Steering manufacturing demand through dynamic pricing

Redefining the paradigm

In this article, we aim to reframe the discussion, advocating for an integrated approach that recognises the interplay between pricing strategies and production processes.

Taking cues from demand-based pricing models like Uber’s dynamic fare adjustments, where prices adjust in real time according to fluctuations in consumer demand, we suggest how similar principles could provide manufacturing organisations with a powerful tool to not only capture value but to create it by smartly aligning demand towards production plans.

Join us as we delve into the potential and challenges of implementing such a strategy, offering actionable insights for a hands-on approach for implementation and outlining the pivotal changes required for success.

The enigma of production plans anchored in sales forecasts

Reliable production plans in manufacturing are the bedrock of operational efficiency. Often, manufacturing companies rely on sales forecasts to formulate their production plans, and these forecasts are generated based on historical sales data, market trends and predictive analytics. However, actual sales can diverge significantly from forecasts as customer behaviour can be unpredictable, and external factors such as economic shifts and competitor actions also influence the actual sales.

When sales underperform, manufacturers are left with surplus inventory that ties up capital and incurs holding costs. Conversely, when sales unexpectedly exceed forecasts, companies struggle to ramp up production quickly enough to meet demand, resulting in lost sales and potentially damaging customer relationships. This disruption can have a ripple effect across the supply chain and puts a strain on production scheduling, resource allocation and supply chain logistics, all of which can erode a company’s competitive edge.

Could there be a more beneficial approach to aligning demand with production, circumventing the perils of traditional forecasting methods?

Using pricing as a proactive lever to harmonise demand with production

Enter the realm of dynamic pricing where prices fluctuate based on real-time demand, offering a compelling solution to the discord between sales and production. Here, we find Uber’s demand-based pricing, also known as surge pricing, where fares are adjusted to balance rider demand and driver supply, ensuring availability while maximising revenue.

Emulating the idea behind Uber’s surge pricing, manufacturing companies can adjust product prices in response to real-time demand fluctuations. This proactive pricing strategy can help align actual sales with forecasts, maintaining a stable production plan. Lowering prices can help clear excess stock and prevent overproduction, while raising prices during periods of high demand can temper sales to harmonise with production capacity.

The outcome? A dual-fold benefit: smoothened demand fluctuations, reducing the costs associated with production volatility, and heightened revenue and profit potential by capitalising on variable customer price elasticity.

Hurdles on the path to adopting a dynamic pricing strategy in manufacturing

Despite the apparent benefits, and while widely applied in hospitality, airlines and FMCG e-commerce, very few manufacturers have managed to tap into the potential of dynamic pricing due to a unique set of hurdles in this sector.

One significant hurdle is the complexity of accurately gauging real-time market demand. Unlike the service industry, where the provision and consumption of services can occur simultaneously, manufacturing often involves lead times. And unlike FMCG e-commerce, complex customer buying processes for investment goods lead to sales cycles of several months, sometimes years. These two characteristics combined provide a challenge for short-term and narrow forecasting windows, which are a prerequisite for dynamic price adjustments.

Another primary hurdle is the complexity of changing long-established pricing structures. Unlike consumer-facing businesses, B2B sales relations often have more rigid pricing due to factors such as channel partnerships and contractual obligations. Also, customers may perceive dynamic pricing as unfair or exploitative, particularly if price increases are frequent or perceived as arbitrary. If not managed carefully, this can lead to customer dissatisfaction or reduced brand loyalty.

An often underestimated third challenge lies in the technical and organisational changes required to support dynamic pricing. On the input side, manufacturers need the data and advanced analytics capabilities to transform them into the required demand insights. On the output, the pricing side, internal end-to-end processes and IT systems must support the flexibility and speed of decision-making required for dynamic price changes.

Dynamic discount steering: kick-starting the journey

A promising solution to tackle the challenges of understanding demand lies in the sales opportunities tracked in a company’s CRM system. These opportunities, which represent potential sales (often referred to as sales pipeline or sales funnel), often come with a probability of winning attached, providing a snapshot of future demand.

As we face limitations in dynamic alterations of list prices, we suggest focusing on a price component that is more fit for our need for flexibility. Most companies do not sell their products at list price. Instead, they use discount frameworks to differentiate prices among customers or negotiate individual prices that fit the individual deal. While we as pricing professionals often seek to minimise discounts for the sake of profitability, they provide a promising option to implement some dynamics.

