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Barasin, A.M.; Alqahtani, A.Y.; Makki, A.A. Performance Evaluation of Retail Warehouses. Encyclopedia. Available online: https://encyclopedia.pub/entry/55204 (accessed on 19 April 2024).
Barasin AM, Alqahtani AY, Makki AA. Performance Evaluation of Retail Warehouses. Encyclopedia. Available at: https://encyclopedia.pub/entry/55204. Accessed April 19, 2024.
Barasin, Abdullah M., Ammar Y. Alqahtani, Anas A. Makki. "Performance Evaluation of Retail Warehouses" Encyclopedia, https://encyclopedia.pub/entry/55204 (accessed April 19, 2024).
Barasin, A.M., Alqahtani, A.Y., & Makki, A.A. (2024, February 20). Performance Evaluation of Retail Warehouses. In Encyclopedia. https://encyclopedia.pub/entry/55204
Barasin, Abdullah M., et al. "Performance Evaluation of Retail Warehouses." Encyclopedia. Web. 20 February, 2024.
Performance Evaluation of Retail Warehouses
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A warehouse is one of the most critical parts of many companies, essential to facilitating trade. With a robust warehouse management system, a business can satisfy customer demand. Additionally, it helps guarantee that the products are affordable, easily accessible, and delivered quickly to a network of customers. However, if not properly structured and managed, it may prevent a business from competing effectively, locally and worldwide. One of the most crucial ways to enhance warehouses and assist managers in continuously monitoring their operations is warehouse performance measurement. Management must establish a variety of criteria to gauge warehouse performance. Based on these criteria, they can determine whether the warehouse is performing well.

performance evaluation retail warehouses performance indicators MCDM tools

1. Introduction

Each retail warehouse in Saudi Arabia’s western region has a set of criteria for measuring its performance. Warehouses occupy a significant position in the economy of Saudi Arabia. They are among the most significant national focuses of development and improvement to increase the efficiency of the country’s economy in logistics [1]. With Vision 2030, the government began to improve Saudi Arabia’s logistics infrastructure, and as it grows, so does warehousing. The warehouse market expanded by 2.8% between 2015 and 2019, and it is expected to grow more in the future.
According to Statista, the operating revenue of warehousing was USD 8.84 billion between 2010 and 2017. The value of e-commerce warehousing in the Middle East could reach USD 500 million by 2024.
Because of the COVID-19 pandemic, the world has encountered numerous warehousing issues, as has Saudi Arabia. The pandemic has significantly impacted logistics, and Saudi Arabia must overcome some challenges to continue warehouse expansion.
According to Colliers International, the average price of warehouse space in Jeddah, Saudi Arabia, was SAR 131 (USD 35) per square meter in the first quarter of 2020. This high price means that retail warehouses must reduce operational expenses, optimize their management systems, and solve problems with cutting-edge scientific techniques.
Most real-world decision-making challenges require the simultaneous consideration of several competing criteria and objectives. Similar challenges occur in various professions, including engineering, medicine, and business. Multi-criteria decision-making (MCDM) is concerned with structuring and resolving problems involving multiple criteria and conflicting goals. With the increase in the number of warehouses in Saudi Arabia, the competition between them has increased, and every warehouse has evaluation criteria to prove its superiority over others.

