The Role of Big Data Analytics in Supply Chain Operations

Big Data Analytics in Supply Chain

Companies and businesses utilise big data analytics in the supply chain to analyse their products or services, optimise the various supply chain operations, predict consumer behaviour and manage inventories. Data analytics has played a crucial role in refining complex sets of data to make some sense out of it for years. Big data analytics is a powerful tool that enables the consolidation and analysis of vast datasets, generating valuable insights to identify emerging opportunities and improve the efficiency of supply chain operations across multiple levels. This blog provides insight into how the supply chain utilises and benefits from the perks of big data analytics.

Define Big Data Analytics in the supply chain

The supply chain is a dynamic business world where the unending cycle of demand and supply helps supplement consumer needs and boost global economic growth. Due to technological growth, supply chain processes have gained speed. The introduction of digitisation-powered automation in multiple industries forms a part of the global supply chain. The supply chain has numerous operations, each of which collects, stores and utilises a plethora of data to perform the required tasks. This is where big data analytics plays a crucial role.

Like its name, big data refers to the entire amount of data collected during a process, including any form of structured, unstructured or semi-structured data. These enormous datasets are stored to be accessed and examined by analysts to produce and unveil required insights for any business, industry or organisation. Big data analytics helps perform advanced analytics on a diverse range of big data sets to present quality analysis and filter the desired output to be utilised.

The new information that gets filtered helps companies and industries build newer strategies and improve their processes to maximise efficiency and boost productivity. Therefore, big data analytics is utilised to extract meaningful insights for businesses and turn the gathered information into tools to drive economic growth.

What is supply chain analytics?

The supply chain involves a series of systematic processes in converting the raw material into finished products and transporting it to the distributor, which is then made available to the consumers. The multiple functions of the supply chain include procurement of raw materials, demand forecast and inventory management, production of finished goods, transportation, resource management and information flow. All these operations lead to diverse data that can be filtered to make sense of and enhance each operation individually.

Supply chain analytics is the process by which companies and industries combine the available data from multiple systems to examine and analyse the various functions of the supply chain. Data related to procurement, production, and transportation help in gaining insights. These insights are utilised by refining complex data sources to gain information about the business model, its working, loopholes, efficiencies and other relevant factors.

When every cardinal point of the different operations of the supply chain is analysed carefully, it becomes easier to transform hard work into smart work and produce better output. Therefore, supply chain analytics is a necessary tool that helps uncover self-productivity, market needs, and competitive behaviour to identify and build on newer opportunities. It also helps solve supply chain management problems, maximising supply chain performance.

Understanding consumer persona with big data analytics

The global supply chain is all about catering to the demands of the consumers, importers and exporters. Different companies and businesses import and export goods to supply them to the consumer market. Building a consumer persona becomes essential as it helps to know who will buy the end product and work on the product so that the product is accepted in the consumer market and gives a good return on investment. Persona analytics provides insights into the trends and patterns in consumer behaviour that help predict future demands so that the market can have enough supplies of finished products.

Here is how big data analytics in the supply chain get utilised. Identifying the working patterns at every level in the supply chain is crucial as it amounts to what the target consumer will be receiving. Any changes in the expenses, quality and quantity of goods produced will directly impact the consumer market. Therefore, an ideal buyer persona is developed by using big data analytics to administer customer satisfaction and retention with the company or business.

The insights gained into the target consumers help manage and regulate the investment in resources, customer satisfaction, customer retention, and customer demands. It also helps in building trust and reliability with the consumer.

Applications of Big Data Analytics in the supply chain management

The internal and external supply chain network generates an enormous amount of data. By deducing the data, manufacturing companies can benefit and upgrade their productivity. The applications of big data in supply chain management help enhance the various operations in the supply chain.

