What are the real challenges in rolling out SAP IBP across an organisation? How do you get a system like that adopted globally across teams that don’t run on the same processes? And what does accurate forecasting actually take in an industry like pharma, where a single planning cycle can stretch over a year?
In this edition, we speak with Gouri Sankar Panda, General Manager – Supply Chain Strategy & Analytics at Intas Pharmaceuticals. An engineer by education in electronics and telecommunication, Gouri’s career in supply chain began, by his own account, “by accident” — with an analytics role at Reliance Retail that exposed him to functions across the business. Since then, he has led supply chain and analytics functions across the consumer and pharma industries and is currently the General Manager, Supply Chain and Analytics, at Intas Pharmaceuticals.
Q: You’ve worked on SAP IBP rollouts across global manufacturing sites. What was the biggest challenge you faced while rolling out this tech?
SAP IBP stands for Integrated Business Planning. The main objective when any company decides to roll out IBP is to integrate all their business processes — because currently, operations are fragmented. Say you’re working with a pharmaceutical or an FMCG company: one site in the US follows different processes than a site in India, and that’s why efficiency and output differ across factories. When a company rolls out IBP, they want to streamline processes so every factory produces at optimum efficiency, everything gets standardized, and management can view it all on a single dashboard.
So the challenge was never about deploying the planning system itself — rolling out a tech system is the easy part. The difficult part is getting people to actually adopt it. Getting people to follow the same process across every global site — that was the most challenging part.
Q: How did you get people to adopt it across all the sites?
There’s a saying — if you can boil the frog first, do it. So we started with the most complex site in the entire organization — the biggest site, with the most complex structure and processes. We didn’t modify all the processes across the board. Instead, we identified which processes could be standardized and which couldn’t, took their existing best practices, and replaced parts of it with something similar that they could easily adapt to. We invested a lot of time getting the functionality ready and getting people to trust the system’s output. Once that launch was successful, the goal was achieved.
Once that biggest, most complex site went live successfully, replicating the solution across other sites became much easier — people saw the case study of the toughest site adopting it well, and that gave them confidence.
And the most important thing: we had to align the leadership team at every level — company leadership, site leaders — and make them part of the development process itself, so they felt secure rather than feeling like a solution was just being handed to them to adopt. Making them part of the team is what made the project successful.
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Q: You mentioned “touchless KPIs” — can you explain what that means and how it’s relevant in supply chain?
Let’s take an example — forecast accuracy. Say the number comes out to 70%. In some cases, that’s good; in some cases, even 50% forecast accuracy is considered good management. It really depends on the scenario. When you present a KPI like that to management, they’ll ask — is 78% good, bad, or ugly? What are the insights behind that number?
Normally, when a KPI comes automatically from a system, it’s just a number — people see it and maybe try to improve it a little. A touchless KPI goes further: it automatically highlights the key products or key issues driving that number, and tells you, for example, that your forecast accuracy of 70% can improve by 6% if you focus on this. It becomes action-driven. When you present it to management, those key actions get highlighted directly, so decisions can be made and action taken much faster.
Q: You’ve worked across pharma, beauty, and FMCG. What’s the single most effective strategy for improving forecast accuracy across these industries? Feel free to share a specific example.
I’ll address this through the pharmaceutical industry specifically, because it’s far more complex and requires much greater planning precision. When I say planning precision in pharma, regulation plays a key part — getting regulatory approvals in some countries can take as long as 12 to 14 months.
Planning is the most critical part of pharmaceutical supply chain because of this. Forecast accuracy becomes a key indicator since you have to plan almost six months in advance of selling a product. If you combine the entire supply chain lead time — from procurement all the way to point of sale — it can take 12 to 14 months. In a dynamic environment with plenty of bottlenecks, maintaining forecast accuracy over that kind of horizon becomes far more challenging than in other industries.
In most other industries, if you fail to plan forecast accuracy correctly in one month, you can recover the next month, because the planning horizon is short — typically three to five months. But in pharma, the planning horizon is almost 14 months, so maintaining forecast accuracy at a benchmark of, say, 70%, is critical.
If you’re working with thousands or even 10,000 product lines, you simply cannot try to improve accuracy across all of them — you’ll get lost. You have to focus on the key signals — the key 20 to 30 products, at most — because improving forecast accuracy on your best-performing products improves your entire P&L and overall supply chain profitability. So the single biggest lever is generating the correct signals and correct exceptions, so you can focus your efforts and improve forecast accuracy even within a very large supply chain.
Q: Finally, what’s your advice to young supply chain professionals entering the field today?
That’s an interesting one — I was in the same place once. But the landscape has changed a lot. Supply chain is becoming one of the most exciting parts of business today, with AI-enabled, data-driven insights readily available — something people used to spend a lot of time generating manually.
My key message to young professionals: don’t focus only on tech, and don’t focus only on business. You need to understand both — the technology and the business side — because that’s what makes your decision-making faster and more efficient.
