How Big Data Improves Supply Chain?

Everyone wants to look cool at school, go to the movies, buy the hottest mobile phone, or be the cool kid at the movies and it's all rolled into one. There are a number of reasons why companies are tapping into Big Data for a variety of reasons in an attempt to gain a competitive advantage. Big Data can help businesses decrease their costs, increase their efficiency, and ultimately make better business decisions. Currently, it is one of the hottest topics gaining traction in the current supply chain industry as one of the hottest topics. There is no doubt that automation is on par with artificial intelligence (AI), machine learning (ML) and artificial intelligence.

Defining Big Data

Big Data is a broad term used to refer to the huge amount of structured and unstructured data that businesses have access to in order to establish trends and patterns in human behavior and interactions, as well as the performance of their businesses. Companies can make better decisions based on customer feedback by leveraging this information in order to determine what their customers want, therefore giving them the advantage of knowing what their customers want.

Nowadays, technology is playing a more and more important role in the strategies of companies, making it imperative that new techniques such as Big Data, AI, and ML are introduced into operations, or else companies run the risk of falling behind their competitors.

Big data can be used to improve the supply chain at each stage in these ways:

Using big data to plan

The use of statistical models in conjunction with information from the complete network of the supply chain can help forecast demand more accurately (for example, sales figures, inventory levels) as part of the planning process. It is possible to prevent out-of-stock situations in retail by maintaining communication between the inventory and replenishment systems. As well as taking into account past and real-time data, these models also include macroeconomic factors, industry trends, and competitor data.

Using big data for sourcing and building

The total costs incurred by an organization can be attributed to approximately 43% to the procurement function. As there are a lot of potential savings to be made in this area, companies are increasingly implementing supply chain analytics to evaluate contractor performance and compliance on a continuous basis instead of on a quarterly or annual basis when it may be too late to intervene and reduce costs. When evaluating contractors, quantitative methods can be used so as to ensure that the cost structure is transparent by assisting decision-makers in detecting hidden costs during the evaluation process.

Using big data for execution

In order to make the most effective use of resources (space, tools, materials, people, etc. ), big data can play an essential role in maximizing output during an activity. Sensors with IoT capabilities can, for example, provide real-time data on equipment that can be used to enhance production capacity and asset performance in the manufacturing sector. The use of analytics can be applied to fault estimation and maintenance planning as well as predictive scenarios. Intel, for instance, is saving $656 million per year using predictive analytics.

Delivery services using big data

Speed (delivery of the product at the right time), accuracy (ensuring that the packages reach their destination), and efficiency (finding the optimal route/combining deliveries) are all on the table. Logistics management can be significantly improved by combining real-time delivery data with external data, such as traffic and weather patterns.

Generating returns with big data

It has been estimated that for certain product categories the return rate is 30%, which is a big deterrent for companies wanting to remain profitable. A reverse logistics company may incur costs for restocking, transportation, shipping, and decision-making related to assessing returned products. It has been proven that analytics can reduce these costs and provide the visibility needed to manage seamless returns by combining data from inventory and sales systems as well as inbound and outbound flows.

Using Big Data To Manage Supply Chains

There has been a significant impact of big data analytics on most sectors of the supply chain economy. Let us examine the examples of how it is being used.

Big data in manufacturing supply chains

With the fourth industrial revolution, powered by the power of data, manufacturing is at the forefront of the fourth industrial revolution. There are also many applications of these technologies - gathering telemetry data so that you can predict maintenance, gathering contextual intelligence so that you eliminate bottlenecks, and forecasting demand are some of them. Domino Printing Sciences was able to gain visibility into daily operations and make their business model more efficient thanks to the integration of data from multiple sources, including ERP, CRM, Oracle, and Salesforce. Moreover, the company shared reports with suppliers, making the impact visible across the entire supply chain.

The role of big data in the consumer goods supply chain

When applied to consumer packaged goods (CPG) companies, big data analytics can prove to be a valuable tool in planning what-if scenarios and determining if certain strategies, such as marketing expenditures, are providing expected returns. This company develops audio/video products and services as well as analyzes IoT data gathered from its products' IP addresses and MAC addresses as well as data collected from its mobile apps in order to gain insight into its customers' experiences. As a result of these insights, Sound United developed new features based on customer preferences. Furthermore, the company was also able to predict demand better by knowing how much inventory retail partners held versus how much they sold.

Big Data In Supply Chains: Case Studies

Amazon

In order to better understand the requirements of their customers, ecommerce giants have to utilize Big Data to gain a deeper understanding of them. In order to receive more revenue, A variety of amazon scraper can be used to extract data for different purposes. Amazon analyzes the behavior of its customers in order to understand what they've purchased recently, what they've added to their shopping cart, and what products they've searched for. The company claims that its personalized recommendation system accounts for 35% of its revenue each year. Whenever possible, Amazon strives to deliver its orders as quickly as possible to its customers. The launch of One-Day Delivery in 2019 took this a step further. By tracking the inventory of manufacturers, Amazon is able to cut costs by 10 to 40 percent and choose the warehouse closest to the vendors and customers, thereby reducing costs by 10 to 40 percent.

Starbucks

There are more than 25,000 Starbucks stores in over 100 countries, making it one of the best known brands in the world. Mobile reward apps have given the company an insight into their customers' spending habits. There are estimated to be more than 17 million active users for Starbucks' mobile app, whereas the rewards app is estimated to be used by more than 13 million. This app provides Starbucks with various information about their customers' favorite drinks and entices them to use the app by providing them with complimentary beverages. Personalized and targeted marketing is another way Starbucks reaches out to new customers. An email is sent out to re-engage a customer who hasn't purchased from them in the past. The email offers a product similar to one they have previously purchased and urges them to come back to the store.

Concluding Remarks

It is reported that 68% of supply chain leaders believe that supply chain analytics are crucial to the success of their company. Even so, one of the most important elements for the successful use of supply chain analytics is the combination of information from all involved parties. As it flows through the system, the boundaries that separate the different elements are essentially gone. As an alternative, if you wish to minimize your efforts and ensure that all the necessary data is collected in the format you require, you may use a web scraping tool such as "Crawlbase".

With crawlbase you are able to collect data from several sources and then derive critical insights from it based on the fact that you are using a reliable source of data. Getting started with Crawlbase today will allow you to reap the rewards of big data when it comes to your business.