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How can you use data to make better decisions as a plant manager?

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As a plant manager, you are responsible for overseeing the operations, performance, and profitability of your manufacturing facility. You have to deal with various challenges, such as optimizing production processes, ensuring quality and safety standards, managing inventory and logistics, and leading your team. How can you use data to make better decisions as a plant manager? In this article, we will explore some of the benefits and best practices of data-driven decision making for manufacturing engineering.

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Krish Sengottaiyan

Senior Director, Industrial & Manufacturing – Helping Manufacturing Leaders Achieve Operational Excellence & Supply…
Nagadeepu VS

Lean Manufacturing Consultant | Accelerate Revenue & Production - No New Hires or Equipment Needed | Applying Practical…
Anand Prakash

Assistant Manager-Mfg,Process & Project Engineering at WIKA GZB.Worked in Poland???? & Germany????on New Projects &…

1 Data-driven decision making

Data-driven decision making (DDDM) is the process of collecting, analyzing, and using data to inform and improve your actions and outcomes. DDDM can help you gain insights into your plant's performance, identify problems and opportunities, and evaluate the impact of your actions. DDDM can also help you communicate more effectively with your stakeholders, such as customers, suppliers, employees, and senior management, by providing evidence and transparency for your decisions.

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Krish Sengottaiyan

Senior Director, Industrial & Manufacturing – Helping Manufacturing Leaders Achieve Operational Excellence & Supply Chain Optimization | Thought Leader & Mentor |
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When a manufacturing plant manager (PM) can access live data, they can make informed decisions. Here's how: Increased Certainty: With data at their disposal, PM can rely on factual information. Efficient Resource Allocation: Data-driven decisions enable PM's to allocate resources more effectively. Enhanced Quality Control: Data allows for real-time monitoring and control Identifying and Addressing Bottlenecks: Data analytics provide insights into the performance of different processes. Effective Risk Management: Data-driven insights enable PM's to identify and mitigate risks associated with manufacturing ops. Competitive Advantage: Leveraging data effectively provides a competitive edge in the manufacturing industry.

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Nagadeepu VS

Lean Manufacturing Consultant | Accelerate Revenue & Production - No New Hires or Equipment Needed | Applying Practical Lean | Lean Guide Manufacturing Strategy & Execution | Greenfield Project Setup expert
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Go see actual problems, Touch feel the actual product and discuss with actual persons on the possible route causes and present detection system failure reasons

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Anand Prakash

Assistant Manager-Mfg,Process & Project Engineering at WIKA GZB.Worked in Poland???? & Germany????on New Projects & Line transfer to India????|NPI|Six Sigma|MOST|Industrial & MFG Engg|Industry4.0|PFMEA|Capex|OE|VSM|APQP
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To ensure data-driven decision-making as a plant manager, implement robust systems for data collection and integration. Clearly define and track key performance indicators (KPIs) aligned with plant goals. Establish procedures for data quality assurance, regularly validating and cleaning data. Utilize advanced analytics tools to interpret and analyze data, extracting meaningful insights. Train team in effective data interpretation, fostering a data-driven culture. Continuously monitor key metrics for real-time awareness and proactive decision-making. Incorporate data into meetings using visualizations for better understanding. Drive continuous improvement initiatives based on data insights, optimizing plant operations.

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2 Data sources and tools

To use data effectively, you need to have access to reliable and relevant data sources and tools. Depending on your plant's size, complexity, and industry, you may have different types of data available, such as production data, quality data, inventory data, maintenance data, customer data, and market data. You may also have different tools to collect, store, process, and visualize your data, such as sensors, databases, spreadsheets, dashboards, and analytics software. You should choose the data sources and tools that suit your needs and goals, and ensure that they are accurate, consistent, and secure.

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3 Data analysis and interpretation

Once you have your data sources and tools, you need to analyze and interpret your data to extract meaningful information and insights. You can use various methods and techniques to analyze your data, such as descriptive statistics, inferential statistics, trend analysis, root cause analysis, and predictive analysis. You can also use various frameworks and models to interpret your data, such as SWOT analysis, SMART goals, PDCA cycle, and DMAIC methodology. You should use the appropriate methods and techniques for your data type, question, and objective, and avoid common pitfalls, such as bias, error, and overfitting.

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Anand Prakash

Assistant Manager-Mfg,Process & Project Engineering at WIKA GZB.Worked in Poland???? & Germany????on New Projects & Line transfer to India????|NPI|Six Sigma|MOST|Industrial & MFG Engg|Industry4.0|PFMEA|Capex|OE|VSM|APQP
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Aspiring to be a plant manager, I'll champion data-driven decisions by mastering analysis techniques, setting clear metrics, and investing in analytics tools. Collaborating cross-functionally, promoting data literacy, and using visualizations will be my strategies. Initiating small pilot projects will kickstart the data-driven revolution. ??????

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4 Data action and evaluation

The final step of data-driven decision making is to use your data analysis and interpretation to take action and evaluate the results. You can use your data to support or challenge your assumptions, hypotheses, and intuition, and to guide or justify your actions and recommendations. You can also use your data to monitor and measure the outcomes and impact of your actions, and to learn and improve from your feedback and results. You should use your data to align your actions with your vision, mission, and strategy, and to create value for your plant and stakeholders.

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5 Data culture and skills

To use data effectively, you also need to foster a data culture and develop data skills in your plant. A data culture is a set of values, beliefs, and behaviors that promote the use of data for decision making. A data culture can help you create a common language, vision, and purpose for your data, and to encourage collaboration, innovation, and learning among your team. Data skills are the abilities and competencies that enable you to use data effectively. Data skills can include data literacy, data analysis, data visualization, and data storytelling. You should invest in training, coaching, and mentoring to develop data culture and skills in your plant.

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Ir. Zairul Amri Zakaria

Director, Engineering Accreditation, Board of Engineers Malaysia
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As a plant manager, embracing a data-driven approach can revolutionize decision-making. Imagine having insights at your fingertips, guiding you through every step of the production process. It's like having a trusted advisor by your side, offering valuable suggestions based on hard evidence. Developing a strong data culture amongst your team empowers everyone to contribute their expertise and insights. By honing data skills, from analysis to interpretation, you unlock the potential to streamline operations, reduce costs, and enhance productivity. With data as your compass, navigating the complexities of plant management becomes not just efficient, but also deeply rewarding.

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6 Data challenges and opportunities

Using data for decision making can also pose some challenges and opportunities for you as a plant manager. Some of the challenges may include data quality, data security, data privacy, data ethics, and data governance. You should address these challenges by establishing and following data policies, standards, and best practices, and by ensuring compliance and accountability. Some of the opportunities may include data integration, data innovation, data collaboration, and data transformation. You should seize these opportunities by exploring and experimenting with new data sources, tools, methods, and applications, and by sharing and leveraging data across your plant and organization.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you use data to make better decisions as a plant manager?

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1 Data-driven decision making

2 Data sources and tools

3 Data analysis and interpretation

4 Data action and evaluation

5 Data culture and skills

6 Data challenges and opportunities

7 Here’s what else to consider

Manufacturing Engineering

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How can you use data to make better decisions as a plant manager?

1

2

3

4

5

6

7

1 Data-driven decision making

2 Data sources and tools

3 Data analysis and interpretation

4 Data action and evaluation

5 Data culture and skills

6 Data challenges and opportunities

7 Here’s what else to consider

Manufacturing Engineering

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