented by AW vector as follows:
AW = (a1W, a2W, …, anW)T
where Aw the Others-to-Worst (OW) vector, ajw refers the preference of the Technology 4.0 driven warehouse practices j over the worst Technology 4.0 driven warehouse practices W and aww = 1.
Step 5. Determine the optimal weights
The optimal weight for each Technology 4.0 driven warehouse practices is the one where, for each pair wB/wj and wj/wW, it should have wB/wj = aBj and wj/wW = ajW. To satisfy these conditions for all j, maximum absolute differences are minimized of the set {|wB − aBjwj|, |wj − ajWwW|}. This problem can be represented as following model:
min max {|wBaBjwj|, |wjajWwW|}.
Subject to:
j w j = 1
w j 0   ;   j
Model (1) can be converted as following linear problem.
min   ξ L s . t . w B w j a B j   ξ L   for   all   j   w j w W a j W   ξ L   for   all   j j w j = 1
  w j 0   for   all   j
The optimal weights of each Technology 4.0 driven warehouse practices ( w 1 * , w 2 * ,   w 3 * w n * ) and optimal value of ξ L was obtained by solving the linear problem by Equation (4). The value of the consistency ratio is compared. Consistency of the comparison depends on the value of ξ L , a value closer to 0 indicates higher consistency and the value less than 0.1 is recommended by Rezaei [56].

3.3. Combined Compromise Solution (CoCoSo)

The CoCoSo method has recently been developed by Yazdani et al. [57], and it is one of the most effective MCDM techniques currently available. By combining an additive weighting model with an exponential weighting model, this method produces an overall result. Based on an evaluation against the criteria (in this study, Technology 4.0 driven warehouse practices), this method ranks the alternatives of warehouse performance measures. There have been rapid increases in the popularity of the CoCoSo approach within the supply chain field and related research fields.
Yazdani et al. [58] developed a decision model based on DEA and R-FUCOM in conjunction with R-CoCoSo to select logistics centres within autonomous communities of Spain. The framework for selecting medical waste treatment technologies was developed by Liu et al. [59] based on Pythagorean fuzzy CoCoSo. Below presents details regarding the steps of the CoCoSo procedure.
Step 1. The initial decision-making matrix related to the selected criteria/practices is prepared by using Table 3’s linguistic terms, as follows
X i j = x 11 x 12 x 1 n x 21 x 22 x 2 n x m 1 x m 2 x m n ;   i = 1 , 2 , n ,   j = 1 , 2 , m
The matrix X m × n shows the initial decision-making matrix which include the m-number of alternative/performance and n-evaluation criteria/practices. Hence, “ x i j ” represents the selection of the ith “warehouse performances” by adopting the jth Technology 4.0 driven warehouse practices.
Step 2. The normalization of the initial decision-making matrix is carried out by using Equations (6) and (7) below (please refer Zeleny, [60]):
For benefit criteria,
r i j = x i j min i x i j max i x i j min i x i j   ;
For non-benefit/cost criteria,
r i j = max i x i j x i j max i x i j min i x i j ;
Step 3. The weighted comparability sequence ( S i ) of each alternative and power weight of comparability sequences ( P i ) of each alternative is calculated using the Equations (8) and (9), respectively.
S i = j = 1 n ( w j r i j )
P i = j = 1 n r i j w j
Step 4. Relative weights of each alternative is calculated using the three aggregation approaches, which are provided by Equations (10)–(12),
k i a = S i + P i i = 1 m P i + S i ;
The Equation (10) shows the arithmetic mean of sums of scores, weighted sum measure ( S i ), and weight power measure ( P i ),
k i b = S i min i S i + P i min i P i
The Equation (11) is used to have a sum of relative scores of weighted comparability sequence ( S i ) and power weighted comparability sequence ( P i ) compared to the best.
k i c = λ S i + 1 λ P i ( λ max i S i + 1 λ max i P i )
The Equation (12) signifies the balanced compromise of weighted comparability sequence ( S i ) and power weighted comparability sequence ( P i ) score. The value of the parameter λ is mostly taken as 0.5. However, it might be different as recommended by the team’s requirements.
Step 5. Based on the value of ki, the weights of the alternatives are calculated by Equation (13).
k i = k i a k i b k i c 1 3 + 1 3 k i a + k i b + k i c
The alternatives are ranked based on the value of k i , that is, the alternative with a significant value of k i is ranked higher than those without.
After discussing the hybrid methodology based on the fuzzy Delphi, BWM, and CoCoSo, the next section includes the case application of the warehousing sector.

4. Case Study

4.1. Case Companies Information

The warehousing sector displays better growth with technological developments in the Makkah region. The level of automation is increasing, which changes the way warehousing operations are carried out to gain a competitive advantage. Technology is being embraced by the government, resulting in the implementation of omnichannel networks and the improvement of the supply chain network. These changes are reflected in the structural changes and storage facilities. A shift, therefore, in the industry toward leveraging technology has led to increased efficiency for the warehousing sector. The warehouses selected have all been converted to smart warehouses using a combination of system-based and data-driven strategies for conversion. Their subdomain functions are focused on inventory management, order-picking and batch-handling, warehouse operations, cyber-physical systems, warehouse management, and operating labour.

4.2. Background of Experts

A range of experts was contacted from the warehousing organization. They were involved in supply chain planning, efficiencies improvements, warehouse maintenance, management operations, inventory management, procurements of raw materials, and vendor management related to technology and equipment were contacted. A careful selection process was followed for the experts’ selection. A panel of Experts constituted had a minimum of 10 and a maximum of 25 years of experience with bachelor’s and master’s in Industrial and Production Engineering; Business Management, and Supply Chain Management.

