When Procurement Met Siri: A Simple Future for Spend Analysis? (Part 2) Jason Busch - April 30, 2013 5:49 AM | Categories: Technology | Tags: Analytics, L1 This post was co-authored by Jason Busch and Ryder Daniels. Yesterday, we introduced the concept of a simplified interface based on voice and text entry to access supplier and spend information. We also shared a number of sample queries (and screen mock-ups) to show how a text-based approach to this interface might work. But the larger transformative power of such approaches will come from more advanced use-cases and queries involving the combination of different internal and external datasets. Imagine being able to simple ask a procurement-focused Siri the following types of questions: What tier two suppliers are experiencing negative social media sentiment? Which of my tier one suppliers are increasing their AP aging with their tier two suppliers? What severe weather forecasts could impact some of our regional or local marketing campaigns this week? Which of my BPO providers has the highest turnover of staff? Which of my suppliers are advertising that they’re doing business with me? Which of my open RFPs are showing bidders that are hiring in order to potentially fulfill the bid? What future price risk do I have in metals? What percentage of export business do we represent for supplier X in China? The systems integration, analytics, computing, text, voice and speech recognition exists to solve these types of problems today – off the shelf. Yet no procurement or supply chain technology vendor (at least as far as we know) is enabling companies to answer these types of questions outside of one-off efforts or standard interfaces and dashboards, such as customized reports and views built off of a spend cube or supplier management/risk management toolset. The data sources one would use to answer each of the queries are surprisingly straightforward: Question 1: What tier two suppliers are experiencing negative social media sentiment? Data sources: Facebook, Twitter, GNIP (aggregator of social media data), vendor master, multi-tier view of suppliers (e.g., gained through demand aggregation software, multi-tier supplier risk management applications such as SAP Supplier InfoNet) Approach: Take data set combinations and conduct query in real-time to show negative social media sentiment through sentiment analysis software (alternative: batch-based approach to analysis such as hourly or daily with “push” capability). Future state: a combination of “triggers” will alert users automatically, in real-time, based on changing sentiment) Question 2: Which of my tier one suppliers are increasing their AP aging with their tier two suppliers? Data sources: vendor master, third-party credit bureau information (e.g., D&B Paydex data), anonymous surveys Approach: Use vendor master and credit information to conduct queries in real-time; combine efforts with an anonymous survey site that could serve as a neural meeting area for tier two suppliers to confidentially shared information about customer (tier one) payment changes and patterns. Question 3: What severe weather forecasts could impact some of our regional or local marketing campaigns this week? Data sources: Media planning and flowchart data, planned media by zip code, any of a myriad of weather data sources (NOAA, etc.) Approach: Analyze media planning and flowchart data in the context of regional weather forecasts by zip code and adjust mix/content accordingly. Future approaches could also show past supplier performance such as the ability to rapidly adjust to changing planning and programming based on last-minute inputs Question 4: Which of my BPO providers has the highest turnover of staff? Data sources: badging/credentialing systems, vendor management system (VMS), job boards advertisements, LinkedIn change of jobs, Glass Door, etc. Approach: Combine internal data (badging/credentialing systems, VMS tools, etc.) with external information sources to provide a view into BPO turnover more broadly as well as specific to the organization. Sentiment analysis of social media sites can also provide insight into BPO culture, turnover, retention, training, etc. In the next installment of this series, we’ll continue to explore future experiential and analytical interfaces into procurement data, as well as the data sources and approaches to solving the following questions: Which of my suppliers are advertising that they’re doing business with me? Which of my open RFPs are showing bidders that are hiring in order to potentially fulfill the bid? What future price risk do I have in metals? What percentage of export business do we represent for supplier X in China? Related Posts: When Procurement Met Siri: A Simple Future for Spend Analysis? (Part 1) Navigating Procurement Data: The Evolution of Spend Analytics (Part 1) Navigating Procurement Data: Simplify to Think Outside the Cube A Procurement Analytics Collaboration: What’s Coming Down the Pike What Marketing Analytics Can Teach Us Discuss this: Cancel reply Your email address will not be published. Required fields are marked *Comment Name * Email * Website Notify me of follow-up comments by email. Notify me of new posts by email.