The diagnosis of the FMCG industry reveals that it has attributes that can’t cope up without machine learning and predictive analytics in the future. Be it large number of sources of data that influence trends or huge volume of day-to-day transactions , traditional analytics will fail to do justice to this huge potential and hence machine learning would be best suited for this kind of analysis. Many horizontals of this industry like improving the effectiveness of marketing campaigns, increasing the performance of the sales team, optimising the supply chain and streamlining manufacturing are tailor made for adoption of machine learning. With the industry being slow in implementing cutting edge technologies , it would be an early mover advantage for whichever company gains far sightedness and goes for machine learning vis-à-vis predictive analytics.

There are a few special characteristics of the business that exemplify the above thoughts :

• The enormous volumes included
• Access to great quality deals information
• Short timeframe of realistic usability
• Current anticipating systems are moderately off base
• Current showcasing systems are not as much as ideal
• Current assembling practices are not as much as perfect
• Current production network systems are not as much as ideal
• Shopper numbers are extensive

1. EXPANSIVE VOLUMES/ACCESS TO GOOD QUALITY SALES DATA

The quantity of offer exchanges accessible to present day FMCG associations is enormous. This information can more often than not be bought from retailers and is of high calibre. This business information shapes the spine for any prescient model as expanding deals ought to dependably be the essential target of any prescient project. Most vast FMCG organizations additionally have great frameworks set up that record information at each phase of an item's lifecycle. From assembling to conveyance to advertising and deals. These frameworks for the most part have amazing information and require next to no information purifying to be profitable.

Given the colossal volumes of exchanges created by FMCG this information is generally difficult to break down physically as it overpowers most overcome examiners. As of now numerous associations have not gone past fundamental examination at a high accumulated level, for example: deals for the week, deals for a store, and so on. Furthermore, where they do bore down further into the information, this is normally done by senior experts with years of experience (and predispositions) at a gigantic cost.

2. SHORT SHELF LIFE

FMCG items as a rule have a short time span of usability implying that the expenses of oversupply and over produce can be critical. Given additionally, the vast volumes of items any streamlining to the oversupply (or undersupply) issue can bring about substantial ROI. The over/under supply issue is again an immaculate possibility for machine learning advancements.

3. DEALS AND MARKETING

On the off chance that you will likely build deals then having exact deals estimating is basic. With an exact anticipating model you can make reproductions that permit administrators to do quality "imagine a scenario in which" investigation. As of now deals estimating is incorrect and senior administration do not have the trust in these numbers. Being able to combine numerous information sources (deals, advertising, advanced, socioeconomics, climate, and so on.) extraordinarily enhances the nature of offers estimates when contrasted with conventional expectations which are customarily done on segregated and totalled deals figures. Once the business information is converged with the promoting information we can begin making extremely exact showcasing forecasts too. Questions like:

• Which item should we advance this month
• What sort of battle will be most beneficial for this item
• What customer section should we target
• How might we get an incentive from our online networking information and utilize current purchaser assessment to make convenient advertising efforts
  
  

4. ASSEMBLING AND SUPPLY CHAIN

Most vast FMCG have great ERP frameworks that hold an abundance of shrouded an incentive in their information. This information can be utilized to make models that can answer a few basic inquiries.

• How might we ensure on time conveyance
• How might we abbreviate an opportunity to fabricate an item
• How might we expand the yield for an item
• How might we limit item returns/dissensions