Supply chain management and logistics have always been complex. Be it sourcing raw materials or delivering finished goods and managing returns, everything comes under the supply chain.
Vendors, manufacturers, warehouses, logistics service providers, etc., are all a part of the supply chain.
Supply chain management (SCM) relied on ERP software and outdated data storage systems.
With the traditional methods taking much time and not delivering enough results, enterprises are looking at data analytics and big data to streamline the supply chain, automate the recurring processes, and increase overall efficiency.
Data generated in the supply chain belongs to more than one enterprise (especially if the manufacturer uses third-party logistics services).
This has made it rather hard to analyze a vast amount of data without using the latest technology. Big data analytics solves the problem.
What is Big Data Analytics?
Data sets are larger and complex than what a traditional data processing system can handle are called big data.
To collect, store, and analyze such vast amounts of unstructured, semi-structured, and structured data, enterprises need to invest in advanced analytics. This use of advanced analytics on big data is called big data analytics.
In Big Data Analytics, data is collected in real-time from numerous sources in multiple formats.
It has high volume, high velocity, more variety, etc., and is processed using artificial intelligence, predictive analytics, and other subsets of AI (like machine learning and natural language processing).
You can also convert big data to smart data using data analytics. Many companies offer big data consulting services to help SMEs and large-scale enterprises process data and gather in-depth insights.
Importance of Big Data Analytics in Supply Chain
So how does big data analytics help in supply chain management? What is the importance of investing in data analytics for SCM and logistics?
Compare and Match Data
As we mentioned earlier, data in a supply chain is produced within the enterprise and outside the enterprise. This data has to be collected, cleaned (remove duplicates, formatting, etc.), structured, and analyzed to derive insights. Both historical and real-time data need to be used to make decisions for improving the supply chain. Decisions about demand and supply, weather conditions, seasonal changes and their impact, etc., are made based on the insights gathered from big data.
Big Data and IoT
IoT (Internet of Things) makes it easy to share information among different devices connected to the network. It helps leverage data within the supply chain. And when this is combined with big data analytics, enterprises can create a network to facilitate continuous and live data exchange.
Speed Up the Planning Process
By integrating data across the supply chain, enterprises can use statistical models and predictive analytics to understand the coming trends in the market. This will help in planning the production, warehousing, and delivery of the finished goods. Data management services are used to processes historical and real-time data and derive insights.
Sourcing Raw Materials
According to the Global CPO Survey 2016 by Deloitte, there’s no clear digital strategy for 60% of the procurement. Many SMEs can save costs by sourcing raw materials based on comprehensive data. Real-time data analytics will help enterprises in the following ways-
- Understand the number of raw materials they need to procure,
- When to reduce the costs,
- Manage production.
Executing the Plans
Planning individual elements in the supply chain will not be enough. The success of a business lies in executing these plans with no errors or glitches. Big data analytics makes it possible to optimize the use of resources while increasing productivity and returns.
Delivering the Final Products
How does a manufacturer get back the return on investment and earn profits? It’s possible by delivering the finished goods to the end customer on time and keeping the customer happy. To avoid delays and uncertainties in deliveries caused by traffic, weather conditions, etc., enterprises can make the necessary changes to speed up deliveries.
Regardless of how good a product is, every manufacturer will have to deal with returned goods. Reverse logistics is costly as it involves additional warehousing and transportation costs while returning the amount to the customers. Data analytics can help enterprises reduce returns by increasing data visibility and aligning the inventory and sales systems.
Customized Simulation Models
By using big data in the manufacturing industry, custom models can be designed to replicate real-life scenarios in a simulated environment. This helps enterprises try out various strategies, make changes to the model, and come up with the best solution to streamline the supply chain in the business.
Ways to Get Ahead of the Competition Using Big Data Supply Chain
Big data analytics can tremendously help manufacturers, logistics providers, and enterprises to streamline their business operations on multiple levels. This empowers them to move ahead of their competitors and make a name for themselves in the market. In our highly competitive scenario, being equipped with the right kind of advanced technology can place a business on the top of the charts.
Improving Inventory Management
Managing inventory is one of the toughest jobs for any manufacturer or supplier. Even 3pl logistics and warehouse service providers need to be careful about how they manage the inventory in the warehouses. Big data analytics helps choose the nearest warehouse, the distribution of inventory, calculation of distribution and transportation costs, and helps track every move.
Also, by automating the process, enterprises do not have to manually check the stock each time to ensure if there is sufficient inventory to cater to customer demands. It is handled by the software, and the employees are alerted when the stock has to be moved.
Streamlining Online Retail and eCommerce
With more customers relying on online purchases, it has become important for businesses to streamline eCommerce operations and make the entire process as smooth as possible. Cloud data analytics services help retailers align the BI processes and tools to improve decision-making.
