Carrier and Freight Forwarders Strategies to Utilize the Immobile Shipping Capacity of Freight Forwarders and Maximize Profits

Carriers and freight forwarders (FFs) play several roles in ensuring the effective flow of goods delivery. They are tasked with accommodating the shippers’ needs in transporting goods via containers, following the carrier’s ship destination plan. In practice, FFs often experience overbook and underbook capacity toward the capacity limit for shipping goods. This has consequently increased FF costs. However, for the carrier, this will increase profits. The aim of this study is to develop strategies for carriers and FFs using a mathematical model approach to obtain the optimal quantity of booking shipping capacity; thus, overbooks or underbooks can be minimized. More broadly, this study also proposes several strategies to increase the profits of all parties, both for FFs through collaboration and for carriers by directly selling marketing shipping capacity to shippers. Optimum booking quantity for goods delivery from each FF is performed through the particle swarm optimization (PSO) approach. Using four FF collaboration scenarios, the model test results yield a profit of $121,270, 2.14% more than the non-collaboration scenarios with a profit of $119,169. The carrier generated an average profit of $39,926 when the FF did not collaborate. Conversely, when the FFs collaborated, the carrier experienced a decline of –1.88% on average profit, which was $39,175. However, if the carrier responds with direct selling, the profit will increase by 9.36%, which is $42,840. It is concluded that collaboration can increase the profits of FFs but reduce the profits of carriers, while direct selling can increase the carrier’s profits.

Average profit; Carrier; Collaboration; Direct sell; Freight forwarder

        Carriers and freight forwarders (FFs) are part of third-party logistics (3PL) in the freight forwarding business. Their role is to accommodate the shipper’s needs in carrying out shipments of goods using containers and following planned demand necessities. As the owner of the shipping capacity of the container ship, the carrier is obligated to deliver the container goods following the shipper’s request. An FF acts as the party that ensures the availability of this capacity in container ships and sells it to the shipper (Gupta, 2008). As a 3PL, the advantages of the carrier and FF depend on how much they can fulfill the shippers’ demands in one delivery period. FFs obtain profit from the difference in the value of the shipping capacity managed to be sold to the shipper against the capacity’s purchased price by the FF from the carrier, following the booking contract of the capacity utilization. The FF will benefit if the booking contract capacities are entirely sold to the shipper. Conversely, the FF will experience losses if the booking contract capacities are not all sold to the shipper. The contract capacity held by the FF, which was not sold to the shipper, and the capacity owned by the carrier, which was not sold to the FF, will be forfeited after the ship is dispatched as a newsvendor’s inventory (Widjanarka et al., 2018).

The FF makes various attempts to minimize losses caused by mismatching the number of goods delivery demands by the shipper against the total booking contract capacity reserved by the FF. Likewise, a carrier strives to fully utilize all the available shipping capacity on its ship. However, the demands for shipping capacity orders by shippers for FFs and the booking contract capacity demands from FFs to carriers are strongly influenced by the uncertainty of the business environment (Hellermann, 2006). In addition, operational problems often occur in the field related to the cancelation of shipping requests, late arrival of goods, or sudden shipping requests (Styhre, 2010).

So far, there have been many studies related to optimizing shipping capacity contracts by FFs to minimize the risk of over-or under-booking capacity for goods delivery. Gupta, in his research, proposed a flexible quantity of capacity booking contracts (Gupta, 2008). Thus, the amount of booking capacity in the next period can be adjusted by referring to the realization of capacity fulfillment that was reserved in the previous period. Meanwhile, Wang and Kao (2008) recommended that FFs need to minimize the risk of goods not being transported due to a lack of booked capacity. However, this does not rule out the possibility of excess booking capacity due to shippers’ low demand for goods deliveries. In contrast, Bing and Bhatnagar (2013) proposed a model of capacity booking to be divided into several periods of the goods delivery. Likewise, another study introduced two capacity procurement patterns based on booking and non-booking capacity (Moussawi-Haidar, 2014). Approaching departure time, the carrier applies direct capacity sales with a first-in-first serve system. This pattern is intended to anticipate an increase in the demand for goods delivery. Research related to air freight proposed a package route for FFs shipping goods by air. The package is intended to utilize the shipping capacity owned by the carrier when there is a low demand for goods delivery (Feng et al., 2015).

