It is a challenging time for the tire industry due to global economic conditions, fluctuations in its front-end automotive industry, and fierce business competition.
It is a challenging time for the tire industry due to global economic conditions, fluctuations in its front-end automotive industry, and fierce business competition. Under these circumstances, cost-effective planning, improved production process, keeping records of manufacturing data, product information, and related assets of tires have more importance than ever before. In particular, a systemic, agile, and centralized data management platform supporting the entire data value chain can significantly contribute to business growth.
The increased disposable income of consumers and their willingness to spend on new technologically advanced models have spurred automobile sales across the globe. Tire replacement, however, being a typical maintenance cost for direct consumers, is a different story – taking a back seat with increasing demand for new cars. Tire manufacturers further face hurdles as the soaring rubber prices cause an uncomfortable stir on the cost of manufacturing and purchasing tires.
Nevertheless, the industry has significantly matured after going through many cycles and stages of change in the last century. Today, it is highly dependent on data and analytics primarily due to several global economic factors, including consumer spending, budget allocation, and/or budget cut downs, among others.
Tire manufacturers are driving intensive research into making their products safer and more efficient than ever before. They are becoming more professional with their operations, making use of the ever-growing volume of data they generate to transform their businesses, which will eventually stabilize the profit. Within the daily measurements of temperature, pressure, and wear, data scientists are looking for patterns that could help improve the safety of the vehicles and prompt more efficient fuel use.
At any tire plant, through the entire material flow – from raw materials warehouse to mixing, compounding, component manufacturing, tire-building, curing, finishing, finished tire warehouse, and shipping – there is a humongous amount of data is being generated. Add to that the mountain data coming in from the company's research labs, test tracks, suppliers, customers, weblogs, and social media; and it is impossible to ignore the enormity of the constant pilling up of datasets.
From production data to recipe management to testing & reporting, discerning handling of all types of data becomes a massive task for enabling 100% traceability of work in progress and finished tires. With market developments, seismic shifts in consumption patterns, and technology getting integrated into almost every stage of tire production and distribution cycle, a comprehensive data management tool is 'mandatory'.
Recording information such as batch numbers, operators, weights, processing times, and test results can ensure accurate product tracking at the individual tire level. In turn, automated analytics can apply these insights to a closed-loop action on the same unit, the entire processing network, or other connected devices. By using readily available data, a tire manufacturer/seller can leverage analytics to generate real-time insights and understand the needs of end consumers and product performance under different operating conditions. Additionally, the data from customers and other sources can help drive contextual analytics and generate further monetization opportunities for cost-containment or revenue-generation initiatives for different stakeholders in the ecosystem.
Having one centralized platform to organize and manage this vast amount of data can significantly improve the time-to-value of data and response time by reducing network bottlenecks and service delays. Such an approach focuses on actionable data and creates long-term benefits. The key is to have one solution, a single-stop destination, that keeps records of all manufacturing data. Such a system allows manufacturers to analyze vast volumes of data in a scalable and efficient way, and thus stay ahead of the curve.
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