Supplier Selection and Multi-period Demand Allocation in a Humanitarian Supply Chain
Supplier Selection and Multi-period Demand Allocation in a Humanitarian Supply Chain
Amol Singh
and controlling the efficient and cost-effective flow of goods of humanitarian SCM
network, includes procurement of goods, storage of goods, and distribution of goods
from point of origin to the point of consumption in such a way that it alleviates the
suffering of the vulnerable people as efficiently as possible. Humanitarian supply
chain management and humanitarian logistics are used interchangeably in the
literature. Purchasing is an important function of humanitarian supply chain management.
The literature in this context significantly focused on choosing the right
suppliers and allocating the appropriate demand of goods to these suppliers. In an
increasingly competitive environment, firms are paying more attention to selecting
the right suppliers for procurement of raw materials and component parts for their
products.
Choi and Hartley (1996) reported that supplier evaluation and selection
together has an important role in the supply chain process and is crucial to the
success of a firm. Similarly, procurement activity in the humanitarian supply chain is
very important owing to demand that is generated from random events that are
unpredictable in terms of timing, type, and size. As a result, the opportunities in this
research area include determining the number of suppliers, managing changing
buyer-supplier relationship, sourcing strategies, and outsourcing decisions for
humanitarian supply chains. The present research work focuses on this issue of
humanitarian supply chain management. The main objective of the study is to
address the problem of optimal allocation of demand of items among candidate
suppliers in order to maximize the purchase value of items. The purchase value of
the items directly relate to cost and quality of raw materials purchased from thesupplier.
Supplier selection problem is a multi-criteria decision-making problem
involving both qualitative and quantitative performance measures. Usually, several
conflicting criteria make the supplier selection problem a complex problem. It is
often desirable to make a compromise among the conflicting criteria.
In this study, a new hybrid algorithm has been developed to solve the problem of
multi-period customer demand allocation among more suppliers under multi-period
demand condition, budget, delivery lead time, and multi-period supplier capacity
constraints. The remainder of the paper comprises six sections. Section 14.2 provides
the review of literature on supplier selection, identifies the research issues,
which form the basis for problem formulation, and then presents the objective of the
study. Section 14.4 discusses the proposed hybrid algorithm to solve the multicriteria
demand allocation problem. Section 14.5 reports the case study and the
findings of the computational experiments. Section 14.6 concludes the study along
with future research directions.
selection is a complex process involving several criteria such as procurement cost,
material quality, delivery lead time, reliability of the supplier, etc. These criteria
can be defined variously as buyers take into account numerous conflicting factors.
Illustratively, low price can offset poor quality or delivery lead time. Dickson
(1966) identified 23 criteria in his study of various supplier selection problems.
He reported that quality, delivery, and performance history are the three most
important criteria. Similarly, Weber et al. (1991) in a review of 74 articles obtained
similar results pertaining to the multi-criteria nature of supplier selection problem.
From a generalized perspective, alternative approaches suggested in the literature
may be grouped into three categories: linear weighting models, mathematical
programming approaches, and probabilistic approaches. However, their study identified
very few articles based on the mathematical programming approach for
supplier selection. Bhutta (2003) provided a review of 154 supplier selection
research articles and alternative methods/techniques adopted. Although most
buyers still consider cost to be their primary concern, new more interactive and
interdependent selection criteria are increasingly being used. Table 14.1 provides a
summary of various criteria used by researchers.
The literature shows a variety of methodologies and approaches used for the
supplier selection problem. Traditionally, linear weighting models, total cost
approach, multiple attribute utility theory, and total cost ownership are used for
supplier selection. None of these approaches have received significant support in
literature or in practice for their limitation to address the issues of real supplier
selection environment. In the last one decade, researchers focused on optimization
techniques, multi-objective programming, analytic hierarchy process, data envelopment
analysis, artificial intelligence, and hybrid approaches. The brief description of alternative approaches in terms of general application, features, and limitations is as follows.
The popular techniques are dynamic programming (Masella and Rangone 2000),
linear programming (Ghodsypour and O’Brien 1998), and multi-objective programming
(Weber and Ellram 1993). Zhang and Zhang (2011) used the MILP
approach to solve the supplier selection problem under stochastic demand. They
selected the suppliers and allocated the ordering quantity properly among the
selected suppliers to minimize the total cost including selection, purchasing, holding,
and shortage costs. Sawik (2011) also applied the MILP approach to study the
problem of order allocation of parts among the suppliers in a customer-driven
supply chain. The study suggested that future research could consider supplier
selection in the customer-driven supply chain taking into account risk and dynamic
multi-period demand. Osman and Demirli (2010) addressed the supplier selection
problem related to an aerospace company and tried to optimize its outsourcing
strategies in order to meet the expected demand and customer satisfaction requirements
under delivery dates and approved budget. They used the goal-programming
approach to achieve the company’s objectives. The optimization approaches suffer
from some drawbacks in that they are unable to accommodate the qualitative
measures of supplier selection problems.
