Manufacturing Digital: Getting a Big Impact from Big Data: Part One

September 24, 2013- 

By Sebastian Mamro, Director, Head of Professional Services EMEA, PROS

Chemical manufacturers today face a host of challenges that have produced extreme pressure on both their sales performance and margins.

Aggressive price competition has been fed by volatility in raw material costs and marketplace demand. In the midst of this highly competitive global environment, chemical manufacturers that produce raw materials, intermediary and specialty chemicals are discovering that big data can significantly improve sales performance and profitability.

Big data is a new technology category that covers a range of analytical tools for gathering, prioritising and deriving critical information from large data sets. Using big data has the potential to transform a commodity industry that has traditionally relied on ‘gut feel’ and cost-plus approaches to pricing and contract negotiations into one that provides customers with more insight about their own operations and opportunities.

In most cases, companies already collect a lot of this data in market tracking and customer transaction information. The key is to analyse that data alongside internal systems information such as ERP and CRM applications. By combining this data, you can bring key insights into play during the contract negotiation process, enabling your sales force to improve its performance with deals that yield better margins and profitability.

Tap the power of big data to manage cost volatility

Harnessing big data through scientific data analysis is fast becoming a necessity for chemical manufacturers. Through the use of automated pricing software, manufacturers can manage the risks and volatility of raw material costs. These solutions allow chemical manufacturers to react quickly to raw material cost changes and implement continuous price updates for tens of thousands of line items.

In one example, a German chemical manufacturer spotted a large price rise in one of its critical raw materials that was about to hit the market. Managers at the company decided to take a proactive approach and look at their own prices so they could take advantage of this foresight. However, they had to spend four months recalculating prices based on the raw material cost increases, and another two months getting approvals and price updates to sales.

By the time the price changes were implemented, the cost of raw materials had gone up and come back down. Customers were naturally reluctant to accept the price increases. In contrast to this situation, chemical manufacturers today can use technology to dynamically capture the cost of raw materials on the open market as well as changes in overhead costs, recalculate prices, get approvals and push the new prices to the sales team in a matter of days.

This means that companies can be more proactive in their approach to pricing and sales. The impact on the bottom line from dynamically managing cost and price decisions using big data techniques can add up to millions of dollars.

Gain visibility into individual customer accounts to maximise profitability

Aside from looking at the wider economic situation, big data can also be used in negotiations with individual customers. Using the latest data science models powered by leading software solutions, chemical manufacturers – especially intermediary and specialty producers – can discover the cost to serve and ultimate profitability of every customer based their own transaction data.  This approach captures what each customer considers their value drivers, from variables like product quality and packaging, through to delivery timing. Sales teams can achieve more profitable pricing by using these insights during negotiations.

By mining collected data to identify costs to serve, value drivers and the “willingness to pay” of different customer segments, these solutions provide a framework that can significantly improve sales performance.  Instead of pricing based on gut feel and incomplete or outdated information, sales can conduct contract negotiations based on product profitability, results and guidance at the time of negotiations.

More importantly, because the exact value provided by the manufacturer to any specific customer has to be tested in the marketplace, big data technology solutions can continually update and analyse actual results, including competitive pricing moves. This enables chemical manufacturers to test new pricing strategies for various market segments and evaluate the results to see if newer, more successful strategies should be implemented for a broader range of customers.

Read part two tomorrow on Manufacturing Digital

5304

By Sebastian Mamro, Director, Head of Professional Services EMEA, PROS

5303