International O.R.: Why should we work together?

Three case studies illustrate collaborative logistics in the forest value chain.

Collaborative logistics

By Jean-François Audy, Nadia Lehoux, Sophie D’Amours and Mikael Rönnqvist

Companies view collaborations with others as an opportunity to improve their competitiveness, access new markets and respect operational, social and environmental constraints. Nevertheless, each company has its own objectives and typically makes its own planning decisions to meet these objectives. Determining how these business entities will work together as well as the value of their collaboration becomes crucial. Specifically, in collaborative logistics, it is necessary to identify how logistics activities will be planned and executed and how benefits will be shared.

In the forest industry, companies compete in selling their products on the market, but also in the procurement of the different wood assortments they need (defined by e.g. specie, quality and dimensions). Logistics to support wood supply and delivery of the finished products is an important part of the value chain. For example, transportation accounts for about a third of the raw material cost. Although several forest companies often operate in the same region or on the same market, collaborative logistics between two or more companies is rare.

This article presents three case studies in the forest industry where collaboration has been analyzed, tested and – for two of them – implemented. The approach has often been to apply standard operations research (O.R.) models and methods including game theory. The results show significant benefits, but the experience and use of the results are quite different. Also, the work has revealed a lack of methodology to meet the real need from the industry.

Wood Supply Collaboration in Sweden

Wood bartering occurs when two companies agree to trade wood. For example, company A will deliver 20,000 cubic meters of spruce pulp logs to company B’s paper mill, while company B will do the opposite. Why is this efficient? Figure 1 shows the solution before collaboration to the left and afterward to the right. Because the supply regions for both companies cover each other, it is beneficial to deliver to the closest mill. Since each company is responsible for their supply, they will still plan the transportation with trucks and routes as before. The only difference is some new customer destinations. It is easy to agree on some levels, but it is difficult to find the optimal level of volume to exchange. In fact, the inclusion of a third party is typically necessary in this process. This type of collaboration is quite standard in Sweden and is behind the case we report.

Collaborative logistics

Figure 1: Illustration of wood bartering between two companies.

In 2004, a group of eight forest companies in southern Sweden wanted to know the potential for coordinated transportation planning and wood bartering [Frisk et al., 2010]. All of the companies viewed their supply and demand as common, and they approached the problem as an integrated one for a single, artificial company. The total volume hauled over one month was 883,000 cubic meters. Table 1 gives in detail the volume available at each company and its proportion, while Figure 2 gives an example of the geographical distribution of three companies. A good coverage, i.e. they work in the same region, between two companies indicates high potential for savings.

Collaborative logistics
Collaborative logistics

Figure 2: Supply areas (indicated in green) and demand points or mills (indicated with red circles) for three of the companies.

The problem can be solved using a DSS system, and the optimization model is a linear programming (LP) model. The potential saving was as high as 14.2 percent. Collaboration accounted for 8.7 percent of savings, while the rest came from improved planning within each company.

Once the first analysis for the eight companies was completed, the results were presented to the managers, triggering a discussion on how the overall cost and/or cost reduction should be split. In the forestry business, the cost is often based on average price per metric ton or cubic meter. Hence, a natural way of splitting the cost is for each company to take a share of the total cost corresponding to its proportion of volume. When the relative savings were computed, it ranged from 0.2 percent to 20 percent depending on the company, so the difference was too high to reach an agreement. The reasons for the difference in relative savings were twofold. First, each company takes responsibility for its own supply and makes sure it is delivered to the new destinations. Second, the geographical distribution differs between companies and this affects the new distribution solution.

In order to come up with a sharing principle that the companies could agree on, several sharing principles based on economic models were tested and analyzed. The motivation was to reach an allocation that provided as equal as possible relative profit among the participants.

As a result of the case study, three companies started collaborating in 2008 by coordinating their planning on a monthly basis. Before each month, each company provided the information about supply and demand to a third party logistics, in this case the Forestry Research Institute of Sweden. Then an integrated plan was computed and the result was given back to the forest companies for their own detailed transportation planning. The sharing principle was based on having the same relative savings applied to each company’s own supply. In addition, some constraints were taken into account to make sure that each company was the main supplier for its own mills, and that pair-wise exchange flows were the same. The latter was to avoid financial exchanges between companies.

With this revised model, it was not possible to guarantee a stable solution. The approach was tested during four months in 2008, and the potential savings ranged from 5 percent to 15 percent each month. A platform has been developed for common plans where a third party logistic provider is not required. (Based on this case study, an educational game [D’Amours and Rönnqvist, 2011] was developed and used in several industrial engineering courses for managers and students.)

Outbound Transportation Collaboration in the Furniture Industry

The second case study refers to the potential collaboration in outbound transportation between four furniture manufacturers in Canada. The transportation involved essentially less-than-truckload shipments (i.e. nine to 21 shipments were needed to fill the trailer). Their mills were located in the same region, whereas their customers were located across the United States (see Figure 3). For this context, the manufacturers wanted to optimize collectively the transportation of their products.