The above stated research is from the perspective of the FF as an independent entity toward its relationship with the carrier and shipper. Another study followed another perspective and examined the cooperation among FFs and the relationships between carriers and shippers to balance the shipping demand and the availability of the shipping capacity for the FF’s side (Kopfer and Pankratz, 1999). Krajewska and Kopfer (2006) reinforced this by developing a collaboration model among FFs that is expected to benefit both parties. Furthermore, a demand collaboration model related to planning more efficient routes of goods delivery was formulated (Krajewska et al., 2008). In contrast, Bookbinder et al. (2015) proposed a freight consolidation model to minimize the effective load for transportation. In another study, forwarders competed in ordering container capacity on a carrier’s vessel. It was found that competition in these activities could benefit all parties, but carriers have the advantage of determining reservation rates where this will be the forwarder’s excess burden cost (Li and Zhang, 2015). Finally, another study proposed a horizontal coalition model between FFs to reduce the cost of booking capacity for idle goods (Widjanarka et al., 2018).

From the ongoing discussions, there is a limitation to booking capacity models, capacity utilization, and the possibility of developing booking capacity models through horizontal coalitions among FFs. In addition, these studies have limitations regarding the available amount of capacity booked by FFs for carriers and the assumptions of the total number of booking capacity contracts that have a fixed economic value. Also, studies considering the perspectives of FFs and carriers remain scarce. Meanwhile, only Lin et al. (2017) proposed a booking capacity model for air cargo by adding negotiations to return excess capacity to the carrier (Lin et al., 2017). The latest research about FFs is related to omnichannel. It is a term used in Lafkihi et al. (2019) study to be broader than a multichannel (online and offline). They defined the omnichannel as a multichannel delivery with a high fluctuation and demand speed; thus, it is complex to consolidate traditional freight organizations. Therefore, coordinating shipping companies, FFs and carriers to maintain a high-demand speed is imperative.

This study has two main objectives: first, to develop a model that allows mitigation of overbook and underbook capacity problems by forming a horizontal collaboration among FFs. Second, the development of an alternative direct-selling business model made possible by carriers to shippers on the unsold shipping capacity to FFs. The achievement of these two goals is expected to provide an overview of the advantages and perspectives from both sides of the 3PL business actors in shipping containers through maritime transportation modes.

    Based on the numerical experiment results, the increase in profit of the collaboration scenario occurs due to the optimization of capacity sharing performed by each collaborating FF to reduce overbook and underbook capacity. However, from the carrier’s viewpoint, the collaboration strategy among FFs decreases the average profit. This is because a new sales channel has emerged as a direct sales channel to reduce carrier losses with the growth and development of information technology. By implementing a direct sales channel strategy, the carrier can increase profits according to the amount of direct sales demand that can be fulfilled in a given period. This can reduce the capital burden on the carrier when the number of demands is less than half of the carrier vessel’s total capacity.

        The authors wish to extend gratitude to: first, PT. Pelindo III, as a corporate state, for permitting its corporation to be chosen as an object in this research. Second, the Ministry of Education and Culture Republic of Indonesia (Kemendikbud) granted research funding, the contract number : 1262/PKS/ITS/2020. Finally, Institut Teknologi Sepuluh Nopember, Surabaya, permitted its resources and facilities to be used to conduct the research.

Baskoro, A.S., Masuda, R., Suga, Y., 2011. Comparison of Particle Swarm Optimization and Genetic Algorithm for Molten Pool Detection in Fixed Aluminum Pipe Welding. International Journal of Technology, Volume 2(1), pp. 74–83

Bing, L., Bhatnagar, R., 2013. Optimal Capacity Booking in Air Cargo Transportation. Journal of Revenue and Pricing Management, Volume 12(3), pp. 235–253

Bookbinder, J.H., Elhedhli, S., Li, Z., 2015. The Air-Cargo Consolidation Problem with Pivot Weight: Models and Solution Methods. Computers & Operations Research, Volume 59, pp. 22–32

Budiyanto, M.A., Shinoda, T., 2017. Stack Effect on Power Consumption of Refrigerated Containers in Storage Yards. International Journal of Technology, Volume 8(7), pp. 1182–1190

