a great article by Jackie Roberts on data governance for Product MDM
One constant truth in the business of data is change. The most critical factor in master data management is agility, within both process and software design. Agility means responding to and managing never-ending changes in the critical data used to support operational decision-making. I firmly believe the ability to respond to changes in the data is a must in the world of Product Information Management, or PIM, as the specialty is sometimes known.
Master Data Management is a broad classification of processes, governance, and software tools used to manage information such as customer and/or product data. CRM (customer relationship management) applications can support the organization’s interactions with employees, members, clients, customers and supply base for marketing, customer services and technical support. Typical data elements in CRM include names, titles, email addresses, physical addresses, phone and fax numbers, etc.
One crucial note is that there is a different expertise required to manage and structure a data governance policy for customer (CRM) data as compared to product (PIM) related data.
The definition of PIM is managing information about products, which may also include services. Product data can include equipment, assemblies, spare part components, and commodity type items such as office supplies or hardware items, i.e. bolts, screws, nuts, etc.
The data management and governance challenges escalate with the vast number of variations used to describe a single product. Adding further complexity to product data management is that of sell-side vs. buy-side. Sell-side data is a controlled data set of product information; as a result, the data and the governance is structured and owned internally by the manufacturer or supplier. If the governance is structured intelligently, the structure enables multiple data uses such as exports to web catalogs, print catalogs, engineering libraries, and more.
Buy Side of Production Information Management
Buy side is more complex, depending on the size of the operation. Buy side is the collection of transaction data (product or service) to support the operation of the plants and facilities. A critical aspect of collection and management of spare part information is to support the planning of physical inventory to enable the uptime of the facility. There is a fine balance of inventory cost versus ensuring the maximum uptime, which can be very challenging. It is not uncommon for a large global manufacturer to exceed 15,000 suppliers / OEMs equating over half a million submitted data records a year. These data records need to be reviewed, verified, classified, structured and referenced to ensure there aren’t any duplicate records created in the ERP system.
There are many variations in the data sources to support the operations of a facility. For instance, spare parts data is submitted from the equipment designers via engineering bills of material, or through individual purchasing requests from maintenance staff. The spare parts data includes the manufacturer name, part number, classification name (noun), unit of measure representing how it is sold, and additional information to describe the physical characteristics. However that same OEM part could be submitted as a supplier part with a different supplier part number, a different classification name and no mention of the OEM part number.
The result is the same part data setup with two different master records, each with different contracts for purchases at different prices and stored in inventory multiple times. This results in either excess cost and inflated inventory or a spare part that is not available resulting in the shutdown of the plant line. This is why the data governance and master data management business processes are so critical to an efficient and streamlined operation.
PIM Data Governance
A product information governance project may appear to be a daunting effort when you’re beginning to structure the data rules.
My best advice is to take the time to develop a data roadmap to provide a clear and precise understanding of the data and its use within the organization. The road map should detail how data is required and submitted for use within the enterprise, account for the multiple uses of the data (purchasing, engineering, marketing, and maintenance), plus the required data elements and structure needed to accommodate each software system.
As an example, let’s explore how the data is provided to the enterprise. There are multiple sources of data, from engineering drawings created internally or provided by suppliers, maintenance requirements, buyer requests, and more. With an understanding of the source data, a clear data requirement enables an improvement in the quality of the data provided to the organization.
Starting at the initial contract to source a new piece of equipment for the plant, you may include with the equipment specification a spare part data requirement plus a template for the supplier to provide the spare part information. The contract deliverable should include the completed spare parts list that was required per data governance. Now your master data management process has been simplified and data quality has improved.
In the roadmap, the required data elements are defined to support the business requirements. For a large global manufacturer the governance may include a structure for equipment numbers, location structure for the equipment and basic data governance elements specific to the master record.
Commonly required elements will include:
- Manufacturer Name,
- Manufacturer’s Part Number,
- Noun classifications,
- Technical descriptive attributes,
- Sequencing of attribute display order,
- Units of measure (typically a purchasing rather than a use UOM, sometimes referred to as a disbursement UOM),
- Volume purchase prices,
- Purchasing category,
- Lead time,
- Warranty information,
- Language translation requirements,
- Other classifications such as ECCN, UNSPSC
- Any other descriptive elements to ensure smart purchasing decisions and stock strategy for items inventoried.
Benefits as a Result of Data Governance
There are many benefits of implementing an innovative data governance and master data management system. Many of the basic benefits, both in process and cost, are:
- Reducing inventory through identification of duplicate items,
- Facilitation of inventory sharing and internal purchasing programs,
- Reduced employee time spent searching for items,
- Common spare part usage strategies,
- Reduced downtime in manufacturing equipment due to lack of information availability,
- Ability to manage inventory using a just in-time model.
Data Governance supports both indirect and direct cost savings. Businesses can begin to embrace the definition of operational data as an asset of the corporation, ensuring improved data accuracy and confidence of the data users.
About the Author
Jacqueline Roberts is the VP at DATAForge™. She is a data enthusiast and firmly believes a successfully implemented MDM program will improve engineering, maintenance and purchasing data processes, which enables inventory management and cost-saving initiatives. You can follow her on Twitter at http://twitter.com/jackiemroberts or visit www.dataforge.com.