Thanks to natural language processing (NLP) AI, buyers could work without Excel.
Procurement: AN EXCITING JOB, A TIME-CONSUMING REPORTING.
A buyer does not make purchases by pure luck. It is an area where one appreciates the constant challenge of negotiations, global strategic thinking or the fact of having concrete, quantified objectives to meet over different deadlines.
In the supermarket sector for instance, it is the wide range of tasks that appeals to junior buyers. Reporting, much less. It is, however, an essential aspect in the work of a buyer, because the proper maintenance of a monitoring table enables them to measure their objectives and quantify their needs. From receiving global data, formatting KPIs and reporting back to their manager, a junior can spend between 4 to 7 hours a week gathering the right information.
DATA: LONG TRAINING SESSIONS FOR BUYERS
As the volume of data grows and becoming increasingly complex, technical proficiency in procurement is expected to become highly important. Proof of this can be seen in the few ads selected below: data literacy is an asset for any buyer. (A rather advanced requirement in this job post to integrate the Supply Chain department where SQL skills are required)
It is therefore essential to train buyers not only to use Excel but also to use an ERP specific to purchasing, not to mention data visualisation tools such as Tableau or Qlik.
One would almost come to confuse the in-depth work on data, which is the prerogative of a dedicated data team, with the "simple" understanding of KPIs or the "simple" access to supplier data without prior filtering, vital to buyers.
Cédric Le Savéant, VP Digital Office Sourcing & Supply Chain Transformation at Technicolor, clearly explains :
"In a general reflection on data access at Technicolor, you need to be able to get answers without having to systematically train employees about pivot tables! “ Source: Technicolor: a databot to access Procurement KPI
NATURAL LANGUAGE PROCESSING TO ACCESS procurement core DATA
Category management typical questions to askR.ai
Category management typical questions to askR.ai
Free Code Camp offers a simple reading chart to measure the impact of a "chatbot as a service". For a bot capable of speeding up low value tasks and embedded in a familiar work environment such as the Slack data assistant, two questions arise: is your bot a source of savings/income? Does your bot improve your reputation?
Is a data assistant provided on purchasing data a source of savings? YES.
At a rate of 30 min per day x 253 working days, for an average hourly wage of €17.3, it is €2188 per employee per year. Today, this is the case for GRTgaz, which saves 30 min per day and per employee thanks to askR.ai.
Does a data assistant improve your reputation? YES.
Maybe not in terms of customer satisfaction, but in terms of employee experience, yes. A professional assistant available 24/7 that saves you time on boring tasks? That's just amazing!
Buyers need more intuitive tools to make it easier to get started, without leaving accuracy and performance on the side. An askR.ai data assistant, based on natural language processing, ensures a quick start and almost instant access to key data in a buyer's daily activity, without the need for Excel training.
Cédric Le Savéant shared this feedback with you during a webinar on Thursday, June 20th at 11:30 am: "TECHNICOLOR & ASKR.AI: GET A CLEAR VIEW OF SUPPLIER EXPENDITURES IN 2 SECONDS"! (french)
* Qualitative survey ASKR.AI, between 2019 april to 2019 may .