Enterprise AI Connect Community Presents

Enterprise AI Connect for Manufacturing

Embracing the power of artificial intelligence, machine learning and big
data to forecast prices, sales, expenses, and propensity.  

Event Overview

We live in uncertain times with business facing perhaps the most challenging times.  In particular, Manufacturing companies face many tough challenges such as increasing labor and production costs, meeting changing customer demands, managing supply chain disruptions, adopting new technology and automation, compliance with government regulations and industry standards all while trying to stay competitive and ahead of competition in these uncertain times.

Research* shows us that:

  • There is a 25% y-o-y increase in use of AI in standard business processes. 
  • While a majority vouched that it has caused an uptick in revenue, 44% say it has reduced costs.
  • In manufacturing, some of the most significant savings come from optimising yield, energy, and throughput. In supply-chain management, respondents are most likely to report savings from spend analytics and logistics-network optimisation.
  • 74% of the organisations that have started adoption of AI in one or more business function are likely to invest more in use of AI across the organisation.

*McKinsey, PwC, Deloitte, OBG & Gartner research from 2020 – 2022
*Data sets for survey respondents were between 100 – 300 & around 3000 in case of Deloitte and across 8 industry verticals

In this Community boardroom discussion, we try to touch base on the critical aspects of using AI in price predictions and the various ways in which manufacturers can make informed pricing decisions, optimize pricing strategies and increase profitability. We will also uncover how AI can be used to identify key drivers of price changes and build predictive models that can help manufacturers understand how various factors impact pricing.  

Boardroom Discussion 1 – Forecasting

Use Machine Learning to Forecast prices, sales, expenses, and propensity

Whilst a large number of organisations are exploring various AI solutions for their needs, they still face challenges such as:

  • Choosing the right AI technology.
  • Lack of buy-in from management / board.
  • Lack of ability to prove business value.
  • Insufficient funding.
  • Lack of technical skills.

Join us to learn from the Industry experts, users and peers how they have deployed AI to optimise their pricing strategy, increase revenue and reduce costs. 

Discussions will be on:

a) Prediction and forecasting:

  • Product pricing: to analyse data on market trends, costs, and competitors to predict the optimal price for a product.
  • Price forecasting: to predict future prices for raw materials, energy and other inputs, helping manufacturers to plan and budget accordingly.
  • Sales forecasting: to analyse data on past sales, customer behaviour, and market trends to predict future sales and revenue, helping manufacturers make decisions about pricing and production.
  • Price optimisation: to optimise prices based on different factors such as production costs, demand, competition, and market conditions.
  • Price Elasticity prediction: to predict how changes in price will impact demand for a product, allowing manufacturers to make informed decisions about pricing strategy.

b)Inventory management to reduce waste, increase efficiency and reduce costs:

  • Demand forecasting: to analyse data on past sales, customer behaviour, and market trends to predict future demand for a product, helping manufacturers optimise production and inventory levels
  • Inventory optimisation: to optimise inventory levels by analysing data on sales, production, and suppliers to determine the optimal amount of inventory to keep on hand.
  • Stockout prediction: to predict when inventory levels will run low, allowing manufacturers to restock before stock-outs occur.
  • Lead time prediction: to predict the lead time of suppliers, helping manufacturers plan and schedule production and inventory accordingly.
  • Inventory forecasting: to predict the future inventory levels, and help manufacturers to plan production, ordering and budgeting accordingly.
  • Order optimisation: to optimise the ordering process by using data on past orders, supplier lead times, and inventory levels to determine the optimal order quantities and frequencies.

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About Enterprise AI Connect

Enterprise AI Connect is a community led initiative of Senior executives in the USA planning
or working on cutting-edge AI projects. To join the community, request an invite.

Proudly supported by

Speakers

Kavita Rao
CMO, Findability Sciences

Richard J Orlando
CIO, Leverage Point

Anand Mahurkar
CEO, Findability Sciences

Isao Matsugaya
Head of AI and VP, Softbank,
SB Telecom America Corp.

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