Maximize your forecasting accuracy

Align operations and realize business benefits

Forecasts are the foundation of every advanced analytics solution.

Example use cases for forecasting range across retail/online store sales, financial planning process, visitor forecasts, power consumption and resource planning.

By improving the accuracy of forecasts in demand variations and spikes allows companies to better align their operations and realize significant business benefits. 

Improve forecasting accuracy with multiple variables

Today, most companies attempt to generate forecasts by using anything from excel spreadsheets to complex financial planning solutions. Typical legacy solutions base their forecasts on looking only at historical series of data with the assumption that the future is determined by the past.

These forecasts can be greatly enhanced by combining additional variables into the forecast model and adding external data sources like location, weather, demographics, pricing, product features, competition, etc. 

How Forecasting-as-a-service works

Houston Analytics' Forecasting-as-a-service applies machine learning and combines time series data with additional variables to generate purpose-built, ready-to-use forecasts for process optimization and decision support.

The forecasting process is customized to client-specific needs, taking full advantage of available internal and external data sources relevant to the specific forecasting requirements.

With machine learning, the forecast models are  trained to find unique contextual patters using your own historical data, and over time the model learns, adapts and improves.

Houston Analytics is able to complement your own data sources with external weather, competition, geographical and demographics data available from various public and 3rd party data sources. 

This allows you to optimize product and service availability across different categories and locations, decrease over/under stocking, and gain improved understanding of sales patterns and their drivers at a more detailed level.

Forecasting workloads for scheduling optimization

To define ideal workforce staffing levels, it's important to create an understanding of the drivers behind workforce and skills needs. 

For retail these typically include forecasts of store visitor volumes, goods flow and forecasts of sales/sales potential. 

In healthcare, staffing demand variations can be affected by forecast weather, such as slipperiness which impacts accident rates. 

After identifying the right demand drivers, we can then select the optimization parameters such as maximizing profitability, minimizing overtime, or enhancing customer service availability during busy time windows.

Financial planning and budgeting

Accurate financial planning and budgeting are fundamental to every business. These tend to be very time consuming manual processes, and can be greatly enhanced with predictive analytics for improved accuracy and speed.

With the help of Houston's forecasting service, companies can enhance their financial planning and budgeting processes with accurate rolling forecasts for metrics such as revenues, profits and payroll expenses across selected time periods. 

Forecast results can be produced as flat files to be loaded into reporting structures and consumed by financial planning systems. Results can also be visualized in interactive dashboards with clients' preferred BI tools. 

Product demand planning and distribution optimization

Houston's Forecasting service can be applied to product inventory forecasting and optimization down to the level of individual locations.

Forecast accuracy is improved by enriching historical sales patterns with pricing, promotions, location demographics, product features, website traffic, weather and more. 

Houston Analytics applies machine learning models and optimization engines to produce accurate forecasts for product demand by each category and individual location. These outputs can be consumed via your existing management systems. 

Frequently asked questions

What is demand forecasting? 

It is a technique for estimation of probable demand for a product or services in the future. It is based on the analysis of past demand for that product or service in the present market condition. 

Why is demand forecasting important? 

Demand plays a vital role in the decision making of a business. In competitive market conditions, there is a need to take correct decision and make planning for future events related to business like a sale, production, etc.

Demand is the most important aspect for business for achieving its objectives. Many decisions of business depend on demand like production, sales, staff requirement, etc. 

What data does it require?

Historical and new data from different sources can be used, including ERP and Customer Relationship Management (CRM) systems, points of sales (POS), sensors, customer demand studies, social media, marketing surveys, different statistics etc.

Predictive analytics enable businesses to combine company information with important economic indicators, promotional events, weather changes, and other factors that affect customer preferences and buying decisions.

What are the most common forecasting methods?

Straight-Line Method — The straight line method is one of the simplest and easy-to-follow forecasting methods. A financial analyst uses historical figures and trends to predict future revenue growth.

Moving Average — Moving averages is a smoothing technique that looks at the underlying pattern of a set of data to establish an estimate of future values. e.g ARIMA

Simple Linear Regression — Regression analysis is a widely used tool for analyzing the relationship between variables for prediction purposes.

Multiple Linear Regression — Multiple linear regression is used to forecast when two or more independent variables are required for a projection.

What are the pre-requisites of a good forecast? 

The predictability of an event or a quantity depends on several factors including; how well we understand the factors that contribute to it, how much data are available and
whether the forecasts can affect the thing we are trying to forecast.

A key thing is knowing when something can be forecast accurately, and when forecasts will be no better than tossing a coin. Good forecasts capture the genuine patterns and relationships which exist in the historical data, but do not replicate past events that will not occur again.