Logistics networks encompass various functions, facilities, and products – raw materials and finished goods – flowing within them. Examples of logistics facilities include vendor ports, production plants, warehousing areas, and distribution centers. Thus, designing an effective logistics network involves developing a system for connecting the various logistics components and functions to ensure an easy flow of raw materials and finished goods through them (Andiyappillai, 2020). The costs involved in a typical logistics network include production and purchase costs and inventory and warehousing charges. For a network consisting of only one warehouse, the most important considerations are picking the optimal location and size for the warehouse, developing an optimal sourcing strategy, and finding the best distribution channel. Here, the goal is to create a logistics network that decreases the yearly network maintenance costs, including production, purchasing, inventory, and transportation costs (Yener & Yazgan, 2019). It is a minimal-annual-cost configuration.
Designing a Logistics Network Consisting of Only One Warehouse
Designing a logistics network consisting of only one warehouse is not always easy, especially for a large organization. However, it is desirable for various reasons, including simplified management, service level improvement, average travel time reduction, inventory cost reduction, reduced safety stocks, low overhead and setup costs, and reduced inbound transportation costs (Lee et al., 2018). The starting point of this design endeavor is either an existing network typology or a potential framework for a new network structure. While the first instance requires the redesign of the physical structure, the second requires building the network system from the ground up (Burganova et al., 2012). Creating a logistics system with one warehouse makes it possible to model any number of facility layers and system transportation channels. The design process may also require classifying the network entities in selectable and non-selectable facilities. Selectable facilities exist and could be closed, while non-selectable ones consist of facilities that are not subject to location decisions.
Regardless of the approach taken in the design process (redesigning the existing system or building a new one), the first step in designing a one-warehouse logistics network is generally the same. It involves the collection and analysis of relevant data (Andiyappillai, 2020). This initial step is important for determining and understanding customer location, product demand, transportation rates by transportation mode, mileage estimation, warehousing costs, shipment sizes, and service level requirements, among other things. The transportation cost is determined by multiplying the transportation rate (cost per mile per stock-keeping unit) by distance, while warehousing costs include fixed and handling costs (Stopka & Ľupták, 2018). The bigger the warehouse, the higher the fixed and the handling costs. Handling costs are proportional to the material flowing through the warehouse, while the fixed costs are not.
After collecting the data, the next step in developing the single warehouse logistics network is aggregating the data. It refers to cleaning and harmonizing the collected information to facilitate effective decision-making. Aggregating and cleaning the data is important because the data collected is usually overwhelming, and the cost of processing and the real data is huge (Chen & Zhao, 2019). Data cleaning and aggregation also facilitate data streamlining, location model sorting, forecast demand improvement, and determination of model effectiveness. Heuristics to aggregate data include customer and product type clustering. The next step is validating and modeling the data to ensure it reflects the network design problem. The easiest way to achieve this objective is reconstructing the existing network configuration using the available data and comparing the model’s output to the available information. This step ensures that the network design model makes sense and the data is consistent. This step allows for corrections and the consideration of alternatives.
The last step in designing the logistics network with one warehouse is optimization. It involves looking at the logistics network and making changes to reduce operational costs, increase efficiency, and reduce delays and logistics problems (Andiyappillai, 2019). Common techniques utilizable in this stage include exact algorithms and heuristics. Both methodologies are mathematical but differ in that the former finds optimal solutions while the latter recommends good solutions that may not always be optimal (Andiyappillai, 2019). At this stage, simulation models would facilitate the evaluation of various design alternatives. The design optimization will also consider various locations alternatives and select the most optimal one based on the owner’s needs, location of the customer, transportation cost, and the cost of running the business.
Steps Needed for an Optimal Network and the Information and Data Needed for This Determination
After developing an effective network, there are various ways of optimizing the system. The first step is continuous improvement. This approach involves considering various elements of the network and changing them over time as new information becomes available (Chen & Zhao, 2019). The next step is developing new algorithms for network optimization, followed by integrating several dynamic real-world factors. For example, the network designer will need to consider network resilience, ocean-lane commitment models, and energy volatility models, among other things. Visualization tools will improve the efficiency of findings presentation and computed solutions ‘frequent outperformance over the best transportation network. The information and data needed to determine this include customer demand, mileage concerns, transportation requirements, and warehouse maintenance costs. It is important to optimize the network, resulting in cost reduction and capacity improvement.
References
Andiyappillai, N. (2019). Data analytics in warehouse management systems (WMS) implementations–a case study.International Journal of Computer Applications, 181(47), 14-17. Web.
Andiyappillai, N. (2020). Factors Influencing the Successful Implementation of the Warehouse Management System (WMS).International Journal of Computer Applications, 177(32), 21-25. Web.
Burganova, N., Grznar, P., Gregor, M., & Mozol, Š. (2021). Optimalisation of Internal Logistics Transport Time Through Warehouse Management: Case Study. Transportation Research Procedia, 55, 553-560. Web.
Chen, J., & Zhao, W. (2019). Logistics automation management based on the Internet of things.Cluster Computing, 22(6), 13627-13634. Web.
Lee, C. K., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L. (2018). Design and application of Internet of things-based warehouse management system for smart logistics.International Journal of Production Research, 56(8), 2753-2768. Web.
Stopka, O., & Ľupták, V. (2018). Optimization of warehouse management in the specific assembly and distribution company: A case study. NAŠE MORE: znanstveni časopis za more i pomorstvo, 65(4 Special issue), 266-269. Web.
Yener, F., & Yazgan, H. R. (2019). Optimal warehouse design: Literature review and case study application. Computers & Industrial Engineering, 129, 1-13. Web.