RISE OF FACEBOOK, AMAZON, APPLE, NETFLIX, GOOGLE DURING COVID-19 PANDEMIC

FAANG is an acronym for Facebook, Apple, Amazon, Netflix, and Alphabet (which was previously known as Google), the five most important technology giants in the United States. The technology breakthroughs of these companies have had a significant impact on global economies, since they create jobs, link people, supply goods, and generate entertainment. As a result, a detailed analysis of FAANG stocks adds to the existing body of knowledge. Furthermore, because stock markets are very volatile and are influenced by a variety of known and unknown causes, it is vital to employ adequate tools for assessing their behavior (Jadhav et al., 2021). The coronavirus disease 2019 (COVID-19) has been spreading over the world since the end of 2019. The first case of COVID-19 was discovered in Wuhan, a city in China, in December 2019. The World Health Organization (WHO) initially issued a global notice on COVID-19 on January 30, 2020(WHO 2020a). The WHO declared it a pandemic as the number of confirmed cases grew to 118,319 cases on March 11th 2020 around the world. Some researchers and media sites commented in March 2020 on how this dreadful epidemic would damage the economies of the afflicted countries (He et al., 2020). Unemployment in the US was on a rise during the pandemic as lockdowns were imposed in various states and small businesses like restaurants, coffee shops, General stores, shopping malls, and massage parlors were forced to shut down for an uncertain amount of time. Public gatherings were banned, and businesses were forced to carry out operations online. For the businesses mentioned above, it was not possible to conduct operations online since they require people to go to the physical location to conduct business. Despite these tough times, FAANG companies thrived and their business grew despite the ongoing pandemic (Hobbs,2020)

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Consistent and Sustainable Supplier Evaluation and Order Allocation: Evaluation Score based Model and Multiple Objective Linear Programming Model

This paper is to develop an integrated approach of supplier evaluation and order allocation to suppliers that suggests the buyer to place more orders to the supplier that has higher evaluation score (consistent order allocation) considering sustainability issues including economic, social, environmental, and disruption of supply chain issues. The proposed approach is handled by an Evaluation Score based Linear Programming (ESLP) Model. Performances of ESLP model is compared with those of Multiple Objective Linear Programming (MOLP) model that does not explicitly consider the evaluation scores of suppliers for order allocation. Experimental results show that ESLP model offers consistent order allocation while MOLP model offers inconsistent order allocation. Moreover, MOLP model has different priorities of suppliers for order allocation when the customer demands are changed. Inconsistent order allocation makes the purchasing process nontransparent, unexplainable, and susceptible for biased decisions. ESLP and MOLP models generate compromised solutions that are nondominated. They are better and worse for some performances. This paper emphasizes a need of further research that develops consistent order allocation methods

New Sustainable Approach for Multi-Objective Production and Distribution Planning in Supply Chain

The paper aims to introduce a sustainable approach for aggregate production and distribution planning in a supply chain (APDP-SC) that considers multiple objectives and fuzzy parameters. The proposed approach addresses sustainability concerns, including maximizing total profit and total sales of the entire supply chain, balancing profit satisfaction between supply chain members, minimizing CO2 emissions from raw materials, production processes, and transportation of goods in the supply chain, and maximizing goodwill score from corporate social responsibility (CSR) activities. To determine the compromised solution, this paper develops a fuzzy multiple objectives mixed integer linear programming (FMOLP) model and a de-fuzzified model. The results of a simplified real case demonstrate that the proposed approach and model effectively determine the compromised solution and outperform comparison models that lack important features. Notably, this manuscript is the first to integrate the decision on conducting CSR activities with the APDP-SC decisions

Vendor Managed Inventory for Multi-Vendor Single-Manufacturer Supply Chain: A Case Study of Instant Noodle Industry

This paper develops a vendor-managed inventory (VMI) model for a multiple-vendor, single-manufacturer supply chain, in which the first stage members can be traders and/or producers and the second stage member is a manufacturer. The model utilizes a realistic transportation cost which is dependent on the sizes (small- or medium-sized) of trucks. It can determine suitable sizes and numbers of trucks that minimize the transportation cost. A genetic algorithm (GA) technique, implemented in MATLAB software, is used to determine the best solution to the problem. A case study in the instant noodle industry is conducted to demonstrate the usefulness of the proposed model. Based on the experimental results, the VMI model has reasonable behaviors using sensitivity analysis. To reduce the inventory level of raw materials, the penalty cost may be set at a relatively high level or the upper inventory limits may be set at relatively low levels, without significantly affecting the average total cost per period of the entire supply chain. When the vendors are allowed to make decision independently, the solution is still the same as the solution from the proposed VMI model, which means that the manufacture does not take advantage of the vendors

