Total Procurement Cost Research Articles

Collaborative and integrated data-driven delay prediction and supplier selection optimization: A case study in a furniture industry

Optimizing supplier selections is an open ended problem, relevant to the operational performance of both individual companies and entire supply chains. Considering the prediction of future occurrences of delays in the optimization of supplier selections is still an under covered problem. Unlike existing literature, this article suggests a more collaborative and integrated workflow to improve the visibility and involvement of multiple stakeholders in the supplier selection decision-making processes. This is achieved through enhanced collaboration between multiple stakeholders (suppliers, customers, decision-makers from different departments, in addition to data sources from information systems), and better integration between data analysis and decision-making, through data-driven-machine-learning and optimization. The specificities of a French company in the furniture industry are considered. A workflow model is designed to support information sharing and to streamline knowledge and interactions between multiple stakeholders from different expertise domains. A Collaborative Predictive Optimization System (CPOS) is designed to classify expected occurrences of delays, to optimize order allocations, and to enable stakeholder collaboration. Delay prediction involves Decision Trees, Random Forests, and eXtreme Gradient Boosting (XGBoost). Supplier selection is solved using mathematical programming, while considering the classification of expected occurrences of delays. Stakeholder collaboration relies on information systems and uses prediction and optimization to support finding satisfactory agreements. The approach is validated using a real 3.5-year dataset, including 139 suppliers, 7,934 products and 89,080 purchase orders. A detailed experimentation, including sensitivity analysis, best–worst case analysis, and a larger scale analysis on company datasets, shows that the suggested approach enhances collaboration and achieves delay reduction and total procurement cost savings. Valuable managerial insights are collected, including the necessity to adopt digital technologies, to adapt company workflows, and to improve upstream negotiations and supplier commitments to yearly plannings.

Optimizing multi-channel procurement planning under disruption risks

Procurement plays a vital role in supply chain management, which directly affects the production, delivery, and reputation of enterprises. This paper investigates the multi-channel procurement planning problem (MCPP-R) considering the impact of disruption risk, uncertain demand and spot-market price. To meet the customer demand, the buyer can make purchases by signing a long-term contract with the primary supplier (PS), which offers certain advantages in price but may be susceptible to disruption risks. Alternatively, the buyer can enter into an option contract to retain the right to purchase from a backup supplier (BS). Additionally, the buyer can also purchase from the spot market with uncertain prices. During the procurement process, the buyer needs to decide the quantity to order from the PS and the quantity to reserve from the BS first, and then decide the quantities to reserve from a BS and the spot market respectively when the customer demand realization and spot-market price emerge. The MCPP-R is to find the optimal procurement portfolio solution with the minimum expected total procurement cost, formulated as a two-stage stochastic programming model. Due to the non-convex and non-continuous nature, the MCPP-R model is solved optimally by transforming equivalently into a shortest-path problem (SPP) with constraints. The solution method does not have specific requirements for the distribution of uncertain demand and spot-market price. We conduct extensive numerical experiments to analyze the impact of risk and uncertainty in demand and spot-market price on the procurement plan. The experimental analysis indicates that BS plays a crucial role in most cases, particularly with high disruption probability or high spot-market price.

Assessing Supply Chain Quality Management Indicators: A Roadmap to Operational Excellence at Al-Zawraa General Company's Altahady Site

This research endeavors to assess and enhance supply chain quality management at the Al-Tahady Site of Al-Zawraa General Company. The primary objective is to evaluate a comprehensive set of supply chain quality management indicators and provide a strategic roadmap for achieving operational excellence. The research problem addressed in this study revolves around the need to improve supply chain quality management practices. Despite the inherent challenges in real-world supply chain research, particularly in data acquisition and information gathering, this study is resolute in its mission. Beyond analysis, the researcher seeks to propose practical application mechanisms to drive tangible benefits for the company in attaining its operational goals. The examination of key supply chain quality management indicators reveals significant insights. Notably, the compliance rate consistently falls within the range of (1.04%) to (1.24%), affirming unwavering commitment to meeting purchase orders and contract standards. Transmission accuracy exhibits a parallel consistency, ranging between (1.04%) and (1.24%), signifying a remarkable precision in deliveries. The punctuality commitment is evidenced by on-time delivery rates, fluctuating from (91%) to (93%). Additionally, this study unveils fluctuations in supplier lead times, varying from (0.16%) to (0.55%). These variations serve as valuable signals, highlighting areas ripe for supplier management improvement. Furthermore, the prudent cost management practices implemented by the company are substantiated by total procurement costs ranging from (239,400,000) to (277,200,000). this research constitutes a concerted effort to elevate supply chain quality management at Al-Zawraa General Company's Al-Tahady Site. Its findings provide actionable insights aimed at enhancing supply chain performance, boosting customer satisfaction, and fortifying competitiveness.

