## 2 Methodology  
LM, GM, and AM articles were identified through a literature search using Google Scholar, Scopus and Web of Science databases. The synonyms for each term were listed first, followed by the identification of keywords, as shown in Table 1. The search process started by searching for single keywords such as lean, green, and additive manufacturing, and it was extended to include a combination of words. The Boolean operator 'AND' was used to combine the keywords, for example, LM 'AND' GM 'AND' AM. To identify different versions of the keywords, wild cards, * and? were used. The screening and article identification process was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The guidelines outlined by [PERSON] et al. [8] in the PRISMA 2020 statement were used. A total of 1000 articles were identified from Scopus, 1000 from Google Scholar, and 1000 from Web of Science, giving a total of 3000 articles. A total of 2000 duplicate articles were eliminated before the screening process. In addition, 500 anonymous articles were removed, while 131 articles were eliminated because they were not written in English. The title and abstract were used to screen the remaining 369 articles, and 53 articles were excluded. The authors did not manage to retrieve 101 articles; hence, they were also eliminated. A total of 276 articles that remained were assessed for eligibility, and 118 articles were excluded as they focused on other industries that were not manufacturing. Finally, 158 peer-reviewed articles and reports whose focus and relevance match with the topic of discussion were synthesized to answer the research questions. The selected papers detailed empirical, conceptual and theoretical findings that matched the selection criteria. To minimize errors and bias in the selection process, the four authors of this study worked collaboratively to review and screen each record and report retrieved by matching them with the inclusion and exclusion criteria. Figure 1 shows the PRISMA method flow diagram indicating how the inclusion and exclusion criteria were implemented during the selection process. The authors extracted the relevant information from each study included in the review, and they were further scrutinized, criticized and synthesized to derive a summary of the required evidence in a systematic way. Information was extracted on LM, GM and AM, with the outcome measures including the following: the impact of AM on LM and GM (primary outcome measure), the impact of AM on the environment (secondary outcome measure), the role of AM in waste reduction (secondary outcome measure), and the impact of LM, AM and GM on sustainable performance (secondary outcome measure). All the results obtained were compatible with each outcome domain for which information was sought. Sometimes, non-value-adding activities can result from other areas linked to manufacturing, such as poor layout, ineffective maintenance, supply chain disruption, and poor work and information flow, amongst others. However, this study assumes that there are no non-value-adding activities resulting from other areas linked to manufacturing apart from the ones from the three techniques of LM, GM, and AM. To assess the risk of bias in the included studies, the authors were paired to assess each study, and all conflicting areas in the two reports were resolved in a collaborative brainstorming session. The impact of the AM on LM and GM was tabulated and compared for each of the outcome measures. The study was limited to the synthesis of the selected articles to draw evidence and conclusions without statistical analysis of the information and evidence garnered from the literature.