Optimal Procurement Strategy Research Articles

Research on Risk-Averse Procurement Optimization of Emergency Supplies for Mine Thermodynamic Disasters

Reducing procurement risks to ensure the supply of emergency supplies is crucial for mitigating the losses caused by mine thermodynamic disasters. The risk preference of decision-makers and supply chain collaboration are the important aspects for this reductiom. In this study, a novel P-CVaR (Piecewise conditional risk value) distributionally robust optimization model is proposed to accurately assist the decision-makers’ decision of risk preference for reducing procurement risks. Meanwhile, the role of cooperation between procurement and reserves are considered for the weakening procurement risks. A risk-averse bi-level optimization model is proposed to obtain the optimal procurement strategy. Furthermore, by applying the Lagrange duality theorem, the complexity of the bi-level optimization model is simplified then solved using a PSO algorithm. Using empirical analysis, it has been verified that the model presented in this paper serves as a valuable guideline for mine thermodynamic pre-disaster emergency material procurement strategies for the prevention of thermodynamic disasters.

A purchaser's optimal procurement strategy under emergencies

This paper investigates a purchaser's optimal procurement strategy under emergencies. The occurrence of emergencies often leads to sharp fluctuations in prices and weakens supply stability, which increases procurement risks for purchasers. In this study, we use the option contract to actively respond to market fluctuations and reduce procurement risks caused by emergencies. If there is insufficient inventory due to occurrence of emergencies, part or all of the shortages can be met by an emergency replenishment. Then, we conduct an in-depth research on the optimal option procurement strategy for purchasers under emergencies. The results show that, a higher emergency replenishment rate leads to a smaller option purchase quantity if the shortage cost is not considered, since the emergency replenishment can save part or all of the shortages. If the shortage cost is considered, both over-purchasing and under-purchasing of options lead to corresponding losses, and then the trend in a purchaser's optimal option purchasing decision is decided by the dominance of the two types of losses. Finally, we summarize the results of the study to give managerial insights into the optimal option purchasing strategies for purchasers under emergencies.

Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a Multi-Objective Approach

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi-objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, the algorithm strategically selects the optimal procurement strategy, either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

Использование методов кластеризации для анализа продаж автозапчастей на СТО грузовых машин

Clustering methods are widely used to divide goods into groups depending on sales volumes in order to build an optimal purchasing planning and inventory management strategy. Cluster analysis methods do not provide an unambiguous partition of the original set of objects, therefore, in the work, existing clustering methods were analyzed to study sales of auto parts at truck service stations. To solve the problem, the following methods were chosen: k-means, hierarchical agglomerative clustering and DBSCAN. Before using the k-means method, the elbow method found the optimal number of clusters. The DBSCAN method is based on object density and automatically determines the number of clusters. The initial data for cluster analysis was information on sales of spare parts at truck service stations for 3 years; clustering was applied to data by year. The DBSCAN algorithm showed unsatisfactory results, since most of the goods (86%) were identified in one cluster, while others contained units of goods. The k-means method gave the best partitioning result, each group has a different volume. The distribution of goods in clusters changes over three years, so managers should study the change in the affiliation of goods to one group or another. The obtained clustering results will help determine the real needs of spare parts at truck service stations and build an optimal procurement strategy.

Sourcing diversification: Strategy selection, sourcing allocation and brand power