Offshore companies offer cloud computing services to collect, store, clean, and analyze data to derive accurate insights. Today’s customers want their orders to be delivered in a single day. For this to work, enterprises need to have a complete control over their supply chain.
Understanding Customer Behavior
The ultimate goal of any enterprise is customer satisfaction. The sales and profits are dependent on how happy customers are with a brand. Real-time data collected from social media and other sources on the internet has vital clues to how a customer feels about a business or its product.
Big data analytics helps understand the customers’ preferences, purchase behavior, and patterns, etc. Being armed with this information allows manufacturers to make changes to their products (be it procuring raw materials from another supplier or releasing more stock into the market to take advantage of the high demand).
Increasing Transparency and Traceability
How do companies like Amazon, Flipkart, Shipping Rocket, or other logistics service providers share the shipment details with customers? They are examples of clients who require data management services and have been successfully using big data analytics to provide accurate product and shipping details to countless customers. Artificial intelligence tools are used to track the movement of goods from the warehouse to the customer. The shipping vehicles have trackers through which data is collected in real-time to share regular updates with customers.
Predicting Market Trends and Outcomes
We’ve talked about this earlier, right? When enterprises know what to expect from the market, they’ll be better equipped to handle the downsides and make the most of the opportunities. Predictive analytics is used to process data from the past and present to compare and find patterns.
Armed with this information, manufacturers can decide if they have to increase or decrease production or continue it the same way. It also helps in choosing cost-effective means of transporting the goods to customers without compromising on duration.
Using Data in Real-Time
Real-time data has gained prominence during the last few years. Database management services help enterprises make the most of this data that comes from various sources. The insights derived from real-time data are highly useful to make fast decisions and avoid the negative impact of customer dissatisfaction. Big data analytics uses real-time data and historical data to generate reports with insights and predictions.
Reducing Time Lags and Optimizing Resources
The biggest data management service example is the use of data analytics to ensure that there are no delays in the supply chain. Many enterprises face losses (or do not get the expected profits) because of the unexpected lags, be it in procurement, moving the inventory from the warehouse, or during transportation.
For example, think of a vehicle carrying the day’s goods gets stuck in a traffic jam and misses the flight that carries the shipments to their destination. This delay will continue and result in the shipment reaching the customer later than promised. This would cause customer dissatisfaction as well as result in additional expenditure required to re-plan the shipping details.
Big data analytics is bringing major changes in the supply chain and helping SMEs and large enterprises to effectively manage the demand and supply fluctuations, anticipate the market trends, identify patterns in customer behavior, and optimize resources. The big data supply chain is already implemented by famous global platforms like Amazon and UPS.
Enterprises can contact data analytics consulting companies to understand which tools and applications will be best suited for their business and how big data can be successfully used in supply chain management and logistics.
Frequently Asked Questions
- What are the five V's of big data and supply chain?
Volume: The amount of supply chain-related data within and outside the enterprise comes under 'volume'. From procurement data to freight invoices, and customer satisfaction, data needs to be collected in real-time for analytics.
Velocity: Real-time data is being continuously generated. It has a high velocity compared to historical data, and this can be processed only when the enterprise has the necessary data management system.
Veracity: This V deals with data standardization by cleaning and formatting huge amounts of data into a known structure. Data needs to be accurate and relevant for it to be used to make supply chain management.
Value: This is the most important V of big data and the one where many enterprises tend to fail. Having high velocity and a large volume of data is of no use if data doesn’t have any value or offer valuable insights to the enterprise.
Variety: Data from different sources comes in different formats. There is a lot of variety in the data, and it can be processed using advanced analytics.
- What are the challenges to using a big data supply chain?
Using big data in the supply chain is not without challenges. SMEs can be successful in streamlining the supply chain when they overcome the following-
Lack of data visibility affects decision-making.
Poor data quality results in wrong decisions
Lack of transparency increases the risk of decision-making.
Not using advanced technology will further slowdown analytics. Big data cannot be analyzed using the manual process in less time.
Customer satisfaction also depends on traceability and sustainability.
- How can an enterprise overcome the challenges of using a big data supply chain?
Enterprises are finding different ways to overcome the challenges presented by big data in the supply chain. By using data strategy consulting services, manufacturers can-
Establish a centralized system and automate workflows to improve data visibility and help in better decision-making. It becomes easier to find alternate suppliers for procuring raw materials. Automating the workflow also irons out the issues with data quality.
Invest in the right BI tools and applications to process and analyze data. Using the correct tools will result in precise predictions and effective decisions.
Share product details with customers to increase transparency and accountability. Many manufacturers these days share relevant data by asking customers to scan the QR code on the products.
Develop a robust governance policy to ensure data transparency without compromising on data security and privacy.