Smarter Swifter Demand Forecasting in CPG; Understanding Predictive Models
If there is one ‘mantra’ for CPG businesses to sail through time’s tidal storms, it is a shift from being ‘reactive to proactive.’ Such an extraordinary shift calls for agile and adaptive strategies focusing on future outcomes with a compelling growth trajectory. With Artificial Intelligence (AI) unlocking possibilities within the supply chain functionalities, predictive models emerge as the quintessential disruptor to this burgeoning revolution. Expected to grow into a $309 billion industry by 2026, 44% of senior executives agree that AI implementation, including predictive analysis, has consistently decreased operational costs and positively impacted the supply chain.
How AI-powered Supply Chain Analytics are Making a Difference
Predictive Models are tools and analytics that analyze patterns, relationships, and relevant external factors to facilitate active, more informed decision-making. Whether optimizing the inventory management process to prevent shortages, forecasting demand at the most granular level, or reducing transportation costs by analyzing real-time traffic or weather data, predictive models proactively enhance operational efficiency and cost-effectiveness.
The key ways for optimizing analytics solutions for CPG are:
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Forecasting Demand
With access to historical sales data, market trends, and consumer preferences using predictive models, companies can mitigate stock shortages and better manage the inventory by forecasting the demand in a timely and accurate manner
Risk Management
Predictive analysis helps maintain the supply chain resilience by identifying any supply disruptions such as logistical delays or bottlenecks, ensuring preparedness and risk mitigation solutions
Logistics Efficiency
Utilizing real-time data on conditions for weather, traffic, and shipment history, predictive analysis helps in route optimization and timely dispatches for enhanced efficiency, speed and reduced cost of transportation
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Inventory and Production Planning
Ensuring the demand matches the supply can be critical to any business, and predictive models facilitate forecast accuracy improvement in CPG by analyzing demand and supply fluctuations and streamlining production timelines
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Customer Satisfaction
Predictive models facilitate timely delivery for the right products and help maintain high customer satisfaction scores through accuracy and seamless logistical experiences
Forces that Drive the Demand for Analytics Solutions for CPG
The following critical factors govern the rising demand for predictive models-based solutions in the CPG industry:
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Rapidly Changing Consumer Behavior
With online and offline shopping platforms becoming equally popular, companies must understand changing consumer preferences while obtaining detailed insights into the evolving trends and consumer segmentation. As a result, there is a dire need to track trends using predictive models
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Technology Adoption and Digital Advancements
The widespread adoption of advanced technologies and IoT across industries has enabled companies to leverage data to generate near-real-time predictive analysis. Cloud data platforms and tech infrastructure investments are widening, allowing companies to future-proof their business models
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Competitive Markets
In a dynamically evolving market, sustaining and growing market share requires CPG companies to scale up their capabilities with insight-led decisions, emphasizing the need for predictive analysis and its derivative inputs for strategic planning
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Data Integration Capabilities
Mammoth quantities of both structured and unstructured data made available through multiple sources (social media, IoT devices, and traditional retail data) make it essential to utilize predictive models to generate insights across the value chain at every stage of the process
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Complex Supply Chain Dynamics
Delays and disruptions impact various stakeholders, from suppliers to manufacturers and distributors to retailers. With predictive models, businesses not only tailor sales forecasting in CPG but also help generate demand prognosis, resulting in better allocation of inventory and logistics
Real-world Applications of Predictive Models and Their Impact
By translating data forms into insights, predictive analysis enhances overall supply chain efficiency and creates a profound real-world impact for CPG companies. Some key examples of CPG marketers leveraging CPG are:
- Walmart harnesses predictive analysis based on consumer online searches and sales data to combat any sales challenges and inefficiencies. This helps the brand regulate the inventory flow for peak days and holiday consumption, strengthening the supply chain and providing better customer experiences.
