The use of total cost of ownership for strategic procurement: a company-wide management information system

We present a general company-wide management information system for defining procurement strategies. We believe that existing practices for determining purchasing strategies can be improved and a new approach developed. The system uses total cost of ownership information. We argue that mathematical programming models should be used for exploiting this information, when evaluating the firm's strategic procurement options. As an example, we show how we have successfully applied our approach to develop a decision support system at Usinor, a European multinational steel company.     

带有交易费用和持有成本的局内采购管理模型竞争分析

采购管理是企业经营活动的一个重要组成部分,更加有效的采购管理策略可以大大减少采购费用,对于企业的经营业绩非常重要。在现实的经济活动中交易费用和持有成本在企业管理费用中占很大一部分比率,而采购过程影响着交易费用和持有成本。所以在前人研究的基础上,将交易费用和持有成本引入到局内采购管理模型中,使得运用该策略无论以后采购价格如何变化,局内人的采购成本总是对应局外问题最优采购成本的一定比例c之内,并得到c与原模型相同。但是引入交易费用和持有成本后每天的采购量将发生变化,原模型是在不考虑交易费用和持有成本的前提下得得到的每天采购量和最优竞争比,如果考虑到现实经济活动中不可忽略的交易费用和持有成本,仍然按照原模型来确定每天的采购量来采购就不能得到最优竞争比c。所以本文考虑到了交易费用和持有成本,并得到和原模型不同的每天采购量,并求出最优竞争比c。     

A robust optimization model for agile and build-to-order supply chain planning under uncertainties

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.     

A mathematical programming model for integrating production and procurement transport decisions

In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study.     

Furniture supply chain tactical planning optimization using a time decomposition approach

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer’s demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.     

A multi-stage financial hedging approach for the procurement of manufacturing materials

This paper addresses the problem of mitigating procurement risk that arises from volatile commodity prices by proposing a hedging strategy within a multi-stage time frame. The proposed multi-stage hedging strategy requires a commodity futures position to be correctly initialised and rebalanced with adequate volumes of short/long positions, so as to reduce the volatility in the total procurement cost that would otherwise be generated by varying commodity spot prices. The novelty in the approach is the introduction of the rebalancing of commodity futures position at defined intermediate stages. To obtain an efficient or near optimal multi-stage hedging strategy, a discrete-time stochastic control model (DSCM) is developed. Numerical experiments and Monte Carlo simulation are used to show that the proposed multi-stage hedging strategy compares favourably with the minimal-variance hedge and the one-stage hedge. A close-form optimal solution is also presented for the case when procurement volume and price are independent.     

An analytical method for cost analysis in multi-stage supply chains: A stochastic network model approach

This paper studies the cost distribution characteristics in multi-stage supply chain networks. Based on the graphical evaluation and review technique, we propose a novel stochastic network mathematical model for cost distribution analysis in multi-stage supply chain networks. Further, to investigate the effects of cost components, including the procurement costs, inventory costs, shortage costs, production costs and transportation costs of supply chain members, on the total supply chain operation cost, we propose the concept of cost sensitivity and provide corresponding algorithms based on the proposed stochastic network model. Then the model is extended to analyze the cost performance of supply chain robustness under different order compensation ability scenarios and the corresponding algorithms are developed. Simulation experiment shows the effectiveness and flexibility of the proposed model, and also promotes a better understanding of the model approach and its managerial implications in cost management of supply chains.     

Multiattribute electronic procurement using goal programming

One of the key challenges of current day electronic procurement systems is to enable procurement decisions transcend beyond a single attribute such as cost. Consequently, multiattribute procurement have emerged as an important research direction. In this paper, we develop a multiattribute e-procurement system for procuring large volume of a single item. Our system is motivated by an industrial procurement scenario for procuring raw material. The procurement scenario demands multiattribute bids, volume discount cost functions, inclusion of business constraints, and consideration of multiple criteria in bid evaluation. We develop a generic framework for an e-procurement system that meets the above requirements. The bid evaluation problem is formulated as a mixed linear integer multiple criteria optimization problem and goal programming is used as the solution technique. We present a case study for which we illustrate the proposed approach and a heuristic is proposed to handle the computational complexity arising out of the cost functions used in the bids.     

An exact algorithm for the Capacitated Total Quantity Discount Problem

In this paper we analyze the procurement problem of a company that needs to purchase a number of products from a set of suppliers to satisfy demand. The suppliers offer total quantity discounts and the company aims at selecting a set of suppliers so to satisfy product demand at minimum purchasing cost. The problem, known as Total Quantity Discount Problem (TQDP), is strongly NP-hard. We study different families of valid inequalities and provide a branch-and-cut approach to solve the capacitated variant of the problem (Capacitated TQDP) where the quantity available for a product from a supplier is limited. A hybrid algorithm, called HELP (Heuristic Enhancement from LP), is used to provide an initial feasible solution to the exact approach. HELP exploits information provided by the continuous relaxation problem to construct neighborhoods optimally searched through the solution of mixed integer subproblems. A streamlined version of the proposed exact method can optimally solve in a reasonable amount of time instances with up to 100 suppliers and 500 products, and largely outperforms an existing approach available in the literature and CPLEX 12.2 that frequently runs out of memory before completing the search.     

