Total Purchasing Cost Research Articles

A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations

Nowadays environmental performance of suppliers becomes more important because of competitive conditions. Besides, the economic performance has been a significant factor for companies to choose their suppliers. In this paper, a new integrated model is proposed for supplier evaluation and order allocation which considers both environmental and economic factors. We use the EDAS (Evaluation based on Distance from Average Solution) method and interval type-2 fuzzy sets for evaluation of suppliers with respect to environmental criteria. According to this evaluation two parameters are defined for each supplier: positive score and negative score. These parameters, together with cost parameters, are utilized to propose a multi-objective mathematical model for determination of order quantity from each supplier. A numerical example is used in this paper to show the applicability of the proposed integrated model. Also, a sensitivity analysis is made to examine the effect of weighting environmental criteria on total purchasing cost and quantity of order from each supplier. The results show that the proposed model is efficient and applicable for real-world problems.

Robust Dual Sourcing Inventory Management: Optimality of Capped Dual Index Policies and Smoothing

Problem definition: How to dynamically replenish inventory from two supply sources or shipping modes with general lead times. The fast source is more expensive than the slow source. Academic/practical relevance: Dual sourcing provides supply chain flexibility to mitigate demand and supply risk. Despite its relevance in practice, characterizing the optimal dual sourcing policy is extremely challenging, and the optimal stochastic policy for nonconsecutive lead times has been unknown for over 50 years. Methodology: We present and solve a robust rolling horizon model for periodically reviewed dual sourcing inventory systems that minimizes the total cost of purchasing, inventory holding, and backlogging. Results: We prove that the optimal robust policy is a dual-index, dual-base-stock policy that constrains or “caps” the slow order and provide closed-form expressions for the three control parameters. The optimality result is established for general lead times and can accommodate nonstationary demand. Our numerical study shows that, as the lead time difference grows, the capped dual index policy increasingly smooths slow orders and for stationary demand, converges to the tailored base-surge policy, which places a constant slow order and has been shown to be asymptotically optimal. Managerial implications: Our capped dual index policy is easy to understand, explain, and implement in practice. In an extensive simulation study, the capped dual index policy performs as well as and can even outperform the best heuristics presented in the stochastic inventory literature.

Robust Dual Sourcing Inventory Management: Optimality of Capped Dual Index Policies

(1) Problem Definition: How to dynamically replenish inventory from two supply sources or shipping modes with general lead times. The fast source is more expensive than the slow source. (2) Academic/Practical Relevance: Dual sourcing provides supply chain flexibility to mitigate demand and supply risk. Despite its relevance in practice, characterizing the optimal dual sourcing policy is extremely challenging, and the optimal stochastic policy for non-consecutive lead times has been unknown for over 50 years. (3) Methodology: We present and solve a robust rolling horizon model for periodically reviewed dual sourcing inventory systems that minimizes the total cost of purchasing, inventory holding and backlogging. (4) Results: We prove that the optimal robust policy is a dual index, dual base-stock policy that constrains or “caps” the slow order and provide closed-form expressions for the three control parameters. The optimality result is established for general lead times and can accommodate non-stationary demand. Our numerical study shows that as the lead time difference grows, the capped dual index policy increasingly smooths slow orders and, for stationary demand, converges to the tailored base surge policy, which places a constant slow order and has been shown to be asymptotically optimal. (5) Managerial Implications: Our capped dual index policy is easy to understand, explain and implement in practice. In an extensive simulation study, the capped dual index policy performs as well as, and can even outperform, the best heuristics presented in the stochastic inventory literature.

