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Sales Forcasting Consumer Goods

Unilever: Sales Forecasting

Unilever is one of the world’s lead­ing multi­na­tion­al con­sumer goods com­pa­nies, with a wide array of prod­ucts in cat­e­gories such as food, bev­er­ages, clean­ing agents, and per­son­al care. Their com­mit­ment to inno­va­tion and sus­tain­abil­i­ty dri­ves their con­tin­u­ous search for ways to enhance oper­a­tional effi­cien­cy and con­sumer satisfaction.

  • ADVANCED MACHINE LEARNING
  • DATA ANALYTICS
  • AI

A collaboration between

  • Arinti
Two team members at Griffith Foods working together on a laptop, discussing data on the screen in a modern office setting.

Challenge

Accu­rate sales fore­cast­ing in a com­plex and dynam­ic mar­ket environment.

Unilever faced the sig­nif­i­cant chal­lenge of pre­dict­ing sales in an ever-chang­ing mar­ket envi­ron­ment. With count­less vari­ables influ­enc­ing demand — from pro­mo­tion­al activ­i­ties to eco­nom­ic shifts — devel­op­ing accu­rate fore­casts that could help opti­mize pro­duc­tion and inven­to­ry lev­els became increas­ing­ly dif­fi­cult. Tra­di­tion­al fore­cast­ing meth­ods were often too slow to react to rapid changes in con­sumer behav­ior, lead­ing to inef­fi­cien­cies in sup­ply chain man­age­ment and poten­tial missed opportunities.

Services used

AI Expertise

Data Science & Machine Learning

Solution

Lever­ag­ing AI to trans­form sales forecasting.

To address this chal­lenge, Unilever part­nered with us to design a cut­ting-edge sales fore­cast­ing solu­tion pow­ered by AI. Our team devel­oped a sys­tem based on advanced machine learn­ing algo­rithms that could ana­lyze large datasets from var­i­ous sources — includ­ing his­tor­i­cal sales data, pro­mo­tion­al sched­ules, and exter­nal fac­tors like mar­ket trends and seasonality.

The AI mod­el pro­vid­ed Unilever with more pre­cise sales pre­dic­tions, account­ing for both short-term fluc­tu­a­tions and long-term trends. The solu­tion was also designed to be scal­able, allow­ing for con­tin­u­ous learn­ing and improve­ment as new data became available.

Technologies Used

Advanced Machine Learn­ing, AI, Data Analytics

The solu­tion lever­aged state-of-the-art machine learn­ing tech­niques, using exten­sive data ana­lyt­ics to refine and enhance the fore­cast­ing mod­el. The use of AI allowed for con­tin­u­ous learn­ing, ensur­ing that Unilever’s fore­casts became more accu­rate over time.

Results

Improved accu­ra­cy and effi­cien­cy across the sup­ply chain.

Reduc­tion of 3% in shelf time pro­duc­tion. This ensures man­u­fac­tur­ing on demand and results in enor­mous optimisations.

With the imple­men­ta­tion of this AI-pow­ered sales fore­cast­ing tool, Unilever expe­ri­enced a sig­nif­i­cant improve­ment in fore­cast accu­ra­cy, which led to bet­ter deci­sion-mak­ing and more stream­lined oper­a­tions. Key ben­e­fits included:

  • More accu­rate pro­duc­tion plan­ning, result­ing in reduced waste and opti­mized inven­to­ry levels.
  • Enhanced agili­ty, enabling Unilever to respond more quick­ly to mar­ket changes and con­sumer demands.
  • Improved resource allo­ca­tion, ensur­ing that pro­mo­tions and mar­ket­ing efforts were bet­ter aligned with actu­al demand forecasts.

By inte­grat­ing AI into their sales fore­cast­ing process, Unilever not only gained oper­a­tional effi­cien­cies but also strength­ened their abil­i­ty to adapt to the evolv­ing needs of consumers.

Conclusion

A future-focused approach to sales forecasting.

This col­lab­o­ra­tion with Unilever show­cas­es the pow­er of AI in trans­form­ing tra­di­tion­al busi­ness process­es. By adopt­ing AI-dri­ven sales fore­cast­ing, Unilever not only solved an imme­di­ate chal­lenge but also set the stage for future inno­va­tions in sup­ply chain man­age­ment. This project illus­trates our abil­i­ty to deliv­er impact­ful, data-dri­ven solu­tions tai­lored to the unique needs of glob­al companies.

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