Embracing the future of sourcing in an intelligent way

At a glance

Sourcing has always been a complex process for organizations.

We, as sourcing professionals, are always under pressure to deliver year-on-year savings and ensure fulfilment under crunched timelines. Apart from this, sourcing professionals face challenges in sourcing against ambiguous or incomplete requirements, managing supplier relationships, dealing with logistics challenges. Now with unforeseen supply chain disruptions due to pandemics and wars, these complexities have increased many folds compelling procurement leaders to reevaluate strategies and look for sourcing automations by adopting innovative technologies and platforms to reduce uncertainties of any kind that have dependencies on the aforementioned factors.  

Sourcing challenges

Traditionally, sourcing professional have been managing sourcing with some help from technology and spreadsheet-driven calculations, thereby ending up doing tactical, repeated work instead of doing the strategic work.

Supply chain disruptions due to pandemics, Environmental, social, and governance (ESG) requirements etc., have forced sourcing professionals to create strategies that weigh less on cost and more in favor of on-time supplies. In simpler words, sourcing professional are looking for solutions which would simplify the sourcing process by embedding cutting-edge technologies and smart algorithms using artificial intelligence (AI), machine automation, and 360-degree transactional insights. 

So, what is this AI that we keep hearing about? Well simply put, it is the years of tacit knowledge that we, as sourcing professionals, have developed and coded into technology platforms to make sourcing repeatable, while sourcing professional focus on adding strategic value to the organization. In other words, the procurement organization of tomorrow will need to evolve from purchasing and sourcing to enabling a seamless digital experience. 

Innovative methods

Let us now look at some of the key innovative approaches that organizations are looking to explore: 

• Embedding AI into sourcing: AI is already beginning to change the face of an organization's sourcing practices. AI drives enterprise-wide visibility into all aspects of the procurement that humans simply can’t imitate at scale. It is helping them to deliver powerful optimization capabilities required for more accurate sourcing, improved productivity, and lower costs. There are multiple AI models that can be embedded into the sourcing cycle which help deliver operational efficiency, add greater strategic value, and gain competitive advantage.

• Smart supplier discovery: Traditionally, the sourcing manager used to mostly depend on manual web search to identify the suppliers. The process was tedious and time consuming. The development of AI-based supplier discovery tools has fundamentally changed the speed at which finding the right suppliers and optimizing a company’s supply base. Just by describing the products or services with a requirement combination such as location, capacity, and geo, sourcing professionals can now get millions of suppliers’ data in a fraction of a second from a variety of sources, including proprietary, public domain, commercial databases, and even global and some region-specific data. The supplier database is further narrowed down by leveraging AI along with natural-language-processing (NLP) algorithms that matches the sourcing professionals’ defined criteria. This smart way of supplier discovery is not only useful during crisis like the pandemic but also useful while companies develop new products, enter new markets, and continually rationalize and optimize their supply bases.

• AI-recommended sourcing events and suppliers: Traditionally, a sourcing manager used to spend a lot of time while creating a sourcing event. This included creating lots, setting up a collaboration team, selecting the right set of suppliers, designing category-based questionnaires, and creating rules for supplier evaluation. Based on the historic events conducted, sourcing applications embedded with an AI model auto prompt the suggested event templates with a pre-embedded questionnaire, supplier, and evaluation rules. In our experience of conducting sourcing events, we have witnessed 40% increase in productivity for our customers. 

• Data-driven e-auction: Traditionally, a sourcing manager used to provide the information of a line item based on which suppliers used to bid while participating in reverse or forward auctions. Because of incomplete information of the bidding items, suppliers more often used to provide invariably high or low bids/quotes resulting in scrapping/cancellation of events and thereby, wasting the sourcing efforts. Leveraging an AI-driven model supported with intuitive UI, the modern-day sourcing applications provide a 360-degree view of each and every item included for auction in advance, which helps suppliers in providing first-time accurate bids. An example is the sourcing tool that did the 2022 IPL auction had an embedded data-driven AI model providing a 360-degree view of the players for the IPL-franchise to bid for a particular player. 

• Autonomous negotiation: A KPMG report shows that 17% to 40% of the value of supplier deals is lost due to inadequate negotiations. Autonomous negotiations are making enterprises nimbler where sourcing professionals can focus on strategy-related work while the machines handle the implementation part. One good example of the bot-enabled autonomous negotiations is the hagglebot. Bargaining is a skill which buyers have been using for decades to negotiate. Since shopping has moved away from shops onto the internet, the joy of bargaining is lost. Hagglebot is one such approach that provides an opportunity to bring back the bargaining experience for buyers where they can bargain over the catalog price with virtual assistance bots. An example of autonomous negotiation is of the hagglebot that allowed customers to set the offer prices which was leveraged by the Indian e-commerce company Flipkart in 2021 during their The Big Billion Days sales event. 

• Commodity price forecasting: AI forecasting in commodities is another area which automatically breaks down structured and unstructured data and creates should-cost models that predict commodity prices with minimal human interventions. This helps sourcing professionals to devise their sourcing strategies based on the commodity forecast.

Conclusion

It is imperative that AI models are going to improve an organization’s strategic sourcing capabilities.

These models will help in managing sourcing in ways that not only save significant amounts of money but also open new and unique ways of procurement to deliver value to a business. Let’s come together in the journey of embracing the future of sourcing the AI way.