Brammer Improves Inventory Control With IBM Predictive Analytics

Distributor Saves £31.1 Million in One Year by Reducing Cost of Carrying Surplus Stock While Improving Customer Service

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LONDON, UK - 29 Jun 2010: IBM today announced that Brammer Group, leading European distributor of industrial spare parts, is using IBM predictive analytics software to analyze its customer data and predict customer demand. As a result, Brammer can now gain real-time intelligence into product supply and demand, revealing where the company could cut back on surplus stock leading to £31.1 million, a 22 percent reduction in inventory in one year. 

Prior to using IBM predictive analytics employees in some parts of the business were carrying out manual calculations and making decisions about inventory levels based on gut feeling rather than fact-based insight.

Brammer, which has more than 2,000 employees across 16 countries, supplies parts to customers of the biggest industries in Europe, such as automotive, pharmaceuticals, chemicals, food and drink, utilities and aerospace, with the millions of mechanical parts and associated services required for the maintenance, repair and overhaul of production line equipment.

By more accurately predicting customer demand for slow moving products such as a specific bearing only sold to one particular customer which might be held for several years before it is required, Brammer Group is now able to identify those slow moving components which have a better chance of sale, and consequently improve the range of products held in stock. This has allowed better level of customer service, validated through annual customer satisfaction surveys.

The added insight and deeper customer understanding provided by IBM predictive analytics has also helped Brammer develop closer relationships with key strategic suppliers. In addition, the Brammer Group has further improved its emergency delivery service for customers experiencing critical machinery breakdowns. The predictive analytics software now enables the company to identify and stock those items that are most likely to be required in an emergency by analyzing historical customer data from the previous 5 years at an item, branch, country, and European level, as well as analyzing price, service and distribution data.

Brammer’s use of predictive analytics is part of a wider initiative to improve the management of growing data across all territories. For example, different countries use different names for the same product which can make company-wide analysis challenging. To overcome this Brammer is using IBM software to standardize data across the different countries, allowing a consolidated view of how inventory is performing at a European level because the software provides them with transparency of all the stock across Europe at a product level.

Yongli Ge, senior logistics analyst at Brammer, said, “IBM SPSS predictive analytics technology has helped us dramatically streamline our inventory by creating stock profiles based on customers’ buying patterns. This enables us to forecast more efficiently what we need in stock and in what quantity, contributing to a £31.1 million inventory reduction.”

Colin Shearer, predictive analytics strategist at IBM SPSS, said: “The global recession is leading companies to rethink their spending strategies and save money wherever possible, and technology is playing a key part in this. Predictive analytics allows smart organizations such as Brammer to significantly reduce costs, without impacting on its customer service.”

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