Academia.eduAcademia.edu

Figure 8 – uploaded by Pankaj Dayama

See full PDFdownloadDownload figure
Fig. 2. Overall Business Process

Figure 2 Overall Business Process

Related Figures (18)

HE purchasing function and the associated procurement processes in organizations, large and small, have tradi- tionally been an important area of operations affecting busi- ness performance. In many organizations, procurement costs constitute a major part of the total costs. For example, about 60 percent of the cost of a car is attributed to procurement costs. Recent trends in the business environment suggest that the importance of procurement is being both reinforced through the emergence of global supply chains and amplified by the growing incidence of outsourcing in many industrial sectors. Simultaneously, the procurement function itself has undergone much transformation. In one large global firm that the au- thors have worked with, decentralized, factored purchasing processes have given way to uniform, centralized purchasing practices, with worldwide purchasing decisions being coordi- nated by a single centralized organization. These changes can, in part, be attributed to the influence that information and communication technologies (ICT’s) have had in reshaping procurement processes both within and between organizations. Index Terms—¥-Procurement, negotiations, auctions, multi- attribute auctions, combinatorial auctions, volume discount auc- tions, game theory, mechanism design, NP-hard problems.
HE purchasing function and the associated procurement processes in organizations, large and small, have tradi- tionally been an important area of operations affecting busi- ness performance. In many organizations, procurement costs constitute a major part of the total costs. For example, about 60 percent of the cost of a car is attributed to procurement costs. Recent trends in the business environment suggest that the importance of procurement is being both reinforced through the emergence of global supply chains and amplified by the growing incidence of outsourcing in many industrial sectors. Simultaneously, the procurement function itself has undergone much transformation. In one large global firm that the au- thors have worked with, decentralized, factored purchasing processes have given way to uniform, centralized purchasing practices, with worldwide purchasing decisions being coordi- nated by a single centralized organization. These changes can, in part, be attributed to the influence that information and communication technologies (ICT’s) have had in reshaping procurement processes both within and between organizations. Index Terms—¥-Procurement, negotiations, auctions, multi- attribute auctions, combinatorial auctions, volume discount auc- tions, game theory, mechanism design, NP-hard problems.
Fig. 1. Taxonomy of procurement scenarios
Fig. 1. Taxonomy of procurement scenarios
NOTATION FOR AUCTION T, AUCTION R AND AUCTION S 1) Model Formulation: Chen, Janakiraman, Roundy, and Zhang [59] present three separate auction mechanisms - Auc- tion T, Auction R, and Auction S. The first two mechanisms incorporate transportation costs explicitly whereas in the third model, only production costs are considered for the allocation decision and transportation decisions are made subsequently. This provides a means to compare the two approaches and the implications for total cost of procurement strategies. Before we present the models, the notation used is introduced in Table Tl
NOTATION FOR AUCTION T, AUCTION R AND AUCTION S 1) Model Formulation: Chen, Janakiraman, Roundy, and Zhang [59] present three separate auction mechanisms - Auc- tion T, Auction R, and Auction S. The first two mechanisms incorporate transportation costs explicitly whereas in the third model, only production costs are considered for the allocation decision and transportation decisions are made subsequently. This provides a means to compare the two approaches and the implications for total cost of procurement strategies. Before we present the models, the notation used is introduced in Table Tl
A Vickrey based payment rule, belonging to the more general truth-inducing VCG family described in [60], is used. The rule, in words, essentially is: The auctioneer decides the quantities awarded to each sup- plier, and the amounts transported from suppliers’ production centers to buyer’s demand locations by solving the following winner determination problem.
A Vickrey based payment rule, belonging to the more general truth-inducing VCG family described in [60], is used. The rule, in words, essentially is: The auctioneer decides the quantities awarded to each sup- plier, and the amounts transported from suppliers’ production centers to buyer’s demand locations by solving the following winner determination problem.