Collaborative logistics

Figure 3: Localization of the shipping volume per manufacturers (indicated with red, blue, green and yellow colors) and the manufacturers’ mills (indicated with red flags).

Audy and D’Amours [2008] explored four different logistics scenarios to establish the collaboration. These scenarios represented different transportation approaches, such as direct full-truck-load deliveries starting from a consolidation warehouse or a less-then-truck-load approach where the different products are picked up at the different mills to consolidate the truck load before transporting goods to the United States. Cost and delivery time reductions, as well as gain in market geographical coverage, were identified in each scenario. The study was conducted in two different regions targeted by the companies (i.e. West Coast and Great Lakes). As in the first case study, the proportion of the total volume hauled by each company was uneven.

Even though a scenario can provide substantial benefits for the group, each company needs to evaluate the scenario according to its own benefits. This individual evaluation can lead to a situation where the scenario with the highest cost-savings for the group (optimal cost-savings scenario) does not provide the individual highest cost-savings to some companies or, worse, provides one or more negative benefits. As a result, without any modification, this optimal cost-savings scenario would be rejected in favor of another scenario that may not capture all the potential cost-savings and may exclude some of the companies.

Audy et al. [2008] integrated in the optimal cost-savings scenario the modifications that satisfy the conditions allowing its establishment by the whole group. By doing so, the result in cost reductions go from 21 percent to 12.9 percent. In other words, an additional cost of 8.1 percent was incurred in the collaborative plan to satisfy the heterogeneous requirements of some partners. Since some companies have more requirements than others and because the impact on cost increase between two requirements is almost never the same, this raises a new question: How should the additional cost incurred to satisfy the special requirements be shared? Using a cost allocation scheme, a new method was proposed and analyzed. This new method allows a share according to the impact of the requirements of each partner on the cost of the collaborative plan. Thus, the partner who increases the cost of the collaborative plan the most absorbs the highest part of the additional cost incurred to satisfy the requirements of all partners.

As a result of the case study, companies with the support of their industrial association initiated a pilot project. As agreed by the four companies, one of them defined a business agreement to manage the collaboration in the pilot project. However, the definition of the business agreement by the leading company was delayed for many reasons and then, two of the three companies cancelled the agreement. One company declared bankruptcy, while the second was suspected by the other companies of opportunistically using the monetary-related parameters, inside the proposition of agreement, to renegotiate downward its current transportation rates with its carriers. The benefit with the two remaining companies was judged insufficient and the project pilot was suspended. Nevertheless, the leading company has recently joined a local organization regrouping several companies from different industrial sectors. Collaborative transportation is one of the strategies the members want to evaluate.

Collaboration Approaches in the Pulp and Paper Industry

The last case concerns a pulp and paper producer who decided to establish a partnership with one of its wholesalers [Lehoux et al., 2009]. Since the production capacity was limited, the producer had to plan operations in order to satisfy the demand of the partner and the demand of other wholesalers (Figure 4).

Collaborative

Figure 4: Illustration of the pulp and paper collaboration case study.

Even though each partner wanted to create a real partnership with mutual benefits, they made planning decisions based on their own costs and constraints. The producer planned operations to minimize production, distribution and inventory costs, while the wholesaler ordered products to minimize buying, ordering and inventory costs. Therefore, the objective of the study was to identify the collaborative approach to implement to ensure efficient products and information sharing, as well as maximum benefits for both the network and each partner.

Four potential collaborative approaches were identified for the case study: 1. a traditional system without any collaboration scheme, 2. continuous replenishment (CR), 3. vendor managed inventory (VMI), and 4. collaborative planning, forecasting and replenishment (CPFR). For each approach, decision models from the point of view of both the producer and the wholesaler were developed. Specifically, mixed integer linear programs (MILP) were used to take into consideration the costs, revenues and constraints involved in using each collaborative approach. Afterwards, models were tested and compared in order to find the most profitable approach for the network. Results showed that CPFR generated the greatest total system profit because of an efficient optimization of both transportation and inventory costs (CPFR inventory cost was up to 44 percent lower than inventory costs of other models, and CPFR transportation cost was up to 18 percent lower than costs of other approaches). VMI was second best since the transportation cost was optimized. CR and the traditional system obtained the lowest total system profit.

After comparing each model using the system profit, the investigation was based on the profit of each partner. Specifically, the different collaborative approaches were compared to verify if the same approach could generate the highest profit for both the producer and the wholesaler. This analysis revealed that CPFR generated the greatest profit for the producer, while CR was the most beneficial for the wholesaler. For this reason, a method for sharing benefits was defined in order to obtain a CPFR collaboration profitable for each partner. The experiments showed that if the producer shared a part of the transportation savings with the wholesaler, the profit of the wholesaler was higher than the profit obtained with CR, and the producer obtained a higher profit than the one generated by the other approaches.

