Tag Archives: Predictive Analytics


Since the industrial revolution, every technological advancement has been viewed through the lens of its effect on jobs. Will I be obsolete? Can a machine do my job better than I can? Are the bots coming for me? If my skills are rendered obsolete, what will I do?

The plain truth is, sometimes machines can do the job better, faster or more efficiently than a human can. Think of the advent of the sewing machine. Even your grandmother’s old Singer model is a whole lot faster, more precise and efficient than she is working with a needle and thread. The art and craft of sewing isn’t lost or obsolete, but for sheer volume and exact replication, you can’t beat the machines.

What’s happening now with artificial intelligence (AI) in manufacturing is a little bit like that. People on all levels of the manufacturing chain want to know if AI is taking over.

The answer is no. Don’t think of it as a takeover. Think of it as more of a transformation. It’s already happening, and it’s not all bad.

AI’s current impact on manufacturing

Artificial intelligence is seeping into the manufacturing workplace in a couple of important ways.

Automation: Much like the sewing machine and indeed all of the industrial revolution, AI has the power to automate repetitive tasks previously done by humans. Operating machinery, tasks on the assembly line, even inspecting products for defects – all of these things are increasingly being automated.

Efficiency: AI can help us optimize processes and procedures, leading to greater efficiency on the line and as a whole.

New job creation. Yes, you read that right. Whereas AI may reduce the amount of jobs focused on repetitive tasks, it is also creating jobs that we haven’t seen before in the manufacturing realm, including specialized programmers, engineers, and technicians. It means companies will need people with different skill sets, and the savvy employers will dig in and train the people they already have to take on these new roles.

Predictive analytics

At USC Consulting Group, we’ve already been using AI with some of our manufacturing clients, specifically in the area of predictive analytics. We spell it all out in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics,” but here is the gist of it in a nutshell.

By now, we all know what AI is — computer systems that perform intelligent tasks, like reasoning, learning, problem solving, decision making, and natural language processing, among others.

Machine learning is a subset of AI. It is, technically, a set of algorithms that can learn from data. Instead of having to be programmed, the computer learns on its own based on data.

Predictive analytics is one output of machine learning. It is the ability to forecast future outcomes based on data. It’s like having a crystal ball that’s informed by vast amounts of complex algorithms and data.

You’re already familiar with predictive analytics but may not know it. You know how Amazon suggests an item for you to buy based on past purchases, or Netflix queues up new shows based on what you’ve already watched? That’s predictive analytics in action.

Much like Netflix’s use of predictive analytics created a seismic shift in consumer expectations, this technology also has the potential to transform operating procedures and processes for many industries.

The benefits of using AI in predictive analytics are many, including:

Bottom line: AI needs us

AI is a powerful tool we’ve used at USCCG to help our clients achieve greater efficiency, productivity, and profits.

But here’s the thing about that. It’s a tool. And it’s only as good as the data we supply. Any variation, and there can be skewed results.

As we all know, life is not a data set. Variation is happening all around us, all the time, even in projects where we need great precision.

That’s why the bots are never going to replace humans. They need us as much as we need them. At USCCG, we have more than 50 years of experience making process improvements, finding hidden opportunities for efficiency, creating leaner systems and helping companies thrive. For the next 50, AI will be one tool we use to help achieve that.

Read more about this innovative technology, including a specific case study about how AI works in practice, in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics.”

AI and Machine Learning - Predicting the Future Through Data Analytics eBook

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If the ongoing supply chain crisis has taught us anything, it’s the critical importance of optimizing warehousing processes. However, managing a warehouse effectively is no mean feat. You’re required to be both strategist and analyst, to identify and capitalize on opportunities for improvement today while outlining and preparing for the needs of tomorrow.

The good news, though, is that managers aren’t alone when it comes to optimizing their warehouse operations. In fact, a host of technologies is emerging to make your warehouse processes more efficient and effective than ever before. This article describes strategies you can use to upscale your warehouse organizational processes through the integration of artificial intelligence (AI) systems.

The Power of Predictive Analytics

If you’ve been working in the industry for a while, you know that the market is always changing, and that means that your warehouse operations strategies must change with it. Fortunately, warehouse managers today have more powerful tools than ever before to analyze market conditions and forecast future trends.

For instance, the predictive capabilities of AI technologies are virtually unprecedented. These systems are capable of analyzing literally billions of data points in mere seconds and, through the power of machine learning, using that data to identify patterns and formulate market predictions.

These insights can be a profound asset when it comes to inventory management and distribution planning.

Remote Sensing

AI technologies aren’t just useful for analyzing data and predicting future market conditions, they’re also superb in monitoring existing conditions and defining optimization strategies as needed. This supports the kind of agility and responsiveness that are essential to avoiding the supply chain disruptions that have threatened the global economy in recent years.

For instance, AI-powered devices connected to the Internet of Things (IoT) can track shipments and even trace individual items as they move through the supply chain, recognizing and documenting delays and disruptions, sending out real-time alerts to stakeholders, and even defining mitigation strategies as needed.

For example, when an AI sensor detects that a shipment is likely to get snagged in congested or blocked transport routes, it can identify and even automatically reroute the most expeditious alternative pathway.

Similar automation processes can also occur within the warehouse itself. So-called “learning warehouses” can monitor internal and external data, from customer behavioral patterns to current weather conditions, and use this data to optimize picking processes, allowing products to be picked and shipped from the warehouse even before orders have been placed.

Driving Workforce Planning

Another critical function of AI technologies in warehouse operations management is to facilitate workforce planning. Warehouse operations can be exceedingly complex, involving a large number of workers and stakeholders performing a diverse array of functions across all stages of the supply chain, from the warehouse to the final point of sale.

Savvy managers can unleash the full potential of AI in workforce planning by deploying organizational planning tools, such as mind maps, to help them more clearly and comprehensively define workflows. This, in turn, enables leaders to identify opportunities to optimize staffing processes organization-wide through AI analytics and optimization.

For example, AI-driven labor planning can prevent overstaffing by combining predictive analytics with internal and external conditions analyses. These systems, in other words, operate holistically to more effectively coordinate operations across all divisions, departments, and job functions in response to existing and expected needs.

The result is greater efficiency and reliability in the supply chain, an enhanced customer experience, and a better overall working environment for warehouse workers, distributors, and shippers alike.

Instituting AI Technologies in Your Warehouse Processes

Integrating AI into your warehouse organizational processes requires some planning. The good news, though, is that you are likely to meet with significant approval from your team, who are likely to have already recognized the immense value of technological innovation in warehouse operations.

The key is to clearly define the short-term and long-range goals to want and expect to achieve with each technology you adopt. Do your research to confirm that your expectations for each innovation are plausible and cost-effective. Then, establish your priorities. A strategic, systematic conversion to AI is likely to be more efficient and effective than a sweeping transformation.

The Takeaway

Optimizing your warehouse’s organizational processes is not easy, but with the integration of artificial intelligence systems, you can achieve improvements in efficiency and productivity that you might never have dreamed possible. AI systems can help managers define optimal inventory and distribution strategies. Remote sensors can track products across the supply chain, enhancing agility and responsiveness at each stage. Learning warehouses can optimize picking and shipping processes by analyzing customer behavior, market trends, and other relevant external conditions. AI technologies can even facilitate workforce planning, helping to prevent staffing shortages or surpluses across every division and job function, resulting in a superlative customer experience and a more efficient and harmonious work environment.

*This article is written by Ainsley Lawrence. View more of Ainsley’s articles here.

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