Winner Determination Problem: If 75(q) is the optimal objective value, x*° the production vector, and m5 *(q) the optimal objective value without sup- plier k, the buyer’s payment to supplier k is given by the rule: The buyer submits a consumption vector q as in Auction T. The auctioneer solves two optimization problems, one to determine allocation and payments and the other to optimize transportation costs, separately.
Winner Determination Problem: If 75(q) is the optimal objective value, x*° the production vector, and m5 *(q) the optimal objective value without sup- plier k, the buyer’s payment to supplier k is given by the rule: The buyer submits a consumption vector q as in Auction T. The auctioneer solves two optimization problems, one to determine allocation and payments and the other to optimize transportation costs, separately.
The buyers total outflow, Ks(q), is given by Ks(q) = ive (a) +N, a Tam *Yxm- The numerical exper- iments clearly show that Auction S minimizes total production costs but leads to higher supply chain costs than Auctions T and R. Transportation Problem: We can observe that this payment rule still retains the VCG structure, and hence the suppliers will submit their true cost functions. Subsequently the transportation costs are determined by solving the following optimization problem:
The buyers total outflow, Ks(q), is given by Ks(q) = ive (a) +N, a Tam *Yxm- The numerical exper- iments clearly show that Auction S minimizes total production costs but leads to higher supply chain costs than Auctions T and R. Transportation Problem: We can observe that this payment rule still retains the VCG structure, and hence the suppliers will submit their true cost functions. Subsequently the transportation costs are determined by solving the following optimization problem:
At the end of each round, the potential allocation x; (¢) is displayed to the supplier k which can be used to better the bid in the following round. The supplier can choose to follow a myopic best response (MBR) strategy embedded within a software based bid suggestion device. To use this feature, the supplier & is required to submit his actual production costs Upi for component i to the auctioneer. The next bid bj (¢ + 1) to be submitted by supplier & is such that he maximizes his potential payoff IT, (b(t)) = 07; (bei (t)—vne)eea(t) in round ¢ by carrying out a suitable computation [69].
At the end of each round, the potential allocation x; (¢) is displayed to the supplier k which can be used to better the bid in the following round. The supplier can choose to follow a myopic best response (MBR) strategy embedded within a software based bid suggestion device. To use this feature, the supplier & is required to submit his actual production costs Upi for component i to the auctioneer. The next bid bj (¢ + 1) to be submitted by supplier & is such that he maximizes his potential payoff IT, (b(t)) = 07; (bei (t)—vne)eea(t) in round ¢ by carrying out a suitable computation [69].
NOTATION FOR ORDINAL RANKING OF ALTERNATIVES TABLE VIII proaches that have been proposed to develop multi attribute procurement mechanisms.
NOTATION FOR ORDINAL RANKING OF ALTERNATIVES TABLE VIII proaches that have been proposed to develop multi attribute procurement mechanisms.
NOTATION FOR INVERSE OPTIMIZATION TECHNIQUES TABLE IX where the weights w; are unknown and 7 is the number of the attribute, and these satisfy the bid rankings. The LP is:
NOTATION FOR INVERSE OPTIMIZATION TECHNIQUES TABLE IX where the weights w; are unknown and 7 is the number of the attribute, and these satisfy the bid rankings. The LP is:
The objective function maximizes the buyer’s utility with the last three terms of (24) making up the winning suppliers price; (36) is supplier s’s maximum drop-out score; (37) and (38) ensure that the top two suppliers participate in round P+1. Computing the optimal scoring rule: After learning the suppliers’ cost functions, the optimal scoring rule for round P +1 is computed by solving the following optimization problem:
The objective function maximizes the buyer’s utility with the last three terms of (24) making up the winning suppliers price; (36) is supplier s’s maximum drop-out score; (37) and (38) ensure that the top two suppliers participate in round P+1. Computing the optimal scoring rule: After learning the suppliers’ cost functions, the optimal scoring rule for round P +1 is computed by solving the following optimization problem:
TABLE X
TABLE X
Fig. 4. Main Elements of a Configurable Bid Fig. 3. Commodity Sourcing Application
Fig. 4. Main Elements of a Configurable Bid Fig. 3. Commodity Sourcing Application
Related topics:
Mechanical EngineeringComputer ScienceGame TheoryMechanism DesignCombinatorial AuctionManufacturing EngineeringAutomated NegotiationElectrical and Electronic EngineeringAutomation Science
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved