The first steps to delivering clean, trusted operational data are data quality best practices such as data cleansing, deduplication, standardization, and harmonization.
A tremendous amount of working capital is tied up in business operations and the data behind it. Complex yet critical business processes make it hard to manage stock or assets, third-party or vendor relations, and inventory errors. With no clear way to standardize or normalize materials schema or taxonomy, organizations have no way to profile, catalog, or properly search for this operational information, and duplicate data (and spare parts) quickly accumulate.
This disparate, outdated, and incomplete data leads to inventory and supply chain issues that pack a punch. The average Fortune 500 manufacturer wasted $40-60 million in working capital tied up in excess parts.
Operational data is the data used to manage the information and technology assets of the organization. Unlike “party” data, which comprises individual or household contact information, operational or “non-party” data includes materials, products, inventory, equipment, spare parts, or components. This may be the stock required to build a specific product or equipment like assembly lines used in keeping certain processes or tasks running. Operational data uses a variety of data points to keep track of goods, including SKUs, serial numbers, cost of goods, quantity and volumes, supplier information, and more.
A crucial part of inventory or supply chain management is using operational data to facilitate the flow of goods and optimize processes. Since operational data is information related to the technology and assets of the organization, it can be used to identify risks and ensure a reliable, steady supply chain.
“We've been impressed with the agility the solution provided in quickly returning initial matching results based on key materials and data objects and its ability to rapidly refine, iterate, and improve those results. This has allowed us to consider multiple data-driven use cases and define business opportunities without a massive effort or the need for expensive, dedicated coders or data scientists.”
- John Price, Manager, Data Management Services, Rio Tinto
Today’s custom-centric data models require data to be integrated across external data (weather, economy, regulations), customer data, supplier data (operational data, material/components), and historical sales to create more accurate demand and inventory plans. Global competition and fast developing product cycles put continuous pressure on costs and efficiency of a company, challenging businesses to stay agile and efficient to deal with new changes.
To manage this disparate material and operational data, data quality software is being used to drive harmonization and reduce spare parts inventory. Data matching solutions that include capabilities such as materials data cleansing, standardization, deduplication, and matching are yielding significant savings for organizations, both upfront and ongoing, including:
Operational matching is helping enterprises quickly find meaningful cost savings and process improvements in their operational data by eliminating unmatched and duplicate operational data with industry-leading, AI-driven ‘operational data’ matching solutions.
For example, cleansing operational data can help you correct inaccurate spare parts data, leading to ineffective spare parts management and BoMs. This allows you to review your parts’ descriptions and characteristics to ensure they are accurate, remove duplicate and obsolete parts, and standardize naming conventions and processes. Optimizing your operational data allows you to manage or reduce the excess inventory you have on hand and maximize asset performance and management.
Whereas dirty operational data can hurt the business’s bottom line, this holistic view of your inventory and operational data helps optimize and reduce the amount of inventory you have on hand. As Syniti CEO, Kevin Campbell, recently stated, “For a Global 2000 company, a reduction in duplicate spare parts alone could save millions – freeing up that capital for more value-add initiatives.”
Learn More: Connecting Data Quality to the Business Bottom Line
The first steps to delivering clean, trusted operational data are data quality best practices such as data cleansing, deduplication, standardization, and harmonization.
To optimize inventory management and reduce duplicate stock, sophisticated record matching and deduplication are required to account for the wide variety of naming conventions, languages, input errors, schemas, and data sets the average enterprise utilizes.
While a seemingly straightforward process, the layered complexities within operational data can cause more errors and inconsistencies when data deduplication is done ineffectively. Without utilizing matching techniques such as contextual scoring, Natural Language Processing (NLP), and purpose-built phonetic algorithms, it’s nearly impossible to account for all the discrepancies found in operational data. Deduplication shouldn't be a siloed event but leverage the insights gleaned from other data management techniques such as normalization and standardization.
Cleansing and wrangling data is an integral part of maintaining a clean database, but a full data harmonization strategy is vital to real-time accuracy. Without a proper methodology in place, one-off or manual data matching can have severe impacts on the business bottom line, such as:
Nearly every industry can benefit from optimized operational data management. Whether purely focused on manufacturing, utilities, pharmaceuticals, life sciences, or even food and beverage, supply chain operators are using technology to provide insight into supply chain performance, anticipate changes in logistics costs and performance before they occur, and gain insights into where automation can deliver significant scale advantages.
“We've been impressed with the agility the solution provided in quickly returning initial matching results based on key materials and data objects and its ability to rapidly refine, iterate and improve those results. This has allowed us to consider multiple data-driven use cases and define business opportunities without a massive effort or the need for expensive, dedicated coders or data scientists.”- John Price, Manager, Data Management Services, Rio Tinto
Operational data management is changing the food and beverage industry with access to real-time data analytics to power a more agile supply chain. By applying data quality measures such as MRO standardization to only 120K in spare parts records, a leading Food & Beverage company realized a whooping €2,025,000 in savings on inventory value, with an ongoing savings of €1,438,250 per year.
It’s no secret that Oil & Gas companies produce a massive amount of production, operational, vendor, and customer data across numerous systems. Collecting and analyzing it all with accuracy while pivoting to changes in supply and demand is what sets apart the true competitors from the rest. Looking to manage their 342K spare parts records, one enterprise in the Oil & Gas industry discovered increased ROI and achieved a potential savings of 5% on inventory value by MRO standardization and 10 to 15% in savings on EAM critical equipment MTTR/MTBF*.
Reducing excess spare parts data and duplicate inventory is vital to harness the massive amounts of data generated within manufacturing organizations. A Manufacturing company looking to manage about 30K in spare parts data was able to realize upwards of 7% of savings on inventory value and an additional 10-15% on EAM critical equipment MTTR/MTBF.
Applying data management to this small subset of spare parts data, in turn, reduced procurement spend per year on spare parts - conservatively 5% or $706,000 in savings. The Manufacturing company was also able to reduce current duplicate or “over inventory” by up to $304K returned to working capital.
It is no surprise that many industries, from utilities to retail, are looking for new ways to manage the incredible volume and variety of data coming into their enterprises. Globalization, technology, and empowered consumers are changing the challenges businesses face in inventory and supply chain management. By applying the right data matching methodology to operational data, enterprises can ensure the correct information is merged, captured, validated, and cleaned – essentially, analytics-ready – and accelerate data-driven initiatives.
To learn more about operational matching and how Syniti can help you improve your data, please register for our webinar, “Why you have duplicates and why you won’t find them.”
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