Demand Forecasting

A systematic estimation of future demands on products

Specifying the objective

Determining the Time Perspective

Choice of method for Demand Forecasting

Collection of Data and Data Adjustments

Estimation and interpretation of results

Demand forecasting refers to a scientific and creative approach for anticipating the demand of a particular commodity in the market based on past behavior, experience, data, and pattern of related events. It is not based on mere guessing or prediction but is backed up by evidence and previous trends. 

 

Forecasting projections is one of the toughest things to get right. Your projections can shift regardless if you’ve been doing it for a while and start to get the hang of it. And even when you’ve been doing it for a while and start to get the hang of it, your projections shift again.

Whether your brand is experiencing gradual sales or is in high-growth mode, we’ll walk you through tips to improve your forecast demand.

Demand Forecasting holds significance in the businesses where large-scale production is involved. Since mass production requires a long gestation period, a good deal of forwarding planning should be done. Also, the potential future demand should be estimated to avoid the conditions of overproduction and underproduction. Most often, the firms face a question of the future demand for their product as they have to acquire the input (labor and raw material) accordingly.

Demand Forecasting is a systematic process that assumes more considerable significance in large-scale producing firms. Demand forecasting may not be a severe issue for small-scale firms that supply a small portion of total demand or produces the product that caters to the low demand or seasonal demand.

Examples

A grocery store looks at sales trends from last year’s Thanksgiving week to prepare adequate inventory levels for the upcoming season. They look at sales leading into that week last year for seasonal products like turkeys, cranberries, and mashed potatoes.

It was a great holiday sale for them. But eight months ago, a competing grocery store opened four blocks away, so they’re unsure how Thanksgiving demand will be affected and if local customers will buy ingredients from their competitor.

At the same time, many families continue to move into the neighbourhood, and they’ve still grown an average of 1% month-over-month since the competing chain opened.

They plan to launch a few more ads than last year through channels that have proven a good ROI for them in the past and offer some new deals to position themselves as the go-to Thanksgiving destination. Their calculations project a 5% increase in sales from last year.

An up-and-coming direct-to-consumer cosmetics brand is proliferating. Currently, they are selling 10,000 orders per month. Based on their past sales data, upcoming ad campaigns, and general market conditions in the industry, they plan to be above 30,000 orders per month at this time next year.

 

Right now, they’re stocking 75,000 units, at varying levels across their 5 SKUs. Their order volume fluctuates quite a bit based on their replenishment cycle, and they restock each SKU at a rate of about every 90 days.

The average units they store will multiply while the cadence will remain the same. The last run of their main SKU was 30,000 units. They’re about to ship in another 50,000 units, and their next series will be of 75,000 units.

They plan to continue to grow at that pace, so they are looking into whether they should purchase land, lease a warehouse, or outsource fulfillment to keep up with demand.