Multi-scenario flexible contract coordination for determining the quantity of emergency medical suppliers in public health events.

Determining the optimal number of emergency medical suppliers for the government to contract with in the context of public health events poses a challenging problem. Having too many suppliers can result in increased costs, while having too few suppliers can potentially expose the government to supply risks. Striking the right balance between these two factors is crucial in ensuring efficient and reliable emergency response and management. This study examines the process of determining the appropriate number of suppliers in emergency medical supply chain. By incorporating option contracts and employing the total cost of government procurement as the objective function, the analysis focuses on the impact of relevant parameters on the optimal number of suppliers. Furthermore, the study investigates the optimal supplier quantities under different types of option contracts. The proposed decision model for determining the optimal number of suppliers in this paper considers three key factors: the supply risk associated with emergency medical supplies, the reserve cost of government procurement, and the responsiveness of emergency medical supplies. Additionally, a method is introduced for selecting the quantity of emergency medical suppliers based on flexible contracts. This approach offers a scientific foundation for the government to effectively address the challenge of supplier quantity selection when faced with risks related to shortages, expiration, and the combination of both.

Research on Intelligent Equipment Procurement Strategy and Optimized Resource Allocation Based on Big Data

Abstract The intelligent equipment manufacturing cycle is long, resource consumption is large, in the manufacturing process often needs for different production stages to take multiple batches of resource procurement strategy and resource optimization configuration. Based on this situation, this paper proposes an intelligent equipment procurement strategy and resource optimization design. The problem description is established, the variables and constraints are introduced, and the construction of the objective optimization model is completed. The corresponding optimization model is solved using the improved Gray Wolf algorithm and the improved differential evolution algorithm. After the training and optimization of the improved gray wolf algorithm, the total cost of intelligent equipment procurement is 180150 yuan, and the procurement strategy is to select the preferred supplier at the corresponding point in time, according to which the procurement strategy can be a realistic intelligent equipment procurement task. In addition, the average deviation of resource optimization allocation based on the improved differential algorithm is only 0.35%, which is better than the performance of the traditional differential algorithm and genetic algorithm, confirming the feasibility of this paper’s algorithm in intelligent equipment resource allocation optimization model aiming at cost optimization.

Sustainable Supplier Selection and Order allocation using Integrating AHP-TOPSIS and Goal Programming

Increased awareness of environmental and social aspects has become an urgent global issue, especially in ensuring supply chain sustainability. In addition, optimizing sustainable supplier selection and order allocation is also crucial for companies to encourage sustainable industries. This research aims to determine the best potential sustainable suppliers and determine order allocation by considering economic, social, and environmental dimensions. This research proposes an approach that integrates AHP-Topsis and goal programming to solve the problem of sustainable supplier selection and order allocation. The AHP-Topsis procedure is proposed for weighting the criteria and sub-criteria of sustainable supplier selection. Meanwhile, Topsis is offered for supplier ranking. This study offers a goal programming procedure for order allocation. Order allocation is based on three goals: minimizing total procurement cost, maximizing total purchase value, and reducing carbon emissions. A case study is presented on the plastic bean processing industry in Indonesia involving three criteria (Economic, social, and environmental) and 17 sub-criteria. The analysis results show that economic criteria have the highest weight, followed by environmental and social criteria. Furthermore, the TOPSIS method selected the three best suppliers out of 5 alternative suppliers to receive order allocation. The order allocation process for three periods was carried out using the Goal Programming method on suppliers C, E, and A. This research contributes to improving supply chain sustainability. It provides practical guidance for companies in selecting suppliers and allocating orders efficiently.

When Is More Merrier? A Cloud-Based Architecture to Procure Impressions from Multiple Ad Exchanges

This study proposes a method to reduce the cost for acquiring impressions for location-based, mobile advertising firms. Such firms act on behalf of advertisers to execute mobile, in-app, ad campaigns. Ad space is sold on ad exchanges that auction impressions one-at-a-time, on a real-time basis. In this paper, we examine whether ad firms should work with one or multiple ad exchanges to minimize the total procurement cost (equal to the cost incurred to acquire impressions plus the computing cost). By working with more ad exchanges, the ad delivery firm can bid lower on each exchange and potentially save on the total procurement cost. However, ad exchanges typically require ad firms to spend a minimum amount on the exchange. Hence, it is not smart to work with an exchange but acquire very few impressions from this exchange. Working with multiple exchanges also incurs a higher computing cost, corresponding to the computing capacity needed to support the bidding architecture. We solve an optimization problem to determine the optimal number of ad exchanges to use to acquire impressions and the optimal bidding policy on each of these exchanges. We also propose a novel, selective bidding strategy where some bid requests are returned with a zero bid. That is, the ad firm deliberately passes on some opportunities to win impressions. Doing so reduces the computing cost (because returning a zero bid expends minimal computing resources). However, the firm needs to bid higher on other opportunities to meet its demand. We find the optimal selective bidding strategy in addition to the optimal number of ad exchanges and the optimal bidding strategy when non-zero bids are returned. Finally, we demonstrate our solution for a real ad firm (Cidewalk) where the firm is shown to reduce its total cost by 33% by working with multiple ad exchanges (instead of working with a single ad exchange) and the use of selective bidding (instead of returning a nonzero bid for every bid request).

Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment

Mathematical relations between supplier capacities, the resulting material supply shortages, together with the impact of material delays on construction projects are not well defined. In response to this, this paper presents a novel multi-objective mixed integer linear programming model that considers the selection of suitable suppliers, inventory management practices, order quantities and the possibility of splitting a material order as integrated decisions to be optimised. The trade-off between the overall procurement cost and the weighted lateness, a measure of material delay impacts, is optimised. Material prices, supplier capacities, and resulting delays are treated as fuzzy scenario-based parameters. The proposed model is tested on a numerical example and computation experiments validate the model performance. An extensive sensitivity analysis is carried out and results suggest that by considering high variations in uncertain supplier capacities, the model would generate lower procurement cost and show less significant delay impacts. Whereas greater variations in uncertain material prices cause the total procurement cost to grow 55%; greater variations in uncertain delay durations also drastically increase the weighted lateness by over 70%. This highlights the importance of having high quality estimates for uncertain parameters. Additionally, the analysis also indicates that a minimum overall satisfaction level of 0.9338 can be achieved depending on the model user's strategies, and the proposed scenario-adjusted problem outperforms problems modelled under deterministic market conditions. The major contribution of this paper lies in the development of a fuzzy scenario-based model to solve the supplier selection and material purchasing problem in construction supply chains.

Technical Note—Bidding in Multidimensional Auctions When the Qualities of All Bidders Matter

Measuring the “True” Quality by Rival Bidder Qualities in Score Auctions In a procurement auction, where price and quality are evaluated by a score, the assessment criteria partially account for the “true” quality offered. The joint information of all the bids can increase the reliability of the measurement process of quality, giving better accuracy of estimation compared with the reported quality. In “Technical Note―Bidding in Multidimensional Auctions When the Qualities of All Bidders Matter,” Lorentziadis examines auctions where the score is adjusted by the qualities of all bidders, who exhibit different production efficiencies and additive separable costs. At equilibrium, the total procurement cost remains the same in the first-score and the second-score auctions. For different adjustments of the score—for example, using a weighted average of all the qualities—we find that the standard unadjusted score auction brings the highest total project cost to the buyer. Procurement managers should give consideration to score rules that account for all bidder qualities.

A hedging policy for seaborne forward freight markets based on probabilistic forecasts

Rate volatilities in seaborne freight markets lead charterers and ship owners to use financial agreements such as Forward Freight Agreements (FFA) for fixing freight rates in advance. The use of FFAs requires effective hedging policies since fixing freight rates in advance might cause both benefits and opportunity costs depending on future rate changes. We propose a data-driven hedging policy prescribing purchasing decisions for FFAs based on comparisons of FFA rates with future spot rate forecasts. The proposed approach is based on probabilistic forecasts instead of point forecasts because the most accurate forecasts in terms of predictive errors do not necessarily lead to the best decisions. We adjust spot rate forecasts by selecting percentiles that result in minimum prescriptive errors (i.e., cost) in cross-validation. Experiments on synthetic data show that the probabilistic forecast-based hedging policy outperforms the point forecast-based policies and benchmark policies, including data-driven policies from the literature. Experiments on Baltic Exchange data from 15 dry bulk and tanker routes confirm the performance of the proposed policy. Compared to two different point forecast-based policies defined in this study, the proposed approach achieves on average 3.31% and 3.02% total procurement cost reduction per route in 15 routes for four years testing period. It results in from 0.67% to 4.79% cost reductions against the benchmark policies.

