Say the words “product lifecycle management,” and the first thing that probably comes to mind is knowing everything about your product—from its inception, its design/recipe, ingredients, flavor, nutritional benefits, market and competition, capacity availability, packaging, suppliers, distributors, market demand, food safety aspects, quality—to name a few. And product lifecycle management (PLM) software helps processors manage many of these variables and make informed decisions on procuring supplies, manufacturing and distributing product and knowing what real-world conditions could potentially wreak havoc in the marketplace.
Traditionally, PLM has been another tool often used in conjunction with ERP, manufacturing execution systems (MES) and inventory systems to better coordinate product design, planning and production, and shipping. Today AI and ML (machine learning) have boosted the power of PLM, MES and ERP systems.
Powering PLM with AI/ML
AI’s predictive capabilities can offer food and beverage processors a critical edge in anticipating trends that ultimately translate to tight alignment with consumer preferences, says Tom Nall, global industry executive – manufacturing and automotive, Avanade. “Sudden shifts in consumer preferences, personalization expectations and the need for a stronger emotional connection between consumers and products each hinder manufacturers’ ability to meet evolving customer demand.”
AI can analyze vast amounts of data from various sources, such as social media, online reviews, sales data and market research reports, says Tyler Newcombe, digital evangelist lead – CP&R, Siemens Digital Industries Software. “By leveraging machine learning algorithms, AI can help identify patterns and emerging trends that are not immediately obvious to humans. Data sources like social media analytics, point-of-sale systems and customer feedback platforms are critical in this process. Software products like PLM, ERP and advanced analytics platforms are instrumental in handling and processing this data to generate actionable insights.”
“AI can predict trends and consumer preferences to identify patterns, correlations and anomalies that may not be immediately apparent,” says Jeff Olchovy, SVP engineering at Crisp. CPG companies have lagged in utilizing data sets such as point of sale and loyalty card information to uncover trends and insights into supply and demand, shopper behavior and consumer preferences across different stores and geographies.
By analyzing vast datasets, from purchasing patterns to supply chain logistics and even global market shifts, AI can uncover hidden insights and project emerging consumer demands, says Nall. Valuable data sources include historical sales records, point-of-sale data, consumer surveys, and external insights from trend-monitoring platforms and retail partners. Leveraging technologies such as PLM, ERP and MES can enable manufacturers to have the agility and flexibility to quickly pivot operations in response to changing consumer preferences. PLM brings disconnected, siloed product development, procurement, production and marketing/sales departments together and helps to overcome today’s challenges.
However, the major challenge in leveraging these insights is the difficulty in integrating data across various organizational silos that different credential holders often manage, adds Olchovy. Companies must integrate data in a single warehouse and stitch together disparate data sets. AI can facilitate this process by allowing data to be joined and cleaned efficiently to identify common elements for analysis. With the proper data integration and technologies, companies can better predict consumer trends and preferences to gain a competitive edge in the market.

While food processors leverage tools like PLM, ERP and MES, advanced analytics platforms like Microsoft Azure, Power BI, Qlik and Tableau further enhance these systems, helping decision-makers interpret complex datasets and adapt quickly to changing consumer demands, says Kate Brown, partner, data & analytics at Wipfli, LLP. “This integrated approach ensures that processors not only understand consumer preferences but also stay ahead of market trends, driving innovation and efficiency.”
PLM: A Single Source of Truth
A single source of truth on product data is vital. Take product packaging: the information needs to be accurate to comply with nutritional information and allergen disclosure regulations and to help ensure product safety. A PLM application means the artwork designer has the information at their fingertips rather than having to hunt for the right person to brief them.
When a product’s ingredients change based on new regulations or when entering new territories, a comprehensive product lifecycle management solution can cover all aspects of a business and help manage multidisciplinary engineering teams. Recipe changes and the scaling up and down of production can occur faster—with greater ease—leading to more opportunities to make an impact in the marketplace.