2. Warehouse Key Performance Indicators

Rouwenhorst et al. [2] classified warehouses from the perspectives of processes, resources, and organizational structures. They conducted a thorough literature review, concluding that most studies focused primarily on analysis and not warehouse design. Gu et al. [3] extensively reviewed warehouse operation planning problems. The problems were classified according to basic warehouse functions: receiving, storage, order picking, and shipping. The literature in each category was summarized, emphasizing the characteristics of various decision-support models and solution algorithms. Storage and order-picking functions impacted the warehouse operational performance the most. Staudt et al. [4] emphasized measuring operational warehouse performance. Performance indicators were extracted from relevant papers using the content analysis method and were categorized based on time, cost, quality, and productivity dimensions. Vatumalae et al. [5] discussed the Malaysian hypermarket retail sector in detail, followed by a thorough literature review on the warehouse management system. The literature review focused on the hypermarket retailers’ warehouses, which are essential in the supply chain, facilitating the movement of materials between the supplier and the customer. A model presented by Ramirez-Malule et al. [6] identified variables that significantly impact warehouse performance with picker-to-parts storage systems, considering the dynamic nature of the model’s processes and the possibility of non-linear relationships among its variables, as well as the simultaneous occurrence of seasonal demand and long and short product life-cycles. Phyllis [7] applied warehouse performance measurement in the case of a medium-sized warehouse in Nakuru Town. The warehouse performance indicators were classified into five categories: productivity, utilization, quality, time, and financial. The findings indicated that the most critical performance indicators were productivity for receiving, space utilization for storage, cycle time for order picking, and productivity for shipping. A study on measuring warehouse performance in third-party logistics (3PL) service providers was conducted by Ghaouta and Okar [8]. Their research had three main objectives: to review warehouse key performance indicators (KPIs) and categories using the systematic literature review method, to investigate categories and determine their relative importance using the Q-sort method, and to validate the order of performance measure categories using a single case study. The research grouped 30 KPIs into five categories and four subcategories using an integrated research methodology.

3. Warehouse Performance Evaluation Using MCDM Tools

Few studies have offered information about the MCDM tools employed in the warehouse performance evaluation context. A study by Kusrini et al. [9] assessed the performance of retail warehouses in supermarkets in Central Java and Yogyakarta, Indonesia. The criteria were weighted using the AHP approach. After assessing the warehouse’s performance, the final score was calculated using the SNORM method. MCDM tools have a wide range of applications in warehouse location problems as well as issues with warehouse architecture and design. Al Amin et al. [10] employed AHP and the technique for order of preference by similarity to ideal solution (TOPSIS) to select the best warehouse among five based on five specified criteria (unit price, stock holding capacity, average distance to factory, flexibility, and layout). Demircioğlu and Ozceylan [11] conducted a thorough literature review using pertinent keywords in several worldwide databases to explore MCDM applications in warehouse layout and design. AHP, ELECTRE, and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) were the most frequently used techniques, which are carried out by applying MCDM methods. To manage the benefits of employing many MCDM methods in a given field, MCDM approaches are employed in an integrated manner. Ulutaş et al. [12] suggested an integrated grey MCDM model including the grey preference selection index (GPSI) and grey proximity indexed value (GPIV) to determine the most appropriate warehouse location for a supermarket. Twelve criteria were employed to compare the effectiveness of five potential warehouse locations. The optimum warehouse location was chosen using a combination of GPSI and GPIV algorithms [12]. Fuzzy extensions of MCDM techniques have also been used in other contexts [13][14][15]. In the context of warehouses, Bairagi [16] employed fuzzy MCDM to assess the location. The warehouse selection indicator, known as the benefit–cost ratio (BCR) [17], is assessed using the aggregate modified weighted value of the warehouse locations’ normalized scores.