Multi-criteria optimization model integrated with AHP for evaluation and selection of COTS components

This paper deals with COTS evaluation and selection for developing a modular software system under single application development task. We consider both quantitative and qualitative criteria which fulfils the specific needs of a software system. We use analytical hierarchy process (AHP) technique for evaluating the fitness of COTS components based upon various criteria and sub-criteria thereby providing overall score of each COTS component. We develop optimization models integrating AHP and multi-criteria decision-making, which aim at: (i) maximize the total value of purchasing (TVP) subject to budget, compatibility and reliability constraints, and (ii) maximize TVP and minimize the total cost of purchase simultaneously subject to compatibility and reliability constraints. The efficiency of the models is illustrated by means of numerical illustrations.

A Multi-objective Mathematical Model for Sustainable Supplier Selection and Order Lot-sizing under Inflation

Recently, scholars and practitioners have shown an increased interest in the field of sustainable supplier selection and order lot-sizing. While several studies have recently carried out on this field, far too little attention has been given to formulating a multi-objective model for the integrated problem of multi-period multi-product order lot-sizing and sustainable supplier selection under inflationary condition. In this study, a mathematical model for multi-period multi-product lot-sizing and sustainable supplier selection under the effects of inflation is developed. The proposed model includes four objective functions which are minimizing total cost, maximizing total social, total environmental, and total economic qualitative scores. The model attempts to simultaneously balance different costs under inflationary conditions to optimize the total cost of purchasing and other objective functions. The applicability of the proposed model is shown by an illustrative example. The results show that the proposed model can provide an effective purchasing plan for the company while monitoring the effect of inflation and assuaging its concerns regarding sustainability issues.

Chaos-assisted multiobjective evolutionary algorithm to the design of transformer

In this paper, multiobjective transformer design (TD) optimization is carried out using multiobjective evolutionary algorithm (MOEA) based on decomposition with dynamical resource allocation (MOEA/D-DRA) for four sets of conflicting TD bi-objectives such as (i) purchase cost and total loss, (ii) purchase cost and total lifetime cost (TLTC), (iii) total mass and total loss and (iv) total mass and TLTC, subjected to 14 various practical constraints. Significant decision variables with enlarged search space are employed for obtaining reliable and efficient TD with minimum losses and TLTC. TD is accompanied by 3D-finite element method assessment to validate the designed no-load loss calculated from analytical equations. To improve the searching ability of MOEA/D-DRA (MDRA) in solving this complex multimodal TD optimization problem (TDOP), this paper proposes integration of chaos with MDRA, enabling chaotic variation in the crossover rate and mutation scaling factor. To prove the effectiveness of chaos-assisted MOEA, logistic chaotic map-assisted MDRA, and iterative chaotic map with infinite collapses- (ICMIC) assisted MDRA (ICMDRA) have been successfully applied to multiobjective TDOP and their TD results are compared with those of MDRA, knee point-driven evolutionary multiobjective optimization algorithm (KnEA), and non-dominated sorting genetic algorithm (NSGA) II. This paper identifies which chaotic map can assist MDRA and solve TDOP by comparative analysis of performance of logistic and ICMIC chaotic maps. Efficient TD results and two MOEA performance indicators confirm the superiority of ICMDRA over all the other MOEAs in terms of diversity and convergence in solving TDOP.

A new agile fuzzy multi-objective model for selecting/eliminating supplier

In today's highly competitive world, supplier selection is an important decision for efficient supply chain management. Appropriate suppliers reduce the purchasing price and lead-time and increase customer satisfaction. However, after supplier selection stage it is possible that some of the selected suppliers' performances are not acceptable and caused more cost for our supply chain. In an agile supply chain management, it is necessary to have a solution for these cases and eliminate these kinds of suppliers. In this paper, we introduce two crisp/fuzzy multi-objective linear models to eliminate the most costly supplier and determine the amount of purchase from the remaining suppliers. Here we have three objective functions: total purchasing cost that must be minimised, quality and service level that must be maximised. Finally, case studies are presented to illustrate how our proposed models work.