4.3. Finalization of Technology 4.0 Driven Practices and Warehousing Performance

Based on the literature review and fuzzy Delphi, a twofold approach is proposed for developing a decision-making model for Technology 4.0 warehouse practices and their performance in warehousing. A literature search enabled the researchers to identify 11 Technology 4.0 driven warehouse practices and 14 warehouse operation performance measures. A semi-structured questionnaire was then developed to finalize the identified practices and performance measures. The experts were contacted for two rounds of responsive feedback. In the first round, responses were collected to finalize the ‘practices’ and their corresponding performances. Regular onsite and virtual meetings, in this regard, were held. The experts use a linguistic scale (shown in Table 2) for their preferred responses for the selection of ‘practices’ and their corresponding performances. Once the responses are gathered, it is transformed into TFN by using Table A1. In this research, decision-making for linguistic groups is based on individual semantics and consensus reaching. Out of 13 practices, nine Technology 4.0 driven warehouse practices were considered most effective for the study, along with 13 warehousing performances out of 15 (see Table 4). The De-fuzzy value of a Technology 4.0 practice and the performance of warehousing is considered significant when it is greater than 0.7; otherwise, it is dropped from further consideration (Chang et al. [61] and Khan et al. [62]). A final questionnaire was prepared to gather feedback on TDS practices and warehouse performance, as proposed in Table 4.

4.4. Prioritization of Technology 4.0 Driven Warehouse

The questionnaire was prepared to collect the expert’s responses for the Technology 4.0 driven warehouse practices (TDP) presented in Table 5 below. Technology 4.0 driven warehouse practices (TDP) are identified by each expert with the help of a questionnaire.
The experts use Saaty’s nine-point scale (1–9) to select the best TDP practices over the other TDP practices, and the same process is repeated for choosing the other TDP practices over the worst practices.
Table 6 exhibits all the experts’ optimal weights calculated by the BWM’s optimization model 2 (Equation (4)). Further, average weight is found for each practice (TDP) and shown in Table 7 and their ranks. Researchers suggested that the average consistency ratio (C.R.) needs to be less than 0.10 to have consistent and reliable results based on the experts’ data. Table 6 has the values of the consistency ratio of each expert, and Table 7 contains the average consistency ratio, which is <0.10; thus, the criteria is achieved in our case.

4.5. Prioritization of Warehousing Performance Measures

The last part of the questionnaire includes warehouse performance as indicated below in Table 8. Each expert used a linguistic decision matrix, as shown in Table 3, to evaluate warehousing performances using Technology 4.0 driven practices as an evaluation criterion. The linguistic terms are transformed with the crisp values (using Table 3) for all the ten experts’ responses.
The average of all matrices is calculated and presented in Table 9.
Next, the normalised matrix is obtained by using Equations (6) and (7) and shown in Table 10. Table 11 presents a weighted comparability sequence and their summation (Sj) for each warehousing performance calculated using Equation (8).
The power-weighted comparability sequence and their summation (Pi) is computed by using Equation (9) and is shown in Table 12 for each warehousing performance are computed using Equation (9) and shown in Table 13 for each warehousing performance. CoCoSo method is based on three aggregation methods to compute the relative weights ( k i a ,   k i b ,   k i c ) of warehousing performance by using the Equations (10)–(12). These relative weights are applied to determine the final weights (as shown by K column) by using Equation (13) is shown in Table 12. Final ranks are found based on ‘K’ weights for the warehousing performances and are shown in Table 13.

5. Results and Discussion

The strategic challenges faced by the industrial setups to integrate Technology 4.0 driven warehouses practices for improved industrial outputs are yet to be achieved. However, our findings suggest that Man-machines or robots for facilitating human ≻ Planning system for management ≻ Storage systems ≻ Order quality and responsiveness ≻ Visualization and application models ≻ Decision-making models for inventory status ≻ Simulation models for inbound transportation management and AGV ≻ Navigation algorithm and sensing systems ≻ Tracking and stock level monitoring models.
The main goal of a warehouse is to obtain all these requirements after removing or reducing non-value-added tasks by the man-machine interactions (ranked as 1st). This reduces downtime in the warehouse and is considered the most effective practice by smart warehouses. For example, robots are used for doing night patrols and collecting mundane data. They can hear sounds such as footsteps or smell harmful air quality with extreme sensing. Robots are powerful and efficient. They are expected to deliver higher quality results by benchmarking standards.
In technologically driven warehouses, the planning system is considered the 2nd most effective method for storing and managing inventory. Management planning systems optimize, control, monitor, and plan various high volume distribution and shipping stages, such as point of sales data, stock, on-hand data, forecasts, and reorders [12].
The storage system rating, at 3, for capacity utilization and cost reduction is low. However, customized warehouses of varying sizes, free zones and non-free zones of locations [66], convenient locations near major logistics hubs, and onsite labour and staff accommodations have not proven as effective as it is expected.
The 4th highest ranked practice is helping the warehouse to increase its competitiveness by setting a cut-off time for next-day deliveries. Warehouses can calculate staging, demurrage, labour, and stock costs using cloud data. As a result of automation, overhead expenses, driving costs, and operating costs are reduced. Reduced energy consumption, less waste, and fewer emissions reduce overhead, driving costs, and operational costs [67].
A visual representation (ranked as 5) provides an overview of shelves in the warehouse, simulated cart movement, and a variety of picking list statistics. We know that optimization models are required to track products and keep stock levels accurately. These models (ranked at 6) are compatible with minimum stock levels, stock reviews, JIT policies, reorder lead times, economic order quantities, and batch control. By using modern computerised technology, the utilization and availability of space can be maintained more efficiently and quickly. In the simulation, computer models (ranked at 7) are used to understand and improve an entire warehouse system in order to achieve the desired results in virtual settings. In terms of investment rate and return, ‘Navigation algorithm and sensing systems for effective equipment utilization’ are the 8th ranked. As a result of a lack of advanced analytics, it isn’t easy to share machine operating parameters such as average speed, cycle time, product output, etc., to the cloud for further processing. As a result, the cloud will not be able to integrate data for valuable real-time insights. By using an inventory tracking system (ranked as 9), a warehouse tracks the movement of raw materials and finished goods to meet customer demand. By a tracking system, inventory is easily visualized at every step of the order process, including shipping, receiving, storing, and fulfilling orders and returning, exchanging, and providing warranty services. Warehouse organisations use cloud and mobile solutions to track inventory in real-time to reduce costs, analyse trends in the supply chain, minimize reverse logistics and increase revenue. With it, warehouses update, select groups of items, implement quality control and batch tracking and integrate their systems with other warehouses. In RFID tags, unique identification numbers enable remote reading and are used to identify items.
The research findings and future directions are summarized in the next section. The research findings and future directions are summarized in the next section.

