An effective implementation of knowledge management is required by supply chains in order to remain competitive. Supply chains are strategic frameworks to ensure customer value, relationships, resources optimization, and practices integration. Through this investigation, inadequacies for an efficient knowledge management cycle, in supply chains, have been identified. Such identified inadequacies avoid completion of the knowledge cycle in supply chains. Mainly, there is a lack of contextualisation and structure for supply chain knowledge (SCK). Consequently, organisations are not gaining the benefits from self-learning, adoption of best practices, which are elements, incorporated in an effective knowledge management implementation. Along supply chains there are two relevant flows: information and materials. Information is the raw material of knowledge, which requires contextualisation in order to become executable; an important difference between knowledge and information. Knowledge in supply chains can be in the form of best practices, however to consider these as cures for everyone is a mistake, instead these can work in different contexts. This paper provides a discussion about the needof a continuous contextualisation of knowledge practices in organisations. Also, a proposal of a knowledge representation to contextualize and diagnose supply chain knowledge is presented. The proposed knowledge representation is a codification to incorporate context in a way that some form of diagnosisof supply chain practices can be carried out, which could reveal possible favourable and unfavourableeffects of practices in a supply chain. In addition, this paper proposal is been constructed in Excel®as a prototype. Especially, with the aim of being used in workplaces to support decisions makingin SMEs supply chains, from which their lack of resources is a typical barrier to become competitive. For this investigation, a number of best practices have been analysed. Also, focus groups and individual interviews to operations managers, from global, small and medium enterprises, have been carried out. Subsequently, it has been possible to integrate the proposed coding representation to enable a contextualisation and diagnosis of supply chain knowledge.