What are the most effective data visualization techniques for demand forecasting?

Demand forecasting is a crucial skill for inventory management, as it helps you optimize your stock levels, avoid excess or shortage, and plan your replenishment strategies. However, demand forecasting can be challenging, especially when you have to deal with complex, uncertain, and dynamic market conditions. That's why data visualization techniques can be very helpful, as they allow you to present, explore, and communicate your demand patterns, trends, and scenarios in a clear and intuitive way. In this article, we will discuss some of the most effective data visualization techniques for demand forecasting, and how they can help you improve your inventory performance evaluation.

1 Time series charts

One of the most common and useful data visualization techniques for demand forecasting is the time series chart, which shows how your demand changes over time. Time series charts can help you identify seasonal, cyclical, or irregular fluctuations in your demand, as well as compare your actual and forecasted demand. You can also use time series charts to show confidence intervals, error margins, or forecast accuracy measures, which can help you assess the reliability and validity of your forecasts. Time series charts can be created using various tools, such as Excel, Power BI, or Tableau.

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Alex Stokes

Unlocking efficiencies through Continuous Improvement in the Supply Chain
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Definitely a tried and true approach to data visualisation, but can still be utilised well and can help articulate outliers and trends.

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2 Histograms and box plots

Another data visualization technique that can help you with demand forecasting is the histogram, which shows the frequency distribution of your demand values. Histograms can help you understand the shape, spread, and skewness of your demand distribution, as well as detect outliers or anomalies. You can also use histograms to compare different demand segments, such as products, customers, or regions. Box plots are similar to histograms, but they show the summary statistics of your demand distribution, such as the median, quartiles, and range. Box plots can help you compare the variability and dispersion of your demand across different groups or categories.

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3 Scatter plots and correlation matrices

A third data visualization technique that can be useful for demand forecasting is the scatter plot, which shows the relationship between two variables. Scatter plots can help you explore the correlation or causation between your demand and other factors, such as price, promotion, competition, or weather. You can also use scatter plots to identify clusters or outliers in your data, or to fit a regression line or curve to your data. Correlation matrices are similar to scatter plots, but they show the correlation coefficients between multiple variables. Correlation matrices can help you measure the strength and direction of the linear relationships between your demand and other variables, and to select the most relevant predictors for your forecasting models.

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4 Heat maps and tree maps

A fourth data visualization technique that can enhance your demand forecasting is the heat map, which shows the intensity of your demand using different colors. Heat maps can help you visualize the spatial or temporal variation of your demand, such as the geographic or seasonal patterns. You can also use heat maps to show the cross-tabulation of your demand by two or more dimensions, such as product and customer segments. Tree maps are similar to heat maps, but they show the hierarchical structure of your data using nested rectangles. Tree maps can help you display the proportion and composition of your demand by different levels of aggregation, such as product categories and subcategories.

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Alex Stokes

Unlocking efficiencies through Continuous Improvement in the Supply Chain
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Heatmaps can be particularly helpful in showing outliers, particularly amongst an audience who may not be intimately familiar with the dataset. Heatmaps can also be useful during routine reviews to understand cross-category impacts.

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5 Dashboards and interactive charts

A fifth and final data visualization technique that can improve your demand forecasting is the dashboard, which shows a collection of charts and indicators that summarize your key demand metrics and insights. Dashboards can help you monitor and evaluate your demand performance, as well as communicate and share your findings with others. You can also use interactive charts, which allow you to filter, slice, or drill down your data, or to change the chart type, format, or parameters. Interactive charts can help you customize and explore your data in different ways, and to discover new patterns or insights that can inform your demand forecasting decisions.

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Alex Stokes

Unlocking efficiencies through Continuous Improvement in the Supply Chain
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Whatever charts are being used, interactivity will always be key. A good Dashboard will allow you to react in the moment to different queries when presenting the data, which can really help drive a productive collaborative meeting.

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6 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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What are the most effective data visualization techniques for demand forecasting?

1

2

3

4

5

6

1 Time series charts

2 Histograms and box plots

3 Scatter plots and correlation matrices

4 Heat maps and tree maps

5 Dashboards and interactive charts

6 Hereâ€™s what else to consider

Inventory Management

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Directory

What are the most effective data visualization techniques for demand forecasting?

1

2

3

4

5

6

1 Time series charts

2 Histograms and box plots

3 Scatter plots and correlation matrices

4 Heat maps and tree maps

5 Dashboards and interactive charts

6 Hereâ€™s what else to consider

Inventory Management

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