How IIoT data can power profitability in lean manufacturing




from How IIoT data can power profitability in lean manufacturing
by Anasia D’mello
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IIoT (Industrial Internet of Things) has ushered in a new era of efficiency and intelligence for the manufacturing industry. Enterprises are now generating an ocean of operational data, which if combined with other data inputs and deployed through new pricing technologies, could multiply the profitability impact of both lean manufacturing and overall business, says Patrick Moorhead, chief marketing officer, Pricefx AG.

Classically, the top down marching order from senior management is always,“Go make more sales.” Of course, everyone agrees on wanting more sales, except more sales will not necessarily produce more profitability (the real goal). An emerging range of new technologies utilising ML (machine learning), AI (artificial intelligence), visual CPQ (Configure Price Quote), and CAD-based digital marketing are allowing organisations to leverage growing data resources to achieve more profitable sales.

These types of data-driven pricing solutions have historically been extremely expensive propositions (multi-million dollar, multi-year investments). The new generation of cloud-based pricing technology allows for incremental and efficient adoption under SaaS (Software as a Service) business models. Long gone are the days of seven or eight figure on-premise hardware installations with 12+ month implementation timelines.

SaaS pricing optimisation platforms like Pricefx offer a solution for profit-based manufacturers who see the opportunity to leverage unique IIoT data resources. Such platforms maximise margin and profitability while maintaining a manageable and high efficiency OpEx investment strategy to accomplish the goals.

So much money is left on the table because manufacturers and sales representatives “price by gut” or tribal knowledge and assumptions of what the customer is willing to pay. These assumptions are often wrong, yet rarely tested. By using price optimisation, an objective and unbiased mathematical analysis determines how customers will respond to different prices for products and services through different channels. It also determines how to maximise profitability.

Process resistance to price optimisation should be expected since pricing authority is currently in disparate pockets across many enterprises. Similar to a Lean Six Sigma implementation, there will be resisters who wish to keep the status quo. They should be engaged in this conversation and have their concerns understood and prioritised; they can become champions of pricing optimisation when they understand and witness first-hand the positive impacts.

The IIoT data used in price optimisation is already accessible for mid-sized and larger manufacturing companies. The data collected from various points open up the possibility of pricing in new ways. Manufacturers can price based on consumption in real-time, take inventory into account to drive price changes, or even adjust pricing according to different parts of the day considering utilisation capacity in a plant, power grid, or other networks. Culling these data from sensors, contracts, surveys, transaction records, incorporating operating costs, add value extracted from big data.

Add more information from competition, economic indicators, promotional successes and failures, and seasonal conditions, much is learned that can be used to optimise prices, promotions, and profitability. Before the rapid data collation access with IIoT, too many markers were duplicated, incorrect, or missing.

Cleansing data and reliably gathering missing data is very expensive and no longer needed in many […]

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