Optimal sizing of storage tanks in domestic rainwater harvesting systems: A linear programming approach

This paper proposes an optimization model to determine the optimal tank size of a single residential housing unit for rainwater harvesting and storage. Taking into account the site specific data such as the rainfall profile, the roof area of the building, the water consumption per capita and the number of residents, an integrated optimization model based on linear programming is proposed to decide on the size of rainwater storage tank to build such that the net present value of the total tank construction costs and freshwater purchase costs is minimized. The proposed model was tested on a case study from Northern Cyprus, the results of which emphasized the feasibility of rainwater harvesting as a sustainable supplement to the depleting aquifers in the region. The study also offers managerial insights on the impact of various parameters such as the number of residents, roof area, discount rate, water consumption per capita, unit cost of building the rainwater tank, and rainfall characteristics on the optimal tank size and on the net financial benefit gained from rainwater harvesting through detailed sensitivity analysis.

Order allocation to cluster suppliers considering supply failure, multiple price discount, and supplier maintenance cost

With increasing geographical proximity within supply chain networks, the development of decision models for ordering materials from co-located cluster suppliers under the broad rubric of risk management in supply chains becomes an important problem given that geographic-specific regional failure with its associated loss is inevitable. This paper proposes a model that optimizes the supplier order allocation of demand among clusters under supply failure risks. The total purchase costs are formulated with supplier maintenance costs, item purchase costs, and the losses incurred due to three types of events in a level of generalization. The proposed model is a general nonlinear programming problem and coded in MATLAB to facilitate the search for the optimal solution with a smaller allocation multiples of 1%. Finally, in an application case, we demonstrated the proposed model’s potential of application.

Order allocation for multi-item sourcing with supply disruptions in shipment quality and delivery

This study outlines a mixed integer nonlinear programming formulation for supplier selection and order allocation in a multiple-item, multiple-supplier sourcing environment. The supplier selection process is driven by the multiple objectives of minimising total purchasing costs, rejections, and late deliveries, along with the constraints of demand, capacity, and price discounts. To reflect a wide variety of operational conditions, four cases of imperfect shipment quality have been developed in this study to analyse their impacts on the total cost. A numerical example was provided to demonstrate the effectiveness of the proposed model in establishing trade-offs among objectives. Sensitivity analysis has been carried out to understand the effects of the model parameters on the purchasing decisions and the total purchasing cost.

Modeling Pooled Purchasing Strategy in Purchasing Consortium to Optimize Total Purchasing Cost

Purchasing consortium (PC) is horizontal collaboration amongst companies to jointly perform their purchasing activities. PC offers companies with better pricing in obtaining goods. One of the critical factors companies to join in the PC is the amount of benefits that would be achieved when they join in the PC compared to when they purchase individually, this factor also called nature of benefits. Strategies to maximize the nature of benefit of PC include two aspects, namely minimizing the total cost of purchasing and maximizing the allocation of saving/gain to each member. This research will be focused on the first aspect of those strategies which is modelling strategy to minimize the total purchase cost. The total purchase cost involves the purchase costs, ordering costs, shipping costs, inventory costs, fix operational cost and penalty cost. Mixed integer programming was developed to model procurement strategy to minimize the total cost of the purchasing system. Sensitivity analysis was also performed to evaluate how different values of parameters affect the total cost. Numerical experiments showed that the total cost of purchasing very sensitive to changes in number of demand from each member of PC. Potential contributions of this research is to provide an overview of the purchasing strategy in the PC by a quantitative approach and determine the condition when a PC needs to make changes on its procurement strategy to maintain and/or improve its nature of benefits during different system's condition.

