Developing an Optimal Parts Management System, Part 2

Medion AG, a manufacturer of consumer electronics and information technology products, has established its reputation through excellent after-sales service. In the first part of this series, we looked at Medion's challenges in forecasting demand for replacement parts. (See: Creating an Optimal Parts Management System, Part 1.) In this blog, we look at how it developed an innovative system to manage replacement part needs.

With the help of the consulting firm Abels & Kemmner, Medion evaluated consumption values for replacement parts. Because of their short consumption periods (less than six months to a year) and unsteady demand, classic methods such as the mean average, exponential smoothing, and median were not suitable for forecasting and had no advantages over the methods Medion had been using. Therefore, past projects were looked at more closely, and consumption values were analyzed with regard to failure behavior.

Patterns emerged in the process. Five different types of failure patterns were identified and assigned what are known as standard curves. The components were assigned to the curves depending on their failure rates in the initial use period, in the middle, and at the end of the warranty period. It came as no surprise that the patterns were not necessarily assembly-specific. Electronics components fell into all of them, and so did mechanical components, so the standard curves could not simply be assigned by product group. Particular rules and indicators had to be used instead.

Methods were then developed to use these standard curves to forecast demand for replacement parts. A prototype of an analytical software tool was specially developed for this very complex task involving lots of calculations. The failure data was processed, and forecasts were calculated.

By assessing the first phase of the consumption period, users of the tool can assign the replacement part to the most suitable failure pattern and calculate demand expected during the warranty period. Options were also built in allowing users to adapt forecasts according to experience-based knowledge. The forecast values can be stretched or compressed by simple clicks, and consumption periods can be reweighted. The tool is very chart-oriented and provides information very quickly on consumption and demand.

The final step saw the methodology being validated. Forecasts based on information known at that time were calculated for certain times in the past and compared with forecasts generated using previous methods. The result was clear — the new methodology makes it possible to make better forecasts for replacement part demand. In particular, the cover for residual demand was improved by a double-digit percentage, enhancing service while reducing stocks. This project was crowned as a double success.

The new methodology has been implemented in the standard DISKOVER SCO software to optimize forecasting and scheduling, so the relevant functions can be used for replacement part management.

3 comments on “Developing an Optimal Parts Management System, Part 2

  1. elctrnx_lyf
    August 23, 2011

    Does this software can esily serve to different types of products without lot of input data. As I understand the replacement parts demand a lot depends on the failures reported by the customers and or distributors or service partners. So how could this software will e more helpful compared to the inhouse softwares used by different comnpanies?

  2. Taimoor Zubar
    August 23, 2011

    Interesting post. I would like to know more about the forecasting techniques used in the system. Does the system forecast solely based on the past trends and information, or it also incorporates the influence of current events? For instance, in the case of an event like an earthquake in Japan, the inventory forecasting can become unreliable if it's simply using the past data. It would be a good idea to have the option to where users can input a certain anomaly in the system and that will be taken into account while forecasting.

  3. mario8a
    August 25, 2011

    very interesting post, I'm also wondering how feasible will be the application.

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