A portfolio approach to managing procurement risk using multi-stage stochastic programming

Procurement is a critical supply chain management function that is susceptible to risk, due mainly to uncertain customer demand and purchase price volatility. A procurement approach in the form of a portfolio that incorporates the common procurement means is proposed. Such means include long-term contracts, spot procurements and option-based supply contracts. The objective is to explore possible synergies among the various procurement means, and so be able to produce optimal or near optimal results in profit while mitigating risk. The implementation of the portfolio approach is based on a multi-stage stochastic programming model in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being determined by simultaneously considering the stochastic demand and the price volatility of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model are performed using demand data from a large air conditioner manufacturer in China and price volatility data from the Shanghai steel market. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability.     

Determining sourcing strategies: a decision model based on activity and cost driver information

Determining sourcing strategies for different material groups provides a major challenge to most companies. There has been little research on the choice of the optimal number of different suppliers for a given product group and the determination of their market shares. In this paper we propose a mathematical programming model using activity based costing information to determine optimal order splitting among suppliers on the basis of the different costs associated with the purchasing decision. We argue that sourcing strategies should be based on the minimisation of the total cost of ownership resulting from external purchases. We demonstrate the model by a real life case regarding the procurement of ball bearings at Cockerill Sambre S.A., a Belgian multinational steel producer. This decision support system is currently used by the company. This case resulted in the decision by management to develop comprehensive, company wide purchasing software using our mathematical program based on the total cost of ownership concept.     

成品油配送多车舱车辆指派及路径优化问题研究

针对成品油配送中多车型、多车舱的车辆优化调度难题,综合考虑多车型车辆指派、多车舱车辆装载及路径安排等决策,以派车成本与油耗成本之和的总成本最小为目标,建立了多车型多车舱的车辆优化调度模型。为降低模型求解的复杂性,本文提出一种基于C-W节约算法的“需求拆分→合并装载”的车辆装载策略,并综合利用Relocate和Exchange算子进行并行邻域搜索改进,获得优化的成品油配送方案。最后,通过算例验证了本文提出的模型与算法用于求解大规模成品油配送问题的有效性。并通过数据实验揭示了以下规律:1)多车舱车辆相对于单车舱车辆在运营成本上具有优越性;2)大型车辆适合远距离配送,小型车辆适合近距离配送;3)多车型车辆混合配送相对于单车型车辆配送在运营成本上具有优越性。这些规律可为成品油配送公司的车辆配置提供决策参考。     

A new mathematical model towards the integration of cell formation with operator assignment and inter-cell layout problems in a dynamic environment

This paper proposes a new mathematical model to solve the cell formation, operator assignment and inter-cell layout problems, simultaneously. The objectives of proposed model are minimization of inter–intra cell part trips, machine relocation cost and operator related issues. Since the objective function of the proposed model consists of none commensurable statements, the preferred solution is obtained by the LP-metric approach. In order to validate the proposed model, some numerical examples are generated randomly and solved by branch and bound technique. Moreover; a real case study is illustrated in order to verify its applicability in an automobile producer company. Moreover the sensitivity analysis of proposed model shows that considering the operator assignment problem has significant impact on the overall system efficiency.     

Robust strategies for natural gas procurement

In order to serve their customers, natural gas local distribution companies (LDCs) can select from a variety of financial and non-financial contracts. The present paper is concerned with the choice of an appropriate portfolio of natural gas purchases that would allow a LDC to satisfy its demand with a minimum tradeoff between cost and risk, while taking into account risk associated with modeling error. We propose two types of strategies for natural gas procurement. Dynamic strategies model the procurement problem as a mean-risk stochastic program with various risk measures. Naive strategies hedge a fixed fraction of winter demand. The hedge is allocated equally between storage, futures and options. We propose a simulation framework to evaluate the proposed strategies and show that: (i) when the appropriate model for spot prices and its derivatives is used, dynamic strategies provide cheaper gas with low risk compared to naive strategies. (ii) In the presence of a modeling error, dynamic strategies are unable to control the variance of the procurement cost though they provide cheaper cost on average. Based on these results, we define robust strategies as convex combinations of dynamic and naive strategies. The weight of each strategy represents the fraction of demand to be satisfied following this strategy. A mean–variance problem is then solved to obtain optimal weights and construct an efficient frontier of robust strategies that take advantage of the diversification effect.     

Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties

This paper considers a manufacturing supply chain with multiple suppliers in the presence of multiple uncertainties such as uncertain material supplies, stochastic production times, and random customer demands. The system is subject to supply and production capacity constraints. We formulate the integrated inventory management policy for raw material procurement and production control using the stochastic dynamic programming approach. We then investigate the supplier base reduction strategies and the supplier differentiation issue under the integrated inventory management policy. The qualitative relationships between the supplier base size, the supplier capabilities and the total expected cost are established. Insights into differentiating the procurement decisions to different suppliers are provided. The model further enables us to quantitatively achieve the trade-off between the supplier base reduction and the supplier capability improvement, and quantify the supplier differentiation in terms of procurement decisions. Numerical examples are given to illustrate the results.     

Fuzzy multi-objective vendor selection under lean procurement

This paper investigates a fuzzy multi-objective vendor selection program under lean procurement based on cost minimization, delivery schedule violation minimization, and maximizing the quality level of the purchased quantity. Specifically, the paper incorporates the vendor production capacity uncertainty into the model to identify an appropriate selection policy for vendors under practical operating conditions. The use of a soft time-window mechanism for the vendor selection model enables decision makers to further incorporate a time based performance metric for vendor evaluation, based on the degree of urgency or need for a part. A solution algorithm using fuzzy AHP is proposed. The results of a numerical example suggest that decision makers prefer vendors who can promise tighter delivery schedules rather than on cost or quality. A sensitivity analysis of the soft time-window on the achievement of the lean procurement objectives is also conducted.     

考虑突发事件状态转移的政府应急物资采购定价模型

突发事件发生后,应急物资需求量呈现爆发式增长,政府首先将常规物资调拨至受灾区,并根据阶段性救灾成果作出应急物资采购决策。突发事件状态总是不断发生转移,使得政府已采购的物资在状态好转时容易造成浪费,在状态持续恶化时又不足以满足突发需求,因此考虑突发事件状态转移情形的应急物资采购定价策略对提升政府应急物资保障能力尤为关键。为此,本研究引入数量柔性契约到政府与应急物资供应商组成的两级应急物资采购供应链,构建了基于突发事件状态转移的应急物资采购定价模型,推导得出政企达成合作的条件与双方最优决策策略,并对比分析了契约合作与分散非合作下的供应商利润与政府成本。进一步采用数值计算与敏感性分析验证该模型的有效性,讨论了若干外生变量对政企最优决策与双方成本收益的影响,提出重要的管理启示。研究表明,考虑突发事件状态转移的基于数量柔性契约的政府应急物资采购定价模型既可以有效提高应急物资储备水平,又能保障供应商的合理收益以及控制政府成本,实现了政企双赢。     

An integrated approach to the one-dimensional cutting stock problem in coronary stent manufacturing

This paper presents a two-stage approach for pattern generation and cutting plan determination of the one-dimensional cutting stock problem. Calculation of the total number of patterns that will be cut and generation of the cutting patterns are performed in the first stage. On the other hand, the second stage determines the cutting plan. The proposed approach makes use of two separate integer linear programming models. One of these models is employed by the first stage to generate the cutting patterns through a heuristic procedure with the objective of minimizing trim loss. The cutting patterns obtained from Stage 1 are then fed into the second stage. In this stage, another integer linear programming model is solved to form a cutting plan. The objective of this model is to minimize a generalized total cost function consisting of material inputs, number of setups, labor hours and overdue time; subject to demand requirements, material availability, regular and overtime availability, and due date constraints. The study also demonstrates an implementation of the proposed approach in a coronary stent manufacturer. The case study focuses on the cutting phase of the manufacturing process followed by manual cleaning and quality control activities. The experiments show that the proposed approach is suitable to the conditions and requirements of the company.     

A practical expected-value-approach model to assess the relevant procurement costs

As an improvement to a series of expected value approach models, this study presents a supplier portfolio decision model, rather than a number of suppliers decision model. The model assesses risks as costs, considers types of heterogeneity in a procurement process and links the assessed terms in the programming model with accounting concepts. The model is then confirmed by using a procurement decision case with real data. The analysis has several implications for modelling, application and practice. In the light of the counterexamples shown, some thoughts pertaining to procurement decisions in supply management are reconsidered.     

A multistage linear stochastic programming model for optimal corporate debt management

Large corporations fund their capital and operational expenses by issuing bonds with a variety of indexations, denominations, maturities and amortization schedules. We propose a multistage linear stochastic programming model that optimizes bond issuance by minimizing the mean funding cost while keeping leverage under control and insolvency risk at an acceptable level. The funding requirements are determined by a fixed investment schedule with uncertain cash flows. Candidate bonds are described in a detailed and realistic manner. A specific scenario tree structure guarantees computational tractability even for long horizon problems. Based on a simplified example, we present a sensitivity analysis of the first stage solution and the stochastic efficient frontier of the mean-risk trade-off. A realistic exercise stresses the importance of controlling leverage. Based on the proposed model, a financial planning tool has been implemented and deployed for Brazilian oil company Petrobras.