Interview with Gurgen Melkonyan, Founder and CEO of Jume

The emergence of a new player in the relatively stable IBP (Integrated Business Planning) market is quite intriguing. What led you to develop a platform in such a competitive space?

Gurgen Melkonyan: Jume’s story began in 2019 with a small team of experts in machine learning and mathematical optimization. We were developing and implementing engines for forecasting, optimization, and decision-making. These solutions were integrated with major global business planning platforms like Anaplan, SAP, and Kinaxis. Once combined with our modules, these “big systems” showed a significant boost in key business metrics—improved forecast accuracy, reduced write-offs, lower logistics costs—so everyone benefited. By 2022, we had reached a critical mass of successful projects.

That’s when we formed a clear vision for our own IBP platform—one where advanced intelligence technologies would be built into the core functionality, not added on as optional extras.

The main challenge was to consolidate all our intelligent modules into a full-fledged integrated planning platform. That’s exactly what we set out to do more than three years ago.

How did you first start working with AI technologies?

Gurgen Melkonyan: Our first major AI-driven project was for Unilever—one of the world’s leading FMCG companies. Interestingly, the project was for its biggest business globally – Unilever USA. It started somewhat by chance. For years, the business team had been using Anaplan for demand planning. But they were struggling: forecast accuracy in certain product categories was stuck below 40%, directly impacting KPIs—massive excess stock in warehouses, poor service levels, and penalties from retail clients for missed deliveries.

Unilever decided to enhance Anaplan with an external forecasting module based on machine and deep learning algorithms. The goal was highly practical: to improve forecast quality and reduce manual effort by using a wide range of internal and external data—price dynamics, promotion calendars, inventory levels, weather, and more.

Our then-small team developed a pilot model, which turned out to outperform all other market alternatives. This led to a full-fledged universal model covering all business units and sales channels, working in tandem with Anaplan and other enterprise systems. At the same time, we created tailored “engines” for Unilever’s Big Three clients—Walmart, Target, and Amazon—based on their specific data and supply chain needs, further boosting forecast accuracy for key retail channels.

Following this, we began developing modules for other areas of integrated planning for other customers—promo budget optimization, procurement, production, logistics, pricing optimization, and more. These either enhanced existing IBP solutions or operated independently.

That’s when we fully realized: the next leap in business performance lies in the practical application of high-intelligence technologies to planning.

How did you manage to develop an IBP platform in just three years? That’s a short timeline for such a complex product.

Gurgen Melkonyan: We didn’t need to enter a completely new market or run customer discovery, because we already had a strong foundation: knowledge of best-in-class solutions, project experience with leading companies, deep expertise in ML, DL, optimization, GenAI, and a team of planning experts from top organizations. We had a very clear understanding of what a modern platform should be.

That’s why, just one year after starting full-scale development, we launched the intelligent Jume platform in March 2023. The first two implementation contracts followed in May and July.

Do you see Jume as a software vendor in the long term, or will you continue delivering implementation services?

Gurgen Melkonyan: At the moment, we’re simultaneously developing the platform and delivering implementations—and we expect to continue in this mode for at least another year. The upside for clients is clear: no one can deliver digital planning transformation and intelligent tool deployment better than the platform’s own creators.

As a vendor, we’re also refining our implementation methodology, which will become the foundation for future training and certification programs for partner integrators.

We’ve already begun preparing to build a network of integration partners who can independently handle sales, implementation, and module deployment. As the vendor, we will ensure warranty support, deliver regular updates, and maintain strong partner relationships.

In your view, is low-code a strength for modern IBP platforms?

Gurgen Melkonyan: Both yes and no. When we started developing Jume, we studied the experiences of global leaders in both hard-coded and low-code solutions. We concluded that the best approach is a balanced one.

We knew that most clients wouldn’t be able to continue developing the solution internally with in-house centers of excellence. So building a fully low-code platform didn’t make sense. At the same time, we wanted to avoid a scenario where every customization required external consultants.

That’s why Jume is built on a hard-coded core with carefully designed zero- and low-code capabilities. This provides businesses with more independence and flexibility in managing and using the platform.

Examples of such functionality include data import and task scheduling, personalized interfaces, self-service reporting, approval workflows, multidimensional views and tables, and more.

Have customer expectations changed over the past few years? What new trends are you seeing?

Gurgen Melkonyan: Absolutely—they’ve changed significantly. In the past, companies would often ask for specific use cases—just financial planning or demand forecasting, for example. Now we increasingly see requests for truly integrated planning, covering the full suite of processes.

Many clients already have experience with global leaders like SAP APO, Oracle Hyperion, Anaplan, and BlueYonder, so their expectations for local IBP solutions are quite high.

Another trend: large and mid-sized businesses often start by looking for outside-the-box solutions—especially when IT leads the system selection process. But as we discuss requirements and dive into the specifics of each business, it becomes clear that significant customization is almost always necessary to align the platform with target business processes.

We also see a growing reassessment of internal centers of excellence (CoEs), once heavily promoted by global vendors. These teams are expensive to maintain, rarely become true drivers of platform evolution—especially when balancing operational tasks—and often struggle to match the expertise of specialized integration consultants.

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It sounds like you see AI/ML and algorithms as key advantages of a modern IBP solution. Is business ready to adopt them?

Gurgen Melkonyan: I’d say business is not just ready—it’s eager to improve efficiency and profitability, which traditional planning systems don’t always deliver.

Today, cutting-edge intelligence technologies are no longer “for the elite.” Many mid-sized and large companies are already using them successfully across different areas: manufacturing automation, warehouse management, computer vision for defect detection or merchandising, generative AI for HR, and of course, machine learning for planning.

In a variety of industries, ML, neural networks, and optimization algorithms have already proven their value in planning and supply chain management. For example, food manufacturers often struggle with high levels of waste from perishable goods—amounting to hundreds of millions of dollars annually, and a significant blow to profitability.

To solve challenges like this, businesses are moving toward integrated planning platforms. At Danone, for instance, we implemented Jume-based modules for demand, commercial planning and supply chain operations. This leads not only to drastic reductions in product waste but also improvements in related business indicators: forecast accuracy, inventory reduction, more efficient use of promo budgets, optimized procurement costs—and ultimately, increased business margin.

It’s a great example of how intelligent technologies in integrated planning can help drive long-term digital transformation and operational efficiency.