Manufacturers know the value of automation on the plant floor. The world is more interconnected, with more competitors and consumers more informed, thus more selective with purchasing decisions. With increased competition and disruption, manufacturers must leverage automation to achieve efficient manufacturing. Automation of any process delivers higher productivity, lower costs, improved workplace safety, enhanced precision and ultimately, allows associates to focus on more valuable activities. Technology, specifically Machine Learning, helps expand the breadth of automation by becoming more accessible and affordable for manufacturers of every size.
Robotic automation on the plant floor has helped companies produce high quality goods, as a robot does not get tired, distracted or endures repetitive injuries. Robots perform dull repeatable steps with reliable accuracy.
Aiming for best practices
Pricing automation is simply transferring the same plant floor efficiencies to pricing best practices. Physical strain is unlikely from a pricing process, but mentally, it can be taxing and often impossible when determining the optimal prices for unique products.
With 2000 customers and 500 active items, that is potentially 1,000,000 unique prices that need to be delivered. Complicating matters include varying market conditions and a competitive landscape by region. Additionally, constantly changing costs on the supply side exacerbates the volume of unwieldy pricing data for manufacturing teams to manage.
Machine Learning algorithms are the robots in the pricing process
Just like robots, manufacturing pricing teams must set some boundaries, train the movements and let the models perform the drudgery of determining the best prices based on predicted revenue, margin and volumes. These models mine all historical transactions on a nightly basis ensuring predictions are based on the most current market realities. This allows a focus on more valuable activities, like selling, managing relationships and negotiating contracts with key suppliers. Pricing is simply too daunting to try without the ‘robots’ (Machine Learning models with process automation).
The costs of not automating pricing are significant
Hard costs, including additional headcount, full-time employee hours, lower margins and lost sales are quantifiable metrics to consider when justifying automated pricing technology. There are also soft costs to consider, from delayed response times as market dynamics shift, opportunity cost of time spent on data entry and manipulation (rather than analysis and customer engagement).
Lower margins and lost sales can be quantified in customer churn as well as the inability to effectively react to market changes, including competitive pricing, demand shifts and product lifecycle management. During the pandemic, industrial organisations have seen what happens when there is an inability to manage supply side changes, including cost changes, supply constraints and product quality challenges.
Focus on the CPQ
Configure Price Quote (CPQ) solutions have slowly deprioritised the most critical and fundamental component, which is price. Without strong pricing science, the configure and quote components are moot. To achieve true efficient manufacturing operations, many may not need a true CPQ, rather a cost-effective pricing solution.
CPQ solutions are designed to enable salespeople and customer service to combine products, features and services; they present the combination as a quote to a customer. Most manufacturers are selling discrete SKUs and do not need to spend additional resources in acquiring, implementing and maintaining a full CPQ system.
The pre-qualification process for CPQ companies
First, those companies with more than $50 million in revenue are prime for rapid CPQ deployment. While smaller organisations would certainly benefit, they may have an overly simplistic pricing process. Most customers want to know how much to charge to capture maximum revenue or profit, which includes examining the solutions or methods currently used to set prices for products.
Too often, key pricing decision-makers have been in meetings discussing pricing changes and have needed to go back and run reports, requiring yet another meeting to evaluate how those changes might impact the bottom line. Even efficient manufacturers, when losing market share to competitors or constantly reacting to price changes, struggle to align customer, location or category-specific pricing to corporate objectives (including growing margins, revenue & increasing market share).
Sadly, very few companies create a visual report and impacts of pricing across revenue, volume, mix and margin. Most manufacturers are not currently using Artificial Intelligence (AI) and Machine Learning to set prices based on customer behaviour, segmentation and demographics.
Getting started with CPQ and analytics for pricing strategies
The onboarding process must start with pre-sales, which requires discovery to determine if process changes and consulting are required to align needs with technology solutions. Once a proven CPQ solution is determined, a post-sale implementation and execution modality is launched. This includes assessing infrastructure readiness, data readiness and systems integration. The return-on-investment (ROI) is achieved when the post-implementation is quantified.
Beyond the qualitative pricing ROI and technologies, are the qualitative best-in-class outcomes of highly efficient manufacturing operations.