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How intelligent order management can help companies stay competitive
Listen to our latest podcast on how intelligent order management powered by AI and automation boosts availability, cuts costs & builds competitive, future-ready supply chains.
In the latest episode of EY India Insights podcast, Nishit Bhatia, Partner and National Leader of Supply Chain and Operations at EY-Parthenon India, discusses how intelligent order management is enabling consumer goods organizations to shift from reactive execution to real-time, data-driven decision-making. He also shares insights on leveraging AI, automation, and concurrent planning to enhance availability, optimize costs, and improve service levels. Tune in to learn why smarter order management is essential for developing competitive, future-ready supply chains.
Key takeaways
By combining real-time data, AI, and concurrent decision-making, companies can reduce stockouts and unlock incremental sales, especially in under-served SKUs.
Intelligent order management helps organizations reduce manual labour, streamline order processing, optimize routing and load planning, thus significantly lowering logistics and operational costs.
With real-time insights and AI-driven decisions, companies can anticipate disruptions, prioritize tasks effectively, and develop supply chains that are competitive, responsive, and ready for an omnichannel approach.
The real value of AI is not automation alone; it is decision support. When AI optimizes priorities, routing, replenishment, and risk, teams can focus on strategy, and supply chains become more resilient, competitive, and future-ready
Nishit Bhatia
Partner and National Leader, Supply Chain and Operations, EY-Parthenon India
For your convenience, a full text transcript of this podcast is available on the link below:
Pallavi
Welcome to this episode of the EY India Insights podcast. Today, we are joined by Nishit Bhatia, Partner and National Leader, Supply Chain and Operations at EY-Parthenon India. Nishit brings extensive experience in advising consumer goods and manufacturing organizations on supply chain transformation, operating model redesign, and digital-led operations. In this episode, we discuss how intelligent order management is helping consumer goods companies strengthen supply chains and improve service levels in an increasingly complex environment.
Thank you for joining us, Nishit for the podcast today.
Nishit
Thanks, Pallavi. Pleasure to be here.
Pallavi
Could you share the key challenges that consumer good companies face today in managing orders across increasingly complex and omnichannel supply chains?
Nishit
The landscape has become incredibly complex in the past few years. Demand is coming from multiple channels – general trade, modern trade, and e-commerce. And it is increasingly becoming very unpredictable, especially given that this demand is overlapping across channels. We have seen cases where the same consumer is using multiple channels at different points of time for the same product.
In fact, close to 40% of consumer purchases in India are expected to be digitally influenced by 2030. On top of that, companies rely on large distributor networks with multiple challenges such as uneven credit availability and heavy dependance on third party logistics. Also, given that so much information, be it inventory at the distributors, retailers, credit status, orders - because orders come from multiple locations, vehicle availability with third parties, none of this is available in real-time. Therefore, organizations often end up operating very reactively, which leads to execution losses, stockouts, and ultimately missed sales opportunities.
Pallavi
Thank you, Nishit. How does intelligent order management differ from traditional order allocation approaches, and why is this shift becoming so critical for consumer goods organizations?
Nishit
Traditional order allocation is manual, sequential and largely rule based. That is assuming that it considers all of these factors that I mentioned – inventory at distributors, their credit status, recent orders, pending orders, and vehicle availability at third parties. The only way they can solve some of these (challenges) is by taking each of these constraints and trying to address them sequentially.
Intelligent order management flips this entire model. It uses real time data, AI/ML intelligence, and most importantly, concurrent decision making. So, instead of working step by step, the system simultaneously validates credit, checks inventory, optimizes allocation and also plans logistics. It brings automation to areas that were previously manual, like order correction or even load building. This enables faster, more accurate decision-making that aligns closely with business priorities. In a world where channels are multiplying and demand is volatile; I do not think this shift is optional anymore. It is the core for FMCG companies to stay competitive.
Pallavi
Based on your experience, what tangible benefits such as improved availability, service level or cost optimization can organizations expect from intelligent order management?
Nishit
The results are real and measurable. I would club this into three big areas:
In terms of availability and top line improvement, we see that stockouts have dropped by 4 to 5%; there is an OTIF (On Time In Full) improvement of upwards of 10%, thanks to smarter allocation and visibility. But the biggest impact on availability we see is on the B-class SKUs, leading to 10 to 15% of incremental sales.
A lot of people focus on A-class SKUs availability, but that does not always translate into an incremental sale because there is enough inventory of A-class SKUs across the entire value chain, either within the organization or within the organization’s partners, channel partners, or even with retailers. But any improvement that you are able to drive on the B-class SKUs translates directly into incremental sales. Real-time prompts are also helping improve promotion execution by almost 15 to 20%.
The second focuses on reducing operational and order-processing costs. At the heart of this lies automation, which reduces manual work by 50 to 60%. We have seen organizations which had teams of 15 to 20 people to manage order-processing; they can now move to a two-to-three-member team using automated order management and drive it with much better outcome.
The additional benefits that we see at the back end are better routing, load planning, both of which lead to improved truck utilization and also cut logistics costs dramatically.
The third area focuses on healthier working capital. Continuous replenishment, non-base stocking is definitely leading to at least 20% lower working capital, and 12 to 15% reduction in overall inventory as well. These are not incremental wins; they reshape a company's cost base and also help drive responsiveness for the organization.
Pallavi
Thank you, Nishit. What role do you think data analytics and AI play in enabling smarter order decisions across supply networks?
Nishit
They are absolutely central to the intelligent order management system. Real-time data removes blind spots and allows for concurrent planning. AI and machine learning enhance decision making, predicting risks, correcting orders, prioritizing customers, even optimizing load building. Additionally, conversational interfaces give instant updates on stock, delivery ETAs, and potential disruptions – all this means that teams can anticipate issues beforehand instead of reacting to them. It just saves a lot of bandwidth for the sales team to focus on what they do best, rather than trying to run around and figure out where the stock is.
In short, AI turns order management from a manual process into a continuous learning, self-improving system.
Pallavi
What would be your key advice to supply chain leaders who are looking to strengthen resilience and competitiveness through intelligent order management?
Nishit
First, invest in real-time visibility, especially around distributed data. Without it, even the smartest systems cannot perform at their best. Second, shift from sequential processes to concurrent ones. This alone can transform speed, accuracy, and responsiveness for an organization. Third, embrace AI not just for automation, but more importantly, for decision support. Let it optimize prioritization of orders, routing, replenishment, and risk prediction, so that teams can focus on strategic decisions rather than firefighting. Companies that make this switch will build supply chains that are more resilient, more competitive, and ready for the next wave of omnichannel growth.
Pallavi
Thank you, Nishit. Now that brings us to the end of this episode. A special thank you to you for joining us and sharing all your insights on intelligent order management system that is reshaping the supply chain dynamics for consumer goods companies.
Nishit
Thank you. Pleasure to be here.
Pallavi
To learn more about this topic for related EY perspectives, please do visit our EY Insights page. Stay tuned for future episodes where we continue to explore strategic innovations in supply chains and operations. Until next time. This is Pallavi, signing off.
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