DUGAA XMRP®: Next generation After-sales MRP system for global manufacturers with Machine Learning for service parts and EOL parts management.
XMRP is built and equipped with our proprietary technologies to tackle the unknowns and uncertainties and provide predictions so manufacturers can have peace of mind to prepare and forecast the after-sales and product repair materials.
MRP (Material Requirements Planning) systems have been around for decades. For instance, in the 1950s, MRP systems used mainframe computers to process information from BOM (Bill of Material).
MRP (Material Requirements Planning) systems are straightforward linear thinking systems. The linear approach is suitable for circumstances where all, or most parameters and values are “pre-defined”, such as JIT (just-in-time) production.
MRP systems are fine when the basic parameters such as Time and Quantities are known, but in real-world repair and after-sales service, these numbers are no longer exact and they are just ranges and possibilities.
When it comes to material planning for RMA (Return Merchandise Authorization), product repair and after-sales service, the fact is the predefined parameters and values will turn into unknown.
Here are some examples of uncertainty that will affect the Material Requirement Planning and the level of sitting inventory.
- When the product will be repaired, is it 3 days after sales or 3 years after sales?
- How many products will be returned? Is it 10 pieces, 1000 pieces or a major product recall?
- Which component needs to be changed?
- Is the component repairable or consumable?
- Is the original supplier still available, due to global regulations and supply chain issues?
- Is the component still available in the market or it is discontinued (EOL)?
- Is there a cheaper and newer component available that can be used instead?
- If you place an order of the components today, how long will it take to be delivered?
- How do you deal with components partial deliveries?
With DUGAA XMRP™, manufacturers now can save huge amounts of resources and money on sitting inventory in repair centers. XMRP helps manufacturers to maintain an optimum level of stock across all repair centers globally for all products and all components.
When XMRP is integrated with RMAONE, you are able to take into account all parameters from service-level agreement (SLA) , multi-tier warranties, end-of-life EOL, repair history and more.
The data can be available globally to all repair sites and ASPs (Authorized Service Provider) in real-time, based on DUGAA’s sophisticated access control system.
The traceability and prediction levels are:
- Unique device-master-id (MID)
- Unique component Serial Number (SN)
- Master part number (MPN)
- Component part number (CPN)
XMRP can be used standalone. It will process batch data and provide the same functionality as the version that integrates with DUGAA RMAONE.
However, there are some differences.
The standalone XMRP:
- It is not in real-time and requires batch data import which can be done manually or automated through DUGAA API.
- Minimal visibility on RMA data.
- Traceability and predictions are narrowed to CPN (Component Part Number) level.
- No visibility in warranty or SLA.
- Minimal repair history is imported
- Minimal EOL data is imported and processed.
XMRP is offered in 2 capacities and architectures.
- XMRP (X1) is the entry-level and it is designed for tens of millions of items and components.
- XMRP (X2) is designed for hundreds of millions to billions of records.
X1 and X2 have the exact functionalities and administration panels. Transitions from X1 to X2 are seamless, but it requires data migration.
This architectural difference is in place to ensure high performance and low latency of the system at all times. All Analytics and Tracer capabilities are in millisecond response time. The information you need from the system is available on your screen instantly no matter the size of data or complexity of the operation.
The results that XMRP generates about the past and the present are always 100% accurate depending on the quality of imported data. We also have DQA (Data Quality Assurance) for all imported data which will check and verify every field and every value. When you import any data in the system, you will have an associated report file that will explain any problem or quality issue with every row of data.
The Future predictions are conservative and it is based on our proprietary algorithms. Our emphasis is on the quality and accuracy of large datasets rather than trial and error with AI black boxes on some excel data. AI black boxes may provide an acceptable result for the data in hand (as a result of possible overfitting) but soon it will break and requires the models to be recalibrated or even remodelled all over again.
We run our algorithms and machine learning on every single device and every component in the device. By doing so, XMRP will produce accurate risk assessments for every component and ultimately for every device. On the other hand, we have anomaly detection which will detect and warn of any sudden change of patterns.
The predictions are in real-time (when XMRP is integrated with RMAONE) or instant after batch import (XMRP standalone mode). Every event in the system will only make the prediction models stronger. Our predictions are stable and time-proof with a high level of accuracy.
|Production quantity known.||Return quantity unknown.|
|Production date is known.||Return date unknown.|
|Planning for production based on orders in hand.||Planning for repair services based on SLA and warranty agreement that may span over X years.|
|Dealing with currently valid suppliers.||Dealing with changes of suppliers or changes of regulations over the years. Dealing with a sudden shortage of components.|
|Dealing with currently available part numbers.||Dealing with part numbers that may be discontinued over the years.|
XMRP is designed to ground up for global manufacturers. Every sales region and repair center site is taken into consideration in the XMRP architecture.
XMRP is aware of pending orders and partially received orders. The system will adjust the prediction results automatically.
XMRP processes the input data and provides real-time predictions over a range of time. For example, 3, 6, 12, 24 month predictions.
XMRP will assess factors from both the master device and its components. The system will identify and analyze the risk at the component level.
Imagine a new product is launched and sold, and it has not got any history of return data yet. XMRP can connect the dots automatically and still provide the prediction.