Supply Chain Management is one among those fields where big data and analytics found obvious applications. In the recent years, however, the businesses identified the potential of big data analytics and start implementing it in SCM than in other areas of operation such as business marketing or manufacturing. Most of the top rated companies analyzed the benefits of implementing big data in SCM and how it can positively impact the company’s overall financial and operating performance.
When businesses implement big data in Supply Chain, they have high expectation and in some cases, many of the companies too face difficulty in adopting it.
According to the survey conducted by Accenture, 97% of the senior executives understand the need and benefits of implementing big data into SCM, but only 17% have implemented this strategy in one or more of their supply chain functions. Accenture further found that on an average of one-third of executives are engaged in serious discussions to apply analytics in SCM and three out of the ten have an initiative to employ analytics.
Accenture conducted a web-based survey that involves 1,014 senior executives from top global companies and 54% of the respondents held of C-level titles, including chief supply chain officer, chief procurement officer, chief sourcing officer, chief operations officer and chief operating officer and the remaining 44% of the crowd involves senior-level supply chain and operations executives. As per the web-based survey, 48% of the surveyed executives are interested in applying big data and analytics into supply chain, which helps them to gain better insights about the future. Nevertheless, the big data analytics can have a high impact on organization overall performance.
The main reason behind the popularity of implementing big data is that manufacturers are devising 80% of their supplier network activity using big data and cloud-based technologies to get requisite details beyond the limitations of Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. This strategy is highly beneficial for businesses whose models are designed for delivering order, shipment and transactional data. As the existing strategy fails to meet the challenges supply chains face today, it is the high time to think and implement new strategies that not only address the existing challenges, but also optimize the overall business operation.
“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran
Being this as a current trend, let us have a look on top 10 big data analytics that revolutionizing Supply Chain Management!
- Scale, Scope and depth of data
The scale, scope and depth of data in supply chains are accelerating day by day, which delivers an ample set of data that drives contextual intelligence. Below given graph defines the 52 different sources of big data that are obtained through supply chains. The graph represents the different data sources that are generated on the basis of variety, volume and velocity under different levels of structured and unstructured data.
As per the above graph, it is clearly visible that majority of the supply chain data is obtained from outside sources. Hence, the point being very clear that it is a high time to concentrate more on outside sources as it directly impacts the results and has the potential to generate maximum data
2. Optimizing Supplier’s network
Big data enables challenging supplier’s network to focus directly on knowledge sharing and association, which optimizes the network to form, grow and propagate into other markets and establish over-time. Big data and analytics not only help supplier network to focus on transactions, but also helps creating a knowledge-sharing hub, which is obtained through the insights of big data analytics.
Above picture represents that the development of supply chain from networks, where sharing the knowledge becomes priority.
3. Optimizing supply chain capabilities
When big data and advanced analytics are implemented into supply chain successfully, it can optimize supply chain capabilities such as demand forecasting, supplier collaboration, integrated business planning and risk analytics at a quickening pace. According to the report generated by Delotte, control tower analytics and visualization are in the pipeline of supply chain that runs with big data pilots.
4. Disruptive technologies of Supply Chain
As per a report, 64% of the supply chain executives believe that big data analytics acts as a disruptive and the most significant technology if it is set for a long-term. Below graph defines how the senior executives prioritize big data over other technologies.
5. Use of geo-analytics
As per the reports generated by Boston Consulting Group, big data can be efficiently implemented into Supply Chain Management to merge two complex networks and optimize then using geo-analytics. According to the report, combining geo-analytics and big data reduces the waiting time drastically and augment the efficiency of overall communication thereby augmenting the transactions within the network. Besides, high accuracy in the network is an added advantage.
6. According to the Accenture Global Operations Megatrends Study, implementing big data and analytics into SCM augments the supply chain efficiency by 41% and the integration across the supply chain will rise to 36%.
7. According to the Accenture reports, entrenching big data analytics in supply chain operations augment its process by minimum of 1.3x over using big data on an improvised basis. Source: Big Data Analytics in Supply Chain: Hype or Here to Stay? Accenture Global Operations Megatrends Study
8. According to the One Network Enterprises, advanced big data and analytics has the potential to generate real-time visibility across SC which augments all the elements associated with it. Source: Turn Big Data Into Big Visibility.
9. Less time and more efficiency: Big data has the potential to generate a huge set of data through numerous sources. Besides, it has the potential to augment traceability performance and reduce thousands of hours of working time to access, integrate and manage the data thereby accelerating the work efficiency.
10. Increases supplier quality through supplier audit and to inbound inspection with final assembly of big data.
“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard