In this article, we are going to be examining how big data is impacting several industries, especially the distribution chain, and how it can be used to create an agile supply chain management.
Big data refers to extremely large data sets that can be analyzed using artificial intelligence or computer systems to reveal patterns trends or associations, often relating to human behavior or interactions. So, it’s no surprise that big data is an incredibly useful analytics tool and is currently having a significant impact on commerce across the globe.
In today’s article, we’ll be shining the spotlight on the supply chain management sector to highlight how big data and the advent of artificial intelligence is transforming the way they operate. From informing decisions on buying trends to streamlining order processing and logistics, we’ll examine just how this innovation is impacting the industry and how it can be used to create agile supply chain management.
Supply Chain Management and the effect of Big Data
Informing consumer buying trends
By analyzing data using neural network pattern recognition systems, big data is able to identify and reveal customer buying trends within a given period. Using this data, businesses are able to locate their most popular products quickly and easily from a large data set – but the benefits don’t stop there. Using this information startup business can identify the immediate and long term trends within the supply chain both from an overall perspective as well as on smaller-scale factors, for example, which seasons or products performed better.
From there, natural language interaction software can analyze big data to compile sales reports. When in the hands of managers, these reports can influence supply chain decisions to create a more efficient process. Stock purchasers can utilize this data to be better informed on which products are performing well and which products may need more promotion or pulling from inventory completely – resulting in rapid stock replenishment and a procurement process that’s in line with consumer demands.
To extend human insight into the supply chain process, natural language generation can provide reports which can be used for further document analysis, all while negating the risk of human error. When it comes to informing consumer buying trends, big data is able to pull the right information quickly to prioritize the efficiency of the supply chain process, while simultaneously saving time and money.
Improved demand planning
Using prediction technology, artificial intelligence and machine learning devices are able to accurately forecast, as well as follow an endless loop of continual improvement in warehouse environments. In turn, this can cut out the requirement for communication between warehouse stockists and purchasers. Gone are the days of traditional stock-taking sessions where data often falls victim to human error, as information technology has provided the antidote to a skill that’s refined beyond our capabilities.
When prediction technology is combined with the intelligence of network pattern recognition programs, computer systems are able to recognize selling trends according to various indicators such as promotional offers, seasons, weather changes and more. By comparing this data alongside forecasting technology, AI systems have the ability to inform inventory buying based on solid data – leaving managers better equipped to calculate appropriate pricing to fuel sales and optimize profit margins.
With big data, businesses are able to cut out the middle-man in the supply chain, leading to faster stock and information turnaround times which strive to maximize customer satisfaction. This can only be a good thing as markets are being increasingly dictated by customer demands.
Streamlining order processing
Big data can speed up warehouse processes, using image recognition technology to improve security and select items as required. AI technology has developed to allow machinery to recognize a simple logo on a high-quality cardboard box amongst many others on highly stocked shelves – making product picking quicker and reducing the risk of human error. As a result, supply chain businesses can improve order processing time – offering better customer experience in the face of a growing ‘next-day delivery’ demand.
Big data has also allowed managers to view order processing throughout the supply chain in real-time – providing better transparency and quicker troubleshooting to resolve issues. In the face of errors, communication can be sent quickly and directly to the involved party, minimizing the ramifications of delays at any point within the supply chain. Monitoring delivery status can have a larger impact on customer service, too – well-informed customers are more likely to view a brand as trustworthy and reliable, resulting in repeat business.
Big data has become invaluable in the business world as a fundamental component to the efficiency and competence of the supply chain as we know it today. We hope that with our overview, you’ll have a greater understanding of how big data can positively impact the supply chain – saving time, money and streamlining processes across the board.