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 visà-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
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.