Stemly

Improving an ML-powered Forecasting Process for the Supply Chain Industry through Intuitive User Experience

Design the enterprise platform from the ground up as a modular system harnessing the power of Machine Learning and other advanced technologies to eliminate silos and ensure a seamless user experience.

Company

Company

Stemly
Stemly

Link

Link

https://stemly.ai
https://stemly.ai

Industry

Industry

SaaS
SaaS

Headquarters

Headquarters

Singapore
Singapore

Founded

Founded

2018
2018

Company size

Company size

60
60

Founded in 2018 within the ING Bank APAC Innovation Lab incubator, Stemly is a pioneering data science platform. Its array of cutting-edge products enables companies to refine strategies and optimize both costs and resources through the application of Machine Learning and other sophisticated technologies. Stemly has garnered notable accolades, including recognition as one of Tech in Asia’s “50 rising startups in Singapore” in July 2021 and inclusion in Forbes Asia’s “100 To Watch 2023” in August 2023.

The initial stages of our development focused on mastering complex demand planning and forecasting methodologies, thorough research, and competitive analysis. At that time, the market was dominated by a few major players reliant on outdated Windows-based and traditional web applications, characterized by cumbersome onboarding processes that required significant customer success efforts. Our ambition was to revolutionize this scenario by introducing a next-generation user experience reminiscent of consumer apps.

Our foremost objective was to validate our product with a select cohort of enterprise clients, continually refining the user experience. The team’s unyielding commitment aimed at establishing Stemly as an independent entity, separate from ING Bank’s APAC innovation labs. This commitment to enhancing both our brand and our offerings was crucial. Our efforts culminated successfully with a significant funding round in June 2021, raising $2.5 million USD from prestigious investors including EDB New Ventures, ING Ventures, Elev8.VC, HH VC Investments, and FutureLabs.

Process

Process

Process

Problem

Enterprise-level supply chain platforms are significantly outdated, lacking the modern features and flexibility required to meet today’s rapidly changing business environments and technological advancements.

Decision-making platforms, historically dominated by established entities such as SAP, Oracle, and o9, have long provided demand planning and forecasting services. Despite their pedigree, these platforms frequently present complexities that can stifle innovation.

Goal

Stemly seized a clear opportunity: to offer a modern SaaS platform that not only ensures an outstanding user experience but also maintains high predictive accuracy—a specific requirement voiced by customers.

The aim is to develop an advanced enterprise-grade SaaS platform for forecasting and optimization, aiming to establish new benchmarks in user-friendliness and speed, while also achieving unparalleled accuracy in prediction.

Constraints/Challenges

Each team member had a different concept of how the platform should be organized, and there was frequent confusion over terminology. To address this misalignment promptly from a design standpoint, the design team created a term dictionary to establish clear definitions for industry-specific terminology, which is crucial in data science platforms.

We then outlined the platform’s structure by benchmarking against top-tier SaaS platforms and adapting those insights to our needs. The framework of organizations, workspaces, and modules proved most effective for us.

Method & Process

At Stemly, the pursuit of innovation is driven by the rapid design and validation of interactive prototypes, tested with real users. This approach led us to develop numerous iterations across a varied suite of planning, forecasting, and optimization products.

These prototypes fulfilled a dual purpose: they not only synchronized our product teams around a common vision but also enhanced our sales initiatives by gathering crucial feedback from prospective customers.

Rapid prototyping was instrumental in facilitating prompt modifications, both within our teams and in client interactions. Our overarching goal has been to streamline the user experience for traditionally complex workflows, making them as seamless as possible.

Result

​​Creating platforms tailored for technical experts requires meticulous attention to detail, ensuring flawless execution in both terminology and functionality. Our engagement with data science products, exemplified by Stemly, highlights the necessity for a deep understanding and continuous education in relevant domains.

In the realm of feature definition and implementation, striking a delicate balance between the founder’s vision and user expectations is paramount to avoid an ever-expanding backlog. The team’s understanding of the effort required for each feature is crucial, guiding the transition from basic functionality to delivering truly exceptional solutions.

For startups in their nascent stages, the adoption of open-source solutions can significantly accelerate product development, particularly for non-core features, thus sidestepping unnecessary reinvention.

The design intricacies of charts, particularly in data analysis, demand meticulous attention, emphasizing the critical role of effective information visualization. We invested substantially in mastering techniques for tabular and chart visualizations to ensure they meet rigorous standards.