Some discount components might be subject to longer-term agreements and cannot be changed on the spot, but in most cases, there is at least one component that leaves room for negotiation and is subject to decision of the individual sales representative. These more flexible components are often supported by target price frameworks, providing the sales teams with a recommended price range to follow when closing deals. Ideally, this recommendation is already differentiated and optimised by the type of product, customer and competitive situation.

Using the insights from the opportunity data in the sales funnel to further refining these flexible discount components and target price frameworks to actively steer supply-demand fit provides a feasible approach to implementing a more dynamic pricing.

Determining the optimal level of discount or target price adjustment requires understanding the relationship between price changes and the expected change in demand, a concept known as price elasticity. In the proposed setup, this would mean the relationship between price reductions and the likelihood of winning any given sales opportunity in the pipeline. For instance, if an additional 5% discount increases the chance of winning a sale by 10%, this insight can be used to tailor discounting practices to align the market demand with production capacities.

Insights into how discounts affect win probabilities can be drawn from analysing discounts and win-loss ratios of past sales opportunities. Having conducted many of such analyses, we know of the various challenges to make them meaningful, isolating the price effect from a range of influencing factors while having to rely on scarce data. Modern machine learning models can help to champion those challenges by better understanding patterns and identifying clusters of similarly behaving customers-product combinations. Another way of generating those insights can be deliberate price tests. If designed in the right way, these have the benefit of providing a more recent and more reliable picture of price elasticity than analysis of historical data.

Embracing change for dynamic pricing success

We strongly believe this approach can create value for many manufacturing companies, however, introspection is imperative as it might not be for all. Therefore, we suggest examining a few things before you embark on this journey.

First and foremost is the adherence with companies’ strategy. In our opinion, a pricing strategy should be a vehicle to drive a company’s overall strategic objectives. If there has been made a strategic choice to emphasise growth over profitability, steering demand down might not be suitable.

Beyond that, there are a few factors that determine the chance of a successful implementation. Some of them are rooted in the very nature of a business and might not be subject to change: the higher the number of individual transactions and customers, and the more distributed transactions are among those customers, the easier it is to understand the price elasticity and to steer the demand. And yet, this is nothing companies will be able to control.

Other prerequisites for a successful implementation might not be in place yet but are possible to achieve if organisations are willing to change and invest. Most importantly, the way they work with discounts. Having some framework in place to steer discounts and having a part of the discount flexible are key enablers for dynamic discount steering. If a company does not give discounts at all, this is great, as it indicates very good pricing power. However, those companies might think of other ways to implement a dynamic pricing approach.

Another success factor is the data and technology landscape. Having a CRM system which holds sales opportunities is a great start. Even better if the data represents a realistic market situation. If not, companies should consider investing in the technology, process and people behaviour to work towards that.

As both the quality of opportunity data and the discounting sit in the responsibility of sales, for manufacturers to implement a demand-based discount steering, a comprehensive shift in both mindset and operations is necessary, which puts the sales teams at the centre of the transformation. This shift requires strong leadership to champion the change and articulate the vision across the commercial organisation.

Beyond that, pricing, sales, marketing, production and finance departments must work cross-functionally to ensure pricing strategies align with overall business objectives. Executives must foster a culture that values agility and innovation, encouraging teams to adapt to new tools and processes.

Finally, manufacturing companies must develop a robust framework to monitor the impact of price and discount changes. This framework should include key performance indicators to measure the effectiveness of steering demand to optimise production efficiency and improving profitability of sales.

Conclusion

Using dynamic discounting to kick-start your journey towards a demand-based pricing model

In conclusion, there is significant potential rewards in adapting a demand-based pricing model in manufacturing and thereby stabilising production plans and grasping value-capturing potential at the same time. However, overcoming the variety of challenges on the journey might seem overwhelming.

We therefore believe in an approach of dynamic discounting to start grasping the potential with a real chance of success. Using sales-funnel data for smart adjustment of discounts and target prices to steer demand is the first step for manufacturers towards a demand-based pricing model.

As with any strategic shift, it will require commitment, adaptability and a willingness to experiment. However, for those willing to embrace this new paradigm, the rewards can be substantial in the form of happier employees, happier customers and improved profitability.