AI ACROSS INDUSTRIES #2

A BLOG SERIES ABOUT OUR EIGHT INDUSTRIES FOR APPLIED AI AND THEIR USE CASES

AI can bring value across various industries, but it is only when you put AI into production, that it will add clear and measurable value. Are you looking for inspiration on how your organization can get started on the AI journey? In a series of posts, we address eight industries and their specific use cases, giving you insights into how implementing AI can increase business value.

INDUSTRY INSIGHTS | JULY | 2019

Last week we introduced our blog series by addressing the industries Finance & Insurance and Retail & Marketing including their use cases, hopefully inspiring you to get started on your own AI journey.

This week we dive into the industries Transport & Logistics and Pharma & Biotech.

INDUSTRY #3

TRANSPORT & LOGISTICS

The use of AI in Transport & Logistics Industries

The sector of transportation and logistics has always been producing a lot of various data and is already to some extent applying AI and Machine learning in its operations. Today the difference is not just only that we have access to big data, computing power, and the algorithms to create new value and competitive edge, it is also very much about being curious in an ever-changing world where the pressure gets higher and higher due to many other emerging technologies.

Today AI can analyze and improve many different areas in logistics and transportation companies, meaning that people can be moved from iterative and low-value activities into more value-creating and customer-facing ones. In the supply chain, there are useful examples of how AI can search among thousands of possible orders, routes, and schedules to find the most optimal selection – all in a couple of seconds. AI can also help with predictive analysis, for example, predict when a customer will be ready to reorder, or when and which vehicle or machine needs preventive maintenance. Canceling of departures is another very costly area of concern for many transportation companies, and the closer to departure, the higher the cost of cancellation is. Here AI can make a significant difference and minimize cost, yet increase NPS as it gets done at the optimal timing, excluding the human factor of good faith.

The area of use is ever-expanding, but most common examples are listed below.

AREAS OF AI APPLICATION IN TRANSPORT & LOGISTICS

  • Predictive and preventive maintenance.
  • Churn for passenger transportation and also B-2-B transportation.
  • Predicting sales order management, including internal procurement and planning.
  • Optimizing fuel consumption.
  • Assisting in customer segmentation.
  • Predicting and improving optimized product pricing.

LEARN EVEN MORE ABOUT AI IN TRANSPORT & LOGISTICS AND THEIR INDUSTRY SPECIFIC USE CASES

INDUSTRY #4

PHARMA & BIOTECH

The use of AI in Pharma & Biotech Industries

The value of AI in pharma and biotech manufacturing lies in reducing product defects, automating quality controls, increasing capacity and streamlining maintenance. Pharma and biotech manufacturers are ideal candidates for sophisticated analytics as they are already well-instrumented with sensors, controllers and storage of historical data.

According to a global consulting firm, manufacturing industries have captured only about 20%-30% of the potential value of data and analytics to date – and most of that has occurred at a handful of industry-leading companies. This is despite that advanced manufacturers have been working with automation and lean ways of working for decades. By embarking on an AI journey, far more complicated and bigger amounts of information can be processed across manufacturing operations and across the supply chain – detecting inefficiencies, relationships and predicting potential issues even before they emerge.

Many applications are using different regression and classification models, and even natural language processing.

AREAS OF AI APPLICATION IN PHARMA & BIOTECH

  • Quality control can be enhanced through vision systems by adding image recognition models based on convolutional neural networks.
  • Downtime of equipment can be minimized by analyzing data for each manufacturing step and/or for a chain of linked operations e.g. moldings importance on assembly output.
  • Models can be implemented so that they continuously learn from data accounting for changing conditions and the current state of a specific machine.

LEARN EVEN MORE ABOUT AI IN PHARMA & BIOTECH AND THEIR INDUSTRY SPECIFIC USE CASES

The areas where various industries can benefit from AI are ever expanding. One way to get started with AI is to look into specific use cases, enlightening you on how AI can benefit your organization and enhance business value.

Stay tuned for next week where we dive into AI in the industries of Utilities and Legal & Accounting.

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