Literature review and research gaps

Research of big data popped up in the 1970s but the publications regarding big data and its utilization in various fields have grown exponentially only since 2008 (Addo-Tenkorang and Helo, 2016). All in all big data has not been extensively researched from any specific viewpoint. Big data researches have for instance aimed and focused at investigating opportunities, challenges and trends of big data. Much research has been made for example from the viewpoint that what big data is as a concept to define it more precisely. Still Addo-Tenkorang and Helo (2016) say there is a restricted agreement about big data performance and what is its most value-adding use. There is not even a single accepted definition to big data even though the big data challenges, impact and performance is handled across different sectors.

According to Waller and Fawcett (2013), and Rozados and Tjahjono (2014) there is not much literature or research about the utilization of big data in supply chain management and how it can have an effect on management of supply chains. Also

Sadovkyi et al. (2014) implies that there is overall a huge lack of novel practical and theoretical progress in the field of supply chain management. Wang et al. (2016) state that there is actually a gap between supply chain practices and theory from the supply chain analytics viewpoint. In addition there are also still many difficulties in applying big data solutions in real life (Militaru et al., 2015). However big data in management of supply chain has been investigated in some specific industries such as in service and manufacturing industries like Zhong et al. (2016) in their study. Some researchers have also studied big data from the perspective of Twitter analytics usage in supply chains that is one sort of social media analytics method according to Chae (2015). Still Wang et al. (2016) state that existing research on big data business analytics on supply chain management and logistics have mostly concentrated on analyzing different perspectives and definitions or recognizing possibilities for supply chain education and research. Overall the big data business analytics are in its infancy and there are still researches to be made to investigate business analytics of big data in different contexts of logistics and supply chain management. Also according to Tiwari, Wee and Daryanto (2018) in the current researches there is not really any consensus about the big data performance in supporting supply chain management.

According to Tummala and Schoenherr (2011) overall risk management of supply chains is an emerging but significant research line in the interconnected and dynamic world that we live in. There are not much guidance or conceptual frameworks on the topic. (Tummala and Schoenherr, 2011) From the viewpoint of risk management big data has mainly been investigated in the bank and financial industry. In the light of supply chain risk management, for instance Ratnasingam (2006) has conducted a research aiming at discovering possible features of supply chain uncertainties and risks. However according to Wu et al (2016) this study but also previous researches mishandle the inter-relationships among different supply chain risks. Wu et al. (2016) have researched big data and supply chain risk and uncertainties management from the viewpoint of sustainability to explore some decisive attributes. Chen, Tao, Wang and Chen (2015) investigated in their study fraud risk management at Alibaba that is based on big data.

According to Chan and Kumar (2007) in the past many researches have focused only on domestic supplier selection and thus left many very critical global criteria un-discussed. According to them there is only limited amount of discussion in previous researches about global supplier selection process. In their research the question of global supplier selection is tackled with fuzzy methods techniques. According to Ruhrmann et al. (2014) methods existing for supplier selection are not taking so well

into account the risks, dynamics and forecasting methods. The existing methods are mainly focused on monetary supplier criterion. Also according to Rao, Xiao, Goh, Zheng and Wen (2017) most of the researches in supplier evaluation do not consider the supply chain environment risk factors and has concentrated on commercial criteria such as lead time, quality, and price. Also according to Chen and Zou (2017) much research has been made about the supplier selection but only few researches has investigated the problem from the point of few of risk aversion. Much research from supplier selection has been made with for example fuzzy techniques as mentioned before and for example with Delphi method. According to Foerstl, Reuter, Hartmann and Blome (2010) there is not much research either on how companies make a decision about what are their suppliers’ risks or how suppliers construct their supply risk management to make sure they are not exposed to the risks caused by suppliers.

According to Patra and Mondal (2015) and Rao et al. (2017) supplier selection is becoming on of the most researched and hottest supply chain questions.

According to Trkman and McCormack (2011) even risk management of supply chains is rather novel concept and thus it is currently a bit chaotic and disorganized. There are multiple different risks and methodologies classifications and usually they concentrate on the forecasting of disruptive events such as natural disaster, terrorist attack and bankruptcy and do not investigate deeply the root causes behind the uncertainties and risks. Constant changes due to a turbulent environment (technology changes, changes in customer tastes or supplier priorities) are not that much investigated. This approach to risk management is not taking into account the fact that environmental, market and technology turbulence in the supplier’s specific market have a high impact on potential disruptions, relationship between supplier attributes and supply chain performance.

Further because multiple suppliers do business in different environments and markets, their turbulence vary and thus the forces having an impact on supplier are also different. That is why all strategies to manage supplier risks do not work for every market and supplier. For example in a market where technology is constantly rapidly changing, company cannot mitigate risks by having a buffer stock. That is why it is important to have a comprehensive approach in SCRM and take into account supplier-associated turbulence and multiple uncertainty sources because of supplier features such as performance, structure and strategy. Companies also need to note that there is no one right way to manage supply chain risks instead risk management is quire firm-specific.