In an age where data is the new oil, industrial organizations are sitting on mountains of untapped potential. Implementing a comprehensive industrial data strategy can help to unlock this potential and drive strategic advantage. From asset performance to maintenance needs, these organizations collect vast amounts of data.
Yet, surprisingly, 60% to 73% of this data is never successfully used for any strategic purpose, according to Forrester. This underutilization results in missed opportunities to streamline operations and grow business.
The Role of Data Strategy in Manufacturing
To successfully maneuver within the manufacturing business landscape, it’s crucial to have a clear view of daily operations at each level – from shop floor activities to supply chain management. The key to achieving this lies in ensuring you are effectively monitoring pertinent information that will provide insights into the health of each function. This is where a robust data strategy comes into play.
A well-devised data strategy lays the groundwork for data-driven operations, ultimately furnishing you with a wealth of data that can be used to create comprehensive dashboards. These dashboards enable you to monitor and assess the overall efficiency of your business, providing the intelligence you need to make strategic adjustments as needed.
When applied to the manufacturing sector, a data strategy can yield a plethora of long-term advantages, including:
Enhanced Operational Efficiency: Through the identification of bottlenecks, inefficiencies, and areas ripe for improvement, a data strategy can help optimize the manufacturing process. By gathering and analyzing data on critical performance metrics such as cycle times and downtime, operational efficiency can be significantly boosted.
Predictive Maintenance: Real-time analysis of data from sensors and machinery can help pinpoint potential issues before they cause significant disruptions. Predictive maintenance can lead to reduced repair costs, increased uptime, and extended equipment lifespan.
Quality Assurance: Data strategies can aid in closely monitoring and controlling product quality. By tracking and analyzing data related to product defects, manufacturers can discern patterns and trends, enabling them to take corrective measures. This, in turn, helps decrease waste, increase customer satisfaction, and boost profitability.
Inventory Optimization: A data strategy can assist manufacturers in optimizing their inventory levels, thus reducing carrying costs. By scrutinizing demand patterns and production schedules, manufacturers can ensure they have the necessary materials and components on hand when needed, without maintaining excessive inventory.
Supply Chain Enhancement: Collecting and analyzing supplier data can help identify risks and opportunities for improvement within the supply chain. This can result in reduced lead times, increased flexibility, and overall supply chain performance improvement.
Product Innovation: By analyzing data on customer preferences and market trends, manufacturers can identify opportunities for product innovation. This enables them to develop new products and services that align with customer needs.
Cost Reduction: A data strategy can help identify areas where costs can be curtailed. By analyzing data on energy usage, raw materials, and other inputs, manufacturers can find opportunities to minimize waste, enhance efficiency, and reduce costs.
Overcome Challenges in Developing Strategies
Industrial organizations are often confronted with numerous hurdles as they embark on developing data strategies for their digital transformation ventures. To ensure success, they need to skillfully navigate obstacles that include data silos, lack of centralization, insufficient security and access controls, and broken organizational memory.
The Challenge of Data Silos
Industrial data, a crucial resource gathered from IIoT devices monitoring plants and industrial assets, often originates from diverse sources. These sources may use different interfaces, varying sample frequencies, and disparate parameters. As a result, this data is frequently stored on local machines, rather than in a centralized database accessible to all employees within the organization.
Such data silos not only complicate the task of ensuring data quality but also create barriers to automated quality testing of machine learning algorithms and other software workflows.
Impediment of Fragmented Data
The fragmentation of data inhibits an enterprise-wide view of operations, making it difficult for industrial organizations to gain real-time insights into how their plants and assets are performing. This lack of centralization strips organizations of the context necessary for informed decision-making, causing missed opportunities to implement proactive strategies such as predictive maintenance.
Security and Access Controls
With fragmented data, the sheer volume of data a company possesses can become obscured, along with the security protocols necessary to protect that data from unauthorized access. Without adequate security measures and data access control, organizations expose themselves to the risk of data breaches. This vulnerability can erode customer trust unless a secure system is in place and complicate the process for customers auditing data.
Broken Organizational Memory
Decentralized data also exacerbates the impact of employee turnover. Without an enterprise-level view of all data, the context for decision-making can be lost when employees leave the organization, taking their knowledge (e.g., understanding why specific data is being collected and how it is being leveraged) with them. Worse still, if the data in question is stored on a local machine, it may be lost entirely when an employee departs.
How to Construct a Robust Industrial Data Strategy
To construct a robust industrial data strategy that paves the way for successful digital transformation, several strategic steps should be taken:
1. Audit Existing Data
The first step in building an effective data strategy is auditing existing data. This involves identifying data that is unique and up-to-date. Low-quality or outdated data is not worth the cost or effort to store and can lead to inaccurate insights and poor decision-making.
During the audit process, it’s important to categorize data based on its source, age, relevance, and accuracy. The goal is to understand what data you have, where it’s coming from, and how it’s currently used. This will help you identify gaps in your data collection and areas where data quality needs to be improved.
Another key part of this process is identifying redundant data. Storing duplicate information not only wastes resources but can also cause confusion and inconsistencies in your analysis. A thorough data audit will help you streamline your data assets and ensure that you’re only storing and managing data that adds value to your organization.
2. Standardize Data
Ensure all data «speaks the same language.» Standardize data formatting and parameters across data sources. Platforms like Shoplogix can serve as a bridge connecting different vendors’ data and organizing it for future analysis.
3. Centralize Data
Shift data storage from local machines to a centralized infrastructure. During this process, conduct automated QA checks to ensure future data quality. Ensure robust and reliable connectivity to the targeted user base.
4. Implement Security and Access Controls
Establish access parameters for internal data usage so employees can access only the data required for their role. This not only reduces the risk of data breaches but also ensures that employees have the data they need to do their jobs effectively. Additionally, all data should be logged and tracked for auditing and security purposes. This helps maintain data integrity and provides a clear audit trail for compliance purposes.
By following these steps, industrial organizations can leverage their data more effectively, leading to more efficient work processes, better decision-making, improved plant maintenance, increased client trust, and overall business growth.
Shoplogix: A Comprehensive Solution for Industrial Data Strategy
Shoplogix is an all-in-one platform that assists organizations in developing and implementing a successful data strategy. It offers real-time visibility into production data, enabling manufacturers to track, analyze, and improve their operational efficiency. By centralizing data, it helps eliminate silos and promotes cross-functional collaboration.
Moreover, the platform’s predictive analytics feature allows for proactive strategies such as predictive maintenance, thereby reducing downtime and increasing productivity.
Experience real-time visibility into production data, eliminate silos, and boost productivity. Let Shoplogix help you transform your manufacturing operations through data-driven insights.
