Consumer Product Sales Forecast
It is imperative for any commercial house to get forecast in terms of financial indicators to have better strategic decisions. It also helps to optimise the manufacturing process or servicing components. The two-way benefit of maximum profit along with cost reduction can be achieved by aligning the business process in tune with the forecast.
Hence, it is imperative for them to have a clear forecast and valuable insights by analysing their past performance in separate time frames. It also helps the user to implement time-dependent production / service process.
Why ACLYSIS ?
Forecast is one of the commonly used mechanisms. You can use it to forecast sales, profits, commodity prices etc.
Even making forecast is challenging. Traditional approaches require a significant amount of human labour, data engineering experience and statistical knowledge. Even non-technical teams can forecast values rapidly and economically by using this analytical podium.
In the parts that follow, we'll examine how to develop and use a predictive model from a historical dataset of a production house by using our Auto Machine Learning platform with some simple clicks.
A key use-case that affects the company's strategy for streamlining production process according to the market demand.
Data in hand
The data consists of monthly sales for the time period from January 2007 to July 2022. We have analysed the data & forecast future sales for next 12 months (from Aug 2022 to Jul 2023) of the company. ‘’Product’’ is our target / time-dependent variable.
We found out the following insights from the analysis
- • Yearly sale is highest in 2015 and lowest in 2009 (2022 not considered as data provided for 7 months).
- • Monthly sales is highest in December & lowest in June on an average. It has an increasing trend in the second half of the calendar year reaching pick in December every year whereas it has a flat trend in the first half of every year.
- • No significant percentage change in increase / decrease in yearly gross sales. 2022 shows less value as only 7 months value are available.
- • It is evident that the months having higher sales also have higher dispersions among yearly sales.
- • In 2015 the average sale reached the maximum. Every year, there is increase in sales percentage during last quarter of the year whereas the scenario is opposite during first quarter.
- • The monthly sales are very high at last quarter every year due to the festivals like Christmas, New Year etc.
Business recommendations
- • The forecasted values for the next 12 months is distributed in the same way the full dataset is.
- • As per forecast, there will be sharp increase in sales during the last quarter of the year with maximum at December 2022. But in January 2023, it will drastically come down and will increase thereafter with a slow pace till April 2023. From May 2023 onwards, monthly sales will be increased substantially.
- • The company has to concentrate in the 1st half of the calendar year to achieve sustained sales and have to imbibe some business strategies to improve sales positions.
- • There is no substantial growth in sales of the company. The business is stabilised at some level. The company needs to do immediate market survey to launch for a new product and explore new strategies to enhance the market share of the existing products. Anyhow, they have to be pro-active for a thorough study to keep the business vibrant at a good level.