Fully automated and insight-driven source-to-pay (S2P) value chains are gradually gaining mainstream attraction as the limitations of traditional procure-to-pay models become a bottleneck to efficiency. Re-engineering such a value chain can open up a plethora of unexpected benefits, including substantial spend optimization, improved working capital, increased margins, better sustainability, seamless buying experience, and much more. However, this entails being at the top of the game in process optimization and integration.
Fully automated and insight-driven Source-to-Pay (S2P) value chains are gradually gaining mainstream attraction as the limitations of traditional procure-to-pay models become a bottleneck to efficiency
When the world's foremost tech giant was in the pursuit of transforming its sourcing and procurement status quo, they stumbled upon a few hurdles at the onset of the journey. Spread across North America, the UK, Europe, and APAC, their S2P business operations were decentralized, fragmented, and non-standardized. High costs were incurred in IT asset management of the complete lifecycle value of the assets. Despite this, forecasting was often inaccurate, and achieving spend optimization targets were challenging.
HCLTech proposed a centralized managed services center for end-to-end IT procurement and asset management, leveraging AI and advanced analytics. Some of the highlights of this unique and innovative project are:
Total cost of ownership (TCO) optimization
HCLTech started with studying, analyzing, and classifying all the cost components of materials and services, covering the end-to-end lifecycle from acquisition to disposition. An advanced analytics solution capable of descriptive, predictive, and prescriptive analytics was deployed, resulting in complete visibility of cost drivers, leading to an optimized total cost of ownership (TCO).
IT asset inventory optimization through improved forecasting process
With as little as ~10% accuracy in asset inventory forecasting, the business faced regular inventory stockouts, over-budgeting, high safety stock, and increased product obsolescence cost. A three-stage approach was established to improve forecast accuracy that started with data selection, where historical data was analyzed. Post that, a best-fit statistical model was leveraged, followed by choosing a forecasting output, particularly a data visualization tool, for a long-term forecast.
In addition to the reduced CapEx/OpEx, benchmarked TCO, informed decision-making capability, contract compliance, and IT-business alignment, the reimagined S2P process boosted their forecasting competence. The business now operates at 76% forecasting accuracy with minimal stockouts and a 30% reduction in inventory carrying costs.
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