Artificial Intelligence (AI) and machine learning are huge topics in tech news today..  AI is being used to identify clusters of pest infestation, identify areas for crop yield improvement, and where areas of low birth rates can get additional assistance.   This is also true in manufacturing, where advances in AI and ML have driven improvements in demand forecasting, new part introductions, and optimized part pricing. 

Not all AI news is positive., however.  Many in the AI community are convinced that nothing truly original in deep learning ML has been identified since Geoffrey Hinton, lead scientist for the Google Brain AI team and former Carnegie Mellon University faculty member, made a breakthrough in neural net machine learning some 30 years ago.  Some believe it is a technology hammer looking for nails.

That’s why it’s important to start from the beginning of the challenge, slowly working your way to the solution. By identifying critical business problems first, and then choosing from a toolkit to find the best solution, machine learning can serve as one of many hammers in your arsenal.  From Mixed Integer Linear programming, linear and non-linear regression, neural networks, and many other approaches, using sophisticated tools like Google AutoML, Gurobi, MATLAB, Rogue Wave, SAS, or even open source options and roll-your-own Least Squares calculations becomes common practice of any good pricing problem solver.  Google’s AutoML and other ML toolkit startups will certainly put more tools in everyday business user’s hands to solve their own problems.

Over the last few decades, AI and machine learning have made major progress in industries like retail, where products frequently sell on ecommerce channels, restocked items have frequent, measurable demand, and there are many competitive substitutes available.  One quick example is my favorite running shoe, the Mizuno Wave Rider, which can be found on Amazon, in the local running store, at my old customer Dick’s Sporting Goods, and even directly from the manufacturer.  The variance here proves that pricing transparency provides prolific data that helps calculate elasticity, optimize prices, and forecast demand based on price changes.

Machine learning applications in pricing for industrial goods can prove more difficult than a simple retail model for a number of reasons.  For one, items are frequently sold at a net price offered to specific customers based on sales volume and customer contracts. Additionally, the long tail of assortments has infrequent price changes and demand, with many items of little or no competitive substitutes. So, what does this mean for OEMs? Simply put, there is there is less demand data and competitive influences to calculate price sensitivity

Thankfully, there are many best practices to combat these challenges, particularly for service part pricing. From bundling parts and labor to price common repairs and maintenance, to identifying quantitative and qualitative attributes for use in Value Based Pricing, complex contract pricing approaches that consider length of the contract, contractual service levels and forecasted parts usage have started to become the norm in service part pricing.

As the shift towards servitization  increases the importance of solving these key manufacturing problems, OEMs can start selling product uptime over selling products. Ultimately, the need for better planning, pricing and use of IoT data will drive proactive service models, which naturally lead to better forecasting of failures. With better AI and machine learning tools at manufacturers’ disposal, rapidly approaching servitization challenges will be easier to face in the months ahead.

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