Multidemand Multisource Order Quantity Allocation with Multiple Transportation Alternatives

This paper focuses on a multidemand multisource order quantity allocation problem with multiple transportation alternatives. To solve this problem, a bilevel multiobjective programming model under a mixed uncertain environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the purchaser aims to allocate order quantity to multiple suppliers for each demand node with the consideration of three objectives: total purchase cost minimization, total delay risk minimization, and total defect risk minimization. On the lower level, each supplier attempts to optimize the transportation alternatives with total transportation and penalty costs minimization as the objective. In contrast to prior studies, considering the information asymmetry in the bilevel decision, random and fuzzy random variables are used to model uncertain parameters of the construction company and the suppliers. To solve the bilevel model, a solution method based on Kuhn-Tucker conditions, sectional genetic algorithm, and fuzzy random simulation is proposed. Finally, the applicability of the proposed model and algorithm is evaluated through a practical case from a large scale construction project. The results show that the proposed model and algorithm are efficient in dealing with practical order quantity allocation problems.

Integrasi Analytic Hierarchy Process dan Goal Programming Dalam Pemilihan Pemasok

Considering that raw material cost could reach 70% of product cost, supplier selection process was become important consideration to improve the efficiency of procurement department. The supplier selection process was multi-criteria problem involving qualitative and quantitative factors. UD Virgo Bersinar was one of fruit chips manufacturer located in Malang. The company often experienced shortage of jackfruits which interrupt production process. Thus, it was necessary to choose its supplier by considering company’s criteria and order allocation of selected supplier. This research would integrate supplier weight from Analytic Hierarchy Process and Preemptive Goal Programming model to determine priority of supplier and order quantity to the supplier. The model was formulated to achieve two objective functions, namely maximizing total value of purchasing and minimizing total cost of purchasing, by considering capacity of supplier and fulfillment of jackfruit. By running goal programming model, it was concluded that supplier from Semarang would supply 7.5 ton of jackfruit in November 2013-Januari 2014 and deficiency of needs would be supplied by supplier from Tajinan.

Cost impact of dynamically managing generation reserves

Generation reserves are needed to maintain the real time balance between power supply and power demand. Because power is noninventoriable, power generation follows power demand. Demand for power varies considerably depending on the time of day, day of the week and season. The predictable portion of power demand is met by purchasing firm energy on a day ahead or real time market. The random unpredictable portion of demand is met by purchasing a set of online and offline generation reserves on an ancillary market. The total energy purchasing cost includes payments for firm energy and payments for generation reserves. The latter include fixed capacity payments for reserve generation capacities and variable payments for the random energy produced from these reserves. The main contribution of this paper is to present an optimization model that captures the dynamism in the selection of the dispatch interval to determine the amount of firm energy and reserve capacities given a set of market prices. This is done by explicitly including in the model the duration of the dispatch period and the frequency this decision is reevaluated. In this model the randomness of the demand is captured by using a Doubly Truncated Normal Distribution. The cost incurred to activate generation reserves is modeled as a Poisson process. The total model captures the price differences from using different reserve sources. An empirical example is presented to illustrate the cost benefits of using the method proposed in this research with two different strategies: a static strategy and a dynamic strategy. It is shown that dynamically setting generation reserves results in cost savings.

Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain

Abstract An organization's environmental performance is affected by its suppliers' environmental performance, and selecting green suppliers is a strategic decision in order to be more competitive in today's global market. The supplier selection problem involves several quantitative and qualitative criteria. In the supplier selection process, if suppliers have limited capacity or other constraints, it is necessary to determine the best supplier and order quantity of each supplier. In this paper, we present an integrated approach, of fuzzy multi attribute utility theory and multi-objective programming, for rating and selecting the best green suppliers according to economic and environmental criteria and then allocating the optimum order quantities among them. At first, the fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal solution is applied in order to analyze the importance of multiple criteria by incorporating experts' opinion and to determine the best green suppliers. Next, multi-objective linear programming is used to consider and to formulate various constraints such as quality control, capacity, and other objectives. The objective of the mathematical model is simultaneously to maximize the total value of purchasing and to minimize the total cost of purchasing. To handle the subjectivity of decision makers' preferences, fuzzy logic has been applied. The efficiency and application of the proposed approach has been illustrated with a case study in an automobile manufacturing company. The obtained results help firms establish a systematic approach for tackling green supplier selection and order allocation problems in a realistic situation. Finally managerial implications, conclusions, and directions for additional research are introduced.

