However, there is still uncertainty about what constitutes LSQ and the best ways to analyze, measure, and improve it. Despite the growing number of papers in this field, there is a lack of comprehensive approaches and models. Through a systematic literature review approach, [PERSON] aimed to provide concrete scientific and practical contributions [13]. Also, this research created new scientific approaches and models for measuring and improving LSQ in different transport and logistics systems. This research literature employed a range of methodologies to assess logistics service quality, which encompassed established frameworks. These methods include SERVQUAL, SERVPER, LSQ, and the Kano model, as well as statistical approaches and adhering to industry standards. Nevertheless, it is essential to acknowledge that the SERVQUAL model does possess certain limitations, including its lack of universal applicability and its emphasis on reliability as a primary dimension. The review of logistics systems and relevant literature revealed a diminished level of quality in logistics service provision. The investigation of factors contributing to poor quality, including technology, information systems, organizational structure, geographical location, customer relationship management, and human factors, is imperative for the purpose of improvement. However, choosing remains a difficulty, particularly for huge assortments that include irregular or broken items. Modern fulfillment systems use manned picking workstations to combine SKU bins and client bins in a small space. A human order picker will be able to assemble orders without much travel with this. The picker is able to reach all bins with their arms and only takes a few steps to the left and right. [PERSON], [PERSON], [PERSON], and [PERSON] presented an example which optimizes the problem for synchronization in an ergonomic picking workstation with a capacity for a single SKU bin at a time and two parallel customer bins [14]. The problem consists of four orders with demands for SKUs A, B, and C. The solution is to consider whether an order demands the SKU but not the requested number of pieces. The picking workstation is equipped with a fully automated injection mechanism for finished customer bins, which can still receive the current SKU. There are two alternative solutions presented: solution (a) requires three SKU bin visits to finish all four orders, while solution (b) demands the delivery of four SKU bins. By reducing the number of SKU bins needed to fulfill a given set of customer orders, there are two advantages: a reduction in preparation time and alleviation of bin supply. Once pick lists are given, the time it takes for a picker to extract the required items from an SKU receptacle and place them in the appropriate customer bins is set. Each SKU bin exchange necessitates a setup time comprised of a toggling delay for the bin exchange, a picker orientation delay, and a potential source of delayed bin delivery. This is called a transportation delay. Average bin exchanges per order is a common metric in many real-world parts-to-picker warehouses, and this KPI has a strong correlation with maximizing throughput performance. The bin supply system is less burdened by reducing the number of SKU receptacles that need to be delivered to a stationary picker. By doing this, the fleet of mobile robots can be reduced and unanticipated delays can be avoided. The purpose of addressing synchronization issues in parts-to-picker systems is to enhance performance by achieving synchronization between the SKUs including the SKU bins and the picking orders which accumulated in customer bins. This research aims to categorize the various synchronization challenges encountered in parts-to-picker systems and highlight their significance. The provided framework offers a systematic categorization system and a quantification of the operational advantages associated with various configurations of workstations.