Sensitivity Analysis

MCDM analyses are prone to error, and the result obtained by these analyses could be influenced by the imprecision of the data, the vagueness of the data, and the subjective opinions of the analysts. Various studies have shown that a small variation in criterion weights can affect the ranking [56]. As a result, it is vital to verify the robustness of the ranking algorithm. A sensitivity analysis is carried out to test the reliability of the results [62].
During the sensitivity analysis, the weights of Sustainable Practices are varied based on the highest weighted categories. In order to generate nine tests of Technology 4.0 practices (Test 1 to Test 9), researchers varied the weight of the highest weighted practice, TDP2 in our case, from 0.1 to 0.9, with increments of 0.1. As a result of the change in TDP2 weight, a corresponding change is also apparent in other Technology 4.0 driven practices weights. See Appendix A Table A1 and Figure 2. This resulted in a difference in the ranking of the warehouse performance as a result of the weight changes in different tests. Using the CoCoSo method, the results of 9 different tests of warehouse performance are shown in Table A2 and Figure 3. Based on Figure 2 and Figure 3, it is evident that most of the Technology 4.0 driven practices and warehouse performance indicators remain the same and are hardly affected in all the tests executed. Accordingly, it is concluded that the proposed hybrid method is sufficiently reliable, robust, and stable to obtain the desired results.

6. Conclusions and Future Research Directions

The study covers the warehousing scenario in the Mecca region, identifying and mapping the practices compared to national and international benchmarked practices. After consulting experts, the decision-making framework is proposed for shortlisting, finalizing, and ranking the practices and performance data. Linguistic scale ratings were used to capture the diversity, subjectivity and imprecision inherent in the human responses.
Through technology, people around the world can connect, and by connecting those at the grassroots level, new knowledge related to the latest prevalent practices can be integrated.
Through the collective efforts of government support and the strength of all industrial sectors, the logistics sector has carried out well compared to global standards. To remain competitive, it must solidify the productive and manufacturing core.
Research findings provide insights to businesses in understanding the implications of different technologies such as IoT, A.I., Big Data, and Blockchain to improve warehouse functions and ultimately increase business and supply chain resilience.

6.1. Managerial Implications

This research is a stepping stone, especially for novice or newly built organizations or those who plan to understand the automation scenario thoroughly. It provides managers with important feedback that can help them embrace change by understanding the impact of various technology-driven practices. It offers insight to organizations competing at international levels on how to improve their practices by benchmarking them against benchmarks, thus enabling the warehousing industry to contribute to the vision of 2030 effectively. Study findings are relevant to a wide range of businesses as the practices chosen are universal and easily applied to any organizations. More contributions are needed to adapt, practice, and perform sustainability from a developing country perspective.

6.2. Research Implications

This study helps researchers grasp the significance and implications of the study and how it can be incorporated into their research to expand their research scope. Researchers consider this study as a continuation of the one conducted by Yavas and Ozen [10], which discussed essential logistics for Industry 4.0 and their relevance to future implications and transformations related to Industry 4.0. In their work, Ali et al. [68] use theory-based SEM evaluation hybrid Machine learning Models for sustainable practices; hence, the same can be used for Industry 4.0 through AI/ML methods for the warehousing sector. Researchers can identify the sectors that benefit from the research results, especially those expected to boost the Kingdom’s economy, how this benefit will be gained, and what requirements each sector must meet. The researcher can use other scales to measure the expert’s response to Delphi and CoCoSo methods. Further research can consider the group decision-making consensus method for the non-cooperative behaviour management of personalized individual semantics. This study can be tested by using other multi-attribute decision-making models such as one proposed by Medic et al. [69] are, the Fuzzy Analytical Hierarchy procedure, and PROMETHEE.

6.3. Limitation

In the study, established organizations are considered in well-developed industrial corridors. The main drawback of the MCDM analysis is the generalisation of the results. The result of this study could be applied only to organizations working in these areas [70]. The other areas might have other challenge. As a result, this study could be tested with organisations representing different operational areas and milestones to have an entirely different point of view on a given smart and sustainable warehouse [71].