Performance Comparison of Two-phase LP-based Heuristic Methods for Capacitated Vehicle Routing Problem with Three Objectives

This paper develops a two-phase LP-based heuristic for the Capacitated Vehicle Routing Problem (CVRP). It considers three objectives: (1) minimizing the total costs of fuel consumption and overtime, (2) maximizing the total personal relationships between customers and drivers, and (3) balancing the delivery weights of vehicles. The two-phase LP-based heuristic (cluster-first route-second) is proposed. First, in the clustering stage, three LP-based clustering models (denoted by C1, C2, and C3) are developed. Customers are grouped into clusters based on real distances between the customers for C1, personal relationships between the customers and drivers for C2, and the delivery weights of vehicles for C3. Second, in the routing stage, an LP-based traveling salesman problem model is used to form a route for each cluster, to minimize the total costs of fuel consumption and overtime labor. The experimental results from a case study of Thai SMEs show that when the C2 clustering model is applied, the performances are the best. Significant contributions of this paper include: (1) it is an original paper that proposes the C2 clustering model, and it has the best performances based on the experimental results, and (2) the proposed two-phase LP-based heuristic methods are suitable for practical use by SMEs since the required computational time is short, and it has multiple models with different objectives that can be selected to match a user's requirements

Selection Model of Subcontractor Relationships by Using Discriminant Analysis

Subcontractors usually handle some parts of special works in construction projects. The development of the subcontractor’s relationship is one of the main issues to ensure the project's success. Many existing models were proposed for evaluating the subcontractor prequalification and performance, but a selection model of subcontractor relationships was still neglected for supporting the decision-making of the main contractor. Currently, main contractors use only their experience and personal preference to choose the type of subcontractor relationships. These practices can reduce the opportunities for finding a suitable subcontractor who could add more value to future explorative work. Moreover, if they mismatch the relationship type with the subcontractor, the main contractors will work with a poor-performance subcontractor. Thus, this wrong selection has hindered the benefit of a long-term relationship subcontractor. This study developed a selection model of subcontractor relationships to solve the problem. The methodology of this research collected data from the primary contractor's assessment of 15 projects, with 93 subcontractors based on factors influencing the current relationship type. Then, the selection model of subcontractor relationships was developed by using discriminant analysis. As a result, time control in planning, work quality, cooperation, and trust factors that influenced the outcome of the model development, were able to classify subcontractors into short-term or long-term relationships. The finding result was also validated and shown at an acceptable level. Therefore, the model development could support the decision-making of the main contractor in choosing the type of subcontractor relationship

Evaluation of SCOR KPIs using a predictive MILP model under fuzzy parameters.

The Supply Chain Operations Reference (SCOR) model is a well-recognized process reference model in the supply chain management field. Based on the literature, there is no research work that proposes a method to estimate and predict SCOR key performance indicators (KPIs) of a company. The objective of this paper is to propose a methodology to assess the SCOR KPIs under uncertainties based on level 2 of the SCOR-Make process metric, including nine KPIs. The proposed methodology consists of predictive MILP models with fuzzy parameters and some algorithms to assess the KPIs related to agility. A case study of a bottled-water factory is conducted to demonstrate the application of the proposed methodology. From the fact that some parameters are fuzzy numbers, the obtained SCOR KPIs are fuzzy numbers, which provide more information than constant values. The findings indicate that the proposed methodology is capable of developing the relationship between the manufacturing parameters and the SCOR KPIs, which enable the effective prediction process especially when the manufacturing parameters are changed or improved

Using Artificial Neural Network for Selecting Type of Subcontractor Relationships in Construction Project

Since some subcontractors could perform their professional skills faster and less expensive, many main contractors have adapted those companies to help their construction works and gained more profits. After the relationship between main contractor and subcontractor was consistently developed by many construction projects, main contractors would be willing to define a potential subcontractor who could ensure a good productivity in the future. Previously, main contractors were experienced by wrong selection of subcontractor in relationship development. Thus, it could cause some controversies between main contractor and subcontractor and hinder benefits with a right subcontractor for a long run business. To minimize the problem of main contractor, this paper used an artificial neural network as a tool for determining the subcontractor in relationship development. As the result, the artificial neural network provided higher accuracy in training and validating data and it could give main contractor more confident in decision making for selecting type of subcontractor relationships