The supply of power batteries is monopolised by a few suppliers, resulting in high purchasing costs for electric vehicle manufacturers (EVMs). Hence, some EVMs have implemented diversified sourcing strategies to seek purchase concessions and reduce their dependence on battery suppliers. Motivated by the sourcing strategies of EVMs, we develop a game-theoretic model to investigate the optimal procurement strategies of manufacturers with different brand power. The model consists of a primary supplier, an alternative supplier, and two competing manufacturers, wherein the focal manufacturer has the option of implementing dual sourcing or partial outsourcing through sourcing allocation, whereas the rival manufacturer only sources from the primary supplier. Specifically, we first study the focal manufacturer’s three procurement strategies under the primary supplier’s uniform pricing: single sourcing (the benchmark), dual sourcing and partial outsourcing. Then, we examine the primary supplier’s strategic response to the manufacturer’s sourcing diversification: maintaining uniform pricing or switching to differential pricing. By analysing the equilibrium profits, we find that compared with the situation under single sourcing, both the primary supplier and the focal manufacturer may obtain higher profits under dual sourcing or partial outsourcing. However, with an increase in the focal manufacturer’s order proportion, the primary supplier’s profit may decrease; and the focal manufacturer’s diversified sourcing strategy may hurt all the supply chain players. Furthermore, we show that when the primary supplier considers switching to differential pricing, the focal manufacturer tends to abandon the diversified sourcing and adopt single sourcing.

Optimal procurement strategy for multi-echelon remanufacturing systems under quality uncertainty

Uncertainties in the quality and price of returned items add to the management complexity of remanufacturing system. To achieve the optimal procurement strategy for improved economic performance, this paper develops a mathematical model to quantify the impact of quality-based categorization of returned products under different return scenarios. Through the stochastic analysis of quality uncertainty with returns, it derives the optimal procurement strategy for cost minimization. The results show that an optimal procurement decision leads to over 80% cost savings in multi-echelon remanufacturing systems. Further, to bridge the gap between optimality theory and practical applications, this paper employs maximum likelihood estimation to speculate the key parameters of quality distribution in an empirical case whereby the optimal procurement strategies are formulated. Thus, the study provides a mathematical foundation for remanufacturers to make optimal decisions for the minimization of total cost. Importantly, the paper innovatively employs a quality coefficient to signal the overall condition in a batch of returned cores before accepting or dismantling, and then establishes the explicit relationship among the quality coefficient and production indexes in remanufacturing.

Managing Emergency Procurement Using Option Contract under Supply Disruption and Demand Uncertainty

Effective and flexible procurement and production strategies are capable of alleviating and mitigating supply disruption and demand risk. Considering the price fluctuation caused by environmental change, we investigate the optimal procurement and production strategies under supply disruption and demand uncertainty based on option contract in a two‐stage supply chain consisting of a retailer who has two procurement opportunities and a supplier who has the emergency production chance. We explore the value of option contract by comparing it with the optimal decision making under no option contract. The result shows that option ordering and emergency procurement can coordinate the optimal strategies under uncertain environment, improving the economic performance of whole supply chain. When the disruption probability is high or the price of emergency procurement is lower, the higher option price can stimulate the supplier to produce more products to satisfy the retailer’s emergency order at a low price, which is beneficial to both, and the value of option ordering is greater. Otherwise, the emergency procurement is worth more for the core enterprise. The moderate exercise price is conducive to the long‐term cooperation of the supplier and the retailer.

Optimal procurement strategy for off-site prefabricated components considering construction schedule and cost

The implementation of prefabricated buildings is hindered by the high construction cost. To address this problem, it is important to determine the efficient leverage of the supply capacity of local factories to assure just-in-time delivery to the construction site, as well as to accurately model the various types of cost to achieve a near-optimal procurement strategy. This paper describes a mathematical model to optimize the procurement of prefabricated components. A genetic algorithm is applied to efficiently obtain the minimum total cost that includes installation cost, business management cost and loan interest cost. Finally, a number of numerical experiments are conducted, showing that the procurement strategy generated from the proposed mathematical model is better than the traditional procurement strategy in terms of construction duration reductions and total cost savings. The component procurement method proposed in this paper improves the scientific management ability of decision-makers, and provides purchasing framework for other prefabricated structural forms.

Integrated optimisation of pricing, manufacturing, and procurement decisions of a make-to-stock system operating in a fluctuating environment

ABSTRACT Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations, they typically utilise operational hedging strategies in terms of pricing, manufacturing, and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically, we study an integrated problem of procurement, manufacturing, and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain, and they directly affect the system's procurement price, raw material flow rate, and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that, we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We, particularly, investigate how production rate, holding cost, procurement price and demand variabilities, customers' price sensitivity, and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore, our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular, the profit enhancement being achieved with dynamic pricing can reach up to 15%, depending on the problem parameters.