Author Contributions

Conceptualization, S.S.A.; methodology, S.S.A.; formal analysis, S.S.A.; data curation, S.S.A. writing—original draft preparation, S.S.A. and R.K.; writing—review and editing, S.S.A. and R.K.; project administration, S.S.A.; funding acquisition, S.S.A. All authors have read and agreed to the published version of the manuscript.


This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. C-1-144-1441. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data Curation is with S. S. Ali.


The authors would like to thank the warehousing and logistics industry experts who contributed to this study by providing support and valuable perspectives for the identification and comparison of enablers and practices, as well as their subsequent validation of findings.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Technology 4.0 driven practices weight and ranks for sensitivity analysis in various test.
Table A1. Technology 4.0 driven practices weight and ranks for sensitivity analysis in various test.
OriginalTest 1Test 2Test 3Test 4Test 5Test 6Test 7Test 8Test 9
Table A2. Warehouse performance weight and ranks for sensitivity analysis in various test.
Table A2. Warehouse performance weight and ranks for sensitivity analysis in various test.
OriginalTest 1Test 2Test 3Test 4Test 5Test 6Test 7Test 8Test 9


  1. Schwarz, M.; Milan, A.; Lenz, C.; Munoz, A.; Periyasamy, A.S.; Schreiber, M.; Behnke, S. NimbRo Picking: Versatile part handling for warehouse automation. In Proceedings of the IEEE International Conference on Robotics and Automation, Singapore, 29 May–3 June 2017; pp. 3032–3039. [Google Scholar]
  2. Jabbour, A.B.; Jabbour, C.; Foropon, C.; Filho, M. When titans meet—Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technol. Forecast. Soc. Change 2018, 132, 18–25. [Google Scholar] [CrossRef]
  3. Atzori, L.; Iera, A.; Morabito, G. Understanding the Internet of Things: Definition, potentials, and societal role of a fast-evolving paradigm. Ad Hoc Netw. 2017, 56, 122–140. [Google Scholar] [CrossRef]
  4. Awa, H.O.; Ukoha, O.; Igwe, S.R. Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. Bottom Line 2017, 30, 2–22. [Google Scholar] [CrossRef]
  5. Kumar, S.; Narkhede, B.E.; Jain, K. Revisiting the warehouse research through an evolutionary lens: A review from 1990 to 2019. Int. J. Prod. Res. 2021, 59, 3470–3492. [Google Scholar] [CrossRef]
  6. Van Geest, M.; Tekinerdogan, B.; Catal, C. Smart Warehouses: Rationale, Challenges and Solution Directions. Appl. Sci. 2022, 12, 219. [Google Scholar] [CrossRef]
  7. Kuo, Y.H.; Szeto, W.Y. Smart transportation and analytics. Transp. B Transp. Dyn. 2018, 6, 1–3. [Google Scholar] [CrossRef]
  8. Nathanail, E.; Gogas, M.; Adamos, G. Assessing the contribution of urban freight terminals in last mile operations. Transp. Telecommun. J. 2016, 17, 231–241. [Google Scholar] [CrossRef] [Green Version]
  9. Issaoui, Y.; Khiat, A.; Bahnasse, A. Toward Smart Logistics: Engineering Insights and Emerging Trends. Arch. Comput. Methods Eng. 2020, 28, 3183–3210. [Google Scholar] [CrossRef]
  10. Yavasa, V.; Deniz, Y.; Ozenb, O. Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transp. Res. Part E 2020, 135, 101864. [Google Scholar] [CrossRef]
  11. Stentoft, J.; Wickstrøm, K.A.; Philipsen, K.; Haug, A. Drivers and barriers for Industry 4.0 readiness and practice: Empirical evidence from small and medium-sized manufacturers. Prod. Plan. Control. Manag. Oper. 2021, 32, 811–828. [Google Scholar] [CrossRef]
  12. Goksoy, A.; Vayvay, O.; Ergeneli, N. Gaining competitive advantage through innovation strategies: An application in warehouse management processes. Am. J. Bus. Manag. 2013, 2, 304–321. [Google Scholar]
  13. Feng, B.; Ye, Q. Operations management of smart logistics: A literature review and future research. Front. Eng. Manag. 2021, 8, 344–355. [Google Scholar] [CrossRef]
  14. Winkelhaus, S.; Grosse, E.H.; Morana, S. Towards a conceptualisation of Order Picking 4.0. Comput. Ind. Eng. 2021, 159, 10751. [Google Scholar] [CrossRef]
  15. Škerlič, S.; Muha, R.; Sokolovskij, E. Application of modern warehouse technology in the Slovenian automotive industry. Transport 2017, 32, 415–425. [Google Scholar] [CrossRef] [Green Version]
  16. Lee, J.; Bagheri, B.; Kao, H.A. A Cyber-Physical Systems Architecture for Industry 4.0- Based Manufacturing Systems. Manuf. Lett. 2015, 3, 23. [Google Scholar] [CrossRef]
  17. Kuo, Y.H.; Pilati, F.; Qu, T.; Huang, G.Q. Digital twin-enabled smart industrial systems: Recent developments and future perspectives. Int. J. Comput. Integr. Manuf. 2021, 34, 685–689. [Google Scholar] [CrossRef]
  18. Barreto, L.; Amaral, A.; Pereira, T. Industry 4.0 implications in logistics: An overview. Procedia Manuf. 2017, 13, 1245–1252. [Google Scholar] [CrossRef]
  19. Reaidy, P.J.; Gunasekaran, A.; Spalanzani, A. Bottom-up Approach Based on Internet of Things for Order Fulfillment in a Collaborative Warehousing Environment. Int. J. Prod. Econ. 2015, 159, 29–40. [Google Scholar] [CrossRef]
  20. Wang, J.; Lim, M.K.; Zhanb, Y.; Wang, X. An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transp. Res. Part E 2020, 135, 101886. [Google Scholar] [CrossRef]
