# A Methodological Approach for the Smartification of a University Campus: The Smart ESI Use Case
## 2 Related Works
### _Smartification_
#### 2.2.1 Methodologies for Smartification
Based on the Eisenhardt method, the work described in [8] attempts to answer the question _How can a methodology for the _santifaction of products be designed?_The authors selected 13 case studies aimed at describing the process of development of smart products by different industries and grouped them between two categories: B2B (Business-to-Business) and B2C (Business-to-Customer). For the phase of data collection, they used description documents and data sheets of each product as a primary source of data, and then they added interviews with users of the products to incorporate their perspectives. As a result of the data analysis, the authors identified a morphology consisting of 8 digital features and a total of 26 digital functions, which were found in the thirteen cases. For a product to be considered \"smart\" it must fit at least one function in each feature. A larger number of functions and a higher degree of product intelligence. From these outcomes, the authors define a methodology for manufacturing smart products by means of the link between the smart product's objective and its digital functions. The methodology consists of three steps: (1) to establish the strategy portfolio; (2) to combine the objective and digital features of the smart product into the so-called \"user stories\", one for each digital feature, that is expressed with the structure: \"As <user> I want <feature, function> to achieve <objective>\"; and (3) refinement of the \"user story\" by adding restrictions and other considerations to, finally obtain the description of a product development's starting point. The Porsche Consulting Method. The company Porsche Consulting GmbH [25] proposed a smartification methodology focused on the smart buildings domain also based on the research of case studies. This domain is elected basically for the urgent need for optimization of indoor spaces for 68% of the world's population living in urban areas by 2050. The authors pose that the current state-of-the-art on this topic lacks widely shared and accepted visions and upside potential offered to various stakeholders and instead, it consists of a set of disconnected applications with different degrees of technological maturity (e.g., security, heating, ventilation, air conditioning, and lighting). To overcome this problem, Porsche Consulting proposes a smartification methodology based on six recommended steps: (1) Collection is the selection of the case studies from well-probed pilots in this or another markets; (2) Evaluation of each case study based on its business impact analysis and its feasibility; (3) Derivation from the previous analysis to determine the position of the company in the smart building ecosystem; (4) Definition of the smartification road map for achieving the target position in terms of development, operation and commercialization, ranging from investment policies up to the hiring of expert personnel in specific areas; (5) Alignment consists of checking the smartification strategy and coincides with the organization's other strategic perspectives (e.g., corporate strategy, business unit strategy, and functional strategies) to detect potential conflicts and tackle them with appropriate solutions; and (6) Smart take off, consists of transferring the strategic initiatives to specific work packages with responsibilities and timelines. Multiple-Criteria Decision Making. Multi-criteria decision making (MCDM) seeks to determine the best alternative by considering more than one criterion in the selection process. According to [26], the MCDM problems can be classified into two main branches: Multiple-Attribute Decision Making (MADM), which concentrates on ranking and scoring a limited number of alternatives that are characterized by multiple, usually conflicting attributes, whilst Multi-Objective Decision Making (MODM) problems involve evaluating alternatives that have been non-predetermined through competing objectives that are required to be optimized simultaneously.