- Another CPG giant, Kraft Heinz, transitioned from Hadoop to Snowflake (cloud platform) to break down any data silos and leverage data-based insights to consolidate their supply chain functions; this integration helped the brand in reducing inefficiencies and also led to incremental revenue growth of 10%
- Constantly facing inventory management challenges, Nestle resorted to machine learning models to help predict inventory needs across regions. Utilizing the data for sales, macroeconomics, promotions, and buying patterns, the company designed a more effective inventory planning process, making them agile to demand response and reducing stock wastage
Roadblocks and Rehauls: Understanding the Common Challenges in Technology Adoption
While the adoption of predictive analysis-based tools is gaining imminence for CPG companies, marketers face the following challenges:
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Legacy systems
Traditional legacy systems are fragmented and obsolete, making integrating advanced predictive analysis practices on these platforms challenging. Therefore, most CPG companies are required to shift and upgrade to more expensive systems
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Data Quality
Despite the availability of extensive data from multiple sources, most CPG companies struggle to leverage it efficiently due to its inconsistent and incomplete nature. Siloed data reduces reliability and accuracy, demanding better data churning
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Volatile Demand
Affected by several external factors such as consumer trends, seasons, promotional offers, pandemics, and even geopolitics, CPG demand is complex and highly volatile, making it challenging for predictive models to capture its dynamics
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Stakeholder Complexities
The CPG supply chain includes several stakeholders- suppliers, manufacturers, distributors, and retailers. Utilizing and aligning data from each of these entities to gather insights is time-consuming and often limits the data optimization possibilities
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Talent Shortages
Deploying machine learning-based models requires special skills and learning around supply chain and AI, and most companies often fall short of trained resources, resulting in slow adoption and limited efficacy of the initiatives taken
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Data Security Concerns
CPG companies are at the center of sensitive consumer and business data, with extensive access to consumer preferences and user history, making it a prerequisite to safeguard critical data and prevent breaches. Maintaining the required compliance and security levels makes it challenging to optimize the predictive analysis
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Real-time Visibility and Tracking
To optimize predictive models, it is important to ensure real-time access to critical information such as inventory, store stock-outs, and shipment updates. Thus, CPG companies without advanced technologies and futuristic devices such as RFIDs and IoT tools often fail to leverage the benefits of predictive analysis
Impacting the Future of CPG Industry Globally
The increasing adoption of predictive modeling to meet rising consumer expectations is evidence that AI- and ML-based models will define the future of the CPG industry. Poised for a remarkable transformation through the next few years, the CPG industry is set to witness the following key trends:
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Trend Anticipation and Product ideation
Access to data-driven insights will aid CPG companies in discovering new ingredients, materials, flavors, packaging styles, etc., catering to evolving customer preferences and market sentiments. These insights will drive the success of new strategies and product launches, improving overall growth
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Shift Towards Sustainability
AI and ML tools will make it possible to sift through otherwise unknown alternate ingredients and formulations by analyzing biological and chemical databases, leading to innovation and sustainable ways of doing things in the future
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Global Collaboration
With extensive insights from databases worldwide, predictive modelling will foster mutually beneficial collaborations among critical teams such as marketing, R&D, and supply chain of various CPG companies
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Enhanced and Real-time Quality Control
By integrating sensor data with predictive models, companies can monitor critical production variables (humidity, temperature, fill levels, etc.) using IoT-enabled manufacturing tools. Flagging anomalies using these smart techniques will help refine formulations and processes consistently
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Effective Bottomline
Leveraging automated simulations and IoT-based smart production technologies, CPG companies will need less time for physical testing, allowing them the bandwidth to innovate and improve their production cycles while cutting costs
Unlocking efficiencies for globally renowned CPG players, WNS deploys predictive modelling and advanced analytical tools to study patterns and identify trends and generate insights. From inventory streamlining to reducing freight costs by up to 50%, WNS employs multivariate predictive models to create accurate demand forecasts for multiple markets, SKUs, and products.
Enabling a leading retail client to monitor their supply chain operations, WNS used ML models to create retail dashboards reflecting every process step, from raw material logistics to sales. This allowed the client to obtain a 360-degree operations view, minimizing disruptions.
Empowering CPG clients with a holistic market understanding through detailed, data-based insights, WNS supports growth and long-term plans by combining domain expertise, AI, and ML tools for predictive modeling. Discover how we can bring a transformative shift for your organization by unlocking the power of AI and predictive modeling. Connect with us to learn more.