Supplier Order Allocation

Pre-qualification reduces a large set of initial suppliers to a smaller set of acceptable suppliers for further assessment. De Boer et al. (2001) have cited many different techniques for pre-qualification. Some of these techniques are categorical methods, data envelopment analysis (DEA), cluster analysis, case-based reasoning (CBR) systems, and multi-criteria decision making method (MCDM). Several authors have worked on pre-qualification of suppliers. Weber and Ellram (1992) and Weber et al. (2000) have developed DEA methods for pre-qualification. Hinkel et al. (1969) and Holt (1998) used cluster analysis for pre-qualification and finally Ng and Skitmore (1995) developed CBR systems for pre-qualification. Mendoza et al. (2008) developed a three phase multi-criteria method to solve a general supplier selection problem. The paper combines analytic hierarchy process (AHP) with goal programming for both pre-qualification and final order allocation. 

Multiple sourcing models Multiple sourcing can offset the risk of supply disruption. In multiple sourcing, a buyer purchases the same item from more than one supplier. Mathematical progranuning is the most appropriate method for multiple sourcing decisions. Two types of mathematical programming models are found in the literature, single objective and multiobjective models. 

Moore and Fearon (1973) stated that price, quality, and delivery are important criteria for supplier selection. They discussed the use of linear programming in decision making. 

Gaballa (1974) applied mathematical programming to supplier selection in a real case. He used a mixed integer programming to formulate a decision 

Besides goal programming, there are other approaches to solve the multicriteria optimization model for the supplier selection problem formulated in Section 6.4.2. They include weighted objective method, compromise programming, and interactive approaches. Interested readers can refer to Wadhwa and Ravindran (2007), Masudand Ravindran (2008, 2009) for more details. Masudand Ravindran (2008, 2009) also provide information on the computer software available for the MCDM methods. 

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Consider a supplier order allocation problem under multiple sourcing, where it is required to buy 2000 units of a certain product from three different suppliers. The fixed set-up cost (independent of the order quantity), variable cost (unit price), and the maximum capacity of each supplier are given in Table 5.15 (two suppliers offer quantity discounts). 

For the three criteria supplier order allocation problem (Section 6.4.2), the preemptive GP formulation will be as follows ... 

We shall now illustrate the four goal programming methods using a supplier order allocation case study. The data used in all four methods is presented next. 

All four goal programming models (Preemptive, Non-Preemptive, Tchebycheff, and Fuzzy) are used to solve the supplier order allocation problem. Each model produces a different optimal solution. They are discussed next. 

In this case study, you will solve a supplier order allocation problem with two products, two buyers, and two suppliers. The problem has three conflicting criteria, namely, total cost, lead-time, and quality (measured by rejects). All three objectives have to be minimized. 

For the critical items in Quadrant 3, we present a three-step method for global supplier selection. In the first step, we present a GP model for country selection this step shortlists a country using various qualitative and quantitative criteria. In the second step, we assess the risks of supply using the analytic hierarchy process (AHP). In the final step, we develop a multi-objective model with price and risk as the two conflicting objectives. For every product, we assign three different suppliers—a global supplier, a domestic primary supplier, and a domestic secondary supplier. Order allocation among the suppliers is optimally decided by the model. 

Sanayei, A. Mousavi, S. E Abdi, M. R, Mohaghar, A. 2008. An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming. Journal of the Franklin Institute. 345 (7) 731-747. 

Supplier selection and order allocation Case study)... 

Consider a supplier selection and order allocation problem with two products, two buyers (multiple buyers represent situations when different divisions of a company buy through one central purchasing department), two suppliers and each supplier offering "incremental" price discounts to each buyer (not "all unit discount"). The objective is to minimize Total cost, which consists of the fixed cost and the variable cost. Fixed cost is a one-time cost that is incurred if a supplier is used for any product, irrespective of the number of units bought from that supplier. 

This mixed integer program with 18 binary variables, 16 continuous variables, and 38 constraints was solved using Excel Solver. The optimal order allocation is given in Table 6.11. Both suppliers are used for purchases. The policy results in a total cost of 93,980. 

Single objective models In Section 6.2, we presented a single objective linear programming model for order allocation using Example 6.3. The model considered supplier capacities, price discounts, buyers demand, quality, and lead-time constraints. The objective was to minimize total cost, which included fixed and variable cost of the suppliers. We shall briefly review here, some of the other single objective models that have been discussed in the literature. 

Case study 4 Supplier Selection and Order Allocation... 

The rank ordered suppliers with allocated purchase quantity shown as in Table 4.11. 

Chakravarty, A. (1979). Supplier performance and order allocation. Journal of Operational Research Society, 30, 131-139. 

Demirtas, E. A. and 0. Ostiin. (2008). An integrated multiobjective decision making process for supplier selection and order allocation. Omega 36(1) 76-90. 

This chapter presents a disruption risk quantification method and a multiobjective supplier selection model to generate mitigation plans against disruption risks. The proposed risk quantification method considers risk as a function of two components—impact and occurrence. Impact is modeled using GEVD distributions, and occurrence is assumed to be Poisson-distributed. The disruption risk quantification method calculates the estimated value of the loss due to disruptive events at a supplier, which is then used in a multi-objective optimization model. The model minimizes cost, lead time, and risk and then maximizes quality and determines the optimal supplier and order allocation for multiple products. The model is solved using four different GP solution techniques—preemptive, non-preemptive, min-max, and fuzzy GP Optimal solutions are displayed using the VPA, and the performance of the solution techniques is discussed. We observe that, for the data set we have tested, preemptive GP, non-preemptive GP, and min-max GP achieve three out of four objectives. 

Demirta , Ezgi A., and Ozden Ustiin. An Integrated Multi-Objective Decision Making Process for Supplier Selection and Order Allocation. Omega 36, no. 1 (2008) 76-90. 

The objective of the model is to choose manufacturers and their suppliers and allocate order quantities between them in a manner such that the expected cost of operating the supply chain is minimized and also the variability of... 

In-house mating is recommended in the Redbook. There are no valid reasons for this. Time-mated animals are available from many reputable laboratory animal breeders and their use has been validated over several decades. The use of time-mated animals allows a predefined number of pregnant females to be ordered and allocated to the study each day so that all procedures can be planned in advance according to the resources available. Also, the supplier has a large colony of breeding males and females available, providing a wide gene pool. 

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