Lessons Learnt from the Economy and How Machine Learning Can Help

In today’s global economy, there are no easy choices. Inflation is running amok, fueled by rising energy and food prices, and the Fed’s stimulus policies in previous years have had the effect of stimulating demand. As a result, real wages in the US have fallen nearly 4% for some 115 million Americans. In addition, disposable income is falling and inventories are rising. Demand is showing signs of weakness, interest rates are rising and the dollar is strong. CEOs of global corporations today face an impossible set of challenges in running their businesses, said Edward Scott, CEO at ElectrifAi.

Today’s CEOs of global corporations face an impossible set of challenges in running their businesses. There are no easy decisions. Inflation is running amok, fueled by rising energy and food prices, and the Fed’s stimulus policies in previous years have had the effect of stimulating demand. As a result, real wages in the US have fallen nearly 4% for some 115 million Americans. In addition, disposable income is falling and inventories are rising. Demand is showing signs of weakness amid rising interest rates and the strength of the dollar.

How should CEOs and CFOs deal with this complexity?

Data is a clear opportunity. Businesses generate vast amounts of structured and unstructured data, yet data remains the last untapped asset on the balance sheet. Why is that? We believe the main reason is that the C-suite does not fully understand the power of its data and ability to drive business value. It all seems so remote and complex. But is it? Consider the following. Today we are able to quickly and easily apply sentiment and natural language processing technologies to call center operations. These operations are at the forefront of the customer experience. NLP has the ability to resolve calls faster and with much higher customer satisfaction. Some businesses that have faster, more successful call resolutions can result in millions of dollars in savings per day and happier customers who are more likely to engage and purchase more products or services in the future.

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This kind of numbers should catch the attention of every board of directors and CEO. Another example is computer vision. Manufacturers of high-value goods can now easily use camera and machine learning systems to detect product defects on the production line and avoid costly product scrap before it’s too late. Boards and CEOs have to make an effort to acquire these fundamental technologies. Failure to do so results in literally millions of dollars not being realized.

See more: How to Build a Career in Artificial Intelligence and Machine Learning

Strategic benefits of machine learning

Machine learning software solutions should quickly enable the C-suite to turn their data into strategic assets to manage this complex environment. It’s about collecting the data, cleaning it, and applying machine learning to uncover insights that help overcome headwinds and optimize the business. This optimization can take place in front, middle or back office operation. Data and machine learning can help large manufacturing, retail, banking, insurance, telecom, energy, chemical, and other companies solve fundamental problems across the board

Here are some examples:

1. Demand forecasting and dynamic pricing: In the post-pandemic world, one cannot rely on “yesterday’s” demand forecast to predict the future. The world has changed, and leaders must grapple with dynamic pricing. This powerful tool gives businesses a distinct advantage by ensuring that price stays in sync with demand and adapts to new customer preferences. With demand forecasting and dynamic pricing, businesses can reduce costs and avoid the risk of overstocking.

Businesses also need to make accurate forecasts. Such a forecasting model typically looks at past data and overlays it with current factors such as economic trends, public holidays, weather conditions, world events and market changes, including competitive forces. The model dynamically adapts to new customer decisions and changing demographics. Better forecasts help improve inventory management and optimize cash flow. Dynamic pricing helps maximize sales and increase profits and market share.

2. Expenditure and Procurement: Every day, CPOs and CFOs ask if they can quickly analyze their spend data to uncover actionable insights and quickly find savings without impacting operations. This can be achieved using machine learning, and the savings are real. This is all done through categorization and classification through machine learning. Think of it as the Hubble telescope illuminating the entire universe.

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Spend Analysis is a great place to identify savings needed in the quarter. One of the many use cases supported by spend analytics is to increase negotiation power with suppliers. We often come across multiple vendors that may be the same due to data entry errors or lack of knowledge of the market where one or more vendors have been acquired. Human errors are an accepted part of all data, but now, with AI and ML tools, this category of errors can be effectively identified and fixed. Data enrichment with market or industry data can give you the best data to make better decisions. Establishing a unique supplier identification leads to an opportunity for volume discounts and builds a strategic relationship with the supplier with even more favorable agreements.

Improving spend categorization by increasing the percentage of spend categorized and aligning it with an industry-specific taxonomy results in quick and immediate returns. This makes it easier for companies to identify problematic expenses, supplier changes, etc. and realize immediate savings. ML-based spend solutions can also provide actionable recommendations on material risks that help mitigate supply chain risk and identify potential credit risks.

3. Customer retention: Many companies are again talking about migration. It matters, and the C-suite needs tools to prevent churn and increase customer lifetime value. Machine learning software solutions that drive deep segmentation, personalization and advertising are essential – do more with the foundations you already have. The effort involved in selling new products and services to an existing customer is far less than acquiring a new customer. A superior CX for an existing client gets you halfway there.

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4. Computer vision: Labor shortages, changing work patterns and rising costs are a fact of life and it is very difficult to put the genie back in the bottle. Certain tasks currently performed by humans can be automated through the use of computer vision. Quality is often better, throughput is higher, and costs are lower.

For example, when it comes to risk and compliance in the oil and gas industry, computer vision can detect safety issues from pipeline integrity, leak and damage detection, out-of-spec contamination, worker safety, and improper use of equipment. Similarly, building inspections in the real estate industry are safer, less expensive, and can be performed relatively efficiently. Computer vision provides a faster and more accurate means of analyzing millions of images and videos to identify potential defects, corrosion, cracks and structural damage.

How do you use machine learning to manage uncertainty and build success? let us know Facebook, Twitterand LinkedIn.


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