Brammer Group, the European distributor of industrial spare parts has saved £31.1 million in reduced inventory after implementing IBM’s predictive analytics software to analyse customer data and predict demand.
Prior to using the software, 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.
Brammer, which has more than 2,000 employees across 16 countries, supplies customers across automotive, pharmaceuticals, chemicals, food & drink, utilities and aerospace, with mechanical parts and associated services including the maintenance, repair and overhaul of production line equipment.
Brammer Group is now able to identify slow moving components which have a better chance of sale, compared to those which are only used by one customer, and consequently improve the range of products held in stock, improving customer service levels.
Relationships with key strategic suppliers have also improved, as has the company’s 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 analysing historical customer data from the past five years at an item, branch, country, and European level, as well as analysing 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 standardise 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 our 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.”