Enterprises are actively reviewing their supply chain strategy according to the highest importance following the unprecedented disruption caused by the Covid 19 pandemic’s unprecedented disruption.
From a risk management and vaccine distribution point of view, the supply chain will continue to be a CXO priority as we step into 2021. Factors like geographical risk, delivery delays, suppliers’ safe work environment, and the like have surfaced risk management as an enterprise’s immediate agenda.
IoT is the current mainstream technology option for risk assessment.
The pandemic has revived interest in the digital transformation of the entire Source to Pay (S2P) process. For example, S2P solutions offer collaborative communication built into the solution itself, enabling real time communication and an audit trail between suppliers, buyers, and internal stakeholders.
This collaboration translates into efficiencies to measure in time savings of more than 15-20%. Contract management and e-procurement are other immediate areas of transformational interest for enterprises.
In their digital transformation journey, enterprises discover that contracts reside in disparate systems and servers with no connectivity or reporting capabilities and no standardized contract language.
Many of them cannot easily report out on the expiry dates of contracts and end up facing financial penalties. There is renewed interest in purchasing under contract to eliminate rogue spending and deals off-contract.
Research shows purchasing under contract returns 3-5% savings on average.
E-Procurement establishes such “spend under contract” disciplines by providing users with online catalogs that have been negotiated and presented in an online purchasing environment. Supplier enablement begins with negotiating purchase agreements with an enterprise’s top 20 suppliers and connecting them through punchout technology.
Punchout (or uploaded static) catalogs validate online purchasing by exchanging authentication credentials digitally so that users can shop with these suppliers, guaranteeing contracted pricing. Users then search for the product, add it to the cart and return the cart to create a Purchase Requisition in whatever ERP or e-Procurement solution is in place.
Enterprises are adopting a full spectrum of technologies such as AI that provide deep supply chain intelligence. There is a high demand for having as much information as possible. AI-enabled autonomous procurement offers humungous advantages over traditional procurement:
1) AI can verify data for standardization in forms submitted across various industries, ensuring submission of clean data for automation.
2) AI can reorder commodities according to inventory.
3) AI can issue purchase request, purchase order, and track supplier’s response.
4) AI can confirm Goods Receipt and generate invoices for payment.
5) AI can monitor professional services, resources, contractors.
6) AI can monitor standard contract forms.
The possibilities are endless. In contract management, AI can read through existing contract language, search for obsolete phrases or keywords and offer alternative phrases, clauses, Force Majeure, and other revisions.
Using that same logic, it can model new contracts to avoid the language traps used in earlier contracts. AI can also be trained to notify contract managers of upcoming expirations and indicate where these contracts may have dated clauses that could prevent exposure. The key is to centralize the contract repositories, clauses, addendums, and attachments that form a complete contract.
An example of how AI benefits an enterprise is in the BFSI industry. Every institution is currently reviewing how to restructure contracts that use London Interbank Offered Rate (LIBOR) in their contracts. An AI designed to identify every reference to LIBOR and make intelligent revisions can equate to real-time savings.
AI is undoubtedly beginning to influence the procurement processes. However, there are a few considerations to be kept in mind while embarking on an AI-led functional transformation:
1) Allow enough time and clean data to start your AI project. While learning how to accomplish a process or task, AI is like an infant taking its first steps. To do so, AI needs valid data, a disciplined approach, and a mentor to correct its thinking along the way.
Autonomous procurement requires clean data and enough time to build a predictable pattern or model of behavior to enable AI to set algorithms effectively. That means that the data an enterprise is currently relying on could negatively affect the AI learning process if it is disorganized or not a disciplined approach everyone follows. This takes time.
2) Begin with a small but very defined and strictly enforced process to teach AI and review how or where to improve the process. Attempting to automate a complex process can be daunting and disappointing, even frustrating.
Take on a clearly defined yet simple process or task. During the learning process, make adjustments, correct incorrect behavior and review what is being automated. Then apply the learnings to the next cycle.
3) Review the outcome and repeat as many times as needed to verify the AI
4) Change management is key to AI adoption. Include all stakeholders and identify positive thinkers, communicators, and mentors for the successful institutionalization of AI.
Historically, procurement efficiencies have been challenging to assess. AI as a solution to increase efficiencies is gaining ground as early adopters see advances in its design and application. However, many enterprises are still unsure about the extent of ROI that can be realized from AI. It all depends on the seamlessly integrated processes and the training.
AI will put forth a recommended strategy for a sourcing event based on criteria it has been trained to provide, such as templates based on commodity, a strategy based on the event’s geography, and standard questions based on established best practices.
For example, best practices gained from the pandemic can automatically replenish supply shortages based on search algorithms, inventory management, supplier risk factors. They can then train AI to point these out and automatically issue a PR for approval.
Anecdotal data shows efficiency gains as much as 20% with increased confidence that accompanies standardization of process and eliminates human errors. To find out more about leveraging AI for procurement, reach out to EAS.Marketing@tcs.com or email@example.com.
Pete Freimayer – Solution Architect – SaaS Procurement Practice, TCS
Richard Waugh – Vice President for Corporate Development, Zycus
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