In enterprise sales, especially when engaging clients with extended procurement cycles, the development of clickable prototypes that surpass existing product features is indispensable. This strategy is invaluable in captivating and securing such discerning clientele.

Problem

Enterprise-level supply chain platforms are significantly outdated, lacking the modern features and flexibility required to meet today’s rapidly changing business environments and technological advancements.

Decision-making platforms, historically dominated by established entities such as SAP, Oracle, and o9, have long provided demand planning and forecasting services. Despite their pedigree, these platforms frequently present complexities that can stifle innovation.

Goal

Stemly seized a clear opportunity: to offer a modern SaaS platform that not only ensures an outstanding user experience but also maintains high predictive accuracy—a specific requirement voiced by customers.

The aim is to develop an advanced enterprise-grade SaaS platform for forecasting and optimization, aiming to establish new benchmarks in user-friendliness and speed, while also achieving unparalleled accuracy in prediction.

Constraints/Challenges

Each team member had a different concept of how the platform should be organized, and there was frequent confusion over terminology. To address this misalignment promptly from a design standpoint, the design team created a term dictionary to establish clear definitions for industry-specific terminology, which is crucial in data science platforms.

We then outlined the platform’s structure by benchmarking against top-tier SaaS platforms and adapting those insights to our needs. The framework of organizations, workspaces, and modules proved most effective for us.

Method & Process

At Stemly, the pursuit of innovation is driven by the rapid design and validation of interactive prototypes, tested with real users. This approach led us to develop numerous iterations across a varied suite of planning, forecasting, and optimization products.

These prototypes fulfilled a dual purpose: they not only synchronized our product teams around a common vision but also enhanced our sales initiatives by gathering crucial feedback from prospective customers.

Rapid prototyping was instrumental in facilitating prompt modifications, both within our teams and in client interactions. Our overarching goal has been to streamline the user experience for traditionally complex workflows, making them as seamless as possible.

Result

​​Creating platforms tailored for technical experts requires meticulous attention to detail, ensuring flawless execution in both terminology and functionality. Our engagement with data science products, exemplified by Stemly, highlights the necessity for a deep understanding and continuous education in relevant domains.

In the realm of feature definition and implementation, striking a delicate balance between the founder’s vision and user expectations is paramount to avoid an ever-expanding backlog. The team’s understanding of the effort required for each feature is crucial, guiding the transition from basic functionality to delivering truly exceptional solutions.

For startups in their nascent stages, the adoption of open-source solutions can significantly accelerate product development, particularly for non-core features, thus sidestepping unnecessary reinvention.

The design intricacies of charts, particularly in data analysis, demand meticulous attention, emphasizing the critical role of effective information visualization. We invested substantially in mastering techniques for tabular and chart visualizations to ensure they meet rigorous standards.

In enterprise sales, especially when engaging clients with extended procurement cycles, the development of clickable prototypes that surpass existing product features is indispensable. This strategy is invaluable in captivating and securing such discerning clientele.

Problem

Enterprise-level supply chain platforms are significantly outdated, lacking the modern features and flexibility required to meet today’s rapidly changing business environments and technological advancements.

Decision-making platforms, historically dominated by established entities such as SAP, Oracle, and o9, have long provided demand planning and forecasting services. Despite their pedigree, these platforms frequently present complexities that can stifle innovation.

Goal

Stemly seized a clear opportunity: to offer a modern SaaS platform that not only ensures an outstanding user experience but also maintains high predictive accuracy—a specific requirement voiced by customers.

The aim is to develop an advanced enterprise-grade SaaS platform for forecasting and optimization, aiming to establish new benchmarks in user-friendliness and speed, while also achieving unparalleled accuracy in prediction.

Constraints/Challenges

Each team member had a different concept of how the platform should be organized, and there was frequent confusion over terminology. To address this misalignment promptly from a design standpoint, the design team created a term dictionary to establish clear definitions for industry-specific terminology, which is crucial in data science platforms.

We then outlined the platform’s structure by benchmarking against top-tier SaaS platforms and adapting those insights to our needs. The framework of organizations, workspaces, and modules proved most effective for us.

Method & Process

At Stemly, the pursuit of innovation is driven by the rapid design and validation of interactive prototypes, tested with real users. This approach led us to develop numerous iterations across a varied suite of planning, forecasting, and optimization products.

These prototypes fulfilled a dual purpose: they not only synchronized our product teams around a common vision but also enhanced our sales initiatives by gathering crucial feedback from prospective customers.

