Reducing complexity of your direct material supplier parts can help drive significant savings within your company. Similar to our recently published blog which described the importance of conducting internal price benchmarking activities as a prerequisite to competitive benchmarking on a company’s direct material purchases. In both scenarios it’s surprising what you find when you are able to cleanse and enrich your purchase part data, it can expose a variety of opportunities to reduce direct material spend without having to make major changes to your products. To reduce the complexity, we recommend grouping your direct material spend into these 3 groups:
Low Complexity/High Volume Items (e.g. raw materials)
Low Technology and Low Complexity Items (e.g. fasteners, wire harness)
High Technology and High Complexity Items (e.g. motors, valves, circuit boards)
Based on our experience, the opportunities in each of these areas will be different depending on the complexity of the item and its purchasing volume.
Low Complexity/High Volume Items
A low complexity group is characterized by a simple attribute profile. Quite often the volumes are high and the benefits extend across multiple product lines and business groups. One example is in the Oil and Gas industries, companies that make drilling related products, where most products are made from tube or bar raw materials. In these cases, we found over 20 instances of the same raw material duplicated with a different part number because they were coming from many different suppliers and business groups. This duplicate part identification represents a significant opportunity for spend and inventory rationalization.
Another consideration is how raw materials are being purchased, for example we found some appliance manufacturers that were buying 180 different thicknesses of steel. They were able to consolidate down to 25 configurations and buy in higher volumes at lower pricing typically achieved by automotive companies. Three raw material groupings to consider targeting in your analysis include: coil, wire and bar stock. The spend savings percentages are lower here than in most groups, 3-8% typically. You will find the high volumes enhance the benefits for this group.
Low Technology/Low Complexity/Medium Volume
This group can be characterized as easily engineered or low complexity items that may have too many variations and since it has too many variations the volumes are not as big. Some good examples would be fasteners, wire harnesses, tubing or hoses. Many time the variations are caused by differing characteristics of length or diameter. The opportunities in this group can be achieved once the characteristics that drive variability are exposed in your data classification effort. Those characteristics that drive variability can be material options, finishes or dimensional characteristics like length. We have seen benefits in the 10-15% range for this group.
High Technology/High Complexity
This group of items can be comprised of many parts, the volumes may be low but the unit price can be high. The trick to finding savings in these type of items is to look at the components that make them up. For circuit boards, it’s the electronic components that make up the boards for example capacitors, resistors, ICs, etc. What we have found in some cases is that smaller suppliers are buying these components at low volumes which will drive up the prices. Rationalizing the spend on the components can lead to significant savings. The opportunities for savings in this group is very high – 15-25%. Other items in this group can also include: Motors, Compressors and Valves.
A key to success for your direct material complexity initiative will be dependent on having complete and accurate data on your products. It’s the data that enables the opportunities, and the data also exposes what drives differentiation and proliferation for each group of items. Products with different levels of complexity and volumes require different approaches to finding opportunities to save money, remember it’s not a one size fits all analysis.