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How do you use data analytics to identify and prevent obsolescence?

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Inventory obsolescence is the loss of value or usefulness of inventory items due to changes in demand, technology, or market conditions. It can result in excess stock, wasted resources, and lower profits. To avoid these negative impacts, you need to identify and prevent obsolescence using data analytics. In this article, you will learn how to use data analytics to monitor inventory performance, forecast demand, and optimize inventory levels.

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Dennis R.

Red seal heavy duty technician

1 What is data analytics?

Data analytics is the process of collecting, processing, and analyzing data to generate insights and support decision making. Data analytics can help you understand your inventory situation, identify patterns and trends, and predict future outcomes. You can use data analytics tools and techniques, such as spreadsheets, databases, dashboards, and statistical models, to perform data analysis.

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Dennis R.

Red seal heavy duty technician
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This is great if your parts dept has a full time parts tech. But if like some of us we wear many hats.and sitting st a computer to check min and max of inventory..can be time consuming..

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2 How to monitor inventory performance using data analytics?

One of the first steps to mitigate inventory obsolescence is to monitor your inventory performance using data analytics. You need to track key inventory metrics, such as turnover rate, fill rate, stockout rate, and obsolescence rate, to measure how efficiently and effectively you are managing your inventory. You can use data analytics to create inventory reports and dashboards that display these metrics and highlight any issues or anomalies. You can also use data analytics to compare your inventory performance with industry benchmarks and best practices.

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Carol-Ann Mysyk

Beauty Boutique Manager
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I have found it helpful to analyze inventory, by reviewing each brand that I carry by the sales per week and to change my minimum stock levels in accordance to current sales patterns while also forecasting upcoming trends. Each time a plano is changed I revisit minimum stock levels to ensure that we are never in an out of stock situation . Our company has a practice of setting everything back to a minimum stock level of 2 every time a plano is changed This avoids an overstock situation. I then go back into the plano and sort it according to either descending sales ascending sales, or number of facings to adjust orders Replenishment orders should never be filling a hole because that represents a potential sale that has been lost.

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3 How to forecast demand using data analytics?

Another important step to mitigate inventory obsolescence is to forecast demand using data analytics. You need to anticipate the future demand for your inventory items, based on historical data, market trends, customer preferences, and other factors. You can use data analytics to apply various forecasting methods, such as moving average, exponential smoothing, regression, or machine learning, to generate demand forecasts. You can also use data analytics to test the accuracy and reliability of your forecasts and adjust them as needed.

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Dennis R.

Red seal heavy duty technician
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How does this work if the inventory of equipment is your own. In terms how do you brocast the future from inventory needed down the road...when you don't know how busy uour going to be?

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4 How to optimize inventory levels using data analytics?

The final step to mitigate inventory obsolescence is to optimize your inventory levels using data analytics. You need to balance the trade-off between having enough inventory to meet demand and avoid stockouts, and having too much inventory that leads to obsolescence and carrying costs. You can use data analytics to calculate the optimal inventory levels for each item, based on the demand forecast, lead time, safety stock, and reorder point. You can also use data analytics to implement inventory policies and strategies, such as just-in-time, ABC analysis, or cycle counting, to optimize your inventory management.

By using data analytics, you can mitigate inventory obsolescence and improve your inventory management. Data analytics can help you monitor your inventory performance, forecast demand, and optimize inventory levels. You can use data analytics tools and techniques to perform data analysis and generate insights and recommendations. Data analytics can help you reduce inventory waste, increase customer satisfaction, and enhance your profitability.

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How do you use data analytics to identify and prevent obsolescence?

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1 What is data analytics?

2 How to monitor inventory performance using data analytics?

3 How to forecast demand using data analytics?

4 How to optimize inventory levels using data analytics?

Inventory Management

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How do you use data analytics to identify and prevent obsolescence?

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1 What is data analytics?

2 How to monitor inventory performance using data analytics?

3 How to forecast demand using data analytics?

4 How to optimize inventory levels using data analytics?

Inventory Management

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