Eyres, D.J., Bruce, G.J., 2012. Ship Dimensions, Form, Size, or Category. Ship Construction, 7^th Edition, Elsevier, pp. 11–16

Feng, B., Li, Y., Shen, H., 2015. Tying Mechanism for Airlines’ Air Cargo Capacity Allocation. European Journal of Operational Research, Volume 244(1), pp. 322–330

Gupta, D., 2008. Flexible Carrier–Forwarder Contracts for Air Cargo Business. Journal of Revenue and Pricing Management, Volume 7(4), pp. 341–356

Hannan, M.A., Akhtar, M., Begum, R.A., Basri, H., Hussain, A., Scavino, E., 2018. Capacitated Vehicle-Routing Problem Model for Scheduled Solid Waste Collection and Route Optimization using PSO Algorithm. Waste Management, Volume 71, pp. 31–41

Hellermann, R., 2006. Capacity Options for Revenue Management: Theory and Applications in The Air Cargo Industry, Volume 575. Springer Science & Business Media

Kopfer, H., Pankratz, G., 1999. Das Groupage-Problem Kooperierender Verkehrsträger. In:  Operations Research Proceedings 1998, pp. 453–462

Krajewska, M.A., Kopfer, H., 2006. Collaborating Freight Forwarding Enterprises. OR Spectrum, Volume 28(3), pp. 301–317

Krajewska, M.A., Kopfer, H., Laporte, G., Ropke, S., Zaccour, G., 2008. Horizontal Cooperation Among Freight Carriers: Request Allocation and Profit Sharing. Journal of the Operational Research Society, Volume 59(11), pp. 1483–1491

Lafkihi, M., Pan, S., Ballot, E., 2019. Freight Transportation Service Procurement: A Literature Review and Future Research Opportunities in Omnichannel E-Commerce. Transportation Research Part E: Logistics and Transportation Review, Volume 125, pp. 348–365

Li, L., Zhang, R.Q., 2015. Cooperation Through Capacity Sharing Between Competing Forwarders. Transportation Research Part E: Logistics and Transportation Review, Volume 75, pp. 115–131

Lin, D., Lee, C.K.M., Yang, J., 2017. Air Cargo Revenue Management Under Buy-Back Policy. Journal of Air Transport Management, Volume 61, pp. 53–63

Moussawi-Haidar, L., 2014. Optimal Solution for a Cargo Revenue Management Problem with Allotment and Spot Arrivals. Transportation Research Part E: Logistics and Transportation Review, Volume 72, pp. 173–191

Sadjiono, I., Istijab, B., Malisan, J., 2018. A Potential Freight Consolidation Center for Asia Pacific Regional Hub?: An Importance Performance Analysis. Jurnal Penelitian Transportasi Laut, Volume 20, pp. 98–108

Styhre, L., 2010. Capacity Utilisation in Short Sea Shipping. Chalmers University of Technology. Ph.D. Thesis. Göteborg, Sweden

Summakieh, M.A., Tan, C.K., El-Saleh, A.A., Chuah, T.C., 2019. Improved Load Balancing for Lte-A Heterogeneous Networks using Particle Swarm Optimization. International Journal of Technology, Volume 10(7), pp. 1407–1415

Wang, Y.-J., Kao, C.-S., 2008. An Application of A Fuzzy Knowledge System for Air Cargo Overbooking Under Uncertain Capacity. Computers & Mathematics with Applications, Volume 56(10), pp. 2666–2675

Widjanarka, A., Wirjodirdjo, B., Pujawan, I.N., Baihaqi, I., 2018. Coalition in Utilization Capacity in Container Transportation Services. International Journal of Applied Science and Engineering, Volume 15(2), pp. 95–104

Zhang, T., Zheng, Q.P., Fang, Y., Zhang, Y., 2015. Multi-Level Inventory Matching and Order Planning Under The Hybrid Make-To-Order/Make-To-Stock Production Environment for Steel Plants Via Particle Swarm Optimization. Computers & Industrial Engineering, Volume 87, pp. 238–249

Zukhruf, F., Widhiarso, W., Frazila, R.B., 2020. A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design. International Journal of Technology, Volume 11(2), pp. 374–387