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Big-Data Initiatives Drive Procurement Strategies

As big-data initiatives create expectations of competitive advantage, organizations are beginning to come to terms with the challenges ahead.

While data quality issues take center stage, the ability to meaningfully integrate structured enterprise data — frequently housed across multiple enterprise resource planning (ERP) systems, planning systems, manufacturing and production systems, customer relationship management (CRM), warehouse management systems (WMS), and transportation and compliance systems — with high velocity unstructured data streams, is causing significant challenges. Organizations with globally dispersed supply chains face an additional layer of complexity. They must also attempt to integrate and leverage useful data that resides with upstream and downstream partners across dozens of nodes in value chain.

In the high-tech industry, most current business applications of big-data analytics are in sales, marketing, customer service, and manufacturing. Demand-related big-data analytics are being leveraged to optimize pricing, promotions, product variations, and product availability, as well as positively impact customer satisfaction. Supply side analytics though, have been slow to take off. With procurement teams and supply sources located globally and working off multiple systems and data warehouses, data accuracy and timeliness of data have posed bottlenecks.

Procurement organizations can transform four important areas by leveraging supply side analytics: planning, procurement, inventory management, and supply risk management. Top procurement organizations recognize that this transformation goes well beyond creating procurement dashboards, spend analysis, and color-coded supplier scorecards. These organizations leverage multi-tier supply base data, supplier financial data, product and process quality data, transportation, regulatory, and other non-spend data to create savings, transparency, procurement intelligence, innovation, risk mitigation, and competitive advantages. Along with the right talent and resource deployment, technology plays a key role, as real or near-real time information delivery and minimal latency are critical to meaningful big-data analytics.

During my conversations and exchange of ideas with other supply chain leadership teams pursuing big-data initiatives in procurement, the following three areas have generated the most interest:

1. Predict supply and supplier performance relative to perfect order fulfillment, predict a supplier's negotiation strategy, optimize sourcing awards, and address downside risks by leveraging:

a.     Supplier financial performance information

b.     Supplier current capacity, future capacity expansion investments, and top customers

c.     Lead time/availability/pricing changes/promise versus actual supply date

d.     Alternate supply source lead time/availability/pricing changes

e.     Delivery expedite instances

f.      Raw material pricing volatility

g.     Currency swings

2. Drive micro-segmentation of supply sources and products based on factors such as manufacturing location, product category, product technology, pricing, lead time, inventory model, supply contracts, carrier contracts, trade regulations, etc., to enable:

a.     Scenario planning during procurement strategy finalization

b.     Inter-enterprise process optimization and process innovation across suppliers, carriers, manufacturing locations, and contract manufacturers

c.     Product and process cost savings, while driving alignment of economic incentives

3. Significantly improve advanced modeling capabilities of component inventory profile across the entire network through:

a.     Integration of planning signals from demand chain, NPI, engineering, and repair/refurbish

b.     Component level traceability, quality, failure rate, and root cause analysis across the value chain

In each case, organizations have begun the journey by creating an MDM strategy and followed that by investing in understanding, harmonizing, and aggregating relevant data from upstream and downstream value chain partners, as well as from third-party sources. In the high-tech industry, distributors like Avnet have traditionally leveraged structured data (including partner data) residing in ERP, supply chain systems, and data warehouses, to enhance capabilities in planning, procurement, and inventory management. Distributors are now leveraging technology and next-generation visualization tools to create competitive advantages for their business partners.

I invite you to share your insight and experiences related to application of big-data analytics in procurement organizations in the high-tech industry in the comments section below.

11 comments on “Big-Data Initiatives Drive Procurement Strategies

  1. TechGuy1313
    October 4, 2013

    I wanted to share a video that I think can be helpful for your readers that deals with planning and executing a Big Data program. (http://www.youtube.com/watch?v=Ow76L0IEZNY) This video is based off of TEKsystems research and delivers the message in a cute way through multiple sci-fi references.  

  2. Daniel
    October 8, 2013

    Lalit, big data is a new domain, where lots of analytical researches are happening. In our day to day activities, lots of datas are generating and its simple lying in our machine. When it's get filtered and analyzed, quality of datas can be derived, which can serve the purpose. Quality is an important factor; otherwise it won't serve the purpose.

  3. Wale Bakare
    October 9, 2013

    I agree with you especially for public sectors – helps government well enough than before in making decisions and budgeting.

  4. Daniel
    October 9, 2013

    “I agree with you especially for public sectors – helps government well enough than before in making decisions and budgeting.”

    Wale, you are right. Government departments are the one, has lots of customer interaction happening at various levels and capacities.  If they are able to analyze this customer query and interactions, they can derive a better solution or can minimize the citizens concerns up to an extent.

  5. t.alex
    October 11, 2013

    Jacob, I think you are right. There has to be well-designed databases so it can capture the necessary data. And some smart people have to develop the algorthims to dig out meaningful information from there.

  6. Daniel
    October 13, 2013

    “I think you are right. There has to be well-designed databases so it can capture the necessary data. And some smart people have to develop the algorthims to dig out meaningful information from there.”

    Alex, datas are generating every minutes and seconds, how to be used it for prediction and analysis is important than preserving it for a long time.

  7. Hailey Lynne McKeefry
    October 25, 2013

    @TechGuy, thanks for the video. It was, as promised, cute–and informative. I don't know if i caught all eleven space and scifi references. Do  you have a sense of how the specifics of the electronics supply chain (with previously siloed systems and global networks fo folks exchanging information) are particularly challenged by big data initiatives? Any best practice advice specific to that market?

  8. Hailey Lynne McKeefry
    October 25, 2013

    @Jacob a first and substantial big challenge is kowing hwere all the data is and then getting an idea of how its stored, backed up, used, etc. I believe a lot of organizations are still in early stages on this.

  9. Daniel
    October 25, 2013

    “first and substantial big challenge is kowing hwere all the data is and then getting an idea of how its stored, backed up, used, etc. I believe a lot of organizations are still in early stages on this.”

    Hailey, I think data administrators may be well aware of that and normally they used to keep a data map to know which data resides where. But I had seen in many companies such data resides in storage disk safely even with out refer it once.

  10. Hailey Lynne McKeefry
    October 27, 2013

    @Jacob, in an ideal world, i think you are right. Unfortunately, many organizations live in a world where various departments load data into cloud based services without the knowledge of IT, where virtual machines are spun up in a hurry without being properly documented, and legacy systems are never really brought into the fold. I would guess that its not uncommon for organizations to be woefully unaware of data.

  11. Daniel
    October 28, 2013

    “Unfortunately, many organizations live in a world where various departments load data into cloud based services without the knowledge of IT, where virtual machines are spun up in a hurry without being properly documented, and legacy systems are never really brought into the fold.”

    Hailey, after the implementation of cloud storage, space is not a constrain for dumping any data and anybody can use it. but as long as its not used or analyzed, its like junk data.

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