Order Processing Delay in Logistics: A Review of Quality and Maintenance Approach
DOI:
https://doi.org/10.48165/dbitdjr.2025.2.02.02Keywords:
Logistics, Order Processing, Quality Manage ment, Maintenance, DelaysAbstract
Delays in order processing are a serious concern in logistics and affect customer service, operational efficiency, and supply chain performance. This paper systematically investigates order processing delays and examines how quality management and maintenance approaches can be employed to reduce them. The paper is based on a Structured Literature Review (SLR) of peer-reviewed academic articles published between 2020 and 2025. We have organized the causes of delays into operational (e.g. equipment failures, manual inefficiencies, data errors) and regulatory aspects (e.g. transport documentation, and legal requirements) and explored quality management approaches to reduce errors, wastage and ensure consistent quality during order processing (Total Quality Management {TQM}, Six Sigma, Kaizen, ISO 9001, Lean Six Sigma). We also reviewed maintenance approaches, starting from a traditional preventive maintenance approach, to a diagnostic maintenance approach that integrates predictive and prescriptive methods using artificial intelligence (AI) to reduce the chance of unplanned downtimes and improve system reliability. This proposal has developed an integrated conceptual model that uses quality and maintenance interventions to target root causes for delays in the logistics value chain. The dual-intervention model presents a whole-scale solution to improve order-filling performance. The framework is essential to provide original value to logistics research and literature and connecting process and equipment perspectives while also providing practical implications for actionable steps for freight forwarders, third-party logistics (3PL) operations, and logistics managers that intend to build their operations to be a resilient and delay-resistance organization. Future research could include empirically testing it and looking at actual data and a systems thinking perspective.
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