Big Data Analytics in Supply Chain Management
In the year 2014, only 14% of senior executives in supply chain management had implemented big data into their infrastructure. That number has grown to around 50%, and continues to climb. Executive leaders in supply chain have recognized the importance of big data analytics to provide essential information to streamlining operations, identifying issues, and fast-tracking progress.
The team at Trax put together this overview of big data analytics in supply chain management. Our goal is to support growth toward a robust and effective use of the increasingly valuable — and vast — data sets in the supply chain and transportation logistics.
Read on to learn how to do more with big data.
For guidance on your way toward transportation spend management maturity, contact Trax today.
What is Big Data Analytics in Supply Chain?
Big data analytics refers to a set of tools, systems, and technology that organizes and mines datasets for meaningful insights. With new capabilities unlocked by IoT, machine learning, and artificial intelligence, even the largest, unstructured data sets can be made useful. Through automations and data integration, leaders in the supply chain can use data to make better decisions.
Supply Chain Analytics Turn Data Into Real Insights
The goal of big data analytics is to create value, and it has not always been as easy or accessible as it now is. According to analysts at McKinsey, “most of the data generated in a supply chain falls outside the scope of just one enterprise or entity,” a dynamic that makes it hard to know if you’re dealing with good data or bad data, or even the original source.
1. Behavior Analysis: Internal, Vendors, and Customers
Predicting human behavior feels like it would be too context-driven to offer up definitive guidance. However, behavior analysis is a growing trend in many sectors, and the conclusions reached are anything but subjective. In fact, behavior is very predictable, once you have established baselines and patterns.
The bigger the data set — in this case, its history/age and size — the better accuracy. As executives in supply chain management evaluate the behavior of every player, they are exponentially better equipped to evaluate performance, identify inefficiencies, offer assistance where needed, and shore up gaps.
2. Order Fulfillment and Realtime Tracking
Supply chain management teams have a need: a need for speed. Bottlenecks, delays, latent demand, unmet demand, all of the little snags add up to big revenue depletion. When it comes to order fulfillment and realtime tracking, there are really two arenas we’re addressing, and both are improved when informed by big data:
Planning — Simplicity in supply chain operations probably won’t come anytime soon. As leaders labor to forecast demand for the sake of planning, data can go a long way in clearing the picture of the market’s current and future state.
Delivery — Supply chain is a logistics game, and big data analytics directly impacts performance. Everything from transportation and timing to accuracy in invoices sends up a data signal, which can then be collected to enhance understanding and improve decision making.
3. Financial Data
This is an area Trax directly works to enhance understanding around data. Freight audit and payment, for instance, support the finance function in many ways. Done right, freight audit and payment systems give clarity around the carrier-shipper relationship, labor costs, fees, and even fiduciary responsibilities. Freight audit and payment programs are data collection and organization machines. Over time, the compound effect will be picture-perfect insights into financial data in supply chain operations.
Best Analytical Techniques Around Big Data in the Supply Chain
A big deterrent to pressing play on big data analytics is the magnitude of the task. It can be helpful to understand the discrete components, and how the right setup yields the right results.
A sophisticated analysis process for big data in the supply chain will include monitoring the “5 Vs” of big data:
- Variety — Are there data sets from a variety of different sources?
- Verification or Veracity — Can the data be trusted? Is it true?
- Velocity — What is the timeliness and power of the data?
- Volume — Is there a high enough quantity of data to adequately represent all of the facts or identify trends?
- Value — Is the data high value, relevant to my supply chain operations?
Those five components relate to intake and organization, which are essential to establishing the right foundation for big data analytics in supply chain management.
From there, if implementation is to positively impact business value and performance, analysis must become routine and insights must be assimilated. This requires the same steps you’d take to form any process:
- Team — Data analysts must be brought on, or existing team members trained to allocate some of their time to overseeing or performing data analysis.
- Integration — Data must be adequately connected to current systems, making it easy and fast to access.
- Sharing — Information must be regularly shared with the right stakeholders, a practice that should be embedded into standard practices to ensure it makes a difference.
Big data analytics is an inherently scientific exercise, often run by professional data analysts. However, relegating these activities to specialists without also creating workflows, touchpoints, and maximal sharing would be a mistake. For executives who wish to glean as much value as possible from big data, the final step of making it accessible, then actionable, is key.
The Future: Predictive Big Data Analytics
The possibilities of data have increased by leaps and bounds in the last decade, and progress shows no signs of slowing. As technology to index and organize data becomes more precise, it will be possible to see the future of the supply chain (SC) more clearly.
Analysts published in the Journal of Big Data explain it this way: “With the advancements in information technologies and improved computational efficiencies, big data analytics (BDA) has emerged as a means of arriving at more precise predictions that better reflect customer needs, facilitate assessment of SC performance, improve the efficiency of SC, reduce reaction time, and support SC risk assessment.”
Experts in this field make it clear: big data is the key to not just understanding what will happen, but being armed to proactively move in advance of any major changes.
Doing More With Data: Using Trax Technologies to Optimize Supply Chain Operations
What big data has the power to do is provide a 360-degree view of supply chain operations. Executives in supply chain management can make data-driven decisions, leveraging the power of big data to improve efficiency and reduce costs and risk. The way companies design, manage, and automate big data analytics processes could be a key differentiator for those that lead the market in the future.
At Trax, we are here to help you win. As you journey toward transportation spend management maturity, and operate from new vantage points, our platforms give you the tools you need to succeed.
Trax is the global leader in Transportation Spend Management solutions. We partner with the most global and complex brands to drive meaningful optimizations and savings through industry-leading technology solutions and world-class advisory services. With the largest global footprint spanning North America, Latin America, Asia, and Europe, we enable our clients to have greater control over their transportation performance and spend. Our focus is on your success.