Designing an efficient vaccine supply chain network using a two-phase optimization approach: a case study of COVID-19 vaccine

The upsurge of the COVID-19 pandemic and its consequences on the world’s population has led to the accelerated production of vaccines across the world. The next challenge in the fight against COVID-19 is to distribute those vaccines to the right person in an optimized way by establishing an efficient vaccine supply network. Consequently, this paper studies an integrated two-phase planning framework for the vaccine distribution network. In the first phase, the population target is classified into several groups to determine their priority for vaccination using a multiple attribute decision making (MADM) technique. The second phase uses a mathematical model to decide on the location of distribution centers, inventory policies, and routing decisions to minimize the total procurement, inventory, and distribution costs. Since the proposed model falls into the category of NP-hard problems, a metaheuristic algorithm entitled population-based simulated annealing (PBSA) is developed to solve the presented model. Besides, the improved grey wolf optimizer (I-GWO) is customized and used as another solution method. A few generated datasets are utilized to evaluate and validate the performance of these solution methods. In addition, this methodology is implemented in a case study of the COVID-19 vaccine supply chain in Iran to demonstrate its practicability.

Assessing Changes in Pulpwood Procurement Cost Relative to the Gradual Adoption of Longleaf Pine at the Landscape Level: A Case Study from Georgia, United States

Longleaf pine once covered 37 million hectares in the southern United States. However, it currently occupies only 5% of the original area. Efforts have been ongoing for the last decade to restore longleaf pine. The expected expansion in the area under longleaf pine has raised concern among wood-consuming mills regarding a potential increase in the total wood procurement cost, as wood availability per unit of land is typically lower for longleaf than for loblolly and slash pines for the first few decades. Therefore, a simulation model was developed in this study, examining the impact of the gradual adoption of longleaf pine by landowners on the total wood procurement cost of a pulp mill located in South Georgia over a 40-year simulation period. Results show no statistically significant difference between scenarios for maximum distance, total cost, and total distance over the simulation period. Our study will guide stakeholder groups to balance the needs for longleaf pine restoration and the reduced cost of wood procurement for wood-consuming mills.

Increased Income to Farming Community through Direct Marketing

ICAR-Taralabalu Krishi Vigyan Kendra, Davanagere initiated innovative marketing strategies for enhanced income to the farmers. The present study focused on analysis of these marketing strategies. The Saturday Organic Weekly Bazaar started during 2013 for organic growers to sell their produce directly to consumers without the involvement of middlemen. The weekly bazaar is making average turnover of Rs. 65865.00 per week from 8 entrepreneurs. The procurement and direct marketing vegetables was initiated through 3 Farmers Producer Organizations. During lockdown period of 2020, 25.23 t of vegetables procured from farmers and sold directly to the consumers in Davanagere city. Total procurement cost Rs. 504600.00 gross earning Rs. 630750.00 gross income Rs. 126150.00 (@ 25% margin), the net income was Rs. 113535.00 (average operational cost @ 10%). KVK is facilitating the marketing of safely ripened mango. In the last 3 years 1.1 t, 2.5 t and 8.5 t of mango was sold directly to consumers earning gross income of Rs. 66000.00 Rs. 200000.00 and Rs. 850000.00 respectively. These marketing interventions initiated by KVK are small steps to increase the producer share in the consumers price and bring in marketing orientation among farming community.

