Information is all around us in different forms. Information is produced in large quantities every day. In this era of the fourth industrial revolution, there is a huge appetite for mining information. This is as a result of the revolution of people, digital technology, platforms, and networks that determine innovation and competitiveness in the market. Big data and analytics are playing a vital role in the success of the business world. Their survival depends on entirely scooping the right information at the right time, this enables them to have a competitive advantage in their prospective fields of operation in the market as they make the right business decision based on the knowledge acquired. Recent reports have shown that data collected in the modern world is expanding rampantly. These are inclusive of both structured and unstructured data that flock businesses daily. Unstructured data constitutes of social media posts, emails, audios, movies, web, and text files which take the large piece of the pie in the world’s digital data. Such data cannot be handled by traditional modes of storing data. Therefore, data reproduction requires new modes of capturing, processing, and storing data. Big data analytics is being used in the agriculture, energy and infrastructure, health, economics and insurance, food and transport, and sports sector worldwide. This article is aimed at attracting the attention of the world in various application and leverage that comes with big data mining and analytics. The paper will also discuss the current trends and opportunities presented by big data and how it has contributed to the creation of successful business strategies and kept businesses in the competition for the market.
Big data is described as huge amounts of data that is basic, semi-basic, and sub-structural and has the capability of being mined while big data analytics is the alteration of auditing large data by uncovering anonymous correlations, market directions, hidden patterns, customer taste and preference and other pragmatic information to business entities. Big data is used in big data applications like Hadoop to analyze data using colossal parallel processing frameworks (Kuchipudi Sravanthi, n.d.). These application software’s clout large scale data that cannot fit in small data input capacity hardware to reveal data using large scale parallel processing equipment. This application is used by logistics to store all data that pertains to their clients’ orders.
Also, it is used in data mining. This applies when using data trees that help users understand what combo of data facet result in a coveted outcome. The decision tree shows the power of relationships and dependencies within data and shows what attributes bring certain outcomes such as fraud risk, and purchases and online signups. It also shows the hidden structure in your data. This is an advantage to logistics as they are able to oversee their pitfalls and are able to come up with a sustainable solution (Kuchipudi Sravanthi, n.d.).
Banks use big data and big data analytics of data such as earnings, mortgages, savings, and insurance policies to determine the creditworthiness of a customer and how long the client is going to pay back the loan and the grace period to be allowed. They also use their customers’ data for the security of their money. This enables clients to transact with their logistics of choice on a safe and secure platform that does not compromise their privacy (Kuchipudi Sravanthi, n.d.).
Big data and big data is also used by credit card companies to detect fraudulent activities as they do rely on the accuracy and speed that comes with database analytics (Konstantinos Vassakis, 2018). By using age-old usage data, they can flag anomalous amounts, retailers, and location and request access from their cardholders before authorizing a malicious transaction (Datameer, n.d.). This empowers logistics to transact safely without the fear of being conned. It also helps the logistics team to make an informed decision as all the data they need is in the palm of their hands. This, therefore, gives them a competitive advantage against their rival businesses.
Logistics team collects consumer preference and purchasing data extracted from raw text from the web, phone calls made to customer call centers and retailers in order to collect every detail being disclosed publicly about their products and services from consumers. This helps them understand better as to why certain products are being bought more than others. Exposure to such data enables logistics to prioritize which order to process first, how to handle them, how they are going to be packaged, and the mode of transportation to be used to the point of destination. This method is known as sentiment analysis. It also allows logistics to know which mode can be used that is ecological in all their operations (Kuchipudi Sravanthi, n.d.).
Big data and big data analytics also helps logistics to evaluate employee performance, how much they ought to be paid, training and examination performance, checking in and out of work, transparency, and employee promotion (Nweke, 2019). Big data and analytics have also helped logistics to create software’s that shows them how well their advertisement works at appealing their products to consumers (Nweke, 2019). This analyzes how stimulating their promotions are in making consumers share these promotions across social media networks making them go viral and thus improving sales.
Logistics have also used big data in creating software’s that have eased the job of logistics teams like documenting by using both the clients’ smartphone and the logistics employee work smartphone. The client orders through the app for a specific product, they can track their products as they head towards their destination. When the package arrives, the logistics team member is required to take a photo of the product with the consumer and the data sent to the company’s servers to be stored in the cloud. The consumer is then required to approve that the package has been received by signing his or her signature on the app. This makes operations easy and logistics can get some extra off time to do their personal duties and leisure after work (Adinarayana Salina, 2016).
Big data and analytics are also used by logistics in warehouses to keep a record of which products have been stored and how many have gone in and out. This avoids wastage and fraud and instead gives leeway to transparency, security, and accountability. It also helps the company to know what amount of space fits their products and how to adopt new techniques to package and store their packages without them being tampered with (Maribel Yasmina Santos, 2019). In addition, big data has helped logistics to offer after services to customers such as sending reminders and recommendation notes about their products for them to make informed decisions.
Big data and analytics have enabled logistics to create customer segmentation that identifies loyal customers and offer them discounts on each product they purchase of a certain value. It has enabled them to also come up with loyalty cards and coupons that have helped create a deep relationship with customers (Pritee Chunarkar-Patil, 2018). Moreover, big data and analytics have helped in pricing optimization. transactional data that is spread across multiple devices helps logistics to understand which service is in demand and which one is not and how to place affordable value on their services (Datameer, n.d.).
