By this time, you’ve probably heard of the term ‘big data’. In the race to stay competitive in the industry, there has been much hype among major organizations in the past decade to accumulate, evaluate and apply big data in their businesses. This phenomenon of ‘big data’ that has emerged in the 21st century is here to stay and will only become increasingly relevant as businesses source for new and innovative ways to differentiate themselves from the pack and rise above stiff industry competition.
Wait…I still don’t know what is big data?
Broadly, big data refers to large volume of data, both structured and unstructured, that can be analyzed for information and applied in machine learning projects, predictive modeling and other advanced analytics applications. Big data rose to prominence for its potential to unlock hidden patterns, unique correlations and critical insights which would not have been possible with the mere analysis of traditional data.
Examples of big data are data found in business transaction systems, customer databases, medical records, internet clickstream logs, mobile applications, social networks, to name a few.
Slow down…what is the difference between traditional data and big data?
Traditional data is typically structured data in fixed formats or fields in a file which is stored in a centralized database architecture. Such data is relatively easy to maintain among all types of businesses, from start-ups to large organizations. Common database tools such as SQL are used to access such data.
Comparatively, big data refers to large or complex data sets which are difficult to manage and manipulate using traditional data-processing application tools. It deals with large volume of both structured, semi structured and unstructured data, and required specialized big data analytical tools to uncover its potential.
Big data in the supply chain – what is the relevance?
In the context of supply chain, a rekindled interest in big data has been brewing. In a research paper published by Production, about 60% of the research on big data applications in supply chain management were published after 2017.
But does your supply chain need big data? Big data in supply chain expands the dataset for analysis beyond the traditional internal data held on enterprise resource planning and supply chain management systems. For example, along the supply chain, point of sale data, inventory data, production volumes data, weather data, social data and other unconventional data points can be analyzed to suggest end-to-end improvements to the supply chain. This article will suggest 3 points to support the case for big data in supply chain:
1) Operations improvement and cost reduction
Interactions with multiple players in the supply chain, from suppliers to manufacturers, distributors, shippers, freight forwarders, retailers and consumers generate big datasets that can be utilized for optimization projects. The analysis of big data has been shown to improve demand forecasts, reduce safety stock, drive optimal delivery plans and reduce uncertainty cost in the supply chain.
For example, big data analytics has helped minimize delivery delays by analyzing GPS data, as well as weather and traffic data, to optimize delivery routes. The United Parcel Service (UPS) uses an internal dynamic route optimization system which has helped them to reduce the amount of wasted miles travelled on delivery routes, all due to the value of big data advancements.
2) Business strategic development
Big data from the factory floors of the supply chain can be applied to the planning and decision-making in the boardroom. For example, big data analytics has been a useful tool for manufacturing companies to help develop strategies, share data, design predictive models and plan factory networks. It has also proven crucial in the design and development of new products and innovations.
For example, the Geek+ Smart Factory integrates robotics, AI, big data, cloud computing, and IoT technologies to achieve a smart and flexible production model. By leveraging huge amounts of digitized data from factories and production processes, the Smart Factory offers an automated solution that guarantees more precise process control and higher accuracy with a straight-through rate for the final assembly exceeding 98%.
3) Enhanced customer experience
When utilized effectively, big data can enhance customer satisfaction dramatically as it allows businesses to pinpoint the preferences or pain points of their customers. This may be valuable data that can be difficult to obtain from the consumer directly.
For example, companies can analyze social networks, mobile and web data to track the way that customers use their products. In more innovative cases, other companies have explored the use of drones equipped with cameras to monitor on-shelf inventory levels.
Big data may very well be the key to creating cost-effective, customer-centric supply chains. Indeed, the relevance of big data is not overstated – despite its notoriety as a yet another fleeting industry buzzword, its impact is here to stay. In our subsequent article, we will explore how companies in the supply chain industry can prepare their supply chain for the successful harvesting and application of big data.