Utilisation des antidépresseurs de seconde génération (SGA) dans la pratique quotidienne du médecin : une synthèse à l’usage du clinicien

Win 2009 French people consumed nearly 36 billion euros worth of drugs, 18% more than in 2004, resulting in growing burden for French Social Security. Expenses refunded by public health insurance amount to 26.8 billion euros or 74.5% of the total cost of drug purchase. While French authorities point to the need for rational prescribing, especially concerning psychotropic drugs, few data on the prescription of second-generation antidepressants (SGA) are synthesized for clinicians’ use. Objective of the studyTo offer a description of effectiveness and tolerability/acceptability for each SGA according to data from the most recent meta-analyzes. MethodFor each SGA we searched for the latest meta-analysis in Medline (1966–present), Web of Science (1975–present) and Cochrane (all journals). ResultsConsidering the benefit–risk ratio and the cost (generic), the first-line treatment for a major depressive episode may be currently sertraline (50mg/day). It may however have more digestive side effects than other SSRIs (due to the serotonin action), which calls for caution while increasing doses. The four most effective antidepressants may be, in this order, mirtazapine, escitalopram, venlafaxine and sertraline. Escitalopram and sertraline may show a better acceptability profile than mirtaapine, duloxetine, fluvoxamine, paroxetine, and venlafaxine. Fluoxetine seems relevant in treatment of negative symptoms of schizophrenia (20mg/day) and in bulimia (60mg/day). Fluvoxamine seems relevant in the case of sexual side effects with a previous SSRI, in treatment of anxiety disorders (it's affinity for sigma receptors may confer a specific action) and in psychotic depression. Mirtazapine may be a treatment of interest when a fast remission of depressive symptoms is warranted but its tolerance profile makes it difficult to use. We found no meta-analysis comparing the efficacy and tolerability of duloxetine with other antidepressants, probably due to the recent availability of the molecule. We found no arguments in favor of first-prescription of tianeptine except perhaps a better safety profile but data were scarce. The presence of generalized anxiety disorder is an indication to start treatment at half doses over a few days, whatever the molecule, to avoid a resurgence of anxious symptomatology. Prescription of antidepressants has demonstrated its efficacy in major depressive episode, in anxiety disorders (for most molecules), in dysthymia but not in seasonal affective disorder (SAD). Efficiency in dysthymia may explain the prescription of antidepressants that exceed the scope of the marketing authorization. ConclusionWe summarize in this article all current data from recent meta-analyzes regarding the effectiveness, tolerance and acceptability of SGA to help practitioners’ choices of prescription.

An integration of a hybrid modified TOPSIS with a PGP model for the supplier selection with interdependent criteria

This paper deals with the supplier selection process when the criteria of selection are not independent. The primary aim is to illustrate that ignoring the interdependencies among these criteria leads to an unrealistic decision. Since supplier selection is a multiple-criteria decision-making (MCDM) process, the general structures of the relative methods are outlined in this paper. The proposed methodology then presents a comprehensive approach to find the best ranking among the alternative suppliers of a typical product. A hybrid modified TOPSIS is integrated with a preemptive goal programming model in this study and then compared to an analytical hierarchy process (AHP) to show the effect of considering interdependencies in the supplier selection process. The results indicate that the proposed methodology provides a higher total value of purchasing (TVP) than AHP while the total cost of purchasing (TCP) of both methods are equal. Empirical proof is offered based on a case study.