  21. Mostafa, H.H.; Mostafa, S.A.; Budiyono, A.; Mustapha, A.; Gunasekaran, S.S. A Hybrid Algorithm for Improving the Quality of Service in MANET. Int. J. Adv. Sci. Eng. Inf. Technol. 2018, 8, 2088–5334. [Google Scholar]
  22. Qiu, X.; Luo, H.; Xu, G.; Zhong, R.; Huang, G.Q. Physical Assets and Service Sharing for IoT-Enabled Supply Hub in Industrial Park (SHIP). Int. J. Prod. Econ. 2015, 159, 4–15. [Google Scholar] [CrossRef]
  23. Choy, K.L.; Ho, G.T.S.; Lee, C.K.H. A RFID-Based Storage Assignment System for Enhancing the Efficiency of Order Picking. J. Intell. Manuf. 2017, 28, 111–129. [Google Scholar] [CrossRef]
  24. Rayes, A.; Salam, S. Things in IoT: Sensors and actuators. In Internet of Things from Hype to Reality; Springer: Cham, Switzerland, 2016. [Google Scholar]
  25. Fragapane, G.; Ivanov, D.; Peron, M. Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Ann. Oper. Res. 2020, 308, 125–143. [Google Scholar] [CrossRef] [Green Version]
  26. Lee, V.H.; Ooi, K.B.; Chong, A.Y.-L.; Seow, C.C. Creating technological innovation via green supply chain management: An empirical analysis. Expert Syst. Appl. 2014, 41, 6983–6994. [Google Scholar] [CrossRef]
  27. Klumpp, M. Automation and artificial intelligence in business logistics systems: Human reactions and collaboration requirements. Int. J. Logist. Res. Appl. 2018, 21, 224–242. [Google Scholar] [CrossRef]
  28. Li, X. Reducing channel costs by investing in smart supply chain technologies. Transp. Res. Part E 2020, 137, 101927. [Google Scholar] [CrossRef]
  29. Kamble, S.; Gunasekaran, A.; Dhone, N.C. Industry 4.0 and Lean Manufacturing Practices for Sustainable Organisational Performance in Indian Manufacturing Companies. Int. J. Prod. Res. 2019, 58, 1319–1337. [Google Scholar] [CrossRef]
  30. Lee, J.; Kao, H.-A.; Yang, S. Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia Cirp 2014, 16, 3–8. [Google Scholar] [CrossRef] [Green Version]
  31. Kuo, Y.H.; Kusiak, A. From data to big data in production research: The past and future trends. Int. J. Prod. Res. 2019, 57, 4828–4853. [Google Scholar] [CrossRef] [Green Version]
  32. Jinxiang, G.; Goetschalckx, M.; McGinnis, L.F. Research on Warehouse Operation: A Comprehensive Review. Eur. J. Oper. Res. 2007, 177, 1–21. [Google Scholar]
  33. Yang, L.R.; Chen, J.H. Information Systems Utilization to Improve Distribution Center Performance: From the Perspective of Task Characteristics and Customers. Adv. Inf. Sci. Serv. Sci. 2012, 4, 230–238. [Google Scholar]
  34. Dotoli, M.; Pia Fanti, M.; Iacobellis, G.; Stecco, G.; Ukovich, W. Performance Analysis and Management of an Automated Distribution Center. In Proceedings of the 35th Annual Conference of IEEE Industrial Electronics, Porto, Portugal, 3–5 November 2009; pp. 4371–4376. [Google Scholar]
  35. Ioannis, M.; Terry, L.A. A case study assessment of the Operational Performance of a Multiple Fresh Produce Distribution Centre in the UK. Br. Food J. 2010, 112, 653–667. [Google Scholar]
  36. Kamarainen, V.; Punakivi, M. Developing cost-effective operations for the e-grocery supply chain. Int. J. Logist. Res. Appl. 2002, 5, 285–298. [Google Scholar] [CrossRef]
  37. Naish, S.; Baker, P. Materials handling: Fulfilling the promises. Logist. Transp. Focus 2004, 6, 18–26. [Google Scholar]
  38. Richards, G. Warehouse Management: A Complete Guide to Improving Efficiency and Minimizing Costs in the Modern Warehouse, 3rd ed.; Kogan Page Publishers: London, UK, 2017. [Google Scholar]
  39. Witkowski, K. Internet of Things, Big data, Industry 4.0—Innovative solutions in logistics and supply chain. Procedia Eng. 2017, 182, 763–769. [Google Scholar] [CrossRef]
  40. Wamba, F.S.; Akter, S.; Edwards, A.; Chopin, G.; Gnanzou, D. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 2015, 165, 234–246. [Google Scholar] [CrossRef]
  41. Wernerfelt, B. A Resource-Based View of the Firm. Strateg. Manag. J. 1984, 5, 171–180. [Google Scholar] [CrossRef]
  42. Hassan, M.; Ali, M.; Aktas, E.A.; Alkayid, K. Factors affecting selection decision of auto-identification technology in warehouse management: An international Delphi study. Prod. Plan. Control 2015, 26, 1025–1049. [Google Scholar] [CrossRef]
  43. De Koster, R.B.M.; Johnson, A.L.; Roy, D. Warehouse design and management. Int. J. Prod. Res. 2015, 55, 6327–6330. [Google Scholar] [CrossRef]
  44. Hao, J.; Shi, H.V.; Shi Yang, C. Adoption of Automatic Warehousing Systems in Logistics Firms: A Technology–Organization–Environment Framework. Sustainability 2020, 12, 5185. [Google Scholar] [CrossRef]
  45. Oláh, J.; Karmazin, G.; Pető, K.; Popp, J. Information technology developments of logistics service providers in Hungary. Int. J. Logist. Res. Appl. 2018, 21, 332–344. [Google Scholar] [CrossRef]
  46. Wanke, P.F. Determinants of scale efficiency in the Brazilian 3PL industry: A 10-year analysis. Int. J. Prod. Res. 2012, 50, 2423–2438. [Google Scholar] [CrossRef]
  47. Mahroof, K. A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. Int. J. Inf. Manag. 2019, 45, 176–190. [Google Scholar] [CrossRef]
  48. Presley, A.; Meade, L.; Sarkis, J. A strategic sustainability justification methodology for organizational decisions: A reverse logistics illustration. Int. J. Prod. Res. 2007, 45, 4595–4620. [Google Scholar] [CrossRef]