Rapid prototyping was instrumental in facilitating prompt modifications, both within our teams and in client interactions. Our overarching goal has been to streamline the user experience for traditionally complex workflows, making them as seamless as possible.

Result

​​Creating platforms tailored for technical experts requires meticulous attention to detail, ensuring flawless execution in both terminology and functionality. Our engagement with data science products, exemplified by Stemly, highlights the necessity for a deep understanding and continuous education in relevant domains.

In the realm of feature definition and implementation, striking a delicate balance between the founder’s vision and user expectations is paramount to avoid an ever-expanding backlog. The team’s understanding of the effort required for each feature is crucial, guiding the transition from basic functionality to delivering truly exceptional solutions.

For startups in their nascent stages, the adoption of open-source solutions can significantly accelerate product development, particularly for non-core features, thus sidestepping unnecessary reinvention.

The design intricacies of charts, particularly in data analysis, demand meticulous attention, emphasizing the critical role of effective information visualization. We invested substantially in mastering techniques for tabular and chart visualizations to ensure they meet rigorous standards.

In enterprise sales, especially when engaging clients with extended procurement cycles, the development of clickable prototypes that surpass existing product features is indispensable. This strategy is invaluable in captivating and securing such discerning clientele.

Problem

Enterprise-level supply chain platforms are significantly outdated, lacking the modern features and flexibility required to meet today’s rapidly changing business environments and technological advancements.

Decision-making platforms, historically dominated by established entities such as SAP, Oracle, and o9, have long provided demand planning and forecasting services. Despite their pedigree, these platforms frequently present complexities that can stifle innovation.

Goal

Stemly seized a clear opportunity: to offer a modern SaaS platform that not only ensures an outstanding user experience but also maintains high predictive accuracy—a specific requirement voiced by customers.

The aim is to develop an advanced enterprise-grade SaaS platform for forecasting and optimization, aiming to establish new benchmarks in user-friendliness and speed, while also achieving unparalleled accuracy in prediction.

Constraints/Challenges

Each team member had a different concept of how the platform should be organized, and there was frequent confusion over terminology. To address this misalignment promptly from a design standpoint, the design team created a term dictionary to establish clear definitions for industry-specific terminology, which is crucial in data science platforms.

We then outlined the platform’s structure by benchmarking against top-tier SaaS platforms and adapting those insights to our needs. The framework of organizations, workspaces, and modules proved most effective for us.

Method & Process

At Stemly, the pursuit of innovation is driven by the rapid design and validation of interactive prototypes, tested with real users. This approach led us to develop numerous iterations across a varied suite of planning, forecasting, and optimization products.

These prototypes fulfilled a dual purpose: they not only synchronized our product teams around a common vision but also enhanced our sales initiatives by gathering crucial feedback from prospective customers.

Rapid prototyping was instrumental in facilitating prompt modifications, both within our teams and in client interactions. Our overarching goal has been to streamline the user experience for traditionally complex workflows, making them as seamless as possible.

Result

​​Creating platforms tailored for technical experts requires meticulous attention to detail, ensuring flawless execution in both terminology and functionality. Our engagement with data science products, exemplified by Stemly, highlights the necessity for a deep understanding and continuous education in relevant domains.

In the realm of feature definition and implementation, striking a delicate balance between the founder’s vision and user expectations is paramount to avoid an ever-expanding backlog. The team’s understanding of the effort required for each feature is crucial, guiding the transition from basic functionality to delivering truly exceptional solutions.

For startups in their nascent stages, the adoption of open-source solutions can significantly accelerate product development, particularly for non-core features, thus sidestepping unnecessary reinvention.

The design intricacies of charts, particularly in data analysis, demand meticulous attention, emphasizing the critical role of effective information visualization. We invested substantially in mastering techniques for tabular and chart visualizations to ensure they meet rigorous standards.

In enterprise sales, especially when engaging clients with extended procurement cycles, the development of clickable prototypes that surpass existing product features is indispensable. This strategy is invaluable in captivating and securing such discerning clientele.

Let's improve your UX

Available for hire/projects

Copyright © 2024 Arianti Silvia. All rights reserved.

Let's improve your UX

Available for hire/projects

Copyright © 2024 Arianti Silvia. All rights reserved.

Let's improve your UX

Available for hire/projects

Copyright © 2024 Arianti Silvia. All rights reserved.