Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.

For the Government projects, Value for Money (VFM) is an important parameter based on which the projects are procured through Public-Private Partnership (PPP) by the public sector. The objective of this paper is to identify and evaluate perceptions of VFM factors affecting Indian road projects undertaken by PPP. Eighteen VFM factors were shortlisted through a literature survey and were validated with experienced professionals. A questionnaire survey was conducted to investigate the most important VFM factors and was grouped into three categories: financial implications, the expertise of the private sector, and contract efficiency using factor analysis. Important VFM factors were identified using the Importance Index Method. The perception was analyzed with various statistical tools. The findings from this study provide more information and understanding about VFM in an early stage, which will be helpful for decision making about PPP.

Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.

The increasingly tight competition has caused problems for small and medium enterprises (SMEs) in increasing their marketing performance. During the last few years, the marketing performance of SMEs operating in the Project woodcraft industry sector in the Badung Bali has moved downwards. The goal of this research is to develop knowledge in the fields of Strategic Management, specifically regarding product innovation to achieve a competitive advantage in an attempt to improve internationalization and marketing performance. The population in this research is all of the SMEs operating in the woodcraft industry sector and is based in Badung Bali. The sample size in this research is determined based on Slovin's formula and the total includes 100 units of SME operating in the woodcraft industry. The method used is the quantitative approach, which examines the role of competitive advantage in mediating the influence of product innovation on internationalization and marketing performance. Based on the research results, product innovation has a positive and significant influence on both, competitive advantage and internationalization. Product innovation and competitive advantage have positive and significant influences on marketing performance. Competitive advantage has a positive and significant influence on internationalization. Meanwhile, it has been revealed that competitive advantage can mediate the influence of product innovation on marketing performance and competitive advantage can mediate the influence of product innovation on the internationalization of SME operating in the Project woodcraft industry sector in Badung Bali. Based on these findings, the project woodcraft industry SME in Badung Bali is advised to be more innovative in producing their products, so that there would be an increase in marketing performance in the international market. It is also recommended for subsequent researchers to add in other variables, such as product quality and market orientation, to enrich the information attained.

The aim of this study is to explore the Days sales of inventory (DSI) as an indicator for inventory management among energy companies in Saudi Arabia for the period ranging from 2012 to 2019. The results of this study indicate that Aldrees company is ranked the first in terms of managing its inventory in which the average value of the DSI for the period 2013-2019 was 5 days. The second rank in terms of the DSI value for the period 2012-2019 belongs to Bahri company in which the average DSI was 23 days. While the third rank was Petro Rabigh in which the DSI average was 31 days. The results of this study should be useful to policy makers at the company management and other external stakeholders such as investors, auditors and lenders.