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PurposeFactors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.Design/methodology/approachIn this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.FindingsThe findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.Practical implicationsThe model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.Social implicationsAlthough the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.Originality/valuePrevious research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

The Engineering Procurement Construction (EPC) contract systems are widely employed in the construction industry. Among the prevalent issues in this sector, cash flow problems frequently lead to decreased productivity and efficiency. To address these challenges, a claim management system is developed based on the Improved Monarch Butterfly Optimization Algorithm (IMBOA) and the principles of EPC. Conventional construction models typically optimize only a single objective, such as time, cost, or delay, which may not effectively enhance overall performance. This study aims to develop a claim management system based on IMBOA and EPC principles to optimize multiple objectives, focusing on minimizing project costs and time delays while ensuring high-quality results. The basic methodology of this research involves integrating EPC and claim management principles with the IMBOA algorithm to create an efficient, high-quality system. This process starts with a comprehensive literature review on EPC, claims, MBOA, and related algorithms. Common disputes and claims in the construction industry are examined, and critical factors influencing these claims are identified. The Monarch Butterfly Optimization Algorithm (MBOA) and its improved version (IMBOA) are explored for their application in optimizing project performance. A case study in China's coal mining industry evaluates the effectiveness of the EPC approach, demonstrating that it minimizes time delays and costs. The IMBOA approach proposed in this study has the potential to mitigate 23% of risks and avoid 32% of risks associated with the action plan of China's coal mining industry. Furthermore, comparative analysis with other optimization models indicates that the developed IMBOA model yields superior results, reducing overall project time by 15% and cost by 18%.

Intense international competition pushes the actors of wood supply chains to implement efficient wood supply chain management incorporating coordinated cost-saving strategies to remain competitive. In order to observe the effects of individual and coordinated decision making, mixed-integer programming models for forestry, round-wood transport, and the wood-based industry were developed and integrated. The models deal with operational planning issues regarding production, harvest, and transport and are solved sequentially for individual cost optimization of each wood supply chain actor as well as simultaneously by a combined model representing joint cost optimization in an integrated wood supply chain. This allows for the first time, benchmarking relative cost-saving potential of the wood procurement strategies coordinated transports, integrated supply chains, satellite stockyards, and higher truck payloads within a single case study setting. Based on case study data from southern Austria, results show the advantages of an integrated supply chain with a cost-saving potential of up to 24%. Higher truck payloads reinforce this potential and enable up to 40% savings compared to the predominant wood procurement situation in Central Europe. Wood supply chain integration for Central European circumstances seems to be feasible only for a limited consortium of a few companies, for example when restricted to a wood-buying syndicate supplying several industry plants or a few large forest enterprises, especially as both groups are commonly steering wood transport on their own. Consequently, further research on the challenging task of implementing integrated supply chains using the opportunities of digitalization to realize existing cost savings potential by deepening cooperation and intensifying information exchange is needed.

Background and objectives: Since 2011 Cameroon has mandated the fortification of refined vegetable oil with vitamin A (target=40 IU/g, range=33-50 IU/g), and wheat flour with iron (60 mg/kg) and zinc (95 mg/kg). A 2012 interim impact assessment in Yaoundé and Douala indicated 76% of wheat flour samples were fortified and indicators of iron and zinc status in women and children were greater relative to prefortification values. However, only 44% of oil samples were fortified and indicators of vitamin A status were unchanged. We assessed Cameroon's food fortification programme using a programme impact pathway framework to identify barriers to optimal programme performance. Methods: Using semi-structured interviews, data on inputs and processes were collected from factories of all active domestic producers of refined vegetable oil (n=9) or wheat flour (n=10). Twelve sentinel sites were selected for market and household surveys, including assessment of frequency of fortified food consumption by women and children (600 households total). Food samples were collected from factories, markets, and households for measurement of vitamin A (iCheck) and iron and zinc (ICP-OES) content. Results: All factories had in-house methods (mostly qualitative) for testing the micronutrient content of fortified products, while 68% presented quality certificates for recent premix purchases. Industries cited premix import taxes and access to external laboratories as constraints. All but one factory oil sample had vitamin A content > 33 IU/g. Among the sentinel sites, 87% of n=393 market oil samples contained detectable vitamin A; 43% had levels > 33 IU/g. Among household oil samples, 86% contained detectable vitamin A; 46% contained > 33 IU/g. All factory flour samples appeared to be fortified, but only ~18% had mineral levels within 10% of the target. Among composite flour samples from markets and households the mean iron and zinc content was 25 mg/kg and 43 mg/kg, respectively, ~45% of target levels. Conclusions: The availability of fortified oil is encouraging and may partially reflect recent growth in the share of domestic oil production. The low levels of iron and zinc in wheat flour indicate the need for programme support, possibly through premix procurement and technical support for micronutrient analysis.

