Big data is one of those terms that's starting to pop up all over the place. Everyone's using it, but frustratingly, there isn't a solid consensus on exactly what big data means. While the term can be defined, the sets of processes, practices, ideas and insights made possible by big data technology are countless. Big data solutions are useful in many contexts and to multiple industries. As such, industry-specific definitions and examples are often more useful to refer to than broad definitions.
In supply chain management, the above holds true. Big data solutions, often combined with automated technology, focus on the analysis and prediction of: demand vs supply, delays, inventory missteps, and the cost to profit ratio of the entire chain. These solutions are already helping businesses reduce the time and expense it takes to fill customer orders. The future looks bright for continued improvements in the accuracy and cost-effectiveness of supply chain management.
What Is Big Data and What are Some Examples of Applications?
There is one common definition of big data that can be easily relied upon. The term 'big data' refers collectively to data sets of such volume and variety that specialized tools are required in order to collect, organize and draw conclusions from them.
By increasing the scope of data collection, we can gain more valuable insights from that data. It seems simplistic to describe big data as simply that; large amounts of data. When data exists in this kind of volume, however, the size of said data makes handling it extremely complex. That complexity, in turn, opens up entirely new ways of thinking about and approaching problems. The following are some use cases for big data solutions in supply chain management and related industries.
One major use for big data tools is in the marketing industry — specifically digital marketing. When you see online ads for products that you've already searched for, or are related to your hobbies and interests, that's big data at work. Algorithms analyze the information you generate online, such as social media profile data, search history, etc. and serve you personalized ads based on that information.
The ability to collect data detailing how people interact with specific web pages is empowering modern marketers in their decision making. The more you know about what people do and how they do it, the better you can predict and guide their online experiences and behavior.
Efficiency and Business Operations
The same technology that analyzes behavior can be used to track items, processes and expenses. Business operations are often described in terms of the value chain, which is all of the activities and processes are undertaken in order to provide value for customers. The value chain emphasizes that providing value for customers should be the end goal of any operation, rather than prioritizing operations that primarily provide value to management and investors This philosophy provides business owners with a more holistic view of all of the operations that contribute to their bottom line.
Big data is helping analyze these chains of production by collecting information that can help inform both cost reduction and customer service improvement opportunities. Data can point to areas in the supply chain where money could be better spent. Information collected here could also improve the customer experience by highlighting which vendors or processes provide the best overall experience and product. Getting a top-down view of the entire chain of activities requires a vast amount of data.
Inventory management is the process of tracking the goods a business owns and intends to sell. It involves making sure there is enough stock to fulfill orders, that the business has enough tools and materials to run efficiently and that certain goods don't get overstocked. Ideally, this tracking occurs from the moment a business takes possession of an item to the moment a final product is sold. Sometimes this involves complex processes where a business will receive parts and raw materials, all of which have their own storage and transport requirements, that they then assemble and manufacture into products for the market. Internal supply chains can complicate inventory management, and management gets more difficult as the size of inventory increases.
In fact, even for small businesses, it's easy to let inventory become disorganized with the wrong tools or lack of careful oversight. Ending up with an overstock of inventory that people aren't buying can put a huge dent in a business' cash flow.
The more inventory a business owns, and the more stages that inventory needs to go through, the more difficult it is to organize and track. That's where big data solutions come in. They allow for the collection and storage of large volumes of inventory data, as well as data concerning the internal supply and production chains of a company. Analysis of these data sets can help predict inventory needs and make the whole process run more smoothly.
How Big Data Works for the Supply Chain
By nature of its broad definition, the term “big data” will carry a different meaning to a marketer than it will to a warehouse manager. For supply chains, big data initiatives are less about predicting how consumers will act. Rather, they are more about tracking how inventory moves from place to place, measuring supply and demand, and comparing supply chain expenses against the profits and revenue from final product sales.
Many businesses err on the side of “too much” — holding extra inventory in case there is a spike in orders. Unfortunately, this ties up cash flow in static inventory that hasn't been ordered yet. Big data solutions are being used to better predict demand for products. By using predictive analytics, companies can leverage demand forecasting to better decide when to buy extra inventory, or when to run a lighter operation. Less need for extra inventory means less total space required, allowing supply chains more freedom to scale — both up and down. Smaller quantities of inventory to be stored and moved means businesses can utilize smaller warehouse solutions, and ultimately create a more cost-effective supply chain overall.