4. MCDM Tools

MCDM tools are used to deal with challenging real-world problems since they can assess several options and choose the optimal one [18]. The literature includes many different MCDM approaches. The ELECTRE tool allows for identifying and eliminating options that are outranked by others, leaving a set of appropriate actions [19]. ELECTRE is a method that experts use to assess the effects of criteria and contrast them with one another based on the anticipated performance [20]. Another MCDM technique, SAW, aims to assess the effectiveness of various solutions [21][22]. The basic concept of SAW is to determine the weighted total of performance ratings for each alternative across all criteria. MCDM also includes stepwise weight assessment ratio analysis (SWARA). When using the SWARA technique, the criteria needs are ranked according to their importance by a group of experts [23]. Saaty [24] proposed the AHP method in the 1980s. AHP provides a logical framework for a decision that must be made by weighing the requirements and available alternatives and connecting those components to the primary objective. The BWM is an MCDM technique that Rezaei introduced in 2015 [25]. It can be used to evaluate alternative solutions in consideration of the criteria and assess the applicability of the criteria to discover a solution to accomplish the issue’s main goal(s). Based on the BWM, a novel approach to group decision-making problems called the G-BWM was developed by Haseli et al. [26]. This approach assists in the analysis of decision-makers’ preferences for employing the BWM structure for democratic decision-making. Another MCDM technique is VIKOR. This technique involves weighing and choosing options based on competing criteria by outlining in detail how close each alternative is to the best hypothetical answer [27][28][29][30]. TOPSIS is another MCDM tool that has been used successfully in many applications [31][32][33][34][35]. Its fundamental objective is to find an optimal solution with the largest and lowest distances to the positive and negative ideal solutions, respectively [36]. The Saudi National Commission for Academic Accreditation and Evaluation (NCAAA) created the Self-Evaluation Scale to evaluate higher education programs. The present research used the TOPSIS technique to compare NCAAA’s original performance criteria and the proposed evaluation sub-criteria [37]. Abdulaal and Bafail [38] developed two new approaches: ranking alternatives based on median similarity (RAMS) and RATMI. RAMS is a developed method that utilizes the ranking alternatives perimeter similarity (RAPS) [39]. The RATMI technique combines the RAMS method with the multiple criteria ranking by alternative trace methodology, using a majority index and the concept of the VIKOR method [40]. RATMI is a new technique that has been applied in recent studies [38][39][41][42].
The rationale for using the G-BWM for criteria weighting and the RATMI for warehouse ranking in evaluating warehouse performance was based on their respective advantages and suitability for addressing the research objectives. The G-BWM technique was chosen for criteria weighting due to its ability to capture the collective preferences of a group of decision-makers. In evaluating warehouse performance, multiple criteria contributing to operational effectiveness and customer satisfaction must be considered. However, assigning appropriate weights to these criteria can be challenging since decision-makers may have different perspectives and priorities. The G-BWM addresses this challenge by involving experts or decision-makers who participate in pairwise criteria comparisons. By identifying the best and worst criteria in each pairwise comparison, the G-BWM aggregates the individual preferences to derive consensus-based weights for the criteria. This group-based approach ensures that the evaluation framework considers diverse perspectives and avoids undue influence from a single decision-maker. The RATMI technique is employed for warehouse ranking to evaluate warehouse performance systematically and comprehensively. Traditional ranking methods often suffer from limitations such as subjectivity, inconsistency, and a lack of consideration for the interrelationships between criteria. The RATMI overcomes these limitations by using the concept of the median performance profile. It traces the performance of individual warehouses relative to the median performance profile, capturing each warehouse’s relative strengths and weaknesses across multiple criteria. This approach offers a more objective and comparative assessment, enabling decision-makers to identify the top-performing and underperforming warehouses in the context of the entire dataset.
The evaluation framework ensures a comprehensive and robust assessment of warehouse performance by combining the G-BWM for criteria weighting and the RATMI for warehouse ranking. The G-BWM incorporates group preferences and diverse perspectives, and the RATMI provides an objective and comparative ranking mechanism. Together, these techniques enhance the evaluation framework’s accuracy, reliability, and applicability, enabling decision-makers to make informed decisions and drive operational improvements in retail warehouses.
In this research, two combined MCDM techniques, comprising the G-BWM technique, were used to group the criteria and give weights to each one. The RATMI technique ranks the alternatives based on the weight given by the G-BWM. The G-BWM allows for group decision-making in determining the criteria weights. Since the evaluations involve subjectivity from multiple warehouse experts, using their collective judgments through G-BWM provides a more democratic weighting process. The RATMI enables a comprehensive ranking of the warehouse alternatives based on the weighted performance scores across all criteria. This helps to systematically produce an overall performance evaluation and ranking. Combining both techniques allows them to complement each other. The G-BWM provides the weights as input for the RATMI, which then uses those weights to generate the final ranking. This makes the evaluation more robust by leveraging the strengths of both methods. Both the G-BWM and the RATMI have been validated in previous studies for effectiveness in group MCDM problems. Applying them together extends their combined application to the warehouse performance evaluation context. The combination approach addresses the limitations of the individual techniques and provides the cross-validation of the results through the convergence of the two methods on a common solution or ranking. This enhances the reliability of findings. In summary, the researchers chose a combined G-BWM and RATMI approach to leverage their synergies, produce a rigorous yet democratic weighting process, and generate a validated overall performance ranking for strategic decision-making.

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