Optimization of the supply chain is one of the steps toward minimizing costs and building effective relationships with suppliers. Linear Programming (LP) models can be effectively used to contribute to the optimization and propose efficient solutions because of providing algorithms to solve the concrete problem and maximize outcomes. In their article, Sandholm and the group of researchers analyze how effective was the focus of Procter & Gamble (P&G) on using REV as the market-clearing engine and software developed by CombineNet to endorse optimization of the supply chain’s work and focus on abilities for providing offers and expressing constraints and preferences.
The authors chose to explore and discuss P&G’s approach to organizing the sourcing networks, the features of REV and aspects of the LP model as the basis, and the benefits for P&G associated with using REV as the tool for optimization (Sandholm et al. 56). Thus, Sandholm and the group of researchers state in their article that REV is an effective engine to organize and optimize P&G’s work with expressions of supply and demand and to provide the optimal solution regarding allocations because P&G’s savings increased along with the effectiveness of the supply chain’s work.
In their article, Sandholm and the group of researchers provide a consistent background to explain why P&G chose to use REV. In the 2000s, P&G used a supply chain of a consumer-driven type to optimize the suppliers and buyer’s relations. The focus was on the computerized submit of suppliers’ offers to make necessary allocations with the help of the reverse auction (Sandholm et al. 56). However, reverse auctions provide poor results in making effective allocation decisions.
The authors explain P&G’s choice of REV accentuating the necessity for the company to utilize the complex program to express constraints and preferences and make complex allocation decisions. REV is an engine that is based on mixed-integer-programming (MIP) techniques arranged to provide tree-search algorithms necessary for completing the optimal allocation (Sandholm et al. 63). P&G selected REV because the engine provides the buyer with the opportunity to work with all options and offers while choosing the appropriate one.
The authors support their discussion stating that during the first test of the software, the problem was resolved by REV in less than nine seconds, and “P&G saved 13.5 percent” (Sandholm et al. 57). The next events demonstrated the opportunities for the company to save millions of dollars while using REV.
To support their discussion of REV’s benefits, the authors note that REV is based on the idea of expressive competition. As a result, suppliers and buyers receive the opportunity to express their supply and demand in the form of detailed offers, constraints, and preferences. The detailed expressions referred to as supply and demand are important to make effective decisions, and P&G benefits from using REV for optimization. The researchers provide the discussion and analysis of two cases related to P&G’s activities and use of REV to support the idea about effective optimization and savings for the company. The results related to the sourcing of displays were 60% of unconstrained savings and 48% of implementable savings (Sandholm et al. 60).
The results regarding the truckload transportation optimized with the help of REV were $47 million related to unconstrained savings (Sandholm et al. 60). By March 2005, the savings of P&G were $294.8 million (Sandholm et al. 60). Furthermore, the company improved its suppliers’ participation and general relationships with suppliers. The authors conclude their discussion of the application of REV to P&G’s environments by providing the appendixes demonstrating the stages and numbers associated with the optimization process.
Analyzing the research’s effectiveness and concluding about its reliability, it is important to note that the goal of the article was to analyze the real-life effectiveness of applying LP models for optimization of the company’s operations with references to the example of P&G as the company aimed to resolve the problem of sourcing networks’ optimization. Thus, the researchers succeeded in explaining the principles of REV’s work with references to MIP techniques and discussed the advantages for the company.
The goal of the research can be discussed as completed. From this point, the conducted research was associated with collecting the numerical accounts and data to demonstrate how the use of REV contributed to minimizing costs and maximizing savings for P&G. The details and results of the research were presented in the form of the analysis of P&G’s cases and events with the focus on concrete results presented in numbers and on the observed improvements presented as the qualitative information.
It is possible to state that the provided results supported the main idea of the article regarding the optimization and improvement of P&G’s operations because they were accompanied by the explanation of the principles of REV’s work and analysis of the received numbers. It was learned with the help of the research that the concept of the clearing problem plays an important role in discussing the options for resolving the issue. Thus, the main goal of REV is to present an effective solution for the company about accepting and rejecting bids while focusing on the other important aspects associated with supply and demand (Sandholm et al. 62).
It was also found that LP models and MIP techniques work effectively to address the company’s expectations when they are integrated along with complex algorithms to respond to all the proposed options (Sandholm et al. 63). From this perspective, the results of the research can be discussed as supporting the argument.
The article provides important information on the use of developed algorithms for LP models to contribute to expressive competition, resolve the clearing problem, utilize new branching strategies and selection techniques. It is also possible to learn more information about the role of scenario navigation (Sandholm et al. 65). As a result, it is possible to learn a lot of important information about the application of LP models while resolving real-life problems.
Sandholm and the group of researchers proposed a convincing argument because their conclusions are supported with the detailed background and theoretical information and empirical data to represent the results of using REV for optimization of P&G’s supply chain. The intended audience of the article is managers and specialists in the sphere of optimization who are interested in using LP models to maximize revenues and savings while focusing on the idea of expressive competition. The article can be used to analyze the work of LP models referring to the example of REV and decide on the appropriateness of using similar models and programs to optimize the work of the concrete company. From this perspective, the article can be discussed as important to present information about the application of LP models.
Works Cited
Sandholm, Tuomas, David Levine, Michael Concordia, Paul Martyn, Rick Hughes, Jim Jacobs, and Dennis Begg. “Changing the Game in Strategic Sourcing at Procter & Gamble: Expressive Competition Enabled by Optimization”. Interfaces 36.1 (2006): 55-68. Print.