Crowdsourcing and sensing have also been used in big data and analytics by logistics to identify the best professionals for performing specific logistical tasks like transportation and warehousing for their clients (Nweke, 2019). Also, it has helped in energy consumption analysis in logistics. The company is able to track its usage in electricity, water and gas. This reduces wastage of resources.
In conclusion, success starts with better decision making. Olden days people used to make choices based on emotion and perception. The abundance in big data and professionals in big data analytics, affordable hardware commodity, and the latest state of the art information management and analytics software has produced a special moment in the diaries of data analysis. This has brought a shift in gear as people are able now to make decisions based on real and well-studied data with informed conclusions. It is evident that we have the proper skills required to analyze unique data sets faster and in a more economical manner. This is an eye-opener for us to see the enormous gains that come with big data and big data analytics and use it for better productivity, increase in revenue and profitability, and efficiency in every business entity including logistics.
References
Adinarayana Salina, K. Y. R., 2016. A Study on Tools of Big Data Analytics. https://www.researchgate.net/publication/310627056_A_Study_on_Tools_of_Big_Data_Analytics.
Datameer, n.d. THE GUIDE TO BIG DATA ANALYTICS. https://www.datameer.com/pdf/big-data-analytics-ebook.pdf?mkt_tok.
Konstantinos Vassakis, E. P. K., 2018. Big Data Analytics: Applications, Prospects and Challenges. file:///C:/Users/hp/Downloads/final_chapter_published.pdf.
Kuchipudi Sravanthi, T. S. R., n.d. Applications of Big data in Various Fields. https://pdfs.semanticscholar.org/987f/6f54d3a68a976d76c374476bc648a87f1152.pdf.
Lora Fleming, N. T. G.-B. L. N., 2017. Big Data in Environment and Human Health. https://www.researchgate.net/publication/322952818_Big_Data_in_Environment_and_Human_Health.
Maribel Yasmina Santos, C. C. G. A., 2019. Enhancing Big Data Warehousing for Efficient, Integrated and Advanced Analytics: Visionary Pape. https://www.researchgate.net/publication/333313432_Enhancing_Big_Data_Warehousing_for_Efficient_Integrated_and_Advanced_Analytics_Visionary_Paper.
Nweke, I. A. A. *. a. H. F., 2019. Big Data and Business Analytics: Trends, Platforms. file:///C:/Users/hp/Downloads/BDCC-03-00032-v2.pdf.
Pritee Chunarkar-Patil, A. B., 2018. Big data analytics. https://medcraveonline.com/OAJS/OAJS-02-00095.pdf, Volume 2.
Big Data in Inventory Management
Big data is used to improve operational efficiency (Nweke, 2019). This is made possible when operations managers oversee real time operations and better approach to information. It empowers logistical teams to enhance performance as opposed to when they were using old methods of operation.
It also helps to maximize sales and profits due to the fact that access to updated data helps finance officers and managers in logistics to monitor steady profit margins with greater understanding. This assures potential investors that their investment will bring profit (Datameer, n.d.).
There is upsurge in customer service levels. Having the data of customer demand pattern helps logistics team to match stock and stock levels with client orders precisely, which will lead to a hike in customer service levels. Data can be studied to foresee seasonal trends, increase and decrease in client demand to ensure the right stock levels are maintained at all cost. Less amount of capital for operations is used as a result of all big data being saved in the virtual world. This leads to a decreased maintenance cost of hardware equipment. Existing systems can be merged with the old systems easily at an economical price with a promise of great returns (Kuchipudi Sravanthi, n.d.).
Big data use in facility layout design.
logistic companies have become enormous, distinct and the factors affecting each industry layout has grown expeditiously. Administering and gauging such huge factors is strenuous in designing and determining facility blueprint problems. These aspects and short comings affects operation time, cost of transportation, packaging and delivery service. The facilities should accustom to these changes over various time periods and this must be addressed while constructing an excellent design. Big data and big data analytics is used along with techniques such as the Hybrid meta-heuristic approach to construct an excellent facility design in regards to customer orders over various periods. The data is optimized in such a way that logistic processes are arranged in the most efficient way within an institute. This goes a long way in reducing operation time as well as improving quality of service.
Big data use in vehicle routing problems
Organizations make use of big data inputs from traffic check servers in cities and brief patterns to establish excellent changing set up paths for logistic terminals after receiving orders and making deliveries. Real time traffic big data is used by supply managers to schedule the most optimal transport routes, avoiding congested roads as well as traffic snarl-ups brought about by factors such as accidents. The use of real time big data in vehicle routing is especially useful in perishable goods industries. Optimal transport rules also lower costs for an organization by ensuring fuel economy as well as maximum deliveries within a certain time period (Datameer, n.d.).
Big data use in minimizing environmental uncertainties
Big data can be used to predict environmental conditions and challenges. When coupled with scientific methods such as machine learning especially, big data has uses in fields such as disaster management, weather forecasting, energy management systems, smart water and as well as remote sensing. When these conditions can be predicted, the supply chain is less affected by environmental uncertainties since an organization can plan ahead and use the most efficient mode of transportation to reach its clients (Lora Fleming, 2017).