Multi-objective model for multi-period, multi-products, supplier order allocation under linear discount

In this paper, a multi-objective mixed integer nonlinear programming model is proposed to solve the supplier order allocation problem. We investigated a problem where a single buyer orders multiple products from multiple suppliers in multiple periods. The model takes into account the linear discount pricing scheme, the suppliers' capacity limits, the delivery rate and the quality of the items, the minimum order quantities, and the budgetary limitations. The buyer should decide on how to allocate orders optimally between selected suppliers to maximize the total value of purchase while minimizing the total cost of purchase simultaneously. Finally, two numerical examples in discount and non-discount environments are given to illustrate the application of the model. The results show that order values can be largely influenced by supplier linear discount.

Cost modeling approach and economic analysis of biomass gasification integrated solid oxide fuel cell systems

This paper presents a cost modeling approach and the economic feasibility for selected plant configurations operating under three modes: air gasification, mixed air-steam gasification, and steam gasification combined cycle solid oxide fuel cell (SOFC) systems. In this study, three cases of biomass gasification integrated SOFC without combined cycle (base case 1) are compared with biomass gasification integrated SOFC-gas turbine (GT) with heat recovery steam generator (HRSG) hybrid configuration (case 2) and biomass gasification integrated SOFC-steam turbine (ST) cycle (case 3) for biomass feed stock. The plant design cases of integrated biomass gasification processes, SOFC, and combined cycles are investigated primarily employing aspen plus™ flow sheeting models. Based on the mass and energy balance results of the system simulations, the economic model calculates the size and cost estimates for the plant configuration equipments. Detailed purchase cost estimations for each piece of equipments and the corresponding total bare module cost were established based on the bare module factor, and the total direct permanent investment were established. The calculated direct permanent investment was used for economic feasibility analysis of the cogeneration plant configuration. The economic feasibility decision parameters, such as purchase cost estimations, total specific plant cost per kW, the financial net present value (FNPV), internal rate of return, and benefit to cost ratio for the system configurations were compared for three design cases. The results shows that the cost of the steam gasification system is shown to be highest compared to the mixed air steam gasification system and air gasification system due to the higher hydrogen production. Other than the SOFC and gasification costs, a significant cost towards the heat exchangers is about 15% to 20% of the total equipment purchase cost. The specific plant cost for the air gasification varied from the 16 600 US$/kW to 19 200 US$/kWe. For the case of biomass gasification-integrated SOFC-GT configuration, the major cost portions are shared by the SOFC, HRSG, and gasifier. The HRSG equipment shared a cost portion of 25% to 30% for all the operating modes. The cost is decreased with a similar trend as that of steam, mixed air-steam and air gasification systems. For the case of biomass gasification integrated with SOFC-ST configuration, the total equipment purchase cost decreases at a rate of 15% to 20% for both mixed air steam and air gasification system. Plant specific cost for the steam gasification mode varied from 15 200 to 17 200 US $/kW which is significantly very high.

The Impact of Group Purchasing Organizations on Healthcare-Product Supply Chains

This paper examines the impact of group purchasing organizations (GPOs) on healthcare-product supply chains. The supply chain we study consists of a profit-maximizing manufacturer with a quantity-discount schedule that is nonincreasing in quantity and ensures nondecreasing revenue, a profit-maximizing GPO, a competitive source selling at a fixed unit price, and n providers (e.g., hospitals) with fixed demands for a single product. Each provider seeks to minimize its total purchasing cost (i.e., the cost of the goods plus the provider's own fixed transaction cost). Buying through the GPO offers providers possible cost reductions but may involve a membership fee. Selling through the GPO offers the manufacturer possibly higher volumes, but requires that the manufacturer pay the GPO a contract administration fee (CAF), i.e., a percentage of all revenue contracted through it. Using a game-theoretic model, we examine questions about this supply chain, including how the presence of a GPO affects the providers' total purchasing costs. We also address the controversy about whether Congress should amend the Social Security Act, which, under current law, permits CAFs. Among other things, we conclude that although CAFs affect the distribution of profits between manufacturers and GPOs, they do not affect the providers' total purchasing costs.