5 ways how data cleansing boosts your asset management

Internet of Things totally reshapes the way we do business today

The changes inevitably start with the Internet of Things (IoT). As Gartner reports, nowadays, we see the companies leveraging IoT in a really big way with IoT device use expected to leap 30 % next year. However, manufacturing is still under enormous pressure. Globalisation, harsh competition, together with a more demanding consumer base, make manufacturers to look for new ways to boost the bottom line. 

With a purpose to improve ROI and respond to customer demands for faster supply at a lower cost, some companies have to run their manufacturing plants 24/7, squeezing every and each capacity from their assets. Given this growing pressure on manufacturing assets, the companies need to go through their long term asset management strategies to ensure they are up to date with the short and long term objectives and demands. 

Connected smart technology considerably transforms the asset management and the way how everything from transportation to construction, power networks and factories is operating. Smart technologies trigger even further changes. Today, location, condition and assets maintenance requirements can be tracked with real-time information. As a result, the companies got the chance to identify operational risks in a near real time and lessen them more efficiently.

Lean manufacturing and operational excellence raise demand in efficiency, cost savings and performance

Lean manufacturing and operational excellence are crucial when doing business these days. Companies must do more with less while increasing throughput to improve the bottom line. It’s logic that material master data and asset management process are among the key challenges for manufacturing and asset-intensive organizations.

In the worst case scenario, low-quality material master data might result in unidentifiable items, duplication, excess inventory accumulation, false stock-outs, equipment downtime, just to name a few.

What is the main challenge when dealing with asset management?

Every second of equipment downtime costs a company lots of money, thus, materials and vendor data need to be consistent, reliable and available at all times. Purchasing and procurement departments also rely heavily on master data for spend analysis, strategic sourcing and inventory control.

As asset-intensive companies are evolving over the time, the amount of data housed within their systems grows too, adding more complexity to the already challenging master data management process. Due to employee turnover and lack of standard guidelines for data entry, master data progressively becomes inconsistent and saturated with duplication.

Is there any solution available?

Yes, there is. The ultimate objective of every data-driven company would be to operate on one common enterprise platform to enable visibility and communication across all business units. In order to accomplish this challenging task, legacy data must be merged together in preparation for migration into the chosen enterprise system. At this point, data cleansing becomes a true necessity.

So, what is that famous data cleansing all about?

Data cleansing is the process of analyzing, correcting and standardizing corrupt or inaccurate data records within a certain dataset. It requires specialized software and strategic project plan to manage an ever-changing dataset.

What are the main steps of a data-cleansing project?

1. To carry out a detailed data evaluation to assess the item master's current state, establish a baseline for key performance indicators (KPIs) and determine project requirements. One performs a series of automated reports to evaluate the current condition of raw legacy data while selecting duplicate records and providing multiple before and after cleansing samples for review.

2. To introduce a corporate standard operating procedure that will be lately enforced across the enterprise. It includes all data components, including naming convention, abbreviations, classifications and formatting requirements. The standard operating procedure and project plan should be tailored specifically for each project.

3. To initiate the quality control review process performed by subject-matter experts. During this phase, each product group and description are carefully reviewed and analyzed to ensure accuracy, completeness and compliance to project standards. A series of software programs further validate attribute information and check for any outstanding spelling mistakes or inconsistencies. Upon review and approval, each item is signed off and marked as "clean."

4. To duplicate and review items. Duplicates are identified by direct match and fit-form-function equivalent. Direct match duplicates are defined as items that possess the identical manufacturer name and part number.

5. To format data and carry out files delivery. Once the entire item master has been cleansed and approved by quality control, it is deemed complete and transferred to a team of information technology specialists. Proper data formatting and configuration at this stage allows seamless uploading into the new enterprise system while promising maximum search and reporting functionality.

   

Data management challenge is not gone once the initial project is over.

Maintaining high-quality data requires a rigid data governance strategy to ensure ongoing accuracy, consistency and compliance throughout all future transactions. For this reason, it’s vital for the corporate standards and duplicate prevention to be enforced across each data entry.

Various data governance solutions are available to provide a process that suits the unique aspects of each company. Each solution is strategically designed as a method of centralizing the data governance process to maximize and maintain data quality while providing seamless system integration. 

Why to implement a data-cleansing initiative?

Master data, which drives critical business decisions and daily operations, is arguably one of the most valuable assets that a company possesses. Implementing a data-cleansing initiative combined with an enterprise resource planning implementation is one of the smartest decisions a company could make. In the long run, it saves lots of time and money, as well as prevents the failure of numerous projects due to the poor asset management.

On the other hand, should businesses be not willing to borrow or sell equity to upgrade plant and machinery, they are less likely to invest in new ways of managing the assets they already have, which would require an overhaul to how they work.

 No doubt, the structures of some industries are simply not set up for the new world of data-driven asset management. The transition could be painful, but it’s definitely worth all the invested time and efforts. Installing sensors is one thing but understanding data is totally another one. New recruitment strategies will be soon required to bring companies towards the new paradigm in asset management.

 As CISCO maintains, “perfectly aligned asset management for manufacturing increases operational efficiency and boosts sustainability by automating the tracking and monitoring of the location, condition, state, and utilization of connected assets from a single consolidated view.” 

Hence, real-time and historical analysis enables customers to identify usage patterns, predict behavior, optimize maintenance schedules and processes, as well as utilize energy more efficiently across the manufacturing site.