Continuous Improvement

Data Collection with KPIs and KPOVs

Continuous improvement (CI) methodology is a systematic and ongoing approach to identifying, analyzing, and improving processes within any industry. In the grain and feed industry, such methodology can be applied to data collection to meet customer expectations and enhance efficiency and profitability.

Data collection is an essential aspect of any industry, and its accuracy and consistency are critical. The CI process helps identify areas where data collection can be improved, resulting in greater efficiency, safety, compliance, sustainability, as well as an ability to meet customer expectations more consistently. CI also may result in a reduction in errors and variability in data collection, leading to more reliable, repeatable, and accurate outcomes.

To achieve reliability and repeatability, data collection must be accurately performed and provide meaningful information that can be used to draw conclusions. Without accurate data, the results may be unreliable or inconsistent thereby making it difficult to reproduce the results.

Performance Metrics

The phrases “key performance indicators” (KPIs) and “key performance output variables” (KPOVs) are metrics used to monitor and measure performance, but they differ in their focus.

KPIs track and measure performance of a business or organization toward achieving its goals. They are frequently used to measure progress towards business objectives, such as revenue targets, customer satisfaction, or employee productivity. KPIs reflect the high-level strategic objectives of an organization and are monitored over a longer period.

On the other hand, KPOVs monitor and measure the output of a specific process. They are usually more focused on operational processes than on high-level objectives. KPOVs are used to identify variations and irregularities in the output of a process, which can then be corrected or improved. They are often monitored in real-time to provide immediate feedback on process performance, and they may change frequently as the process evolves.

The main difference between KPIs and KPOVs is that the former are focused on measuring high-level business objectives over a longer period, while the latter are focused on monitoring specific process outputs in real-time.

Practical Examples

Here are some examples of KPIs and KPOVs that could apply to a grain elevator operation:

CI methodology is used to analyze the KPOVs and identify areas for improvement, such as optimizing equipment or modifying processes. This data can be used in combination with CI methods to optimize processes and improve service. For instance, let’s consider grain quality data. If the data indicates that the product consistently has a high moisture content, the CI process can be employed to identify potential causes, such as inadequate drying methods or storage conditions. Once the root cause is identified, corrective actions can be taken to implement corrective actions, such as additional training, changing standard operating procedures, upgrading equipment, or modifying the storage environment, with the goal of reducing the moisture content and improving the product’s quality.

Similarly, if equipment performance data indicates that maintenance is needed more frequently than anticipated, the CI process can help identify the root causes of equipment breakdowns, such as worn-out parts, inadequate operator training, or poor maintenance practices. This data can be used to optimize equipment repair schedules, implement preventive maintenance measures, and staff training, or even invest in newer, more efficient equipment altogether.

It’s essential to match your KPIs and KPOVs to customer needs. By understanding customer requirements, you can create CI metrics that are aligned with customer expectations and guide decisionmaking processes that prioritize the customer’s needs. For example, KPIs such as the percentage of on-time deliveries, accuracy of invoicing, or grain quality specifications may be essential to meet customer needs. KPOVs such as test weight, moisture content, or foreign material levels may be key inputs to aligning grain quality standards with specific customer requirements.

By aligning KPIs and KPOVs with customer needs, organizations can enhance customer satisfaction, build brand loyalty, develop more profitable relationships, and potentially differentiate themselves from their competitors. In the customer-centric grain industry, matching KPIs, and KPOVs with customer needs is essential to creating a competitive advantage.

Common Barriers

Common barriers to installing CI methodologies generally involve changes in business operations that may impact employee roles, responsibilities, and attitudes. Some barriers that may be encountered when enhancing the data collection process include:

1. Resistance to change: Some workers may resist new data collection methods due to a lack of familiarity or comfort with new technology or processes, leading to a reluctance to adopt new procedures.
2. Lack of resources: Limited resources, such as funding, staff, or technology, can hinder the ability to collect relevant data.
3. Inadequate training: It’s essential that workers are trained to collect data accurately and consistently. Without proper training, data may be incomplete or inconsistent.
4. Data privacy and security concerns: The collection of sensitive data, such as customer information, must be done with extreme care to ensure privacy and security.
5. Limited access to technology: In areas where technology infrastructure is insufficient or unreliable, the collection of accurate and timely data may be challenging.

Ensuring data integrity

To ensure the quality and accuracy of data collection, data should be sorted, stored, and used according to the following best practices:

• Data should be sorted in a way that is intuitive and easily accessible for future analysis.
• Use a secure platform to store data to prevent loss and ensure confidentiality.
• Validating data, comparing to regular performance, and finding trends are important to draw insights.
• Data should be regularly monitored and analyzed to identify potential anomalies, trends, and patterns.
• Data visualization techniques, such as charts and graphs, can help identify issues and make the results easy to understand.

In summary, managing a grain elevator operation without sufficient data poses significant risks to production efficiency, quality control, customer satisfaction, and overall profitability and sustainability.

The CI data collection process provides insights into the day-to-day and long-term operating trends. By tracking these KPIs and KPOVs, grain elevator operators can gain insights into their operations that facilitate better decision-making. Continuous monitoring of these variables can highlight areas where practices could be improved, streamlining workflows, improving product quality, reducing downtime, and enhancing the overall efficiency and profitability of the facility.

Jim Voigt is the president of JFV Solutions Inc. and has over 48 years of experience in management and operations in the feed, grain, and grain processing industries. Jim is also trained in and has over twenty years’ experience in continuous improvement methodology.