How Data Analytics Can Be Used In Chemical Companies
Every day, larger and larger amounts of unstructured data are created. In order to generate added value from the data through analysis and to be able to use this for business processes, appropriate technologies and extensive know-how are necessary. This has coined the term Big Data that includes the collection, analysis, and evaluation of large amounts of data. Today, organizations from all sectors and branches of industry – including the chemical industry – are faced with the resulting opportunities and challenges.
A survey conducted by Fraunhofer IAIS showed that industrial companies use Big Data primarily to make faster and more informed decisions, build strategic competitive advantages and improve operational processes. The aim is to save costs, increase sales, improve product and service quality, develop new products, and increase productivity.
Companies are often aware of the relevance of Big Data Analytics: According to an Accenture study, 79 percent of executives believe that companies that do not use Big Data will lose their competitive position or even disappear from the market completely. 83 percent of the executives who have pursued Big Data projects aimed to gain a competitive advantage.
Challenges when using data analytics in the chemical industry
The mere collection of large amounts of data does not yet make it possible to use them profitably. This requires employees who have the necessary know-how in the field of data collection and analysis. The challenge is to translate data so that it can be used by executives in decision-making processes. But qualified employees are difficult to find. Recent surveys show that less than 20 percent of companies have the necessary know-how to handle data effectively.
Further challenges are caused by the high volume of data, the great variety of data structures, the high speed at which new data is created, and the uncertainty regarding data quality.
Especially in the complex chemical industry, there are numerous factors that cause the industry to be in a state of constant change: globalization, the sharp rise in demand from Asia, oil production and its impact on raw material costs, global competition, the increasing compression of product life cycles, and the rapid establishment of new technologies for developing business models. In addition, the target markets of chemical companies are broad-based and therefore very extensive. This is mainly a result of the many application areas and industries to which chemical products are supplied.
Thus, large amounts of data are generated quickly and continuously, requiring analysis in real-time. At the same time, a certain degree of agility is required in the decision-making process among management teams to react effectively to changing factors and generate competitive advantages.
Usages of data analytics in chemical companies
By using Big Data correctly, chemical companies can not only reduce costs, but also increase margins, optimize processes, and allocate resources precisely according to their own objectives. In addition, companies can understand markets better and anticipate future customer needs to meet demand quickly and stay ahead of the competition with shorter lead times.
In the following, we show five opportunities for chemical companies to use Data Analytics.
Planning & Production
Companies that analyze inventory, production, and sales data in real-time can identify discrepancies between supply and demand and take appropriate action to influence the situation (for example price changes, promotions, etc.). Forecasts of demand behavior, based on environmental factors, also optimize warehousing and indicate required logistics capacities. In this way, it is possible to prevent old product inventories from blocking storage capacities or creating shortages.
In the production of chemicals and plastics, the use of data analytics helps to improve productivity and efficiency. Every minute, production plants generate vast amounts of information, for example, data on the speed and quality of production or the condition of the production plants. Intelligent analyses of this data can be the basis for decisions or automated processes. It provides insights into the factors that influence productivity, the reasons for production stops, the ways to save energy, or improve product quality. In this way, machine failures can be avoided, capacity utilization increased, and preventive maintenance measures initiated.
In addition, energy-intensive production steps, for example, can be carried out in such a way that fluctuating energy prices are exploited. Moreover, data analyses can optimize demand planning so that all raw materials and consumables for production are always in stock. „Pedictive“ analytics, meaning data analysis to forecast activities and trends, can anticipate future demand behavior, make appropriate forecasts, and thus prepare production processes accordingly.
In supply chain management, data analysis can be used to optimize costs, such as freight costs, raw material prices, and storage costs.
The data analysis enables a particularly efficient design of the supply chain and provides a real-time overview of the operation. Deviations from standard delivery patterns and bottlenecks that slow down supply chain processes can be identified. This is the basis for forward-looking risk management.
Especially in the transport logistics sector, data applications can analyze and identify weather forecasts and events that could lead to interruptions or delays in the supply chain. Examples are natural disasters, strikes, fires, or insolvencies. This allows companies to create contingency plans, define alternative suppliers, and optimize transportation to keep production running smoothly and to take appropriate action before the competition.
Pricing – Procurement & Sales
Pricing in procurement and sales is another important area where the use of Big Data Analytics offers companies significant advantages. Pricing decisions and strategies for chemicals depend on numerous, sometimes highly variable factors. Examples are volatile raw material costs, energy costs, market demand, competitors‘ prices and strategies, exchange rates, and others. The pricing of chemicals is therefore very complex.
Time-consuming, manual procedures for setting prices make it almost impossible to identify price patterns that could create increased value. Especially for large companies, it is simply too costly to capture and analyze the ever-changing pricing variables for thousands of products at a granular level. As a result, many rely on simple factors such as chemical production costs, standard margins, prices for similar products, volume discounts, and so on.
By using intelligent data analysis, manufacturers and distributors can use the resulting information to monitor price trends and thus determine when important purchases should be made. This allows companies to pursue cost reduction strategies to promote profitability and make more competitive pricing decisions.
For each product, companies should be able to find the optimal price that a customer is willing to pay. By using data analysis insights, chemical companies can make more informed pricing decisions and optimize their sales prices according to the competitive and market situation. Real-time data analysis also enables dynamic pricing and price variation based on all factors – including those that might otherwise have been overlooked. As a result, companies can increase profit margins by three to eight percent by dynamically setting prices on much more granular customer and product segments.
Data analysis in the field of warehousing helps to optimize processes and thus increase productivity. Today, a great amount of inventory data is already collected and used to optimize costs and efficiency. Chaotic warehousing approaches, for example, enable companies to use storage space more efficiently and reduce the distances employees have to walk. High-bay warehouses that can automatically transfer pallets at night are also used to optimize schedules for the next day.
Other data collected, for example by sensors, video systems, or from forklift trucks, can be used to monitor picking, warehouse productivity, and inventory accuracy. Data on picking performance in different areas of the warehouse helps companies optimize personnel deployment. This real-time analysis also helps to make decisions to optimize processes and productivity (for example, adjusting the handling/routing of forklifts) and identify the causes of picking and storage errors.
While marketing in the past years was often characterized by mass communication, the focus today is on the individual customer with its specific behavior and needs. This results in large amounts of data and customer profiles consisting of demographic data, connection data, information about customer behavior before and after the purchase, and many more. This data can be used to gain valuable insights and make appropriate decisions.
Data Analytics can identify complex correlations in these large amounts of data, derive valuable insights as well as forecasts, and enable appropriate decision-making. Thus, companies receive information about customer and demand behavior, product and market trends, and future customer needs. This opens up a wide range of possible applications in marketing:
- Customer acquisition: Knowledge about customers enables insights about who they are, where they are, what they want, what type of contact they prefer and when.
- Customer retention and loyalty:
- Create personalized offers or decisions about which product or service a customer is offered via which channels and at which conditions
- Customer value analysis can accelerate sales cycles, maintain customer relationships, and improve customer retention
- Optimize marketing performance: Data-driven insights help marketing departments to customize target audiences, create customized offers and marketing content, and adjust marketing campaigns.
This not only allows an increase in sales but also a more focused and successful use of budgets and resources.
Of course, there are also opportunities to use Big Data Analytics in chemical companies beyond these five business sectors. Companies can also benefit from the use of data analysis in product development and innovation, human resources, and many more.
Data Analytics with chembid
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