## 4 Conclusions and Future Work  
This article presented the extended heuristics and performance-based validation of AOSF's associated AOSR strategy in an SME-oriented warehouse environment. The applied test scenarios were discussed in comparison with standard warehousing strategies (Zoning with FIFO logic and zoning with fewest logic), particularly for managing the number of products in key warehouse areas, such as racks, RA, and EA, with different corresponding system states (with or without possible conflicts). The results from different scenarios demonstrate that the AOSR algorithm performs better in comparison with standard WMS strategies, especially in System State (1), which is a normal running state of a warehouse. However, it is impossible for a single solution to be universally applicable, so the presented system also has some limitations. For example, performance has only been tested in a prototype, not in a real-time distributed cloud architecture, where results may vary slightly. Furthermore, this system does not include in-built cloud server security, which is another rich area of research. Although the AOSR strategy can cater to requests coming from smart-devices, connecting manual industrial hardware components to this system may raise some more areas of optimisation.  
For large-scale distributed enterprise setups, the AOSR's parent architecture of the AOSF framework can be utilised for inter-enterprise integration as it includes OLAP based systems and server architectures on the cloud layer. The AOSF framework provides a high-level guide for manufacturing industrial management for SMEs, but could be developed further to cater for concerns related to privacy and security. For AOSR, there could be some more dimensions to work upon in the future, such as movement of products within the warehouse shop floor using forklift trucks, utilising collapsible racks or small-scale drones (as some industries offer a high-tech robo-oriented solution such as GrayOrange [45] and Unleashed [46]). These solutions provide nice cutting-edge features but come with an additional infrastructure cost. Similarly, information systems based on cloud-based cognition, Big Data-driven and Deep Learning-assisted process planning and decision-making [47; 48] could be implemented for large industrial environments. The moderate level, semi-autonomous, low-cost solution provided by AOSR can also be used for incremental improvements in the future. For example, the system is flexible enough that conveyor belts and picking machines or Big Data support could be added to the system if needed. Additionally, as we have implemented the AOSR strategy in Demo3D [43] in liaison with a local industry which offers consulting services to diverse industrial clients from Australia, South East Asia, and North America, we are in the process of getting more involved in offering this solution to a range of their clients.  
Conceptualization, F.U.D., D.P., F.H., M.W.; Methodology, F.U.D., D.P., F.H., M.W.; Software, F.U.D.; Validation, F.U.D., D.P. and J.R.; Formal analysis, D.P., J.R.; Investigation, F.U.D., D.P., J.R., F.H., M.W.; Resources, F.U.D., D.P.; Data curation, F.U.D.; Writing--original draft preparation, F.U.D.; Writing--review and editing, D.P.; Visualization, F.U.D.; Supervision, D.P., J.R., F.H., M.W.; Project administration, F.U.D., D.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by an Australian Government Research Training Program (RTP) Scholarship under the ID: C3263747 at The University of Newcastle, NSW, Australia.  
Data Availability Statement: The details of different classes and categorisation of products in the test cases used in this article are extracted from the online source provided by DGI Global and Eurosped warehousing and logistics companies. Conflicts of Interest: The authors declare no conflict of interest.