  1. Managing supplier relationships – Knowing about the key suppliers or the network of suppliers in the supply chain management is essential to avoid the risks of delays, failures, and last-minute cancellations. A healthy collaboration with suppliers ensures cost-effective and on-time delivery of high-quality raw materials or products. Supplier relationship management helps enhance the interactions between suppliers in the chain and establish systematic and strategic planning for the delivery of goods. In this, big data helps evaluate the ROI of every supplier and gain insights and analysis into potential suppliers to select from a pool of suppliers available, thereby enhancing the organisation’s profitability.
  2. Improving product design – Big data analytics is an effective way of smart manufacturing whereby manufacturing companies can utilise multiple technologies and test the efficiency and customer response to the product or services. Predictive analysis through big data helps in understanding customer needs and preferences and testing for newer upgrades in the product design over time. All parties involved in the supply chain must be considered while designing a product for which data from multiple sources is analysed to integrate newer features and trends into newer modified designs.
  3. Predicting demand – It is vital to be updated with demand forecasts for inventory management so that companies can avoid last-minute chaos. Big data helps in crawling through a diverse set of past data to trace patterns in demand growth so that companies can plan production or pre-order and store the goods to fulfil the demands. Predictive analysis in big data helps to know and evaluate potential supply chain risks that can hamper productivity and find suitable approaches to tackle the problem.
  4. Managing logistics chain – Accurate and real-time data exchange of information is crucial to improve communication and collaboration between shippers, receivers, shipping companies, transport authorities, distributors, and retailers. It helps in assisting supply chain management. Data from different partners is dynamically stored and retrieved to carry out logistics operations consistently. Big data analytics helps manage the logistics chain by filtering necessary insights out of the enormous data collection to manage warehouses, transport systems, IoT sensors and supply chain executions. Using pervasive analytics, insights and suggestions from partners can be integrated into the organisation to improve productivity and efficiency.

Advantages of Big Data Analysis in supply chain operations

  1. Operational Efficiency – Using big-time data in the various supply chain industries helps in figuring out the pros and cons, tackling future potential risks, and changing development strategies in the industry. It helps in minimising the inefficiencies and enhancing productivity.
  2. Consumer satisfaction – By tracking changes in consumer feedback, requirements and newer trends using big data, businesses can customise their products and services to offer a personalised experience to their consumers.
  3. Advanced management – Predictive analysis in big data is an effective means of forecasting the demands to avoid last-minute burdens and have alternatives in case of uncertain or unexpected delays or cancellations from the supplier.
  4. Transportation management – The various transport modes, i.e. air, water, road and rail, need optimised routes to provide accurate ETAs to their shippers and deliver goods faster and safer. Big data analyses historical data to filter efficient routes that help avoid traffic and minimise GHG emissions, making shipping energy efficient.

Challenges in implementing big data analysis in the shipping industry

  1. Data security – Since enormous amounts of data are exchanged and stored by industries, data breaches and cyber crimes are potential threats to the shipping industry. It is crucial to secure the classified data of the company and the personal data of the consumers and clients. Therefore, enterprises must invest in data security systems to minimise hacking and data manipulation risks.
  2. Data integration – The enormous amounts of data collected from diverse sources must be combined on a single platform to draw necessary insights. Many face a challenge in integrating and unifying this data onto a platform. Industries invest in newer tools and technologies that can seamlessly collect data from multiple sources to perform analysis.
  3. Need for skilled analysts – Data analysis is a growing job opportunity since every supply chain industry uses big data analytics to enhance efficiency. There is a need for more skilled data and business analysts that can make sense of the data and provide beneficial outcomes. Therefore businesses must invest in training programs to offer employees the necessary skills and knowledge to perform data analytics.

Big data analytics in the supply chain helps industries and businesses enhance their operations and contribute towards increasing the efficiency and productivity of the supply chain.

LOTUS Containers is a global marketer of premium shipping container solutions that use new technology in the intermodal transportation of containers. We provide different types of shipping containers to suit the supply chain requirements.