6 Transformations for Manufacturing for Change Agents

This isn’t an easy time to be in business. Frankly, I think it is one of the most challenging times in recent memory due to the uncertainty that exists in the economic climate. However, it is during uncertainty that real leaders transform markets and bring about instrumental change. This is the time when bold manufacturers are seizing the opportunity to gather new revenue and optimize their operations while competitors hope that “return to normal” is just around the corner. I’ve seen tremendous acceleration of change in my best customers simple because they are led by change agents who establish bold visions and don’t take no for an answer. They are obsessively customer centric, think about the customer’s needs, and drive to meet the new market where its at… but aren’t content to leave it there. In the manufacturing market there are six major changes I’m seeing that are meaningfully impacting the bottom line, customer perception, and customer retention, including:

  • Automation of the Supply Chain
  • Automating Sales Quoting with Intelligent Agents
  • Predictive Maintenance (manufacturing and customer)
  • AI for Good Picking Enablement
  • Connected Products
  • Customer Insights

Automation of the Supply Chain

This area of the business has seen tremendous opportunity simply because of the incredible waste that exists in this domain of the business. Yes, every business has planners, ERP systems, and spreadsheets, but they aren’t good enough. We’ve found supply chain improvement as much as 40% over the original planning solution, typically resulting in tens of millions in a $2-3 billion dollar company and hundreds of millions in larger companies. This is not aspirational and ignoring this area of optimization is like leaving money on the table.

In the image below you can see the typical spikes a company experiences over the course of a year. You can see the goals being to actualize the amount of inventory held at any point to the demand and based on the economic model of the selling cycle.

Automating Sales Quoting with Intelligent Agents

The competitive sale has many factors but one of the greatest is time. The business that engages its customer real-time, provides the correct quote, and aligns its business value with the need will typically win. We’ve seen this transformation on the consumer side, such as the example below with Starbucks (self-service ordering), but we haven’t necessarily gotten used to it in B2B or B2-BigC sales. However, this is where much of the opportunity exists to increase revenue relatively easily. Even a 1-5% increase in revenue can mean millions of dollars.

Think about the Starbucks example below, where in the time of the pandemic they adapted the self-service model to efficiently take orders, understand time-of-day selling, and optimize queuing in stores by routing to the closest available with the shortest line.

This same concept is applied to selling motions by building techniques like Natural Language Processing into B2B sales channels, such as email, bots, or apps, facilitating accelerated quoting by recognizing complex SKUs, and CPQ scenarios. This is especially important in larger selling teams where the constant churn of sales reps and large SKU sets (sometimes as large as 100,000 different SKUs) can make quoting difficult. By removing barriers between understanding the request and delivering the quote a company can substantially increase its win rate and bring in more revenue. I’ve seen even email scenarios being meaningful targets here, where quote time can be reduced from 1 day to minutes, if not real-time.

AI for Good

I’ve written extensively about AI for Good in the past, but I want to bring this to the surface again. We’ve now seen the fruits of engaging artificial intelligence in smart manufacturing environments where individuals with cognitive disabilities, as well as highly functioning workers can achieve significantly increased productivity by leveraging augmented reality technologies in safe manufacturing environments. The two primary scenarios we’re seeing are:

  • Leveraging augmented reality to enable individuals with cognitive disabilities to do work that they previously would need a human partner to do, but could do with AI input
  • Leveraging the same augmented reality to decrease pick times and improve picking efficiency for highly functioning workers in complex warehouse environments

After much engagement in this space we’ve made this Open Source, in partnership with Clover Technologies to accelerate usage across the globe. We want to see every manufacturer receive the benefits of enabling augmented reality for their workers and also the joy of engaging workers who alternatively may not be doing this level of work.

Connected Products

The next scenario is connected products, which has increasingly become table-stakes in the product market. I recently purchased a new stove and I immediately wanted a smart product, almost regardless of whether I had an actual purpose for it. The enablement of smart products creates a new value stream for consumer attach, great add-on experiences, and a way to partner with the distribution network to maintain customer relationships. This is the opportunity to create a recurring revenue stream attached to every sale that brings long term additional value to consumers.

You can see an example below of what Brunswick is doing with their connected boat platform which brings additional value to boaters. The Nautic-On platform provides both on-boat and off-boat experiences which ensures that boaters have a great time on the water and mitigates disasters to their day.

The key to connected products is understanding the customer use scenario or (job to be done) and looking for ways to improve their experience. For example, with Nautic-On they are mitigating annoying problems with boats like engines not started, pump not working, battery dead, etc., which make for a much better boating experience. I’d gladly pay to have a monitoring service if it means that my boat isn’t floating away from the dock with it not starting, kids-and-all!

Also understand that connected products are an excellent way to increase the price of a product in a way aligned with additional value. Over time you’ll find yourself competing less on the capabilities of the hardware and even more on the capabilities of the software.

Predictive Maintenance

Prediction is a key capability of AI/ML that outpaces what human intuition can compute based on large amounts of data. This isn’t to say that time based prediction or intuition are worthless, but they are only two inputs into a larger mathematical puzzle that can be modeled with AI. There are two major ways that AI/ML is being used in the predictive maintenance space:

  • Manufacturing efficiency by preventing unplanned downtime
  • New revenue through value-add services to existing sold products.

The creation of new revenue or optimizing manufacturing processes comes from the combination of a connected (or observed) product in conjunction with prediction of expected operational state.

The ability to understand when something is going to occur based on existing data has obvious benefits to the manufacturing environment (in continuous operation) and to customers (in new service opportunities). This is a build-on to the connected product strategy and especially in large value sales (think large cranes, machines, trucks, etc.) we can see tremendous opportunity for regular and meaningful engagement.

Customer Insights

The final story surrounds the tremendous value driven from data as an aggregate, vs. through individual stories. In the earlier examples we are solving for a scenario, such as inventory, route, or manufacturing optimization. In this scenario we are bringing large datasets together to look for trends across the ecosystem and then looking at how that data can be monetized. The data can be sold back to customers as ecosystem insights and guide the entire industry to float its boats higher through shared best practices, customer identification, or continuous improvement.

The diagram below depicts the journey from isolated data sources to aggregate sets which empower wisdom.

The growth of an organization up the maturity curve allows it to drive increased revenue because they understand the surrounding world better and are able to create new opportunities that are dormant in the data. In many cases big data story telling enables the discovery of new revenue streams that allow a company to out-pace the market by bringing new ideas to the table and insights that inform leader-to-leader conversations.

I have seen the impact that each of these scenarios make in customer environments and the way they transform to meet their customers, partners, and employees in new and powerful ways. Now is the time to look at our businesses differently and differentiate in the market and find operational savings that allows for a new way of working, especially as we enter into 2021!

Nathan Lasnoski

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