Predictive Maintenance Solutions: Boosting Productivity for CPGs

In the recent past, the global consumer packaged goods (CPG) industry has witnessed the toughest competition due to an oversaturated market, changing consumer behavior, and the incorporation of advanced technologies. In this light, predicting the course of action of the market forces that affect demand and supply is important to developing the right business plan.

Embracing tech-based predictive analytics and maintenance solutions is crucial to keep pace with the constantly varying consumer demand quotient and add value to the entire marketing and supply chain process. Many leading consumer-goods companies have started leveraging CPG data analytics in manufacturing, marketing, supply chain, and delivery processes to boost productivity.

Now, coming to the million-dollar question, how to start? Moreover, how do maintenance analytics boost productivity?

Let’s dig in!

What are Predictive Maintenance Solutions?

These solutions can help you assess the condition or state of your machinery and other equipment and make maintenance suggestions based on those predictions. This approach saves costs compared to preventive routine maintenance by performing maintenance only when it is warranted.

If you are a CPG manufacturer, implementing these solutions can ensure timely equipment maintenance and prevent breakdowns- saving on downtimes and costly repairs. Moreover, this also enables better asset maintenance and improves efficiency and quality, boosting the overall productivity of CPG companies.

How to Get Started?

Although predictive analytics can transform your maintenance program, a world-class maintenance solution is generally not built in a day. Here is how you can drive smart maintenance in your organization by following the simple steps discussed below:

Step 1: Begin with a small pilot system

A pilot maintenance software can be built and implemented within a month for one or two critical assets. This initial system will consist of data streaming connections, embedded sensors, and a basic dashboard for performance visualization.

Step 2: Asset health monitoring

With the available data from the maintenance system, you can get started with asset health monitoring. Any asset failure prediction data will be displayed on the system, and failure thresholds will be optimized.

Step 3: Leverage data science

Expert data scientists can analyze this system data to create predictive models based on machine learning algorithms and help you avoid equipment failures that result in unplanned downtimes and costly repairs.  

Ways in Which Predictive Maintenance Solutions Boost CPG Productivity

Predictive maintenance establishes a cyclic flow of digitized data from physical assets and analyzes physical data digitally to enable CPG organizations to take actions in advance. This approach enables agile operations and removes any disruptions. Besides, by offering a holistic view of data, it provides comprehensive assistance to CPG companies in several other ways as follows:

#1 Accounts for variances to ensure smooth operations

From routine maintenance works to assessing the effect of weather changes or seasonal festivities on demand, labor supply, and raw materials, predictive analytics and maintenance tools ensure smooth operations and reduce unnecessary expenditures with time allocations. The digitized IoT framework allows relevant information to be accessed in a centralized way so the repairs due to machine wear and tear can be conducted periodically based on the real-time data generated from the IoT systems.

#2 Innovation in inventory management

Earlier, CPG companies used to make calculated estimations based on the information from retail managers regarding inventory management. But, with the advent of IoT and predictive analytics, collecting, analyzing, and maintaining data at the granular level has become extremely easy.

A McKinsey report published in 2015 demonstrates that automating inventory management can reduce inventory carrying costs by approximately 10 percent, which could ultimately lead to savings of $5 billion to $15 billion per year in 2025. Therefore, CPG companies must enable automation in inventory level identification, reordering systems, tracking inventory dimensions, and assigning products to retail destinations to gain a competitive edge. In addition, ioT-driven predictive analytics and maintenance tools conduct micro-level data analysis to provide excellent quality control, decrease operational costs, increase productivity and make product delivery from the warehouses to the consumers a cakewalk.

Hence, by leveraging the capabilities of predictive analytics and maintenance solutions, CPG organizations can scale to new heights of success. With the advent of new technologies, now is the right time to make the predictive maintenance transformation!

The Tredence Advantage

Tredence enables CPG organizations to transform their predictive and preventive maintenance approach. We leverage our expertise in CPG data analytics, data management, and visualization to equip you with a customized CPG analytics platform that will help you extract the best business process outcomes and accelerate your digital transformation journey.

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