Demand Forecast
Tiki is an e-marketplace with its Retail business operated by Tiki itself. Demand forecasting is a crutical step in the procurement process of any retail business. We created a new tool that replace and automate some parts of this process.
Challenge
In need for a tool to automate and optimize the demand forecast process.
My role
User Researcher
Product Designer
DELIVERABLES
Conduct a user observation to study the current method.
Design the product.
Support the users closely on their adoption process.
result
Overestimation rate reduced significantly to only around 10%.
Launch the first version with very low adoption curve.
Challenge
Inventory is a crucial investment in retail. Overstocking ties up cash, while understocking leads to missed revenue. The right inventory level meets customer demand, a challenge assigned to the Procurement team. Each month, they must forecast demand and plan stock procurement and replenishment.
A manual approach worked in the early days when there were fewer products and cost optimization wasn’t a major concern. However, as the business scaled to 100,000+ SKUs, the Procurement team faced stricter coverage and accuracy requirements. The manual process became inefficient, making
automation solutionessential.To automate and optimize this decision-making process, we need to
study and analyzethe current workflow to identify pain points.Since demand forecasting is data-heavy and complex, my role as a designer was to create an interface that
balances comprehensiveness with simplicity—empowering users without overwhelming them.
Tiki (tiki.vn) is an e-commerce platform with a retail business (Tiki Trading) operated by Tiki itself. Tiki Trading generates the majority of Tiki’s profit.
This is the workspace the Procurement team uses to perform the task.
Research
I conducted a
Contextual Inquirywith 12 users, observing their end-to-end workflow and interview (semi-structured) them during the process.Questions to be answered
How is the demand forecasting task currently performed? What the process is like? What calculation they make?
Why they do what they do?
Identify the problems or challenges which could lead to low coverage and accuracy.
After collecting the data, my
thematic analysisrevealed their methods & process, along with problems affecting accuracy and coverage. The report guided our team (PM, Data Analyst, Developers) in further discussions to define the product concept.
Research plan and conducting
Research report using Thematic Analysis. This was a pretty complex research project.
Findings
Key takeaways drawn from the research
1. Some parts can be automated
Although different product types (e.g., food versus electronic devices) may follow different processes due to their unique characteristics, there are key similarities that could enable a unified decision-making process. For example, at some steps, they all:
Rely on similar data references (e.g., historical sales data)
Share common goals (e.g., identifying historical sales trends),
And use comparable methods to achieve these goals (e.g., calculate average sales per day).
As I was able to synthesize all of those patterns, these serve as a knowledge base for the data/dev team to build their forecasting model/algorithm/set of rules for our AI product.
2. However, there are also parts that could be a challenge to automate
Certain ad-hoc or unstable factors require users to rely on intuition and market experience rather than calculations. For example:
Seasons (e.g., Christmas holidays, back to school season)
Products that are ‘on trend’ or ‘out of trend’ (e.g., via Tiktok/social media) (could have unusual future demand)
New product → no historical data to refer to.
Unusual cases like supply crises (e.g., material shortages or rising gas prices)
These are areas where the system struggles due to limited data and predefined rules, making user input, audit, and review essential.
 |  |
|---|---|
Back to school season products | An example of on-trend product |
3. Many risks our product could prevent
During the forecasting process, users must navigate multiple Excel files and tools spread across different sources, leading to wasted time switching between platforms and performing repetitive tasks. A significant portion of their time is spent collecting and centralizing data into a single Excel file, followed by manual calculations to derive necessary metrics.
Additionally, human errors and biases frequently arise, such as mistakes from copying and pasting or irrational decision (e.g., relying on overly short time frames for analysis). These issues compromise the accuracy of the forecasts.
Automating these steps would streamline the process, reduce errors, and enhance the reliability of forecasting outcomes.
Design
Next, I designed the interface, focusing on prioritizing data for decision-making. The biggest challenge was determining which data to display and how—whether as numbers, tables, or charts—to optimize clarity and usability.
Base on the research findings, a document is created to better manage the data hierarchy
Data visualization
Result
We achieved excellent results in this first phase although the system using a relatively simple forecasting method*. The Procurement team significantly improved accuracy,
reducing overestimationfrom a large margin to just around 10%.In terms of
product adoption, the transition from Excel to our tool was smooth. Despite the unfamiliar interface and task complexity, the adoption curve was low—only a few of the 30 users needed assistance.