The unexploited opportunities of big data analytics in supply market intelligence

The opportunities of big data analytics (BDA) were studied in a recently published master’s thesis ‘Opportunities of big data analytics in supply market intelligence to reinforce supply management’ (http://urn.fi/URN:NBN:fi-fe201705226754) by Salla Paajanen, a Research Scientist at VTT. The big data characteristics of high volume, variety and velocity (3Vs) require utilizing advanced analytics. In this blog, we will present five messages that every supply management professional should recognise. Finally, we will unwrap some of the future trends of BDA according to big data professionals.

Message 1: Insight-based decision-making in supply management creates competitive advantage for a company.

Supply market intelligence (SMI) that can be defined as the ability to develop deep insights into key supplier market characteristics is important in supply management especially in driving innovation, sourcing strategy formation and supply risk management. SMI is a key element in creating competitive advantage, but its opportunities are still not identified and exploited in many companies, and insight-based decision-making is insufficiently utilized in supply management. Recognising the company’s needs is important in data-driven decision-making. The needs can derive from the point of view of sales, supply management or product development. Supplier technology road mapping in collaboration with other business units, like R&D, enables enhanced product and business development. When the perception of the supply markets changes, a need may be derived to reform existing business and strategy. Thus, SMI contributes to forming strategic partnerships with suppliers and developing new ecosystems.

Message 2: BDA has great potential in creating systematic SMI.

Companies can create competitive advantage by analysing big data according to their needs by applying analytical capabilities. It is notable that value via BDA is realised only when the analysis is used for data driven decisions or actions, such as identifying and implementing (collaboration) opportunities or selecting the best suppliers. Furthermore, BDA reduces unexpected events (e.g., supplier’s bankruptcy or market price fluctuation), as well as response time through proactive action planning. Supply markets’ risk assessment and opportunity identification are challenging, but once successful, can offer new business development opportunities.

Message 3: Big data can be categorised based on its form (structured; semi-structured; unstructured), and ownership or access to it (proprietary; public; purchased).

Some of the most important data and information (referred to as ‘pre-knowledge’ in the above figure) that is needed from the external supply markets are examining and forecasting future market trends, innovations and technologies, suppliers’ quality and delivery performance, existing suppliers’ abilities, but also new suppliers and solutions, global price levels as well as product/service availability.

Examples of BDA applications in creating SMI according to existing literature and previous studies can be found via the following link. This link on the other hand presents examples of data and information for different pre-knowledge categories as per the above figure based on the conducted research.

Message 4: Value creation with BDA is more efficient in collaboration with a solution provider than when implemented entirely in-house.

When utilising the capabilities of an external BDA solution provider, the company does not have to invest in basic analytics, but has access to more comprehensive data sources and analytical skills. Many companies attempt to achieve large gains from BDA too fast and with too little resources. This results in encountering a tipping point quickly, often from 3–6 months, as well as nonrecurring and disconnected experiments.

An external solution provider enables the creation of systematic SMI by integrating internal and external data from different sources and databases into a cloud warehouse management system. By utilising advanced analytics and interactive management tools, the solution provider can create an easy to use single point of truth user interface for the decision-maker.

Message 5: Pre- and post-analysis processes are more challenging than the actual analytics implementation.

Data aggregation and organisation are prerequisites for the analysis, which is labor-intensive due to large amounts of unstructured data in different formats (e.g., textual, numeric or graphic) and in different sources (e.g., corporate internal databases, social media or literature). Analysed information needs to be shared and absorbed internally in the company, which emphasises understanding the analysis and collaboration between the business units. Different parties can be responsible for these processes, but the most important step in creating value via BDA is understanding the key elements of the supply market and the analysed data. Closing the gap between analysts and decision-makers who are actually utilising the analysed data is a determining factor for realising the value from BDA. Solution providers need to tailor their services according to customer needs, but enlightenment, interest and involvement of supply management professionals in utilising BDA is equally important. Hence, asking the right questions is one of the requirements for a successful analysis.

Future trends of BDA

In the future, artificial intelligence (AI) -based machine learning and automation will continue to increase importance in analysing big data. Technological developments, such as cognitive computing, robotics, mobile technologies and Internet of Things (IoT) create future opportunities that still require further research in order to achieve the best benefits. Digitalisation of supply management and the entire supply chain changes processes and creates new methods of working.

Salla Paajanen VTT

Salla Paajanen, Research Scientist

Anna Aminoff VTT

Anna Aminoff, Senior Scientist
Twitter: @AminoffAnna

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