Прикладні аспекти дослідження ринку дизайну на маркетингових засадах

The Ukrainian market of graphic design, which belongs to the sphere of creative industries, demonstrates high rates of development. The derivative nature of commercial demand for graphic design services, including professional design services, is determined by the dominance of video content in the network, the introduction of innovative technologies through digitalization of virtual space in the formation of social media feeds, loyal attitude of the target audience to integrate advertising, personalized and relevant offer active development of stock image, video and music platforms. The total amount of taxes paid by the design industry during 2016-2018 amounted to UAH 259 million. In 2019-2020, the amount of taxes paid by this sector will increase one and a half to two times a year. The individualization of advertising appeals has led to the need for in-depth study of the content and characteristics of attributes and values of goods that influence consumer purchasing decisions by marketers, designers and other professionals in related fields. A separate area of research is to study the impact of the psychology of design decisions on increasing the loyalty of end users and increase sales of companies in the sector. The application of a customer-oriented approach in the Ukrainian graphic design sector actualizes the identification of the tasks facing design from the standpoint of marketing approach. The aim of the study is to present the results of the study on the impact of psychological aspects of design decisions on the effectiveness of customer companies through such marketing tools as increasing brand awareness, increasing loyalty of end users, deepening interaction with content, feedback and more. Graphic design is an integral part of industrial and trade policy, as well as one of the marketing tools used in the process of product creation and customer service, pricing, advertising, branding, merchandising and more. The main tasks of design stem from the mastery of basic psychological principles in creating the concept of design (von Restorf effect, Gestalt principles, visceral reaction, color psychology, psychology of form, double coding theory, cognitive load theory). The expansion of marketing areas of design in the practice of service providers has led in the largest cities of Ukraine to the formation of sustainable design communities, the beginning of professional design activities, the introduction of niche publications, public procurement and more. As a result, there is an increase in the capacity of the design market – the total cost of procurement of design services for the period 2016 – by August 2019 increased to UAH 62 million. It has been established that the biggest competitors in the market of graphic design of Ukraine are now the design agency «MUZIKA.UA», graphic design studio SHANKA; Bypaul design studio; BRANDME; Wezom; Marat and Abordazh. On the example of projects of the agency «MUZIKA.UA», a leader in the design market, analyzed the effectiveness of design decisions in terms of: brand awareness, by measuring the average website traffic, levels of interaction with content, sales growth and market share due to design solutions per month. Based on the study of the economic situation in the design market of Ukraine, the practical involvement in the market of graphic design of one of the authors allowed to identify the main problems of market development. By identifying the main problems of the development of the graphic design market, directions for solving problems have been formed and proposed for practical use. The intensification of intra-industry competition requires a study of the levels of concentration in the target markets of graphic design, taking into account the risks of marketing and financial activities, which should be the subject of a separate study.

Examining the unit costs of COVID-19 vaccine delivery in Kenya

BackgroundVaccines are considered the path out of the COVID-19 pandemic. The government of Kenya is implementing a phased strategy to vaccinate the Kenyan population, initially targeting populations at high risk of severe disease and infection. We estimated the financial and economic unit costs of procuring and delivering the COVID-19 vaccine in Kenya across various vaccination strategies.MethodsWe used an activity-based costing approach to estimate the incremental costs of COVID-19 vaccine delivery, from a health systems perspective. Document reviews and key informant interviews(n = 12) were done to inform the activities, assumptions and the resources required. Unit prices were derived from document reviews or from market prices. Both financial and economic vaccine procurement costs per person vaccinated with 2-doses, and the vaccine delivery costs per person vaccinated with 2-doses were estimated and reported in 2021USD.ResultsThe financial costs of vaccine procurement per person vaccinated with 2-doses ranged from $2.89-$13.09 in the 30% and 100% coverage levels respectively, however, the economic cost was $17.34 across all strategies. Financial vaccine delivery costs per person vaccinated with 2-doses, ranged from $4.28-$3.29 in the 30% and 100% coverage strategies: While the economic delivery costs were two to three times higher than the financial costs. The total procurement and delivery costs per person vaccinated with 2-doses ranged from $7.34-$16.47 for the financial costs and $29.7-$24.68 for the economic costs for the 30% and 100% coverage respectively. With the exception of procurement costs, the main cost driver of financial and economic delivery costs was supply chain costs (47–59%) and advocacy, communication and social mobilization (29–35%) respectively.ConclusionThis analysis presents cost estimates that can be used to inform local policy and may further inform parameters used in cost-effectiveness models. The results could potentially be adapted and adjusted to country-specific assumptions to enhance applicability in similar low-and middle-income settings.

A goal programming model for the optimization of log logistics considering sorting decisions and social objective

Log logistics include sorting, processing, and transporting of logs from their place of harvest to demand locations. These activities account for a significant portion of the total log procurement costs; therefore, attempts were made in previous studies to optimize some aspects of log logistics. However, operational details, such as sorting decisions, truck compatibility requirements, and social objectives, are often disregarded in the optimization literature. Incorporating these details into the model makes the results more realistic and applicable. To address these gaps, a bi-objective mixed-integer programming model is developed in this paper to optimize log logistics. The first objective is to minimize total logistics costs, and the second objective is to provide a balanced workload for trucking contractors. The bi-objective model is solved using the goal programming approach. The model is applied to log logistics of a large Canadian forest company, where trucking contractors use heterogeneous fleet of trucks to carry various log sorts from cutblocks to sort yards for sorting. The planning horizon is 4 weeks with daily decisions. The goal programming model generates balanced workloads for the contractors with less than 0.4% increase in total costs compared to the single objective model where only the total cost is minimized.

Data science and reinforcement learning for price forecasting and raw material procurement in petrochemical industry

Petrochemical industry is one of the major sectors contributing to the world-wide economy and the digital transformation is urgent to enhance core competence. In general, ethylene, propylene and butadiene, which are associated with synthetic chemicals, are the main raw materials of this industry with around 70–80% cost structure. In particular, butadiene is one of the key materials for producing synthetic rubber and used for several daily commodities. However, the price of butadiene fluctuates along with the demand–supply mismatch or by the international economy and political events. This study proposes two-stage data science framework to predict the weekly price of butadiene and optimize the procurement decision. The first stage suggests several the price prediction models with a comprehensive information including contract price, supply rate, demand rate, and upstream and downstream information. The second stage applies the analytic hierarchy process and reinforcement learning technique to derive an optimal policy of procurement decision and reduce the total procurement cost. An empirical study is conducted to validate the proposed framework, and the results improve the accuracy of price forecasts and the procurement cost reduction of the raw materials.

Electric bus fleet composition and scheduling

The low energy density of batteries and the long recharging times constitute a significant barrier for electrification of public transportation (PT) systems since electric buses (EB) require too heavy and expensive batteries to achieve the operational availability of their combustion engine counterparts. New recharging technologies such as fast chargers and dynamic wireless power transfer (DWPT) emerge as promising solutions to overcome these challenges. Optimizing the bus fleet composition and the schedules is essential to take advantage of these emerging technologies and achieve electrification of PT in a cost-efficient way. To address this challenge, this paper proposes an integer (binary) programming formulation to find the optimal electric bus fleet composition and scheduling that minimizes the total procurement cost of the buses and the operating cost of the schedules. A column generation (CG) approach is devised to obtain provably high-quality solutions, for large problem instances. The success of the approach is due to a novel dynamic programming algorithm we develop to solve the generalized resource-constrained shortest path problem that needs to be solved in each CG iteration to find out new schedules to include in the model. Extensive computational studies on large real-world PT networks attest to the efficacy of the suggested methodology and reveal valuable managerial insights from a system-wide perspective.

Toward global comparability in renewable energy procurement

<h2>Summary</h2> Prices from the competitive procurement of renewable energy are increasingly used for the comparative evaluation of financial and technology performance. Comparing prices from auctions or power-purchase agreements at their face value is often not meaningful, particularly across jurisdictions or over time. Differences in support regimes and the market, tax, and regulatory environment can make a like-for-like comparison convoluted and result in misleading conclusions. Here, we estimate project revenue and value holistically for eight global offshore wind projects. Using a cash flow model, we consider applicable support regimes; market sales; and the monetized value of tax incentives, depreciation, and transmission. We find considerable variation in the absolute levels and relative composition of project revenue and value, which must be considered for deducting costs from procurement prices. The resulting metric enables decision makers and research to compare the total cost of procurement on an equal footing and supplements established cost metrics.

An optimization approach to design forest road networks and plan timber transportation

The provision of ecosystem services depends on the landscape-level distribution of forest management options over extended planning horizons. Nevertheless, transportation costs are an important fraction of total procurement costs. Therefore, forest managers are challenged by the need to make decisions regarding the design of the road network needed to have access to forest management units (e.g., stands) as well as the transportation of forest products over this network. The objective of this research is to develop an approach to integrate the design and maintenance of a forest roads’ network and to provide solutions to the timber loading location site and the timber transportation problems. The emphasis is on the combination of mathematical programming formulations and heuristic solution techniques to minimize the sum of road building, road maintenance, timber loading and transportation costs. Specifically, a mixed integer programming model is built to represent the problem while a heuristic algorithm, based on a Graph Simplification procedure and a big-M Estimation procedure, is proposed to solve the underlying optimization problem. Results from an application to a large-scale forest management problem in Northwest Portugal involving 1137 management units are discussed. Results suggest that this approach may help address efficiently the need by forest managers to integrate road design, road maintenance, timber loading location and transportation concerns.