Demand Forecasting with ERP Data

Businesses across industries need the answer to two critical questions before they start building for the long run. “What does the market need?” and “How much does the market need?”. Using tools like Power BI and Tableau combined with Python/R, one can provide reasons why demand works the way it does.

Predicting how your business is heading is always a game of probabilities, but with time, our product, RubiCube, only gets better at giving the right direction. We provide 75-80% accurate predictions based on historical data and current scenarios. We visualize the order for you to make the pivots needed.

But how does it all tie up with understanding the demand for your offering? Read this case study to know more.

What is demand forecasting?

Demand forecasting is estimating the future demand for a product or service based on historical data, market trends, and other relevant factors. It is vital in supply chain management, production planning, inventory management, and sales forecasting.

• Production planning: Adjust and plan your production schedules effectively by anticipating demand and planning accordingly.
• Inventory management: Manage inventories more effectively by ensuring you have enough stock.
• Supply chain management: Procure all the raw materials necessary to execute logistics operations by meeting customer demand.

ERP Data: What data does it gather, and how does it collect them?

An ERP system is one of the mainstream implementations that businesses have. It brings different data sets to bring information such as sales, inventory, production, and employee data to one platform.

• Sales Data: Customer orders, sales invoices, and customer payments.
• Inventory Data: Product stocks, locations, and stock movement.
• Production Costs: Production process, labor cost, and production times.
• Financial Data: Information about expenses, revenue, and profits.
• Employee Data: Employee performance, attendance, and payroll.

Limitations of prescriptive analytics

Prescriptive analytics display a version of the future based on the past and doesn’t consider many factors. In this section, we uncover the limitations of prescriptive analytics and how it affects business.

• Data quality: The accuracy and reliability of prescriptive analytics depend on the data quality used.
• Limited Scope: It is designed to provide recommendations based on specific data and parameters.
• Factor identification: It is limited by how the logic runs from past to future without the context of the present factors.

Implementing demand forecasting

Now that business leader knows why they need to step beyond the past and get analytics based on the current situation, let’s understand how accurately demand forecasting is implemented and its benefits.

Steps to implement demand forecasting

• Define the need for forecasting- Select the right KPIs
• Collect and analyze historical data
• Provide the current variables that affect the metrics
• Integrate the forecasting model into the ERP
• Monitor and refine the forecasting model

Methods of demand forecasting

The tools and techniques you need for forecasting can vary on the methodology you choose and the way you want to proceed.

1. Time Series Analysis
• Analyzes data like sales, revenue, and consumer behavior collected over time using moving averages, exponential smoothing, and trend analysis.
2. Regression Analysis
• Analyzes the relationship between two or more variables, like demand vs. price, promotion, weather, and economic indicators.
3. Market Research
• Gathers present market information about customers, competitors, and market trends.
4. Customer Feedback
• Details and informs about product/service satisfaction and KRAs to anticipate future demand.

What role does AI play?

A statistical model can be trained to do what it needs to do, but when it’s given the capacity to learn what it’s doing, it can keep iterating to predict and get to the root of the cause accurately. AI algorithms can:

• Identify patterns across large datasets.
• Process data with the needed context.
• Optimize various relevant metric levels.

Conglomerates like Amazon, Walmart, and Coca-Cola use AI-led algorithms to understand their consumers and find the ideal position within the market for boosting sales and RoI.

Real-life problems, tech-led solutions

Technology and consumer lives are invariably overlapping over the last few years. But what happens when the approach misses one crucial KPI that connects the statistics to the present?

In this section, we discuss how a different strategy for analysis would have served as a warning system for the complete power grid failure in Texas.

Due to an unforeseen winter storm in Texas on February 2021, the temperatures dropped to -39 degrees Celsius, causing a massive upsurge in power demand. It took about 12 days for the power grid to be fully functional again; this lack of power led to an infrastructure failure that directly/indirectly caused more than 200 deaths.

Could this have been predicted?

Using data available from the Department of Energy of the USA, we built a data lake. We simulated and built a model using R and Python with an advanced regression testing model on the data lake. We did econometric modeling to refine the statistical system. Econometric modeling solves situations where prescriptive demand forecasting fails, because

• The linear model is built on accurate datasets based on the past record of experiences,
• Regression analysis uses time series data to understand and predict trends.
• Econometric model quantifies the relationship between KPIs and the factors that affect them.

What are the advantages of demand forecasting?

There is a wide range of reasons why you need ERP demand forecasting, and utilizing the power of AI is crucial when attempting to build functionality.

• Revitalizes stock and workforce management depending on business needs.
• Optimizes companies’ allocate resources efficiently.
• Enhances customer satisfaction and provides personal insights.
• Reduces resource wastage and gives a competitive advantage.

How can a software product company push forward with demand forecasting?

Predicting demand based on the likelihood of outcomes is always an iterative process. It uses a combination of data analysis, statistical algorithms, and demand forecasting with machine learning to pinpoint the most likely results from particular choices.

The following are the reasons why a software product company can help you:

• Machine Learning – Connect historical data to current market trends.
• Data visualization – Make data easy to read through charts and graphs.
• Supply Chain Optimization – Tailor-made solutions to specific challenges in transit.

A predictive model can run on any organized data arrangement and does not always need to be on ERP Data alone. This means you can estimate and build the scope of your business depending on the market instead of going on personal intuition.

CI Global: Predicting business growth with RubiCube

With ERP expertise, we built RubiCube with problem statements we discovered across our 25 years of experience. Understanding the demand for what you offer is vital in any market and business, but more than a linear graph of what already exists is needed. RubiCube doesn’t just predict market behavior with your business projections but also represents them through visualizations that point to connecting the dots.

The tool has more than 25-pre built dashboards and analytically boosts operational efficiency by 30%. Want to know how your business can benefit from it? Reach out today.