A cloud-based PLM application helps establish a digital approach that integrates program, project and product lifecycles. This means manufacturers can easily manage brand hierarchy and brand assets while streamlining program planning. The value is widespread:
- Increased security and reliability: Instantly enable the highest standards of privacy and data security.
- Flexible provision: Scale up or scale down your platform anytime.
- Lower costs: Reduce the cost of ownership with predictable operational expenses and minimal IT infrastructure. Flexible business models and contracts lead to reduced costs.
- Global access: Instant PLM access for all stakeholders, anytime, anywhere
- Accelerate product development: Leverage existing knowledge to support design re-use, manage change and speed cycle time
- Streamline product development and manufacturing processes with internal and external stakeholders
- Create a multi-domain bill-of-materials to visualize your entire product
—Tom Nall, Avanade
AI Models Manage Recipe and Ingredient Optimization
ERP suppliers are building in AI models to their cloud-based systems, for example, IFS Cloud, where IFS.ai is the backbone of the system, according to Kevin Miller, IFS chief technology officer. “IFS Cloud offers food and beverage companies seamless quality control encompassing compliance, audits, in-process controls, positive release and traceability—all of which empower manufacturers to effectively monitor and proactively evaluate processes for potential areas of concern. Companies can add specialized capabilities for each process, such as recipe/formula management, lot/batch tracking, yield optimization, by-product management, shelf-life monitoring and expiration date management.”
AI models can help optimize recipes and ingredients by analyzing extensive datasets on ingredient costs, nutritional information and consumer preferences, says Siemens’ Newcombe. “These models can suggest ingredient substitutions that maintain or improve the nutritional value while reducing costs. For high-end products, AI can identify unique ingredients that meet specific quality criteria and consumer demands. As AI continues to grow it will eventually be able to accurately simulate and predict taste, flavors and mouthfeel by analyzing chemical compositions and historical data on sensory profiles, ensuring the final product aligns with desired characteristics.”

“Firms like Givaudan are leveraging AI models to streamline food and flavor formulation,” says Avanade’s Nall. “AI models can analyze ingredient costs and suggest alternatives that maintain the desired quality while reducing expenses. This ensures that products remain economically viable without compromising on quality. PLM systems, enhanced with machine learning, can integrate data-driven insights, which allow companies to experiment with and refine formulations. This technology enables processors to bring products to market with confidence in both quality and profitability.”
AI can optimize recipes to meet specific nutritional goals, such as reducing salt, sugar or fat content while maintaining the same taste, adds Nall. It can even simulate and predict the textural properties of food, ensuring that the final product has the desired mouthfeel. This is particularly important for products like meat alternatives, where texture plays a crucial role in consumer acceptance.
While AI models can suggest innovative ingredient combinations to meet dietary needs, enhance flavor profiles or create unique recipes that cater to niche markets like health-conscious or specialty consumers, generative AI models are even more powerful, says Wipfli’s Brown.
“Generative AI models take this a step further by designing entirely new recipes based on culinary patterns and consumer trends, enabling food processors to explore uncharted possibilities in product development,” says Brown. “When it comes to high-end products, AI can analyze premium ingredients and their sensory attributes—such as taste, flavor, and mouthfeel—to ensure that the final product delivers both on quality and market expectations. This capability empowers processors to balance cost, nutrition and innovation, all while tailoring products to evolving consumer demands.”
AI/ML and the Supply Chain
Various AI-powered software solutions are designed to enhance supply chain management. ERP systems integrate inventory, production and sales data to provide a comprehensive view, enabling more accurate demand forecasting and better inventory control, says Wipfli’s Brown. SCM (supply chain management) software specializes in optimizing logistics and ensuring products are available at the right time and place. PLM tools, on the other hand, manage the entire product lifecycle from development to distribution, aligning demand forecasts with market needs and product availability.
The benefits of AI models are they process vast amounts of data to identify patterns and trends that traditional methods might miss, says Avanade’s Nall. Generative AI tools can enable employees to “talk to the data,” analyzing a wide range of data sources, including historical sales data, weather patterns, loyalty program data, social media trends and more. In addition, AI provides real-time data analysis, allowing companies to adjust their forecasts and strategies dynamically as new information becomes available. What’s more, AI tools facilitate better collaboration across different departments, ensuring that everyone is working with the most up-to-date information.