Amol Singh
Introduction
Humanitarian supply chain management, the process of planning, implementing,and controlling the efficient and cost-effective flow of goods of humanitarian SCM
network, includes procurement of goods, storage of goods, and distribution of goods
from point of origin to the point of consumption in such a way that it alleviates the
suffering of the vulnerable people as efficiently as possible. Humanitarian supply
chain management and humanitarian logistics are used interchangeably in the
literature. Purchasing is an important function of humanitarian supply chain management.
The literature in this context significantly focused on choosing the right
suppliers and allocating the appropriate demand of goods to these suppliers. In an
increasingly competitive environment, firms are paying more attention to selecting
the right suppliers for procurement of raw materials and component parts for their
products.
Choi and Hartley (1996) reported that supplier evaluation and selection
together has an important role in the supply chain process and is crucial to the
success of a firm. Similarly, procurement activity in the humanitarian supply chain is
very important owing to demand that is generated from random events that are
unpredictable in terms of timing, type, and size. As a result, the opportunities in this
research area include determining the number of suppliers, managing changing
buyer-supplier relationship, sourcing strategies, and outsourcing decisions for
humanitarian supply chains. The present research work focuses on this issue of
humanitarian supply chain management. The main objective of the study is to
address the problem of optimal allocation of demand of items among candidate
suppliers in order to maximize the purchase value of items. The purchase value of
the items directly relate to cost and quality of raw materials purchased from thesupplier.
Supplier selection problem is a multi-criteria decision-making problem
involving both qualitative and quantitative performance measures. Usually, several
conflicting criteria make the supplier selection problem a complex problem. It is
often desirable to make a compromise among the conflicting criteria.
In this study, a new hybrid algorithm has been developed to solve the problem of
multi-period customer demand allocation among more suppliers under multi-period
demand condition, budget, delivery lead time, and multi-period supplier capacity
constraints. The remainder of the paper comprises six sections. Section 14.2 provides
the review of literature on supplier selection, identifies the research issues,
which form the basis for problem formulation, and then presents the objective of the
study. Section 14.4 discusses the proposed hybrid algorithm to solve the multicriteria
demand allocation problem. Section 14.5 reports the case study and the
findings of the computational experiments. Section 14.6 concludes the study along
with future research directions.
Review of the Literature
Supplier selection is one of the growing research areas. Studies show that supplierselection is a complex process involving several criteria such as procurement cost,
material quality, delivery lead time, reliability of the supplier, etc. These criteria
can be defined variously as buyers take into account numerous conflicting factors.
Illustratively, low price can offset poor quality or delivery lead time. Dickson
(1966) identified 23 criteria in his study of various supplier selection problems.
He reported that quality, delivery, and performance history are the three most
important criteria. Similarly, Weber et al. (1991) in a review of 74 articles obtained
similar results pertaining to the multi-criteria nature of supplier selection problem.
From a generalized perspective, alternative approaches suggested in the literature
may be grouped into three categories: linear weighting models, mathematical
programming approaches, and probabilistic approaches. However, their study identified
very few articles based on the mathematical programming approach for
supplier selection. Bhutta (2003) provided a review of 154 supplier selection
research articles and alternative methods/techniques adopted. Although most
buyers still consider cost to be their primary concern, new more interactive and
interdependent selection criteria are increasingly being used. Table 14.1 provides a
summary of various criteria used by researchers.
The literature shows a variety of methodologies and approaches used for the
supplier selection problem. Traditionally, linear weighting models, total cost
approach, multiple attribute utility theory, and total cost ownership are used for
supplier selection. None of these approaches have received significant support in
literature or in practice for their limitation to address the issues of real supplier
selection environment. In the last one decade, researchers focused on optimization
techniques, multi-objective programming, analytic hierarchy process, data envelopment
analysis, artificial intelligence, and hybrid approaches. The brief description of alternative approaches in terms of general application, features, and limitations is as follows.
Optimization Techniques
The popular techniques are dynamic programming (Masella and Rangone 2000),linear programming (Ghodsypour and O’Brien 1998), and multi-objective programming
(Weber and Ellram 1993). Zhang and Zhang (2011) used the MILP
approach to solve the supplier selection problem under stochastic demand. They
selected the suppliers and allocated the ordering quantity properly among the
selected suppliers to minimize the total cost including selection, purchasing, holding,
and shortage costs. Sawik (2011) also applied the MILP approach to study the
problem of order allocation of parts among the suppliers in a customer-driven
supply chain. The study suggested that future research could consider supplier
selection in the customer-driven supply chain taking into account risk and dynamic
multi-period demand. Osman and Demirli (2010) addressed the supplier selection
problem related to an aerospace company and tried to optimize its outsourcing
strategies in order to meet the expected demand and customer satisfaction requirements
under delivery dates and approved budget. They used the goal-programming
approach to achieve the company’s objectives. The optimization approaches suffer
from some drawbacks in that they are unable to accommodate the qualitative
measures of supplier selection problems.
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