Big data solutions are also being used to improve on production and delivery time, as well as to diagnose and solve problems in supply chains that businesses didn't know existed in the first place. These solutions allow businesses to expand operations and offer the same kinds of power and insight to global and local companies alike.
The ability to take large data sets and use software analysis to draw conclusions, make predictions and eliminate mistakes is making all the difference in supply chains. In an industry where a late shipment or a wrong order can set back the entire chain of production, efficiency is key.
The Connection Between Big Data and Supply Chain Automation
Big data is a powerful tool that assists data scientists in identifying problems and solutions, but just having the data isn't itself a solution. Big data relies on automated algorithms that use machine learning to recognize inconsistencies and patterns that the human eye can't — or would take significantly longer. Automation is a key factor in making big data manageable, but other types of automation can be used to implement the efficiency changes that data scientists draw attention to.
Big data technology can help to identify weaknesses within a company's supply like inefficient processes, lack of organization, or long turnaround times. As a result of these analyses, companies will often turn to automated solutions, like robotic warehouse technology. Automating processes, like order filing and inventory organization, can help fill the gaps that big data solutions identify, all while integrating seamlessly with automated big data tracking measures that have already been implemented.
Big Data and Analytics in E-Commerce and Retail
Big data plays an increasingly pivotal role in e-commerce and retail. It has been used to develop new methods of tracking and delivering orders, to maintain compliance, and to support various other advancements.
In e-commerce environments specifically, big data is allowing companies to serve customers better. The ability to increase the accuracy of demand planning while keeping track of inventories across decentralized warehouses represents one of the significant logistics benefits of big data.
That old adage, “fast, good, cheap; pick two,” isn't really relevant anymore when it comes to customer expectation, because e-commerce giants like Amazon have trained customers to expect all three. Especially for businesses that sell products through Amazon, inventory management for Amazon orders affects not only outcomes for customers, but the business's position and discoverability on the platform as well. The predictive power that big data brings, as well as the time and cost reduction initiatives informed by these predictions, are absolutely essential to any business hoping to compete online in the modern age.
Customer and Retailer Concerns About Big Data
The main concern that customers have about big data is privacy and security. The more data that is collected, the more damaging a data breach can be. Across so many platforms, often improperly secured, it can be easy for hackers to access it.
In addition, as the data evolves to contain more sensitive and personal information, it can become more vulnerable to theft. High profile breaches such as the Equifax breach, and news about improper data collection and use by major companies have made many a modern consumer wary of data privacy. A breach can be extremely damaging to a company's reputation, scaring away current and potential customers.
For companies, there are concerns as well. Not all data generated is useful or even genuine. External security can stop hackers, but the more that data is used and shared internally, the more opportunities there are for internal theft or negligence. Treated without proper care, an abundance of data can create problems, especially when it comes to outsourcing operations that require the sharing of private customer data.
The Future of Big Data
The potential for big data in future applications can be great, although difficult to predict. After all, technology has been providing solutions to problems we didn't know existed for years.
One of the main advancements coming to the forefront of big data involves increases in computer processing power. With this, we'll be able to see large amounts of data being reasonably processed and analyzed at increased speeds. We'll also begin to see improvements in the scope of operations that algorithms can be taught through machine learning.
The more data that these automated algorithms have access to, the more accurate and reliable they become. Problems that have been identified about a process, by looking at the data associated with it, should have more potential solutions to implement. Furthermore, as we process more data, and as we do it faster, businesses will eventually be able to rely on “real-time” analytics — receiving information about their business as events occur, via minute-by-minute updates.
As all of this develops, we may well see an increase in the number of autonomous tools used in business — algorithms, programs, and robots that require zero user input to not just perform a job, but also to make decisions about that job.
Still, big data is very likely to face continuing privacy and security concerns, especially in cases where it is employed in place of human oversight. That very development, on the other hand, may provide the key to increased security as we learn how to create better encryption. The future is bright for big data.