In logistics, time, place, and space utility present the concepts that determine the choice of transportation modes. Time utility refers to the availability of the products when they are demanded by customers (Lecture 9, slide 8). Place utility is understood as moving products from the locations with lower value to those with higher value to ensure that customers have them where needed. As for space utility, it is associated with accessible storage of products that can be transported depending on place and time utility. Transportation is the core means of utilizing all three mentioned concepts in practice. For example, taking jewelleries as an example, it is possible to state that they need a high level of place and space utility security due to their expensiveness. Properly designed logistics allows for avoiding lateness and safety concerns, which is based on the maximization of available space and speed of transportation (Punakivi & Hinkka, 2006). In this connection, logistics operations are constantly developing to find better solutions, such as offering substitute products, focusing on warehouses, et cetera.
A process of mode selection involves five basic choices, including rail, road, air, sea / inland waterways, and pipelines. While globalization promoted greater cooperation between countries and distant locations, it also increased fuel costs and environmental concerns (Lecture 9, slide 10). The consideration of risks and rewards serves as the basis for choosing a transportation mode. Among the strategic factors that should be taken into account, there are inventory and facility costs. The possible quality factors include security, safety, and standards of transportation systems, which impact the products’ presentation and further customer satisfaction (Scott et al., 2011). Ultimately, the strategy selected by a company should provide a positive contribution in financial terms (Scott, Lundgren, & Thompson, 2011). Both advantages and disadvantages of modes should be investigated regarding a particular product and target destination. Nevertheless, it is possible to distinguish between some common factors. For roads, advantages are flexibility, variety of vehicles to choose from, and low costs, while its disadvantages include small load sizes, congestions, and negative environmental imprint. Consequently, this mode is the best decision for comparatively close distances to deliver products directly to required points.
A set of tactical factors that influence the selection of transportation modes can be composed of management policies, inventory levels, customer service, and so on. For instance, some pharmaceutical companies in Finland decided to extend their product lines by offering alternative medicines to customers, which allowed increasing logistics utilization (Punakivi & Hinkka, 2006). Customer service integrates such factors as reliability, product knowledge, and speed or lead time. The operational considerations also require paying attention to physical attributes: location points, size of products, delivery limitations, and delivery frequency / quantity. For example, if a company needs to move tons of construction materials from one continent to another, it is better to choose air or sea ways to minimize costs and time. However, if there is a need to send 20 packages of clothes between two nearby cities, road mode should be prioritized. Nevertheless, legal and infrastructure constraints can impact the final decision, which makes it especially important to anticipate potential barriers to efficient transportation.
In addition to the existing modes of transportation, the future of logistics is related to innovative fuels. Among the alternative sources of power generation, there are liquid petroleum gas (LPG), hydrogen cell, as well as biomass and natural energy as renewables (Lecture 1, slide 14). Myerson (2015) considers location decisions as a lean concept that determines the effectiveness of the entire supply chain. The author insists that a thorough analysis should be conducted to optimize a distribution and manufacturing network (Myerson, 2015). For example, the impact of time utility can be associated with the expenses on building and maintaining railroads, as well as following schedules, which restricts time availability. In contrast, space utility can be related to a limited flexibility of delivery points or overcoming relatively long distances. With the wider introduction of electric vehicles, it would be possible to reduce the environmental impact and make transportation even more flexible Lecture 1, slide 18). More to the point, the development of Nano Flow Cell Technology would promote using charged electrolyte as a fuel, which can be applied not only to road but also air and rail modes.
The primary goals of using warehouses are the maximization of resource utilization to meet customer needs or improve customer service. According to Punakivi and Hinkka (2006), since companies from various industries need warehouses to store their products in preferred locations, there is a need to maximize such service. Outsourcing warehousing operations seem to be a relevant approach to advancing logistics. 20% of warehouse operating expenses are allocated for storage of products, while 50% accounts for picking orders (Lecture 3, slide 19). In addition, warehouse decisions depend on the availability of various resources, organizational policies, and government incentives (Lecture 10, slide 17).
