## 4 Literature Review Outcomes
### Interpretation of the Outcomes  
In this section, the outcomes of the SLR were critically analyzed to identify the gaps and trends in each perspective. To this end, Table 3 describes the main trends and gaps identified for each perspective adopted in the present SLR. As a general point about all of the investigated aspects, and based on the results discussed in Section 4.1, the great majority of papers investigate Lean DSC by considering Industry 4.0 as a general concept. This can arise from the novelty of Industry 4.0 as a newborn concept in SC. In this regard, the majority of previous works aimed to provide an overview of that to address its potentiality in Lean DSC. As a result, it could be convincing evidence to motivate the researcher for focusing on studying the Lean DSC from the perspective of each single digital technology of Industry 4.0. 1. [label=()]
2. Supply chain processes. These outcomes imply the necessity of studying the application of different technologies to eliminate waste from each SC process. By looking now at individual processes, the application of Lean and digital technologies is mostly discussed in manufacturing (Figure 5). Such an outcome stems from the fact that Lean principles were first introduced in production processes. However, the Lean philosophy can reduce non-value-adding activities in all the other SC processes. In fact, one of the main lacks of the present state of the art in Lean DSC is that no research is carried out on Procurement, Transportation, Demand Forecasting, Marketing, and Pricing processes. This lack could be stem from the fact that these issues are not directly connected to manufacturing as the main focus of the previous works in the realm of lean studies. As a result, this issue gives rise to finding fewer studies about the connection of mentioned issues and Lean DSC as a newborn field. However, for instance, SCs deal with marketing issues to forecast the demand specifically in short life cycle products and they should have better forecasting by removing non-value-added activities to the customer. To this end, about the demand forecasting, BDA by analyzing SC data in different echelons, can provide a better understanding of customer needs that results in improvement of inventory waste [72]. BDA application can also provide a great opportunity to promote marketing policy and reduce the associated costs by identifying the most potential groups for advertisements as target customers [3]. As a result, it can enhance defining the marketing strategies and increase the efficiency of advertisement activities to attract the utmost customers regarding the time and cost of advertisements. To this end, the marketing group can dedicate its effort to defining the best strategy for each group of customers rather than identifying them. Therefore, that leads to reducing the over-processing waste in marketing activities. Furthermore, BDA helps to perceive the best time for considering discounts on perishable products with a limited shelf life to reduce inventory waste. To achieve that, practitioners might combine BDA with IoT to collect and analyze real-time data and CC to share information among different echelons. Additionally, BC by providing tamper-proof data can make a reliable and trustful database for applying BDA in forecasting the demand and marketing strategies for luxury goods or strategic items such as vaccines [73] during the COVID-19 pandemic. For instance, in the case of strategic items such as vaccines during the COVID-19 pandemic, governments can be sure about future decisions by more reliable demand forecasting to distribute the appropriate amounts of vaccines for different parts of the country. To this end, they can rely on the trustful data collected by BC and analysis provided by BDA. Moreover, about the luxury goods, BC can provide reliable information about the final customer data such as the location, final selling price, etc.