Integrated solutions, such as supply chain management and ERP systems enhanced with AI, are especially suited to this task, as they provide a holistic view of demand dynamics and empower companies to align production with market needs effectively, adds Nall. In today’s dynamic environment, flexibility and agility can be crucial competitive advantages.

An AI-powered ERP not only elevates supply chain operations beyond manual operations, but also provides enhanced EAM (enterprise asset management) and service management (FSM and ITSM) functionality, according to IFS’s Miller. “The IFS.ai Copilot feature now goes deeper than ever, surfacing insights from across the organization. The context-aware Copilot has preconfigured industries capabilities that get even more powerful when integrated with customer data sources, and it knows where users are in IFS Cloud and provides accurate insights related to it.”
IFS.ai can take unstructured data from, for example, a new manufacturing customer PO and auto-create a new order so the production process can be accelerated. The impact of this new order onto the shop floor can then be modeled and analyzed with the new manufacturing scheduling optimization (MSO) simulation capability. IFS.ai does the heavy lifting, enabling production managers to improve capacity planning and meet customer demand. Meanwhile, asset managers can use the simulation capabilities to more accurately predict and plan essential asset maintenance based on different scenarios.
“Through MSO simulation, we have been able to increase capacity utilization for a more realistic production schedule to better meet customer demand,” says Miller. “What-if analysis also allows companies to maximize the usage of available resources as part of creating an optimized manufacturing schedule.”
KFC Western Europe Selects PLM System To Maximize Data-Driven Brand and Customer Experiences
KFC Western Europe, the quick service restaurant business known for its finger-lickin’ good chicken, is implementing Trace One Product Lifecycle Management (PLM) to build deeper efficiencies and data connectivity to unlock improved brand experiences for customers. KFC, a subsidiary of Yum! Brands, selected Trace One over competitors because of Trace One’s dedication to carefully understand its challenges, working closely with the teams at KFC to configure Trace One PLM to effectively support their business needs.
With Trace One PLM, KFC gains the ability to unify and connect its copious amounts of data to leverage a product lifecycle management approach. Trace One’s cloud-based tools with baked-in analytics enable KFC to transform the way it manages its product development, packaging and networking processes for global enhancements. By formalizing project and data management processes, KFC will be able to deliver better customer experiences via improved operational efficiencies and reduced error rates.
KFC will be able to connect and consolidate data from across disparate operational departments. This will equip business leaders with a single-source-of-truth view of the company through standardized processes, reports and more readily identifiable opportunities for optimization and product portfolio growth.
KFC is launching Trace One PLM with the full suite of modules to manage critical business operations that include end-to-end product lifecycle management, packaging development, documentation, data insights and supplier collaboration. KFC will support the following organizational processes with Trace One PLM:
- Connected data management
- A unified operational vision
- Security and regulatory compliance
- Greater brand viability
- Improved supplier relationship management
AI/ML and Demand Forecasting
AI is becoming a crucial tool for food processors to optimize demand forecasting, whether for new products or those already in distribution, says Wipfli’s Brown. By analyzing historical sales data, AI can identify patterns and trends to predict future demand. This capability becomes even more powerful when combined with external factors like weather patterns, seasonal changes and market trends, enabling processors to adjust production and inventory strategies proactively. For new products where historical data may be unavailable, AI can analyze purchasing behavior within the target market to fill the gap until product-specific data is collected.
For existing products, AI and ML can significantly enhance demand forecasting by analyzing historical sales data, weather patterns, market trends, and removing human bias, says Siemens’s Newcombe. “These technologies can identify complex patterns and correlations that traditional methods might miss, providing more accurate forecasts. Software products like supply chain management systems, ERP systems and demand planning tools equipped with AI capabilities can support this type of supply chain optimization. These solutions allow companies to anticipate demand fluctuations, optimize inventory levels, and reduce waste, ensuring a more efficient supply chain operation.
