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The warehouse industry is one of the backbones in the logistics operation which involves several activities i.e. storage, receiving, picking and shipping of goods/cargoes. This study analyzes the most important warehouse productivity indicators for improving warehouse operation efficiency.

This study presents an empirical methodology of the fuzzy analytical hierarchy process (FAHP) method, an integration between the fuzzy logic method with an analytical hierarchy process (AHP) method incorporated with the adoption of quantitative and systems theories under the modern management theory approach.

The results indicate that the weight values of the main criteria which lead by the criterion “Space (0.4005)” at the top ranking, followed by Information System (0.2445), Labor (0.2065) and Equipment (0.1484). In addition, the weight values and ranking of the 16 sub-criteria are also highlighted which the sub-criterion “Warehouse Management System (0.2445)” scores the highest weight value and followed by Storage Space Utilization (0.1043) and Throughput (0.0722) accordingly.

Finally, this research contributed to enrich the literature, while highlighting a series of recommendations on the top three most significant productivity performance indicators that can be useful in further research.

A generic analysis model developed with the adoption of three study theories: quantitative, system and productivity theories.

The purpose of this study is to investigate the impact of logistics capabilities on the relationship between information sharing (INS) and logistics performance of Sudanese industrial companies.

This descriptive study investigates the relationship between INS and logistics performance in Sudanese industrial companies. A five-point scale questionnaire surveyed a non-probability sample of 262 logistics, supply chain and operations managers. Structural equation modeling has been used to test the relationship between the variables.

The results revealed that there is no direct positive relationship between logistics INS and logistics performance, while there is an indirect relationship through logistics capabilities. The findings confirmed that there is a positive relationship between logistics INS and logistics capabilities.

There are several limitations in this study. This study is limited to certain businesses and has a small sample size, which may impact the capacity to apply the findings to a broader context. The sample was based on the current manufacturing companies in Sudan, which are distinguished by certain characteristics in terms of number, business stability and other factors. Because of these factors, the findings may not accurately reflect the actual situation. Industrywise, this study focused on manufacturers, whereas logistics is based on a chain of partners and involved parties. Including them in any future investigation could lead to meaningful findings and discoveries. Furthermore, the data collection’s cross-sectional form may not comprehensively reflect the temporal dynamics of logistical activities. Future research may address these constraints by investigating the efficacy of diverse logistical capabilities in other businesses and circumstances. The construct of the variables is a single-dimension construct, which does not reflect all the practices associated with INS, logistics capabilities and logistics performance. Furthermore, there is an opportunity to explore further the impact of INS on important logistical performance metrics, such as order lead time, on-time delivery and inventory management. One potential area for future investigation is the study of logistics information-sharing technologies, specifically utilizing data analytics and machine learning. In such a context, deep insight and understanding of logistics capabilities and performance require more qualitative analysis; therefore, future research could fill this gap and provide deeper insight.

For practitioners in the small and medium-sized enterprise sector in Sudan, these findings suggest enhancing their operations, particularly by investing in information-sharing technologies that improve stakeholder coordination. Actively engage supply chain partners in the logistics information system to ensure timely and accurate information flows to all. However, it’s important to be aware of potential challenges, such as the high costs and complex design of these systems and potential resistance to change within businesses. Policymakers play a crucial role in this process, as they can use these findings to establish industry-wide standards or incentives that promote the use of advanced logistical capabilities; besides that, policymakers need to invest in building genuine information channels and systems that inform the industry and clear the ambiguity.

This study establishes a relationship between INS and logistics performance, which will be interpreted by logistics capabilities to enhance the logistics performance of Sudanese logistic businesses in an underdeveloped context characterized by a weak logistics industry and logistics capabilities. It suggests that companies should prioritize logistics INS and investment in INS technology to enhance their logistics capabilities.

The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance.

A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators.

The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities.

The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes.

Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.