Procurement Optimization

Procurement Optimization

+ Manufacturing organizations perform supplier analysis to lower procurement costs while maintaining flexibility and lowering risk. + Disparate, siloed systems – as a result of organic and M&A growth, as well as needs of different business units – cause variety and scale of data to grow. The challenge is unifying the disparate data so that all of it can be consumed for analysis. + Traditional methods of integrating data quickly fall short, necessitating new approaches to unifying and enriching data. + Unfortunately, the systems used to capture customer information are often dedicated to single functions or geographies. This creates silos of data that are difficult to integrate cleanly with other sources, causing a large bottleneck for downstream analytics. Traditional ‘top-down’ approaches of standardizing data quickly become insufficient when dealing with the scale of data variety found in today’s enterprise.

Turning disparate supplier data into actionable insight

Procurement managers need complete, accurate, and actionable information to identify cost saving opportunities across the supplier base, while minimizing risk and maximizing supply chain flexibility. Yet, as companies continue to make large investments in enterprise, financial and supply chain information systems, very few organizations have been able to leverage all of the resulting data for analysis. This whitepaper discusses the reasons behind this, why unification of supplier data is now mission-critical for analysis, and how these views can improve efficiencies in supplier pricing or managing risk. Many questions, few answers The modern supply chain relies on a variety of ERP, PLM and Supply Chain systems that generate enormous amounts of heterogeneous data. Unfortunately, few enterprises can unify the information generated from these systems to answer important P&L questions: + How can find the highest quality part, at the cheapest price, from all of my suppliers? + How do I optimize supplier payment terms across different business units and territories? + How do I ensure that I minimize supplier risk that I am exposed to? 

While enterprises have evolved into data-rich environments due to continual investment in information systems, the subsequent datasets they produce are often difficult to connect with other relevant internal and external data sources.

This occurs due to a variety of reasons:

Globalization

Today’s enterprise is global, operating through multiple business units in territories around the world. As a result, data is often dispersed across the organization and stored in varying formats and standards.

Disparate Systems

While instrumentation has occurred across much of the supply chain, the systems involved are expensive to consolidate and not easily synchronized. This becomes even a bigger issue when companies grow through M & A, and when systems are not consolidated after acquisitions.

Old Approaches

Analysts spend as much as 80% of their time preparing data, leaving only 20% for actual analysis. This is due to the fact that traditional ‘top-down’ and ‘rule-based’ approaches to integration are not alone sufficient when dealing with the extreme variety of data typically found in large enterprise ERP, PLM and supply chain systems. In summary, while the supply chain can be considered a ‘data-rich’ environment, the disparate nature of the underlying data makes unified analysis exceedingly difficult, obscuring long-tail opportunities to improve efficiency and save money. Further, traditional approaches to solving this problem quickly become impractical as the scale and variety of data increases. Why Unification Matters Unified datasets are critical to understanding how suppliers interact with different parts of the enterprise. Unified data-sets enables analysts to: + Optimize Supplier Payment Terms – Using a unified view, one can identify opportunities to improve payment and discount terms by analyzing suppliers across multiple business units and territories. + Optimize costs -By leveraging unified part data, one can quickly and easily find the most affordable supplier for a given part, while considering other important factors such as quality and historical supplier performance. + Reduce risk – Unified data enables analysts to easily create and update a ‘golden record’ of suppliers across different countries and business groups, providing a more comprehensive view of the organization’s dependence on suppliers, and identify key points of risk Making Enterprise Wide Spend Analysis a Reality Tamr embraces the reality of extreme data variety within the supply chain, enabling analysts to analyze spend opportunities using data generated across all supply chain systems. Patent-pending technology using machine learning algorithms performs most of the work, unifying up to 90% of supplier, part and site entities by: + Referencing each transaction and record across many data sources + Building correct supplier names, addresses, ID’s, etc for a variety of analytics + Cataloging into organized inventory of sources, entities, and attributes When human intervention is necessary, Tamr generates questions for data experts, aggregates responses, and feeds them back into the system. This feedback enables Tamr to continuously improve its accuracy and speed. As a result, Tamr dramatically reduces the time and cost to build the unification metadata required to analyze the long-tail of transactions and events, because it learns and applies institutional knowledge automatically.