Scale AI-supported New Product Introductions with SAP IBP
Recent McKinsey research shows product innovation is one of the greatest potential opportunities for growth for consumer packaged goods (CPG) companies. In fact, it shows that digitally enabled product innovation helps CPG companies bring new products to market 50 percent faster, at a third lower cost, and with double the return on investment. ?
Yet recent research across over 83,000 CPG product launches in the USA shows one in four (25%) new SKUs were no longer bought 1 year later—a rate that increased to approximately 40% 2 years post-launch.
CPG companies face challenges with consistent growth and new product time-to-market, exacerbated by omnichannel challenges and consumer trends: Nielsen data showed that over 85% of 30,000 new CPG products in the US fail within 2 years, while 85% customer purchases were from brands they’d previously bought, across 80 categories.
GenAI offers an opportunity to accelerate incremental and breakthrough innovation and testing for new products, while embedded AI processes and planning can recommend production quantities, where to produce and pack, based on previous quality results.
How can AI reduce manual NPI efforts and improve planning and project results for new products and portfolios, helping CPG companies deliver consistent growth with fewer NPI failures and less product waste?
Benefits of an End-to-End (E2E) NPI Platform
Market-leading CPG and fashion retail companies need to sense changing demand chains and connect these digital stimuli to product innovation and supply chains, to dynamically fulfil NPI first-mover advantage and extensions of existing brands, with connected internal and external manufacturing, distribution, and dealerships.
Collaboration and automation are key factors across NPI teams to connect these demand chains to their supplier chains, and AI can dramatically streamline product innovation, planning, and decisions.
Product teams can optimize NPIs with SAP probabilistic planning to hedge against disruption across the full internal enterprise and external supplier and distributor network. Working within ranges can help with agility and reduce overproduction risks in the critical weeks after launch.
SAP’s PLM integration through ERP to Integrated Business Planning (IBP) could further enhance digital alertness and agility for supplier and contract manufacturer choices based on bill-of-material changes.
For example, reducing or replacing NPI plastic components or packaging could mean alternative plants and sourcing strategies. ?
Using like-modeling capabilities in SAP IBP’s Manage Product Lifecycle app, planners are able to drive forecast automation and segmentation for new products that currently have only little or no sales data.
Agile design processes and design changes can be synchronized with planning for rough-cut capacity changes directly to constrained capacity and detailed scheduling, and manufacturing order dispatching.
Moreover, with SAP’s flexible E2E Min-Max Scheduling guardrails consistent between planning and scheduling, ?product managers can apply probabilistic planning approaches across product groups.
Connected yet flexible E2E synchronized planning reduces the risk of fixed production plan cutoff dates, to enable more resilient, agile, and adaptive NPI production plans and inventory levels across the network. Production flexibility is key when so many new NPI SKUs will likely fail.
For further risk-resilience, CPG brand teams or product managers can use SAP IBP’s inventory network visibility, manufacturing, and transportation optimizer calculations to steer fulfilment based on embedded AI supply lead predictions for product/locations, down to SKU level.
Applying like-modeling logic on lower levels, such as the product-ID level, when forecasting on a higher level, such as the product-group level can strengthen support for product managers for new products, and products phasing out, during forecasting on an aggregated level.
Thousands of products and their superseding replacements can be aggregated and disaggregated to plan phase-in and -out processes or dynamic decoupling points for specific brands and product groups.?
Different product groups and brands can use SAP IBP with different planning areas, and depending on access controls, regional or central planning teams can plan across groups on one data model.
SAP customers run the E2E optimization of millions of planning variables and SKUs using our dynamically scalable IBP cloud platform, steered and scaled by AI.
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AI-driven NPI Planning with SAP IBP
SAP connects AI-supported planning processes such as demand sensing and supply lead time predictions with production and allocation flexibility in real-time.?
Our embedded AI processes can translate demand sensing and stimuli across networks into recommendations for supply scenarios, considering constraints and inventories with the flexibility to fulfil with full visibility.
With demand sensing microtrend handling planners gain more flexibility to plan new product launches or product discontinuations with multiple launch dimensions.
SAP’s AI-driven planning suite can support the forecast automation for customers across thousands of new product introductions, promotions, bundling, and product supersessions every quarter.
AI-guided master data cleansing saves NPI planners considerable efforts and empowers them for automated mass master data identification and alignment for new products.
SAP’s IBP platform can support planners and forecast accuracy, probabilistic scenarios, phase-in and phase-out options, with the automation of massive data calculations based on historical data such as trends and seasonality.
Using IBP’s AI curve-based forecasting, product teams can swiftly automate and scale NPI probabilistic projections based on individual or combinations of previous product profiles.
AI-driven sales-curve similarity analysis in SAP IBP analyzes historical sales curve patterns and defines similarity groups as an NPI preprocessing step, helping product teams and planners move to more automated NPI forecasts.
Using external factors or customers’ own Python or R algorithms in the IBP forecast can enhance IBP’s time series analysis and change point detection capabilities that could transform NPI processes.
SAP IBP customers use weather data and external factors and data to sense and change their plans and adjust supply range forecasts for NPIs. Consumer analysis or even innovative influencer demand sensing can steer NPIs with IBP.
This E2E capability can help transform digital alertness for customers, launching and redirecting products depending on changing seasonal weather patterns, changing distributors due to disruption, or because of consumer trends or influencers.
Outlier detection and extreme gradient boosting can be used to further transform product launches at scale, while reducing waste and overproduction and inventory carrying costs through accurate production.
Flexible collaboration with n-tier suppliers, raw material vendors, copackers and distribution partners directly from SAP IBP, combined with supply lead time predictions, helps refine the production plan just-in-time and avoid just-in-case overproduction risk.
GenAI with SAP—Joule—can empower NPI planners with E2E insights across processes and data to support just-in-time with details and analytics in management business reviews or risk boards.
In the future, characteristic-based NPI Planning, combined with these AI automation capabilities in SAP IBP, will enable automation at ever-increasing scale.
Conclusion
SAP’s E2E orchestrated planning and scheduling tools can deliver new insights for the NPD and NPI processes with detailed visibility, for example predicting lead times with transportation lanes and stock in transit, for optimal service levels at balanced cost across the full network.
SAP IBP can aggregate NPI changes in product/location demands to ‘fix the mix’ out to regional distribution centers and issue purchase orders automatically based on rules, streamlining planning.
SAP’s customers run synchronized NPI planning end-to-end, from research and development, through commercial and supply chain to production and logistics. Our embedded AI supports these connected NPI processes and data.
GenAI and embedded AI can further automate and accelerate NPI, while supporting recovery and recycling. Customers should focus on E2E AI’s impact to gain competitive and profit advantage in the NPI process, while reducing waste. Get in touch if like to see how our customers already achieve this.
SAP PP | QM | PLM | IBP Consultant
6 个月Guy Clutton-Diesen Great insights. Will IBP though include a feature similar to" product similarity" feature in SAP CAR that suggests reference products based on attribute values ?
Supply Chain Planning Expert at Westernacher Consulting
6 个月Great insights Guy. I think the topic is somewhat more complex than it seems, as it includes a new technology that needs be leveraged both by business processes and IT solutions. Curious to see how the future pans out for AI in supply chain planning.