  49. Esteves, A.M.; Franks, D.; Vanclay, F. Social impact assessment: The state of the art. Impact Assess. Proj. Apprais. 2012, 30, 34–42. [Google Scholar] [CrossRef]
  50. Bank, R.; Murphy, R. Warehousing sustainability standards development. In IFIP International Conference on Advances in Production Management Systems; Springer: Cham, Switzerland, 2013; pp. 294–301. [Google Scholar]
  51. Nikolaou, I.E.; Evangelinos, K.I.; Allan, S. A reverse logistics social responsibility evaluation framework based on the triple bottom line approach. J. Clean. Prod. 2013, 56, 173–184. [Google Scholar] [CrossRef]
  52. Ishikawa, A.; Amagasa, M.; Shiga, T.; Tomizawa, G.; Tatsuta, R.; Mieno, H. The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets Syst. 1993, 55, 241–253. [Google Scholar] [CrossRef]
  53. Tseng, M.; Wu, K.; Chiu, A.; Lim, M.; Tan, K. Service innovation in sustainable product service systems: Improving performance under linguistic preferences. Int. J. Prod. Econ. 2018, 203, 414–425. [Google Scholar] [CrossRef]
  54. Ali, S.S.; Paksoy, T.; Torğul, B.; Kaur, R. Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: A fuzzy hybrid multi-criteria decision making approach. Wirel. Netw. 2020, 26, 5759–5782. [Google Scholar] [CrossRef]
  55. Chen, Z.; Ming, X. A rough-fuzzy approach integrating best-worst method and data envelopment analysis to multi-criteria selection of smart product service module. Appl. Soft Comput. 2020, 94, 106479. [Google Scholar] [CrossRef]
  56. Rezaei, J. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 2016, 64, 126–130. [Google Scholar] [CrossRef]
  57. Yazdani, M.; Zarate, P.; Kazimieras Zavadskas, E.; Turskis, Z. A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis. 2021, 57, 2501–2519. [Google Scholar] [CrossRef]
  58. Yazdani, M.; Chatterjee, P.; Pamucar, D.; Chakraborty, S. Development of an integrated decision-making model for location selection of logistics centers in the Spanish autonomous communities. Expert Syst. Appl. 2020, 148, 113208. [Google Scholar] [CrossRef]
  59. Liu, P.; Rani, P.; Mishra, A. A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology. J. Clean. Prod. 2021, 292, 126047. [Google Scholar] [CrossRef]
  60. Zeleny, M. Compromise programming. In Multiple Criteria Decision Making; Cocchrane, J.L., Zeleny, M., Eds.; University of South Carolina Press: Columbia, SC, USA, 1973; pp. 262–301. [Google Scholar]
  61. Chang, P.; Huang, L.; Lin, H. The fuzzy Delphi method via fuzzy statistics and membership function fitting and an application to the human resources. Fuzzy Sets Syst. 2000, 112, 511–520. [Google Scholar] [CrossRef]
  62. Khan, S.; Singh, R.; Haleem, A.; Dsilva, J.; Ali, S.S. Exploration of Critical Success Factors of Logistics 4.0: A DEMATEL Approach. Logistics 2022, 6, 13. [Google Scholar] [CrossRef]
  63. Fletcher, S.; Johnson, T.; Adlon, T.; Larreina, J.; Casla, P.; Parigot, L. Adaptive automation assembly: Identifying system requirements for technical efficiency and worker satisfaction. Comput. Ind. Eng. 2020, 139, 105772. [Google Scholar] [CrossRef]
  64. Cirulis, A.; Egils, G. Augmented reality in logistics. Procedia Comput. Sci. 2013, 26, 14–20. [Google Scholar] [CrossRef] [Green Version]
  65. Hofmann, E.; Rüsch, M. Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind. 2017, 89, 23–34. [Google Scholar] [CrossRef]
  66. Fumi, A.; Scarabotti, L.; Schiraldi, M.M. Minimizing Warehouse Space with a Dedicated Storage Policy. Int. J. Bus. Manag. 2013, 133, 312–318. [Google Scholar] [CrossRef] [Green Version]
  67. Faber, N.; De Koster, R.B.M.; Smiidts, A. Survival of the fittest: The impact of fit between warehouse management structure and warehouse context on warehouse performance. Int. J. Prod. Res. 2018, 56, 120–139. [Google Scholar] [CrossRef]
  68. Ali, S.S.; Kaur, R.; Persis, D.J.; Saha, R.; Pattusamy, M.; Sreedharan, V. Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment. Ann. Oper. Res. 2020. [Google Scholar] [CrossRef]
  69. Medić, N.; Anišić, Z.; Lalić, B.; Marjanović, U.; Brezocnik, M. Hybrid fuzzy multi-attribute decision making model for evaluation of advanced digital technologies in manufacturing, Industry 4.0 perspective. Adv. Prod. Eng. Manag. 2019, 14, 483–493. [Google Scholar] [CrossRef]
  70. Ali, S.S.; Kaur, R.; Khan, S. Evaluating sustainability initiatives in warehouse for measuring sustainability performance: An emerging economy perspective. Ann. Oper. Res. 2021. [Google Scholar] [CrossRef]
  71. Ali, S.S.; Kaur, R. Effectiveness of corporate social responsibility (CSR) in implementation of social sustainability in warehousing of developing countries: A hybrid approach. J. Clean. Prod. 2021, 324, 129154. [Google Scholar] [CrossRef]
Figure 1. Steps of decision-making framework.
Figure 1. Steps of decision-making framework.
Mathematics 10 01252 g001
Figure 2. The illustrative ranking of Technology 4.0 driven practices in nine tests.
Figure 2. The illustrative ranking of Technology 4.0 driven practices in nine tests.
Mathematics 10 01252 g002
Figure 3. The illustrative ranking of warehouse performances in nine tests.
Figure 3. The illustrative ranking of warehouse performances in nine tests.
