In modern business environments, an effective supply chain management (SCM) is crucial to business continuity. Competition between supply chains (SC) has replaced the traditional competition between companies. Lean, Agile, Resilient and Green (LARG) paradigms are advocated as the foundation of a competitive SCM. To make a supply chain more competitive, capable of responding to the demands of customers with agility and capable of responding effectively to unexpected disturbance, in conjugation with environmental responsibilities and the necessity to eliminate processes that add no value, companies must implement a set of LARG SCM practices and key performance indicators (KPI) to measure their influence on the SC performance. However, the selection of the best LARG SCM practices and KPIs is a complex problem, involving dependencies and feedbacks. This paper proposes an integrated LARG analytic network process (ANP) model to support decision-making in choosing the most appropriate practices and KPIs to be implemented by companies in an SC. To validate the model in an exploratory approach, a case study in an automaker supply chain is presented.

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.