Data forecasting in the Retail Industry

Retail organizations today must strive to satisfy the unique demand of each of their customers. Gone are the good old days of the mass market where a single assortment of standard pricing and a single “average location” forecast would satisfy consumer demand in all stores. Often, predicting demand for a single item in a single store can be a difficult proposition.

Forecasting data service demand for all stock-keeping units (SKUs) across all stores and all geographies is a much greater challenge. Forecasts need to adjust to changing demand and quantity patterns, handling seasonality, including difficult-to-predict demand for slow-moving goods. Forecasting data service providers are really invested in making the retail industry much more efficient.


Within an organization, generating forecasts is an important first step in many planning and decision-making processes. Typically, forecasting is performed on a regularly repeating basis for a wide variety of planning purposes. For example, future demand for products and services may be forecast in order to support production planning, marketing activities, resource scheduling, and financial planning. Forecasters often follow the same iterative process with each forecasting and planning cycle.

Alpha Data is a forecasting data service provider in Dubai covering businesses from all over the UAE, hence being one of the best forecasting data service providers in UAE.


THE PROCESS OF FORECASTING IN RETAIL:

The process usually involves generating updated forecasts based on the most recent data, reconciling forecasts in a hierarchical manner, identifying and remedying problematic forecasts, adding judgmental overrides to the forecasts based on business knowledge, and publishing the forecasts to other systems or as reports.

Upon completion of the forecasting process, the planning process determines what actions the organization will take in light of the forecasts. Planning processes not only take into account the forecasts but also the constraints upon the business and overall corporate goals. Much of an organization’s time and effort is spent in the planning process. Improving the reliability of statistical forecasts that feed these processes can result in huge rewards. Improved forecasting often leads to greater operational efficiency, reduced expenses, and increased profits.

There are multiple planning functions within an organization that the forecasts can serve. Examples include forecasting future demand for production planning purposes, supporting marketing plans (promotion planning), resource planning, financial planning, and reporting. Forecasting managers typically follow an iterative process for each forecasting and planning cycle. The steps usually include obtaining the latest data, generating updated forecasts, fixing problem forecasts, conferring with other internal or external parties about the forecasts, adding ad hoc adjustments based on business knowledge, obtaining executive review and change approval, and publishing the forecasts.

FORECASTING TECHNIQUES

Quantitative methods:
Qualitative methods:
Unpredictable:

When you have an in-depth analysis of past performance combined with plans and forecasts of future customer demand, you can more accurately allocate and restock merchandise across channels and stores. Truly understanding customer demand patterns, not just what was purchased, but what those patterns reveal about future potential, enables you to send the correct assortments, size, and case-pack distributions to the correct stores.