Mathematics 10 01252 g003
Table 1. Emerging economics and Technology 4.0.
Table 1. Emerging economics and Technology 4.0.
Emerging EconomiesInvestment in Artificial IntelligenceGrowth of Industry 4.0 in G20 4Operating Environment for Implementation 5Global Sustainability Index
China1st ($22 Billion) 412136 1
India3rd ($2.5 Million) 641963 2
Saudi Arabia2nd ($4 Billion) 421161 3
Table 2. Linguistic scale and their associated TFNs.
Table 2. Linguistic scale and their associated TFNs.
ScaleLevel of SignificanceTriangular Fuzzy Number
1Very low(0.1, 0.1, 0.3)
2Low(0.1, 0.3, 0.5)
3Medium(0.3, 0.5, 0.7)
4High(0.5, 0.7, 0.9)
5Very high(0.7, 0.9, 0.9)
Table 3. Linguistic scale and their associated TFNs.
Table 3. Linguistic scale and their associated TFNs.
Linguistic ScaleCrisp Value
Very Low (VL)1
Low (L)2
Medium (M)3
High (H)4
Very High (VH)5
Table 4. Identification of the Technology 4.0 driven practices and warehouse performances.
Table 4. Identification of the Technology 4.0 driven practices and warehouse performances.
TDP PracticesMinGeometric MeanMaxDe-FuzzyDecision
Man-machines or robots for facilitating human0.50.7937250.90.731242Accept
Tracking and stock level monitoring models0.50.8139260.90.737975Accept
Planning systems for management0.50.7740260.90.724675Accept
Navigation algorithm and sensing systems 0.50.8139260.90.737975Accept
Fair Acceptance0.10.3297230.70.376574Reject
Visualization and application models0.50.8139260.90.737975Accept
Simulation models for inbound transportation management-AGV0.50.7937250.90.731242Accept
Managing optimization, controlling, monitoring, and planning stages0.50.7548160.90.718272Accept
Decision making models for Inventory status0.50.8139260.90.737975Accept
System for facilitating0.10.2785540.70.359518Reject
Human efforts0.10.203450.70.334483Reject
Order quality and responsiveness0.50.8139260.90.737975Accept
Inventory status updates, risk, and investment minimization0.10.2785540.70.359518Reject
Storage systems0.50.8139260.90.737975Accept
Warehousing PerformanceMinGeometric MeanMaxDe-FuzzyDecision
Improved inventory management0.50.7937250.90.731242Accept
Improve human skills0.10.2785540.70.359518Reject
Effective storage management0.50.7937250.90.731242Accept
Better adoption of digital technology0.10.2785540.70.359518Reject
Improved resource planning and utilization0.50.7937250.90.731242Accept
Improved distribution and shipping or delivery process0.50.7937250.90.731242Accept
Increased return on investment0.50.7937250.90.731242Accept
Decrease in non-value-added activities0.50.7937250.90.731242Accept
Improved information integration and sharing0.50.7937250.90.731242Accept
Improved benchmarking standards0.50.7937250.90.731242Accept
Improved C.E. based smart culture0.10.2785540.70.359518Reject
Improved capacity/space
Reduced operational cost0.50.7937250.90.731242Accept
Reduced downtime0.50.7937250.90.731242Accept
Reduced TAT for delivery performance0.50.8139260.90.737975Accept
Table 5. Technology 4.0 driven practices.
Table 5. Technology 4.0 driven practices.
PracticesDescriptionAuthored by
Planning systems for managementOptimization, control, monitoring, and planning of point-of-sale data, inventory information, customer projections, and planned orders for high-volume distribution.[63]
Man-machines or robots for facilitating humanAutomated Mobile Robots (AMR) using sensors are needed in warehouses to facilitate/manage human efforts and interventions.[27]
Order quality and responsivenessBy integrating Technology4.0 into inventory management, inventories can be reduced, order fulfilment can be increased, order processing time is reduced, and orders will be fulfilled correctly the first time. This will reduce customer inquiries, simplify customer support, and increase customer satisfaction.[11,14]
Visualization and application modelsIntelligent agents are used to define complex operations, managing speed and accuracy to deliver the products.[64]
Tracking and stock level monitoring modelsAdopting assessment models that update automatically whenever products get delivered, sold, lost, or destroyed. This is to eliminate inefficiencies and have the correctness.[38,65]
Decision-making models for Inventory statusFor warehouses to be able to update inventory status, minimize Risk, and investment, decision support models are needed.[65]
Simulation models for inbound transportation management-AGVThe key steps of the inbound receiving process are handled by Automated Guided Vehicle (AGV), which is built on a simulation framework.[15]
Storage systems: Automated Storage and retrieval (AS/RS)The automated storage and retrieval (AS/RS) system has been developed to achieve capacity utilization, standardized and accurate picking operations, and cost reduction. This system uses a computerized control system to automatically retrieve and place the products.[17,65]
Navigation algorithm and sensing systemsMagnetic positioning and RFID indoor positioning systems are helpful for integrating industrial trucks and lift trucks in the operations.[43]
Table 6. Best and worst Technology 4.0 driven warehouse practices along with the optimal weights from each expert.
Table 6. Best and worst Technology 4.0 driven warehouse practices along with the optimal weights from each expert.
