Digital Twins: The Missing Piece in CPG Industry Success

CPG companies operate in a fast-paced and extremely competitive market where efficiency and agility are vital. A CPG company’s ability to satisfy consumer demand, optimize production and manage their supply chain is compromised by specific challenges when it doesn’t leverage AI-enabled digital twin technology.

Digital twin technology provides real-time replicas of actual systems to better understand their expected performance, reduce costs, boost efficiencies and extend the life of stock by leveraging real-time data from the IoT and data lakes to inform business decisions.

Challenges CPG Businesses Face Without Digital Twin Technology

1. Limited Predictive Maintenance

CPG companies operate production facilities that are heavily dependent on machinery and equipment to achieve production targets. Without digital twins, they are unable to determine equipment failures in advance, leading to unexpected shutdowns and disruptions to production plans and affecting product availability. Digital twins will also prevent them from taking a reactive maintenance approach, which leads to higher maintenance costs, shorter equipment lifespans and reduced profitability.

2. Inefficient Production Processes

CPG companies often face a lack of visibility into their manufacturing operations, unable to see where exactly they can cut costs and boost capacity. They lack a means to identify and address inefficiencies in areas such as resource allocation, equipment utilization and production scheduling. As a result, they tend to fall victim to longer cycle times, higher production costs and lower overall productivity. A digital twin can help CPG companies spot opportunities to drive savings.

Also Read: How Digital Twins Help the Oil & Gas Industry

3. Poor Supply Chain Visibility

CPG companies depend on efficient supply chains to get raw materials and goods on time. But without digital twins, companies have no real-time visibility into the supply chain. This makes it difficult to predict and react to changes in demand, inventory levels and supplier performance. Instead, companies are stuck not knowing the impact of delays or issues that affect people, plants and inventory. This creates risks of stockouts, excess and supply chain disruptions, ultimately affecting customer satisfaction and the overall brand.

4. Inaccurate Demand Forecasting

Accurate demand forecasting is critical if CPG companies want to efficiently align inventory balances with sales forecasts and production schedules. Without digital twins, companies are unable to incorporate real-time data and advanced analytics in their demand forecasting process, and CPG companies often use demand forecasting. Lack of which results in overstocking, stockouts and missed sales opportunities. In addition, poor demand forecasting impairs strategic planning by making it more difficult for CPG companies to engage in strategic decision-making.

5. Limited Decision-Making Insights

Effective decision-making is critical for CPG companies to remain competitive in a rapidly evolving market. However, without digital twins, companies lack the insights needed to make informed decisions. The absence of real-time data analytics and simulation capabilities hinders companies' ability to evaluate different scenarios, assess the impact of strategic initiatives, and optimize their business processes. This leads to suboptimal decision-making, missed opportunities, and an inability to innovate and adapt to changing market conditions.

To Sum Up

Put simply, without a digital twin no manufacturer in the CPG industry can adequately track and optimize their production facilities, or effectively understand their supply chain. Without the visibility afforded by the digital twin and its real-time, predictive features, leveraged by powerful machine learning, CPG leaders would not be able to simultaneously act upon the changing needs and demands of consumers while keeping unit costs down and competing in a marketplace that is increasingly competitive.

Learn about GEP’s AI-powered supply chain software.