Mathematical Program for Optimal Procurement of Liquefied Natural Gas Cargos with Split Deliveries

The steady growth in the demand for liquified natural gas (LNG) as a transition fuel has resulted in an increase in its trade all along. Consequently, there have been various innovations in its trade and transportation. There are opportunities for an LNG importer that is currently using only long-term contracts to source LNG to optimize their costs by utilizing short-term opportunities in the spot market. Previously, we had proposed a mathematical model to identify an optimal procurement strategy that considers existing ongoing long-term contracts and spot market cargos. We extend the model in this work and incorporate split delivery, an innovation that has been recently introduced in LNG transportation, which enables further operational flexibility. We propose a mixed-integer linear programming model that utilizes split deliveries to reduce the overall procurement costs for a set of terminals. Over a set of scenarios, the proposed model saved, on average, $1269.5 million over a 12-month period when split deliveries are included as an option. We also propose two benefit allocation schemes: one based on the well-known Shapley value and another simpler one to fairly distribute the savings among the terminals. These ensure that every terminal would gain by participating in a coalition for split deliveries.

Dual sourcing: Creating and utilizing flexible capacities with a second supply source

We study a finite horizon, single product, periodic review inventory system with two supply sources and a salvage option. These supply sources are typically capacitated and capacity levels often need to be reserved or installed in advance of the operational planning horizon. The supply sources may thus be differentiated by their lead times, capacities, and fixed and variable order costs. Salvage options allow for inventory reductions and incur fixed cost and variable revenues. We first analyze the tactical problem of determining an optimal procurement strategy under given capacity profiles at the two suppliers. We then address the strategic model in which optimal capacity profiles, both static and dynamically adjusted, are obtained based on two‐part capacity contracts. We characterize the structure of optimal procurement strategies when the lead times of the two suppliers differ by a single period and the lead time for salvage opportunities matches that of one of the suppliers. For general lead time combinations, we show that the optimal procurement strategies satisfy monotonicity and limited sensitivity properties, and construct effective heuristics and upper and lower bounds based on our structural results.

공급사슬 중단 및 수율 불확실성을 고려한 최적 공급업체 수와 주문량의 결정 및 보완주문 전략에 관한 연구

In this study, we developed an optimal sourcing strategy considering compensation orders to mitigate the supply chain disruption risks and yield uncertainty. Additionally, we proposed an approximation method that can perform quick calculations to ascertain the optimal number of suppliers and initial order quantity. Finally, by using a numerical example, we confirmed that if there is a disruption probability, it is desirable to distribute the risk by dividing the order into small portions from multiple suppliers. This study proposes an optimal procurement strategy under disruption risks and serves as a guide to establishing a procurement strategy that maximizes a firm's profit in the long term.

Designing flexibility procurement markets for congestion management – investigating two-stage procurement auctions

Increasing infeed from renewable energy sources poses considerable challenges to system operators (SOs) who are in charge of power system reliability. Accordingly, the frequency of network congestion and the corresponding congestion management costs have increased dramatically over the last years and give reason to extensively discuss alternative approaches. Among these, flexibility markets bear the potential to complement existing congestion management practices by incentivising decentralised resources with large potentials of flexibility to participate in relieving congestion. For this reason, multiple demonstration projects across Europe are currently testing different flexibility market designs.We contribute to this on-going discussion by investigating the auction design of such a flexibility market. We analytically derive the optimal procurement strategy of a SO within a flexibility market platform, recurring to the well-established methodology of the classical Newsvendor problem and extending it in a stochastic programming framework with two stages. We apply our model to a case study of a transformer that is frequently congested due to high infeed from wind farms. Based on an analysis of relevant sources of flexibility, differentiated concerning lead time and cost structure, we explore the effects of demand uncertainty and information updates between auctions. The results of the case study, including a comprehensive sensitivity analysis, reveal insights that are used to provide policy advice on how to design flexibility procurement markets under specific conditions.