Purpose – The purpose of this paper is to analyze the relationship between supply planning, trust and integration, and the influence of them on operational performance. Design/methodology/approach – The paper used a survey with 335 respondents from three different industries. The paper analyzed the data with structural equation modeling. Findings – The results suggest that supply planning and trust are positively related and both influence supply integration and operational performance. At the end the paper proposed a classification for supply integration based on planning use and trust. Research limitations/implications – The sample is composed by companies from only three industries (machinery, electronics and automobile), what does not allow generalization. Practical implications – Managers are challenged to develop simultaneously supply chain planning practices and trust-based relationship within buyers and suppliers. They must pay attention to different integration drivers and use them accordingly and in the context analyzed. The study suggests a 2×2 matrix that might help managers’ decision making. Originality/value – Despite the importance of planning in supply and manufacturing management, few papers analyzed the role of supply planning integrated to trust. The combination between these aspects brings a more realistic and pragmatic view of the supply chain management.

Defense planning faces significant uncertainties. This report applies robust decision making (RDM) to the air-delivered munitions mix challenge. RDM is quantitative, decision support methodology designed to inform decisions under conditions of deep uncertainty and complexity. This proof-of-concept demonstration suggests that RDM could help defense planners make plans more robust to a wide range of hard-to-predict futures.

Da Beschaffung und Einkauf sowie die Lieferkette in der heutigen, sich schnell verändernden Welt von entscheidender Bedeutung sind, gibt Ihnen dieses Buch einen umfassenden Überblick darüber, was Beschaffung und Einkauf sind. Der Schwerpunkt liegt auf den Einkauf 4.0 und dem, was die Zukunft uns allen in diesem Arbeitsbereich bieten wird, nicht nur für Einkaufs-Profis, sondern auch für alle, die ihr Wissen zu diesem wichtigen Thema vertiefen wollen. Jedes Unternehmen beschafft Waren und Dienstleistungen. Erfahren Sie, wie Sie Ihre Prozesse an die Zukunft anpassen können und lernen Sie mehr über neue Technologien wie Blockchain, KI, Robotic Process Automation und viele mehr.Agile Methoden sind mit all diesen Prozessen verbunden, und wir werden uns auch mit Lean-Prinzipien befassen. Zum Schluss erfahren Sie, wie Sie einen Smart Contract mit Ethereum aufsetzen können. Machen Sie sich fit für die Zukunft und lassen Sie uns \"Einkauf neu definieren\".

The aim of this study was to determine factors affecting milk production cost in dairy cattle farms. The main material of study was formed data obtained from surveys applied to 175 dairy cattle farms selected by stratified random sampling method from dairy cattle farms in Biga district of Çanakkale province between January 2015 and December 2015. Data of study were analyzed by using multiple regression method. The results of study indicated that 41.7 % of farms have the lowest number of cattle (5-10 head), the prominent age group of farmers’ ranged from 36-46 years (45.1 %) in farms and 12.5 % of farmers’ have the highest income (≥ 24000 TL). According to the results of multiple regression model, it was determined that some factors such as the time that is spent in dairy cattle farm, farmers' dairy farming experience, farmers' educational level, farmers' feed procurement, livestock diseases and maize silage production in farms had significant effects on milk production costs. In conclusion, these factors were explained to have important effects on decreasing of farmers' milk production costs.