Commentary: Six best practices for new product introduction
In the digital economy, products are becoming more complex, lifecycles are getting shorter, and market environments continue to evolve. While much has changed in new product introduction (NPI) over the years, the process that supports it hasn't.
Static solutions cannot keep up with dynamic conditions. LNS Research reveals that 91% of the market still uses spreadsheets and electronic documents to track product requirements. With innovation largely focused on the design and engineering phases of product development, the NPI process has been left behind.
Unexpected product delays and costs happen to the best of us. In 2012, Apple announced that it would make a Mac in the U.S., but when it started manufacturing the computer, finding sufficient parts domestically was difficult. The unraveling began with a threaded nail—a custom screw. Because the NPI team did not review the entire bill of material and confirm all sources of supply down to the screw level, the new Mac product launch was delayed for months, according to The New York Times.
To improve time to market, increase gross margins, and successfully navigate risk, it's time to overhaul our outdated approach to NPI. Change starts by making the process more strategic, collaborative, and digital with these six best practices.
1. Pre-mortem planning. Hindsight is 20/20, so a post-mortem exercise conducted after product release, with lessons learned informing a better path forward, is a recommended best practice. But we can benefit from this point of view sooner by conducting scenario planning earlier in the process, anticipating failures, and mitigating the probable sources. Which suppliers are at risk? Is manufacturing in an area subject to higher tariffs? Which expedited options are available if the schedule slips?
Pre-mortem planning is a powerful way to identify possible problems. Traditional challenges such as quality, risk, and forecast accuracy are amplified by shorter product lifecycles and unpredictable markets. Anticipating the unexpected minimizes the potential impact.
2. Earlier sourcing input. By sourcing the bill of materials (BOM) right the first time, schedule delays, expensive redesigns, or additional charges from rush fees or tariffs are less likely to cause disruption at an inopportune time. Procurement can help optimize the build by providing sourcing recommendations including synergies with other products for deeper discounts, tariff implications, and shipping alternatives.
With an estimated 60-80% of the product cost and risks determined at the design stage, late changes can be more expensive than necessary. By aligning processes, technologies, and metrics to bring sourcing knowledge closer to the point of design, trade-offs happen earlier, such as reducing material expenses and increasing parts reuse. The cost to qualify a supplier or shift to a new location is incredibly high; engaging sourcing teams sooner will help NPI teams navigate supplier and part selection in a fluctuating global economy.
3. Target-based costing. Identifying the desired profit margin and maximum allowable cost to meet that margin upfront emphasizes cost in the design phase and distributes competitive pressure across the supply chain. When all team members understand targets and key budget drivers at the start, these factors will be included in decisions about the product from design to production and shipping. The upstream decisions made during NPI can significantly impact the timeframe and cost of a product during its lifecycle. When the product moves to manufacturing and ramps to target volumes, it is difficult to change plans, find new suppliers, or cut expenses.
Target-based costing was led by the automotive industry and is a key reason that automakers in Japan gained a competitive advantage over the U.S. and Europe. Integrating artificial intelligence (AI) into this methodology increases the speed and accuracy of projections when examining market conditions, reviewing target price against projected costs, and setting a target margin. Teams then work backwards to identify the constraints around allowable cost and drive strategies with suppliers on component target-level costing and alignment.
4. Data integration. Here's where advanced technology and big data analytics can really make a difference. Distributed teams are trying to compare all the risks, costs, and trade-offs to select the right suppliers, locations, and alternate parts for more products in compressed timelines. Disparate tools, such as spreadsheets, make it difficult to understand the factors that can influence the full product lifecycle. Manual methods of managing information and tracking changes limit the ability to collaborate and scale.
Cognitive NPI solutions consolidate diverse data sources in a centralized repository, allowing AI to analyze the information. This outside-in view?aggregating external data, such as market intelligence and raw material input costs, with internal data, such demand forecasts and purchase history?derives new insights for additional visibility.
5. Cross-functional coordination. Additional efficiency in NPI requires cross-functional coordination between manufacturing, quality, marketing, packaging, customer service, regulatory, and sourcing departments. Systems with shared tools and automated processes are one way to improve team integration. These tools can also provide an audit trail so stakeholders can easily evaluate changes, fix errors, work on exceptions, answer questions, and make feature-cost tradeoffs to launch at the optimal level of risk.
6. Post-mortem analysis. The final step in the NPI process, post-mortem analysis identifies improvements for the future. Did pre-mortem planning predictions bear out to avoid pitfalls, or are there new scenarios to consider for next time? Again, procurement should be involved in this step to evaluate the impact of decisions to direct material cost and risk attributes.
Post-mortem analysis also highlights opportunities to reevaluate the NPI process and implement innovation. Where are the bottlenecks? What steps can be streamlined or automated? Are the right technology solutions in place? There's very little AI-based decision-making within NPI today, which provides a massive opportunity for improved cross-functional collaboration and intelligence.
By following the steps laid out here and focusing on increasing planning and collaboration across functions, you can improve your new product introduction process and decrease the chances of following in Apple's footsteps. Hopefully, your next heavily anticipated new product launch will not be delayed for want of a screw. Better data visibility and integration efforts can go a long way toward enabling this planning and collaboration. When your data is fragmented, it hinders your ability to optimize costs, improve supplier selection, and mitigate risk. Applying artificial intelligence to this data can help enterprises produce valuable insights that can accelerate time to market, improve gross revenues, and decrease risks.
All of these efforts to strengthen the new product introduction process will help companies save time and money by increasing their ability to respond to ever-changing customer and market demand.