If a food manufacturer has a new product and wants to understand how it might perform in stores, this typically requires costly in-person market testing, says Crisp’s Olchovy. With AI and ML, however, companies can be proactive by using an AI/ML-based recommender system or optimization algorithm to surface similar products and see how the new product may perform based on pricing, ingredients, demographics and store location, allowing them to test product ideas using a data-first approach.
“Data platforms and integration tools can be outfitted with GenAI and large-language model (LLM)-powered data enrichment and feature engineering capabilities that allow data scientists and analysts to create more sophisticated data sets and models from data an organization already possesses,” adds Olchovy. “For example, if a readily available join key does not exist between retail sales and internal ERP data sets, LLMs can derive or infer a connection that allows records to be probabilistically joined with one another. Additionally, the use of mundane, publicly available data like historical weather reports can be made more efficient and impactful by analyzing and deriving features from them using LLMs. A typical daily grain weather report for a given location can be transformed into a time series of severe weather events that can be overlaid on top of sales and inventory data for use in proprietary predictive models.”

AI Tools, Supplier Management and Food Safety
AI-based software tools are transforming supplier management by addressing the complexities of the food supply chain, says Wipfli’s Brown. “A key benefit of AI is its ability to monitor supplier compliance with industry regulations and standards in real-time. By analyzing data from audits, performance metrics and other sources, AI systems can quickly identify deviations from required standards, reducing the risk of regulatory issues and costly recalls. Additionally, AI enables processors to analyze historical supplier data—such as performance, ingredient quality and environmental factors—to predict potential quality issues before they occur.”
AI-powered supplier management tools also make it easier to find new suppliers when new food safety rules come into play. “In October 2023, the governor of California signed The California Food Safety Act,” says Avanade’s Nall. The bill proposed banning four food chemicals known to increase cancer risk, hyperactivity in children, thyroid and liver problems and more. This meant changing the recipe of popular foods. Product Lifecycle Management tools, like Siemens Teamcenter on Microsoft Azure, are invaluable at accelerating the reformulation of products. In addition, integration with ERP systems means the new bill of materials is instantly and accurate conveyed to upstream suppliers.”
AI-driven software tools are key for food processors looking to streamline supplier management, which in turn helps ensure compliance, quality, food safety and end-to-end cost efficiency, adds Nall. Through AI-powered analytics, processors can continuously monitor supplier performance, detect anomalies and predict risks based on historical compliance data, quality metrics and cost trends. Advanced platforms integrate capabilities to offer actionable insights, enabling food processors to make data-driven decisions and foster strategic supplier relationships that support sustainable growth—or identify potential alternate suppliers should the need arise.
AI-based software tools can help food processors with supplier management by automating and enhancing the evaluation of supplier performance, compliance, quality and cost-effectiveness, says Siemens’s Newcombe. These tools can analyze data from supplier audits, delivery records and quality assessments to identify reliable suppliers and flag potential risks. AI-supported platforms like supplier relationship management (SRM) systems, PLM software and ERP modules can manage and monitor supplier compliance with food safety standards, track quality metrics and optimize procurement costs. Additionally, AI can predict potential supply chain disruptions and recommend alternative suppliers, ensuring a resilient and efficient supply chain.

AI Tools for Better Decisions
AI significantly enhances PLM tools by enabling more intelligent and data-driven decision-making throughout the entire product lifecycle, says Newcombe. From initial concept and design through development, production and market launch, AI can streamline processes, improve accuracy and reduce time-to-market. It can facilitate predictive analytics for market trends, automate routine tasks and optimize resource allocation. Integrating AI with PLM tools helps companies stay competitive by fostering innovation, improving product quality and ensuring that products meet evolving consumer demands and regulatory requirements efficiently.
AI tools ensure that food processors remain competitive and responsive in an ever-changing market, says Brown. “Their ability to adapt quickly to evolving conditions makes them indispensable for businesses striving to meet consumer demands while maintaining operational excellence.”