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Is procurement ready for big data analytics?

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by Beroe Inc.
16 February 2017

With inputs from Shruti Snigdha Prusty, Research Analyst

Digitization and big data have opened up opportunities for companies across all industries. A huge amount of data is being generated on a regular basis, which could be leveraged to create business advantage. However, business leaders are approaching the idea with caution.

Recent studies conducted around big data implementation show that initiatives have failed to fetch the expected results. A study by Accenture on the prevalent perception about the big data revealed that 97 percent of supply chain executives understand how big data analytics can benefit their supply chain. But, only 17 percent have already implemented analytics in one or more supply chain functions, according to Industry Week.

While most companies believe in the impact of big data analytics in their supply chain, many have had difficulty adopting it. Businesses need to understand that implementation of programs relating to big data analytics cannot follow a one-size fits all approach. A capability assessment study is imperative to a successful implementation.

Inherent challenges in big data implementation

In the procurement context, challenges include collating data from different sources and processing them for optimization. Drawing meaningful inference from the patterns is another challenge. This would require a major overhaul of existing procurement practices in some cases.

Moreover, any initiative costs money. And a strong business case is warranted to highlight the ROI of doing such an exercise so that CPOs can successfully incorporate big data analytics as part of procurement practice when the organization as a whole decide to leverage it. Key drivers include:

  • Reducing the costs of SCM
  • Compliance and risk
  • Traceability
  • Agility

As per the Hackett Group’s research, the top two priorities for procurement in the coming years are becoming a trusted business advisor and making business more agile. There exists a capability gap when it comes to accomplishing these objectives, as the conventional analytics practice does not meet the following demands:

  • Collect and comprehend end-to-end supplier data like registration, contract, RFX, performance, payments and termination
  • Collaborative planning by using data shared by suppliers
  • Leverage the unstructured data generated in the market place, which in turn can assist in anticipating supplier risks, predict the spend trajectory and fine tune category strategies
  • Finally, the organization culture need to be flexible enough to adapt to data centric procurement eco-system

Big data analytics is not a ‘one size fits all’ solution

According to a 2015 Informatica Business Value Assessment, companies could save up to $6 million annually in supplier spend, by leveraging trusted supplier data quality. 61 percent of those who had an enterprise-wide strategy said Big Data analytics helped them shorten their order-to-delivery cycle times, while only 14 percent of those using a process-focused strategy saw similar results.

Big data is not a ‘one size fits for all’ solution and is heavily dependent on the organization size, industry and the market forces. It is imperative that thorough due diligence be performed to assess the specific needs of an organization and determine business insights required to meet those needs. To extract tangible value from big data analytics, the ecosystem should be able to manage the volume of data as well as the velocity or speed with which the data gets generated and analyzed. 

Pointers for procurement organizations

According to a study by Accenture and General Electric, 84 percent of the companies surveyed believe that big data analytics could "shift the competitive landscape" for their industry within the next year and 89 percent believe companies that fail to adopt a big data analytics strategy could lose both market share and momentum.  If successfully implemented, big data initiatives can reduce supplier risk, uncover new savings, predict negative external environment and helps in better collaboration with suppliers.

Procurement organizations can begin by testing the waters of integrated procurement analytics using big data. However, such an investment will require reshaping of the procurement process completely from bottom up, which can be a tedious and cost-intensive effort. A thorough cost-benefit analysis needs to be done before committing to an investment.

On the other hand, if Procurement is entrusted to select a vendor for big data implementation, here are the pointers:

  • Define goals according to the business problems before selecting any tool and vendor
  • Vendor selection must be based on past industry experience, geographical presence, revenue, size of the company, clients served and product offering
  • Choose vendors who build their products based on industry-wide data standards
  • Ensure technical documentation is part of vendor’s deliverable to prevent vendor lock-in; give an audit trail and contribute to the internal body of big data knowledge
  • Check whether the vendor is able to meet the security requirements
  • Vendors who offer support for maintenance, bug fixes, or user questions after implementation of tools
  • Look for a vendor who takes a consultative or partnership approach on the team level to help maintain and grow client’s Big Data knowledge internally
  • Independent assessments of tool's performance, quality, value, functionality and cost are perhaps more useful for business stakeholders than just going through press releases or vendor-produced case studies. 
  • Choose vendors, who provide tools which are user friendly
  • Providers must have appropriate level of scalability
  • Select a big data vendor whose pricing model is easy to understand, suits the budget of the internal stakeholders and in line with the business goal, and industry benchmarks.

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