It’s crucial to prioritize data management before jumping into implementing analytics. Because different departments and processes produce different data types, a common data language is a good place to start to help structure the data and break down silos.
How Investments in Supply Chain Technology, Analytics and Visibility Strategies Can Improve Manufacturing Organizations
Q&A with Geoff Coltman, Vice President | Catena Solutions
Tell us about yourself and your role with Catena Solutions
My name is Geoff Coltman, a vice president at Catena Solutions, a supply chain consultancy that specializes in supporting supply chain organizations with their business initiatives including digital, financial, human capital, and supply chain transformations. As a Vice President, I am responsible for strategic sales growth, client development, and operations planning for companies seeking to solve their supply chain problems.
What are some key benefits of introducing data analytics to manufacturing processes? How can investing in supply chain analytics help companies minimize the impact of disruptions?
Introducing data analytics to manufacturing processes leads to greater accuracy, decreased response time, savings on labor costs, better inventory management, greater visibility, and error prevention, all of which contribute to higher revenue and more efficient operations.
Companies are experiencing disruptions like never before, causing leaders to harness the power of supply chain analytics. Advanced analytics systems are incredibly helpful in uncovering patterns and identifying and predicting risks along the supply chain network. This allows leaders to formulate responses to potential disruptions before they wreak havoc on supply chain operations.
However, the human element is still crucial in this process, as data analytics professionals are needed to analyze potential disturbances and take action to avoid them.
What are the key steps to successfully implementing supply chain analytics? Are there any important factors supply chain leaders should consider when beginning implementation?
Implementing supply chain analytics involves a systematic approach and consideration of several key steps and factors. Supply chain leaders must:
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Define clear objectives: Stakeholders must agree on the challenges and objectives they want supply chain analytics to solve. Without a true north, it’s unlikely implementation will go according to plan.
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Prioritize data collection: Companies must ensure they have access to the right data sets and confirm a large amount of data is able to be analyzed. Proper analyzation is nearly impossible without high quality data.
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Procure the right tools: There’s no shortage of analytics tools on the market, and it can be intimidating to select the right systems. Working with an implementation partner can help companies pick the right tools for their goals.
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Invest in change management: Successful technology implementation goes hand in hand with proper change management and creating a comprehensive plan for employees to adopt the new systems.
I want to emphasize the importance of clean, reliable data for successful analytics implementation. It’s crucial to prioritize data management before jumping into implementing analytics. Because different departments and processes produce different data types, a common data language is a good place to start to help structure the data and break down silos.
Can you discuss how manufacturing companies leverage technology solutions, such as analytics and real-time monitoring, to enhance their supply chain visibility? What manufacturing-specific challenges might arise during the implementation of these technologies?
Manufacturing organizations can leverage advanced analytics, including predictive and prescriptive analytic strategies, to identify patterns, trends, and potential disruptions across manufacturing activities. Through these strategies, manufacturers can forecast demand, optimize production schedules, respond to market trends, and prepare for maintenance needs.
Additionally, when paired with real-time monitoring dashboards, manufacturers gain a live view of supply chain operations and can track production progress, inventory levels, order status, and shipments in real time. This is especially helpful for achieving visibility into the logistics and transportation process. Real-time analytics detect inefficiencies and suggest modifications to move assets more effectively, optimize routes, reduce delays, and more.
During implementation, manufacturing organizations may struggle with data standardization as data is often siloed, and standardizing the quality and consistency of data from diverse sources can be challenging.
Additionally, adoption is a common challenge. Employees may be slow to adopt new tools and processes, so it is imperative companies create comprehensive educational and training programs to successfully transition employees to using the new systems and dissolve resistance. Working with a change management expert is often needed for successful implementation.
Can you discuss the role of data analytics in facilitating demand forecasting and inventory management, enabling manufacturers to optimize their production levels and reduce excess stock?
By leveraging data analytics for demand forecasting and inventory management, manufacturers can reduce carrying costs associated with excess inventory and minimize stockouts through modeling technologies which provide insights into a company's stock data.
For example, supply chain analytics systems can identify shortages and overstocks, warn if stock levels are at odds with expected demand, and suggest corrective actions to take.
Data analytics also help manufacturers ensure the availability of raw materials, components, and products to optimize order fulfillment and manufacturing processes.
In what ways are predictive analytics being employed to anticipate equipment maintenance needs and prevent costly downtime in manufacturing plants?
By identifying maintenance needs in advance, predictive analytics help manufacturers avoid unplanned downtime, costly emergency repairs, and production disruptions, which translates into substantial cost savings. Additionally, predictive analytics can also be used to forecast the need for spare parts, helping prevent delays in obtaining replacement parts. By leveraging data from sensors, historical maintenance records, and other sources to predict when equipment is likely to fail or require maintenance, organizations can fix issues before they arise.
That’s why data analytics systems are revolutionizing Maintenance, Repair & Operations (MRO) processes. MRO are the activities that keep a manufacturing plant operating efficiently, from facility maintenance to systems and equipment upkeep. Historically, MRO was a manual and labor-intensive job, but new analytics tools can predict MRO needs in real-time, which leads to less downtime and fewer production stoppages.
What types of data sources are commonly collected within manufacturing environments, and how are these data points being leveraged for informed decision-making?
Manufacturing environments collect a wide range of data which are crucial for gaining insights into production processes, quality control, equipment performance, and more. Common data collected includes machine speeds, changeover time, machine downtime, maintenance records, repair histories, scrap rate, amount of product produced, and more.
With so many data points in the manufacturing process, it can be challenging for manufacturers to know what data to focus on. To correct inefficiencies and improve operations, we recommend focusing on the largest bottlenecks and collecting data that can inform the root cause of issues.
Geoff Coltman is Vice President of Catena Solutions, a supply chain consultancy that specializes in supporting supply chain organizations with their business initiatives including digital, financial, human capital, and supply chain transformations.
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
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