15 Ways Big Data Analytics can help improve the Supply Chain and 3 potential barriers
It is a strange time in supply chain management for big data analytics. According to a 2014 study conducted by Accenture, although an overwhelming 97 percent of supply chain managers understand the benefits of big data, only 17 percent have actually taken steps to integrate big data analytics into their operations.
Why is there such a significant gap between recognizing the benefits of big data and using big data to improve the supply chain? The answer may be simple: executives and managers do not yet know how to quickly and successfully develop and deploy big data strategies and capabilities.
In this post, we will discuss three main topics: what big data analytics is, how it is currently being utilized by companies, and how big data analytics can be leveraged improve the supply chain.
What is Big Data Analytics?
Very simply put, big data analytics is the process of collecting and interpreting large volumes of information in order to reveal new patterns, information, and trends. Big data analytics has recently been made possible through new software and technologies, new data management strategies, and newly developed databases. These advances have allowed data scientists to process and analyze extremely large and ever-changing streams of information in new and innovative ways.
The one overarching benefit of big data analysis is information: being able to sift through mountains of diverse and messy data and processing it all into useable knowledge. Generally, big data can be used to:
- Reveal trends
- Spot waste and inefficiency
- Assess risk
- Understand behaviors
- Reveal correlations and relationships
- Improve service
- Develop marketing strategies
- Improve revenue, and even
- Predict outcomes
The new technologies that have allowed for big data analytics to develop are so new that data scientists are still discovering new ways that we can benefit from big data.
15 Ways Big Data Analytics Can Help
While businesses, organizations, and corporations of all kinds have been using big data analytics to improve their systems, there are specific ways in which these new tools, strategies, and technologies can be used to meet challenges in the supply chain.
Currently, the most advanced supply chain managers have been using big data analytics to:
- Understand and improve supplier and vendor relationships.
- Boost the ability to engage in real-time decision-making.
- Enable more complex and more collaborative supplier networks.
- Create detailed supplier and vendor profiles.
- Create detailed customer profiles.
- Improve risk management strategies.
- Optimize inventory management and distribution.
- Improve supply chain traceability.
- Better understand customers and their needs.
- Improve speed through shortening reaction times and delivery times.
- Decrease customer dissatisfaction and improve customer interactions.
- Enhance planning, such as sales and operations planning (S&OP)
- Understand how to offer better product recommendations.
- Find opportunities for cost reductions.
- Improve overall supply chain efficiency.
It is again important to note that big data analytics is new enough that the full potential of harnessing large volumes of supply chain data is not yet known. There are certainly opportunities for companies to develop new and exciting ways to improve the supply chain through the utilization of big data.
3 Barriers To Implementing Big Data Analytics
With so many obvious benefits, we must return to our initial question: why aren’t supply chain managers harnessing the power of big data analytics as quickly as possible?
1.Expense. Change is expensive, as is hiring truly skilled and experienced data experts who can successfully analyze information. While big data analytics can save companies a significant amount of money after it is in place, the price tag on integrating it into an already complex system is considerable.
2.Access to data. Locating and managing data can be just as difficult as analyzing it. Big data analysis is not effective if the data being analyzed are low quality, incomplete, or out of date. Supply chain managers must have organized databases before they can move forward to taking advantage of that data.
3.Lack of expertise. In order to reap the benefits of big data analysis, you must have experts working at every step of your plan, from coming up with a big data strategy, to organizing your data, to coming up with strategies based on your analytical findings. At present, there are simply not enough big data experts to implement plans.
Even considering these barriers, many supply chain managers are in the process of making the move towards integrating big data into their systems. According to the Accenture study mentioned above, 37 percent of companies are engaged in serious talk about how to implement big data information into the supply chain, while another 30 percent are planning on implementing big data analytics in the next year.
What are your thoughts? Do you currently work in supply chain management and do you use big data analytics? What has been useful and what has not about leveraging big data in your supply chain?
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