Optimal Procurement Strategy for Supply Chain with Trade Credit and Backorder under CVaR Criterion

Some capital-constrained and risk-averse retailers may unable to obtain financing from banks due to insufficient collateral and high loan costs, so some retailers tend to use trade credit financing to ease their financial pressure. For the two echelon supply chain composed of a well-funded supplier and a capital-constrained retailer with risk-averse preference, a trade credit strategy model with the supplier-led is established in this paper. By analyzing both parties’ benefits, we derive the model solution and provide optimal decisions to all petitioners. The results obtained in this paper show that the optimum order quantity under the Conditional Value-at-Risk (CVaR) criterion declines w.r.t. the confidence level, and the wholesale price of the supplier increases w.r.t. the confidence level. The reason is that when the retailer makes fewer orders, the supplier will correspondingly increase the wholesale price to maximize their profit. On the other hand, the ordering policy with allowing backorder will make the retailer place fewer orders. Finally, the proposed model is indicated by the given numerical experiments.

Procurement competition in the presence of IoT-enabled B2B E-commerce

• We explore whether manufacturers give up traditional channel under B2B spot market. • Two risk-averse manufacturers engage a Cournot competition. • Manufacturers choose the optimal procurement strategy based on spot price variance. • The FN strategy may benefit the whole downstream manufacturers. • The FF subgame always benefits the whole supply chain. The Internet of things (IoT) has accelerated the development of e-commerce and enabled business-to-business (B2B) spot markets to emerge. This study considers a supply chain composed of one supplier and two manufacturers. Two manufacturers can procure raw materials from dual sources, that is, from a supplier with a forward contract or from a B2B spot market. Two manufacturers use raw materials to produce the final product and take part in Cournot competition in the final customer market. On the basis of the procurement strategies of two manufacturers, three competition structures exist between the manufacturers: (NN) both manufacturers procure raw materials only from a B2B spot market; (FN) one manufacturer procures raw materials from dual sources, namely, a forward contract and a B2B spot market, while the other manufacturer only obtains them from a B2B spot market; and (FF) both manufacturers procure raw materials from dual sources. We investigate which procurement strategy is better for manufacturers. Our study finds that the optimal setting is for manufacturers to procure raw materials from dual sources if and only if the spot price uncertainty exceeds a threshold value. Furthermore, the optimal wholesale price in the FN subgame is less than the price in the FF subgame. The optimal order quantity in the FF subgame is less than the quantity in the FN subgame. Whole downstream manufacturers can benefit from the FN strategy if the supplier’s risk aversion exceeds a threshold value. Meanwhile, the FF subgame always benefits the whole supply chain.

Procurement risk management in a petroleum refinery

ABSTRACTWe analyze a petroleum refinery's procurement strategy, explaining how risk management affects optimal sourcing from long‐term, spot, and swap contracts. We use time series analysis to model the interaction between petroleum prices, transportation costs, and gross product worth. These models are then used to generate the scenarios incorporated in the stochastic program applied to compute the conditional value‐at‐risk. We prove the necessary and sufficient conditions for the optimal procurement and risk management strategies, and show that risk aversion can be better represented by the weighted average between expected profit and conditional value‐at‐risk, deriving the respective ISO curves. We estimate that an increase in the degree of risk aversion decreases the use of swap contracts. Our model is applied to the analysis of a refinery based in Singapore. Using regression analysis, we show we cannot reject the hypothesis of a statistically significant relationship between the way Saudi Arabia prices the long‐term contracts and the shape of the forward curve. We then study how risk aversion influences the procurement strategies, profitability, and risk exposure of the refinery. Finally, we analyze the pricing of long‐term (forward) contracts by Saudi Arabia, and study how the country could benefit from a different pricing policy.

The impact of multiple products and limited capacity on routine sourcing decisions

We study the influence of limited capacity and multiple products on the optimal sourcing decisions for a manufacturer subject to supply and demand uncertainty. The manufacturer produces two products. The component of one product is sourced from two suppliers, one perfectly reliable and the other cheaper but unreliable, failing to deliver the order with some probability. We develop a mathematical model for a single-period setting and study the associated optimality conditions. Our results show that the supplier diversification strategy is significantly different when the buyer has limited capacity. Specifically, we find that single sourcing from an expensive and reliable supplier may be an optimal procurement strategy even when a cheaper option exists. In addition, when producing multiple products, the optimal sourcing strategy may hold inventory of one component to reduce the amount of capacity idling when the supply of a different component is disrupted. We use stochastic programming to validate our single-period findings in a multi-period setting. Although carrying inventory of finished products provides another tactic to mitigate supply uncertainty in a multi-period environment, we observe the key findings from single period, like single-sourcing from an expensive but reliable supplier, remain valid.

Supplier selection mechanism in electric coal procurement under sustainability.

The electric energy mainly comes from thermal power generation fueled by electric coal, and the selection of electric coal supplier is of great practical significance for the development of electric power enterprises. In this paper, the mechanism of supplier selection in electric coal procurement is designed from a perspective of sustainability. Concretely, eleven evaluation indexes from three aspects of economy, environment, and society are selected to construct a new evaluation index system for selecting sustainable electric coal suppliers. Based on this index system, a new method of supplier selection in electric coal procurement based on 2-tuple correlation coefficient analysis is proposed. In this method, considering that the evaluation index values have the features of multi-source heterogeneous data, i.e., precise real number, interval number, and fuzzy linguistic variables coexist, all index values are converted into 2-tuples. Then, a 2-tuple deviation maximization method is proposed to determine the weight of each evaluation index, and a new method of 2-tuple correlation coefficient analysis is proposed to select sustainable electric coal suppliers. Moreover, the effectiveness and feasibility of the model are highlighted by an application example of selecting the electric coal suppliers. Combined with the development status of electric power industry and the research results of this paper, several countermeasures and suggestions are provided on how to formulate the optimal procurement strategy of electric coal for electric power enterprises and how to improve the competitiveness for electric coal suppliers.

A decision-support system to assist forest industry planning and investment when data are scarce: the case of hardwood veneering in subtropical eastern Australia

ABSTRACT An aspatial mathematical model has been developed to simultaneously support log processing investment decisions with respect to processing scale, facility location and log procurement when data are scarce. A key design principle was to make the model suitable for use by industry. The objective function maximises gross margins per hour of log processing time, and the model accounts for potential processing efficiencies with larger-diameter logs. To support log procurement decisions, the model estimates mill-delivered log costs at which a log procurement officer should be indifferent between purchasing alternative log types. The utility of the model is demonstrated with an application to rotary veneer processing of hardwood logs in subtropical eastern Australia. Complex interactions between processing scale, facility location and log procurement strategies were revealed by substantial differences in gross margins between modelled scenarios. Log procurement decisions were found to have the greatest potential impact on gross margins, followed by facility location and processing scale. The model highlighted that substantially higher returns can be earned from optimal log procurement strategies relative to approaches that either minimise log costs, maximise product recovery or do not differentiate between log types and simply utilise all available log volume.

Optimal Ordering Policy for Supply Option Contract with Spot Market

This paper considers the procurement mechanism with two supply channels, namely, an option contract purchase and a spot market. For the mechanism, under the stochastic demand and the stochastic spot price, we consider the portfolio procurement with the spot trading liquidity and the option speculation respectively. To maximize the buyer’s profit, we establish two optimal portfolio procurement strategy models for those two scenarios. Based on the buyer’s cost-benefit analysis, we present a solution method to each model and provide an optimal ordering policy to the buyer. By the obtained results, we analyze the role of the spot trading liquidity and option speculation in a buyer’s expected profit. Some numerical experiments are presented to show the validity of the formulated models.