Order picking implies producing pick lists, scheduling operations, and providing the necessary documentation, which is often done in terms of a Warehouse Management System (WMS) (Richards, 2018). Accordingly, the process of warehouse automation and the introduction of warehouse technologies should be preceded by a preparatory process, including proper documentation and analysis of current processes in order to identify low-quality areas and further improve warehouse operations (Scott et al., 2011). The central task, in this case, is to organize the process in such a way as to shorten the route of a picker when bypassing the places of storage of products, excluding unnecessary movements (Bartholdi & Hackman, 2019). Hausman, Schwarz, and Graves (1976) rationally note that warehouse automation turnover-based rules are effective for maximizing picking efficiency. In particular, they increase throughput capacity and balance it with storage capacity.
Cost, flexibility, and service factors are critical for the automation process, while cross-docking and added value activities are considered promising operations. Baker and Halim (2007) argue that the benefits of the mentioned strategies are inventory centralization whilst maintaining acceptable costs. Moreover, demand chain management is reported to be another strategy that improves the pace of picking orders. One of the key speed increase efficient operations is the organization of different categories of products in the warehouse by storage and pick-up locations. They should be sorted according to the rate of expenditure: the most popular and fastest-consumed category products are to be located closest to the place of order picking (Baker & Halim, 2007). For less popular products, areas are to be allocated behind goods of the first category. The least consumable products are to be positioned in the farthest places. In many warehouses, products are mistakenly arranged by types of products, while the rate of their consumption is not taken into account. The organization by demand contributes to a faster picking process.
Using voice picking in the process of working in a warehouse increases picking efficiency. Being equipped with a headset with a microphone, an employee connects to the WMS system wirelessly. Performing the picking, an operator interacts with the warehouse management system using voice commands. In turn, the system transmits sound information about the place and the quantity required for the selection of goods. The key advantages are improving picking accuracy and productivity, minimizing data entry errors, and multilingualism. Capital costs and dehumanization of work are drawbacks of this picking method (Lecture 3, slide 38). In comparison, the Pick to Light system is a digital selection system that provides electronic control of all movements of goods. Information regarding all orders within the company is transmitted electronically to the WMS system, and an operator receives specific assignments (Lecture 3, slide 37). The light modules indicate the location and quantity of goods to be picked up. Simplicity, reliability, and powerlessness of operations are the main benefits, but expensiveness and manual backup problems present challenges.
Due to technology development, augmented reality glasses can be used as a working tool for warehouse employees, which make it possible to work using a Pick by Vision system. During the execution of operations for orders to ship products from a warehouse, all information is automatically transferred from the WMS management system to an employee’s visual interface of the glasses with a detailed description of the task, comprising the required range of goods for the set on request, exact location in the warehouse, the required quantity, and other details (Lecture 3, slide 39). Such advanced technologies as Pick by Light and Pick by Vision are simple to use and require now previous knowledge to use; nevertheless, the traditional approaches to picking are more affordable solutions that bring tangible results. Consequently, one may conclude that further research and new solutions are required to ensure that new picking methods would be widely implemented in practice since they contribute to optimization of warehouses and order picking.
References
Lecture 1. Future logistics fuels.
Lecture 9. Transportation logistics and modal choice.
Myerson, P. (2015). How location decisions impact a lean strategy. Web.
Punakivi, M., & Hinkka, V. (2006). Selection criteria of transportation mode: A case study in four Finnish industry sectors. Transport Reviews, 26(2), 207-219.
Scott, C., Lundgren, H., & Thompson, P. (2011). Guide to supply chain management. Berlin: Springer.
Baker, P., & Halim, Z. (2007). An exploration of warehouse automation implementations: Cost, service and flexibility issues. Supply Chain Management: An International Journal, 12(2), 129-138.
Bartholdi, J. J., & Hackman, S. T. (2019). Warehouse and distribution science. Atlanta, GA: Supply Chain and Logistics Institute, School of Industrial and Systems Engineering, and Georgia Institute of Technology.
Hausman, W. H., Schwarz, L. B., & Graves, S. C. (1976). Optimal storage assignment in automatic warehousing systems. Management Science, 22(6), 629-638.
Lecture 3. Put away, storage and picking processes.
Lecture 10. Distribution models and network design.
Richards, G. (2018). Warehouse management: A complete guide to improving efficiency and minimizing costs in the modern warehouse (3rd ed.). London: Kogan Page Publishers.
Scott, C., Lundgren, H., & Thompson, P. (2011). Guide to supply chain management. Berlin: Springer.