Big Data Analytics for Supply Chains
Data analytics are nothing new to supply chain managers. Companies have long been applying ‘analytics’ or simple evaluative techniques to aid in everyday decision making. However, these analytics have largely consisted of simple techniques applied to segmented or specific situations, like using internal data to calculate optimal facility designs, or to determine the best ways to restock inventory. Internal data analysis is common and has rarely stretched beyond the boundaries of company know-how or internal talent resources, but things are changing.
Recent shifts in technological capabilities are allowing companies to not only collect more internal data, but also to collect data from more varied external sources. Big data really is big: six billion people, with the aid of mobile devices, audio recording tools, and geo-location technology are sending, requesting, and remixing digitized information, and often sharing valuable data while doing so. Of course, the Internet of Things (IoT) contributes an additional 20-40 billion reliable, information generating and sharing players to the big data realm .
The analyzing power of new technologies continues to improve, and while most supply chain managers remain excited about the potential of big data, many are unsure of the next step to formulating a big data analysis strategy. This accumulation of big data holds great promise, but it requires more powerful and skilled techniques for sifting through the seas of information. But what exactly does big data analytics mean, and how can it impact your supply chain management?
Most supply chain organizations are still unsure of how to define big data, as well as how to break down large data in order to drive supply chain performance. The concepts are new and not well understood. According to the SCM World 2014 Chief Supply Chain Officer Survey, Big Data Analytics takes the cake for the most “disruptive and important” technology in supply chain management. Supply chain managers are aware of big data and its revolutionizing capacity, but they are unsure about how to how to use it. Big data differs from internal or traditional data andi refers to the ability to process and analyze large quantities of data with Velocity (in real time or close to real time), Variety (in time and context), and Volume (specifically high volume) .
Here are a few key differences between traditional and big data supply chain analytics:
Traditional Data Analytics
- Centralized data sources are controlled and monitored (usually internal data that can be recorded and displayed through relational database formats).
- They are updated on a regular basis.
- Predictive analysis are largely optimization and simulation engines.
Big Data Analytics
- Big data involves the use of both external as well as internal data.
- Analytics involve structured and unstructured data elements from a variety of physically distributed data sources.
- Large memory resources are required to deal with vast swaths of data and analysis.
A primary difference in data analysis for supply chains is the integration of external data sources. External sources include data from suppliers, customers, and outsourced partners, as well as contextual information like weather. IoT data is especially enticing to global supply chain managers because it allows for deterministic modeling of supply chains based on factors like temperature, distance, and weight, which could improve accuracy and help mediate risks.
For example, by overlapping geographical supplier locations with weather statistics, a supplier can calculate weather risks and orchestrate its supply chain to reduce risks or plan back up options. If you’re like most supply chain executives, you’re interested in big data and on the verge of implementing analytics in your supply chain, but you have yet to figure out how to use big data to improve your overall business. As you begin preparing for your leap into big data, keep these critical success factors in mind:
- Once a commitment is made to integrating big data into operations, those managing both IT and supply chain logistics need to be fully invested in finding their business specific benefits through big data. To compete in a global marketplace, companies need to have an enterprise wide strategy for quick and effective adaptation to big data.
- Start by keeping it simple. While the possibilities for exploring and boundary breaking are tempting, companies are more likely to see returns on big data analysis by focusing their initiatives on a few small, focused areas in the beginning. Not only will this allow for precise feedback, but it will ensure that the requisite time and energy are put into asking the right questions and finding the right answers.
- Reengineer business processes to meet changing demands. “Business as Usual” is not the same as it once was and shouldn’t be treated the same. Successful companies will evaluate how changes can be made to effectively use the insights gained from big data.
- Run internal big bang data analytics, using existing resources to implement a supply-chain wide data resource. Implement new technologies, training, and tools that can accommodate current internal data as well as incoming external data.
- Ask an expert. Hire external capability to shed light on what big data could do for your specific supply chain issues.
The potential of big data analytics is monumental, as it can have a significant impact on a company’s overall operating and financial performance. Although investing in data analysis is costly and the repercussions unsure, the capability of big data analytics to generate significant business value means big data is here to stay.
 O’Marah, Kevin. The Big Data Leap of Faith. Beyond Supply Chain, SCM World. 4 July 2014.
 Big Data Analytics in Supply Chain: Hype or Here to Stay?. Accenture. 2014.
 Big Data Analytics in Supply Chain: Hype or Here to Stay?. Accenture. 2014