As of this writing, the partners work together using a CR technique (Lehoux et al., 2010), but they plan to implement CPFR in the future. Therefore, the producer will certainly have to share benefits with the wholesaler to maintain a win-win relationship. Otherwise, it is possible that the wholesaler may prefer to work with someone else.

Comments and Future Challenges

Many O.R. tools can be used to study inter-firms collaborations. In some cases, economic models are applied to address costs and benefits sharing; in other cases, optimization models and simulations are used to plan operations and estimate potential shared costs (see [Audy et al., 2010] for more details). Moreover, collaborations can be analyzed from different planning levels. It may concern the design of the coalition, the strategies or the technology to implement, the day-to-day activities to jointly execute the operations, and so on. Collaborations can also be applied to different types of business contexts, from transportation activities to service companies.

Nevertheless, collaborations between organizations are complex, and many problems are still very difficult to deal with. These problems often call for interdisciplinary solutions. In our case studies, the theoretical results showed significant benefits and costs savings. However, partners were not necessarily ready to change their way of doing things, to share their knowledge or to collaborate with other stakeholders to achieve these benefits.

In the first case study, only three of the eight companies decided to collaborate, requiring new constraints related to the wood exchange that contributed to reduce by 1 percent to 2 percent the potential in savings. In the second case study, the collaboration failed mainly because of the opportunistic behavior of one of the partners. As observed in the two first case studies, large differences in size seem to reflect the negotiation power in building the partnership. In the third case, the producer wanted to implement VMI, but the wholesaler was afraid to lose control over operations and not necessarily ready to shift some responsibilities.

Another issue is competition. In the process of building the partnership, some entities may be strong competitors. In such a context, trust may play an important role in the decision process. Legal issues are also very important. Many countries are concerned with potential collusive activities and therefore legislate to avoid them. In addition, the theoretical sharing process may, in reality, need to deal with benefits that are difficult to evaluate and impossible to share (e.g. value of faster deliveries). Finally, the collaboration is rarely fixed in time. The environment changes constantly, as do the parameters considered when designing the collaboration. How should this dynamic be considered upfront? How often should the terms of the collaboration be reviewed?

For the forest industry, collaboration between network members is a real challenge. Companies that are willing to share resources and information as well as risk and benefits, undoubtedly stand to gain from working together. This will certainly capture practitioners’ attention in the coming years.

Jean-François Audy (Jean-Francois.Audy@cirrelt.ca) is a doctoral student in Industrial Engineering at Université Laval in Quebec, Canada. Nadia Lehoux (nadia.lehoux@cirrelt.ca) and Sophie D’Amours (Sophie.Damours@forac.ulaval.ca) are professors in the Department of Mechanical Engineering at Laval. D’Amours is the director of the FORAC Research Consortium, a center of expertise for the advancement of the forest products industry. Lehoux and Audy are members of the FORAC team. Mikael Rönnqvist (mikael.ronnqvist@nhh.no) is a professor at the Norwegian School of Economics and Business Administration in Bergen, Norway. All four co-authors are members of the Inter-university Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT).

References

  1. J.-F. Audy, N. Lehoux, S. D’Amours and M. Rönnqvist, “A framework for an efficient implementation of logistics collaborations,” Report CIRRELT-2010-24, Université Laval, Canada, 2010. Forthcoming in International Transactions in Operational Research.
  2. J.-F Audy, S. D’Amours, “Impact of benefit sharing among companies in the implantation of a collaborative transportation system – An application in the furniture industry,” “IFIP International Federation for Information Processing, Pervasive Collaborative Networks,” Springer, pp. 519-532, 2008.
  3. J.-F Audy, S. D’Amours, L.-M. Rousseau, “Cost allocation in the establishment of a collaborative transportation agreement – An application in the furniture industry,” Report CIRRELT-2008-50, Université Laval, Canada, 2008. Forthcoming in Journal of the Operational Research Society.
  4. S. D’Amours and M. Rönnqvist, “An Educational Game in Collaborative Logistics,” Report CIRRELT-2011-04, Université Laval, Canada, 2011.
  5. N. Lehoux, S. D’Amours, A. Langevin, “A win-win collaboration approach for a two-echelon supply chain: a case study in the pulp and paper industry,” European Journal of Industrial Engineering, Vol. 4, No. 4, 2010.
  6. N. Lehoux, S. D’Amours, A. Langevin, “Collaboration and decision models for a two-echelon supply chain: a case study in the pulp and paper industry,” Journal of Operations and Logistics, Vol. 2, No. 4, VII.1-VII.17, 2009.
  7. M. Frisk, M. Göthe-Lundgren, K. Jörnsten and M. Rönnqvist, “Cost allocation in collaborative forest transportation,” European Journal of Operational Research, Vol. 205, No. 2, pp. 448-458, 2010.