Table 7. Weight and rank Technology 4.0 driven warehouse practices.
Table 7. Weight and rank Technology 4.0 driven warehouse practices.
Technology 4.0 Driven Warehouse Practices (TDP)WeightsRank
Planning system for management (TDP1)0.1972
Man-machines or robots for facilitating human (TDP2)0.2261
Order quality and responsiveness (TDP3)0.0994
Visualization and application models (TDP4)0.0955
Tracking and stock level monitoring models (TDP5)0.0449
Decision-making models for Inventory status (TDP6)0.0776
Simulation models for inbound transportation management and AGV (TDP7)0.0537
Storage systems (TDP8)0.1563
Navigation algorithm and sensing systems (TDP9)0.0538
Average Consistency Ratio (CR) = 0.0735
Table 8. Warehousing performances.
Table 8. Warehousing performances.
Practices Description Authored by
Improved distribution and shipping or delivery process (WOP1) As soon as new orders are placed, the automated process starts with picking items from inventory, packing boxes, and making sure packages reach their destinations. This improves efficiency.[14,33]
Increased return on investment (WOP2)Streamlining the process with technology ensures better ROI.[28,36]
Improved benchmarking standards (WOP3)Based on the current best practices, AI/ML helps to generate actions and drive improvements in warehouse operations.[43,63]
Reduced operational cost (WOP4)Due to integrated A.I./ML-based processes receiving, storing, order picking, inspection, packaging, dispatching, delivery, kitting, pricing, labeling, and product customization have less costs. [14,38]
Improved information integration and sharing (WOP5)With a technology-driven process, all the data is consolidated in one place, and the digital performance management for product location, quaintly on hold, etc., is supported by IIoT.[65]
Reduce reverse logistics (WOP6)In warehouses, return and reverse logistics are very important. Smart systems enable identification, implementation, and tracking.[54]
Reduced downtime and GoLive (WOP7)Robotic process automation, ERP, Digital work instructions, augmented reality-based operator assistance, and basic retrofit automation for loading, conveyors etc., are accelerated adoption irrespective of existing technology infrastructure. [16,17]
Improved resource planning and utilization (WOP8) Utilizing technology 4.0 driven resource planning allows for demand-driven planning for human deployment, increasing productivity and efficiency on various work stations and maximizing space utilization.[2,10,63]
Decrease in non-value-added activities (WOP9) Machine monitoring system connected to the IIoT (Industrial Internet of Things) is the best way to capitalize on the value-added warehousing system. It helps to reduce the yield losses by collecting real-time operator feedback and connecting them from anywhere which improves administrative functions. [65]
Effective storage management (WOP10)Storing items in a class based on the fixed items are considered best. Optimum number of boundaries of storages and volume are considered for random and class-based storage. However, travel time is fairly insensitive to the number of storage classes. [65]
Reduced TAT for delivery performance (WOP11)Warehouses are having differential speed of adoption with advantage to those with existing technology infrastructure such as Operator training using virtual reality, advanced analytics (AI/ML) for operations, automation of plant/warehouse logistics (AGV etc.) [21]
Improved inventory management (WOP12)Control and safeguarding of the inventory is an essential task for a successful warehouse for better business in terms of cost, turnover and accuracy by using AI/ML, big data and cloud computing. [39]
Improved capacity/space utilization-availability/usage (WOP13)Technology-driven system specially simulation, ERP etc., help to direct put away to manage the space discriminately, allow the material handling. It relatively provides the most economical means of storage in terms of equipment cost, use of space, damage to material, handling labour and operational safety utilization. [10,33]
Table 9. Initial decision matrix.
Table 9. Initial decision matrix.
Table 10. Normalized decision matrix.
Table 10. Normalized decision matrix.
Table 11. Weighted comparability sequence matrix.
Table 11. Weighted comparability sequence matrix.
Table 12. Exponentially comparability sequence matrix.
Table 12. Exponentially comparability sequence matrix.
Table 13. Relative weights, final weigh and raking of warehousing performance measures.
Table 13. Relative weights, final weigh and raking of warehousing performance measures.
Performance MeasuresKaRankingKbRankingKcRankingKFinal Ranking
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ali, S.S.; Kaur, R. Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach. Mathematics 2022, 10, 1252.

AMA Style

Ali SS, Kaur R. Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach. Mathematics. 2022; 10(8):1252.

Chicago/Turabian Style

Ali, Sadia Samar, and Rajbir Kaur. 2022. "Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach" Mathematics 10, no. 8: 1252.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop