Demand Planning KPIs: A Guide to Essential Metrics to Know

Demand planning is a crucial process for any business that wants to optimize its inventory, production, and distribution. Important factors of demand planning include: forecasting future demand for products or services, meeting the customer’s needs and expectations, and the planning thereof. However, it is important to not underestimate the task of demand planning, as it involves many uncertainties and complexities. It is, thus, of utmost importance for a business to ensure that its demand planning process is effective, efficient, and accurate.

Key Performance Indicators (KPIs) are measurable values indicating a business’s performance and how well it is doing in achieving its objectives. KPIs can help a business monitor and evaluate its performance, identify and solve problems, and improve its decision-making. In this article, we will discuss the importance of KPIs in demand planning, the essential and advanced KPIs that a business should use, and the practical implementation of KPIs in the demand planning process.

The Significance of KPIs in Demand Planning

KPIs are essential tools for demand planning, acting as a methodical and objective mechanism to better measure the effectiveness and efficiency of a company’s demand planning process. Businesses can afford to benefit from the use of KPIs in many ways. Let us assess. KPIs compare and contrast a company’s actual demand with its forecasted demand to help determine its accuracy and reliability. From this, it can identify the sources of deviation within the original predicted demand, allowing companies to take corrective actions to improve upon these errors.

Maintaining a competitive edge is something all businesses should strive for. KPIs help this goal become more of a reality through their ability to track the progress and performance of a company’s demand planning process while benchmarking it against best practices and industry standards. The awareness that KPIs provide of your company’s strengths and weaknesses in terms of demand planning can provide ample opportunity to better align your processes with business goals and strategies.

Essential KPIs for Demand Planning

Demand planning is based on three main categories: demand forecasting, inventory management, and the purchasing process. As previously stated, demand forecasting
involves predicting future demand for products or services and planning accordingly to meet customer needs and expectations. With all of the complexities and challenges that demand forecasting poses, how can a company ensure that its demand forecasting process is effective and efficient? The answer is to use KPIs!

Some of the most important KPIs for demand forecasting include:

• Forecast Accuracy, or forecasting KPIs:  Indicating how close the demand forecasts are to actual demand.
• Monitoring Signals: Indicating whether the demand forecasts are within an acceptable range of error.
• Bias: Indicating the tendency of the demand forecasts to be systematically higher or lower than the actual demand.

Inventory management involves controlling and optimizing inventory levels and supply chain flows to ensure the availability and quality of products or services at all times. Inventory management is a crucial challenge, as it involves balancing demand and supply, the movement and whereabouts of inventory, and has a great impact on customer satisfaction, business reception and reputation, and overall profitability. Some of the most important KPIs for inventory management are:

• Inventory Turnover: Indicating how many times inventory is sold and replaced in a given period.
• Stock-out Rate: Indicating the percentage of times a product or service is not available in inventory when requested by the customer.
• Average Inventory Value: Indicating the average value of the inventory over a given period.

The purchasing process involves selecting and purchasing the materials, equipment, and services necessary for the production and distribution of products or services. The purchasing process is a strategic challenge, as it involves managing supplier relationships, assessing risks and opportunities, and reducing costs and time. Some of the most important KPIs for the purchasing process are:

• Reordering Frequency: Indicating the number of times a company places a purchase order in a given period.
• Supplier Risk: Indicating the level of uncertainty and vulnerability associated with a supplier, in terms of quality, quantity, price, and delivery.
• Effort and Time Consumption: Indicating the time and effort required to complete the purchasing process, in terms of research, negotiation, contracting, and payment.

While there are many KPIs that can be used to measure and improve the demand planning process, some KPIs are more important and relevant than others. This is largely dependent upon the business context and objectives. Let us have a look at which essential KPIs should be used by which type of companies, as well as how to calculate and interpret them.

Forecast Accuracy

Forecast accuracy measures how close the demand forecasts are to the actual demand, and thus falls into the category of highly important KPIs. It can be calculated as the ratio of the forecast error to the actual demand, usually expressed as a percentage. Forecast error, on the other hand, is the difference between the forecasted and the actual demand, either in absolute or relative terms. A lower forecast error indicates a higher forecast accuracy, and vice versa.

There are different ways to calculate the forecast error, depending on the level of aggregation and the time horizon of the demand forecasts. Some of the common methods are:

• Mean Absolute Deviation (MAD): The average of the absolute values of the forecast errors.
• Mean Square Error (MSE): The average of the squared values of the forecast errors.
• Mean Absolute Percentage Error (MAPE): The average of the absolute values of the forecast errors, divided by the actual demand, expressed as a percentage.
• Symmetrical Mean Absolute Percentage Error (SMAPE): The average of the absolute values of the forecast errors, divided by the average of the forecasted and the actual demand, expressed as a percentage.
• Weighted Mean Absolute Percentage Error (WMAPE): The average of the absolute values of the forecast errors, weighted by the actual demand, expressed as a percentage.

Each method has its own advantages and disadvantages. For example, MAD and MSE are more sensitive to large errors, while MAPE and SMAPE are more sensitive to small errors. WMAPE can account for the variability and importance of the demand, while SMAPE can avoid the problem of division by zero when the actual demand is zero.

If your company is looking to improve its forecast accuracy, there are a number of tasks to implement. Begin with ensuring that the forecasting method and technique being used are suitable to the type and pattern of the demand data. Demand forecasts should also undergo regular updates and revisions, with each bit of new information and feedback that is received. Incorporate external factors and events affecting demand, such as seasonality, trends, promotions, and competitors, and continually compare the forecast accuracy across different products, regions, and time periods to help identify areas needing improvement.

Tracking Signals

Tracking signals indicate whether the demand forecasts are within an acceptable range of error, and can be calculated as the ratio of the cumulative forecast error to the mean absolute deviation (MAD). A positive tracking signal indicates demand forecasts are higher than the actual demand, while a negative tracking signal indicates demand forecasts are lower than the actual demand. A company should strive for a tracking signal close to zero, as this indicates stronger accuracy, whereas a tracking signal far from zero indicates a biased forecast.

Three tasks businesses can employ to monitor their tracking signals include setting a control limit, plotting the signals over time, and analyzing the causes and patterns of the signals. A control limit should be based on the desired level of confidence and tolerance. For example, a common control limit is +/- 4, which means that the tracking signals are within the 95% confidence interval of the forecast error distribution.

The plotted tracking signals need to be checked whether they fall within the control limit or not. If they fall outside the control limit, it means that there is a significant deviation in the demand forecasts, and corrective action is needed. Once the patterns have been analyzed, it is important to adjust the forecasting method and parameters accordingly. For example, if the tracking signals show a consistent positive or negative trend, it means that there is a systematic bias in the demand forecasts, and a smoothing or correction factor may be applied.

Bias

Bias is another important KPI in demand planning, measuring the tendency of the demand forecasts to be consistently higher or lower than the actual demand. Bias can be calculated as the average of the forecast errors, either in absolute or relative terms. Demand forecasts are overestimated with a positive bias, while a negative bias indicates an underestimated forecast. A zero bias means that the demand forecasts are unbiased, and there is no systematic error.

Bias can have a negative impact on the demand planning process, as it can lead to over or understocking, which ultimately affects customer satisfaction and profitability. Businesses should aim to minimize their forecasting bias and ensure that they are as close as possible to the actual demand. This can be achieved through numerous manners:

1. Use a suitable forecasting method and technique, based on the type and pattern of the demand data.
2. Update and revise the demand forecasts regularly, based on the latest information and feedback.
3. Incorporate external factors and events that may affect the demand, such as seasonality, trends, promotions, and competitors.
4. Compare the bias across different products, regions, and time periods, and identify the sources and reasons of the bias.
5. Apply a correction factor or a smoothing technique to adjust the demand forecasts, and eliminate the bias.

Advanced KPIs in Demand Planning

Beyond essential KPIs, there are also some advanced KPIs that a business can use to further enhance its demand planning process. These are generally more complex and sophisticated, often providing more insights and value. Let us delve into these KPIs and what makes them more advanced.

Long-Term Capacity Requirement Forecasting Accuracy

Long-term capacity requirement forecasting accuracy is an advanced KPI in demand planning. It measures how well the demand forecasts match the long-term capacity requirements of the business. It can be calculated as the ratio of the long-term capacity requirement forecast error to the long-term capacity requirement, expressed as a percentage. Long-term capacity requirement forecast error is the difference between the long-term capacity requirement forecast and the long-term capacity requirement, either in absolute or relative terms. A lower long-term capacity requirement forecast error indicates a higher long-term capacity requirement forecasting accuracy, and vice versa.

Long-term capacity requirement forecasting accuracy is important for the demand planning process, as it can help a business plan and optimize its long-term capacity investments. These might include building new facilities, acquiring new equipment, or hiring new staff. It is therefore of utmost importance that it remains accurate. Here are a few examples of improvements that can be made to long-term capacity requirement forecasting accuracy:

• Use a suitable forecasting method and technique, based on the type and pattern of the long-term demand data.
• Update and revise the long-term demand forecasts regularly, based on the latest information and feedback.
• Incorporate external factors and events that may affect the long-term demand, such as macroeconomic trends, technological innovations, and environmental regulations.
• Compare the long-term capacity requirement forecasting accuracy across different products, regions, and time periods, and identify the areas of improvement.

Intelligent Demand Planning KPIs

Intelligent demand planning KPIs use artificial intelligence (AI) and machine learning (ML) to enhance the demand planning process. Intelligent demand planning KPIs can leverage the power of AI and ML to:

1. Analyze large and complex data sets, and extract meaningful insights and patterns therefrom.
2. Generate more accurate and reliable demand forecasts, and adjust them dynamically based on the changing conditions and scenarios.
3. Optimize the inventory, production, and distribution levels, and balance them with the demand and supply.
4. Detect and prevent anomalies and outliers in the demand data, and handle them appropriately.
5. Provide recommendations and suggestions for improving the demand planning process, and support the decision-making.

Below is a list of some examples of intelligent demand planning KPIs:

• Demand Sensing: Sense and respond to real-time changes in demand, such as customer behavior, preferences, and feedback.
• Demand Shaping: Influence and modify the demand, such as through pricing, promotion, and product design.
• Demand Segmentation: Group and classify the demand, based on the characteristics and attributes of the products and customers.
• Demand Simulation: Create and test different scenarios and assumptions, and evaluate their outcomes and impacts on the demand.

Should your company want to implement intelligent demand planning KPIs, here are a few recommendations to take into consideration:

1. Use a suitable AI and ML platform and tool, based on the type and complexity of the demand data and problem.
2. Train and validate the AI and ML models, and ensure that they are accurate and robust.
3. Integrate the AI and ML models with the existing demand planning system and process, and ensure that they are compatible and consistent.
4. Monitor and evaluate the performance and results of the AI and ML models, and update and improve them accordingly.

Intuendi represents a state-of-the-art AI-driven demand planning tool

Practical Implementation of KPIs

Despite what we have covered, the use of KPIs in the demand planning process is not limited to calculation and measurement but additionally acts as a matter of implementation and application. To make the best use of KPIs in the demand planning process, a business should:

1. Define and select the most relevant and appropriate KPIs for its demand planning process, based on its business context and objectives.
2. Set and communicate the targets and expectations for the KPIs, and align them with the business goals and strategies.
3. Collect and organize the data and information needed for the KPIs, and ensure that they are accurate and reliable.
4. Calculate and analyze the KPIs, and interpret and understand their meanings and implications.
5. Visualize and report the KPIs, and share and communicate them with the stakeholders.
6. Act and improve based on the KPIs, and take the necessary actions and initiatives to optimize the demand planning process.

Visualizing KPIs with a Demand Planning Dashboard

One of the most effective ways to implement KPIs in the demand planning process is to use a demand planning dashboard. A demand planning dashboard is a graphical representation that displays and monitors the KPIs in a clear and concise way, while also highlighting key information and insights. The dashboard, furthermore, compares and contrasts the KPIs across different dimensions and perspectives. It identifies and tracks trends and patterns in the KPIs, and spots the changes and fluctuations. Because of this, it additionally detects issues and alerts companies of the issues within the KPIs, triggering actions and solutions.

It is no surprise that after delving further into the idea of demand planning dashboards you might be looking to incorporate one into your own business. If that is the case, you can and should:

1. Use a suitable data visualization tool and technique, based on the type and format of the KPIs and data.
2. Choose and design the most appropriate and appealing charts and graphs for the KPIs, such as tables, bars, lines, pies, gauges, and maps.
3. Organize and arrange the charts and graphs in a logical and coherent way, and ensure that they are consistent and compatible.
4. Customize and enhance the charts and graphs with relevant and useful features and elements, such as titles, labels, legends, colors, and filters.
5. Update and refresh the charts and graphs regularly, and ensure that they are current and accurate.

Software Tools for KPI Monitoring

Above and beyond this, software tools can also be used for KPI monitoring. Software tools for KPI monitoring are applications and systems that can help a business to:

1. Automate and simplify the calculation and analysis of the KPIs, and reduce the errors and efforts.
2. Integrate and consolidate the data and information from different sources and platforms, and ensure that they are complete and consistent.
3. Store and manage the KPIs and data in a secure and accessible way, and ensure that they are protected and available.
4. Generate and deliver the KPIs and data in a timely and convenient way, and ensure that they are relevant and useful.

Some examples of software tools for KPI monitoring that you already might be familiar with are Microsoft Excel, Microsoft Power BI, Tableau, and SAP. To use these tools for KPI monitoring, a business should:

• Select the most suitable and compatible software tool for its demand planning process, based on its needs and preferences, and ensure that it is functional.
• Connect and integrate the software tool with the data sources and platforms, and ensure that they are compatible and consistent.
• Use and operate the software tool, and ensure that it is effective and efficient.

Connecting Demand Planning KPIs with Other Business Sectors

We know that demand planning is not and cannot be an isolated process, but rather, a connected and integrated one that affects and is affected by other business sectors. One might, then, consider connecting and aligning one’s demand planning KPIs with the KPIs of other business sectors, such as sales, marketing, finance, and operations.

By doing so, a business can ensure that the demand planning process remains consistent and coherent through all levels of the overall business process and strategy, reducing opportunities for conflicts and contradictions. It can also improve company morale, fostering a culture of collaboration and communication between the different business sectors – with all levels working with a common understanding and vision.

Some examples of how to connect and align the demand planning KPIs with the KPIs of other business sectors are:

1. Sales: The demand planning KPIs can be connected and aligned with the sales KPIs, such as sales volume, sales revenue, and sales growth. By doing so, a business can ensure that the demand planning process supports and matches the sales objectives and targets and that the sales performance and results reflect and validate the demand planning process.
2. Marketing: The demand planning KPIs can be connected and aligned with the marketing KPIs, such as market share, customer satisfaction, and customer retention. By doing so, a business can ensure that the demand planning process considers and incorporates the marketing factors and activities, such as customer behavior, preferences, and feedback, and that the marketing performance and results influence and improve the demand planning process.
3. Finance: The demand planning KPIs can be connected and aligned with the finance KPIs, such as cash flow, profitability, and return on investment. By doing so, a
   business can ensure that the demand planning process contributes and adds to the financial objectives and targets and that the financial performance and results evaluate and justify the demand planning process.
4. Operations: The demand planning KPIs can be connected and aligned with the operations KPIs, such as inventory turnover, production efficiency, and distribution cost. By doing so, a business can ensure that the demand planning process optimizes and balances the operations levels and resources and that the operations performance and results support and enhance the demand planning process.

Importance of KPIs in Improving Demand Planning

Let’s rehash! KPIs are vital tools for improving the demand planning process, as they can help a business to:

1. Measure and evaluate the effectiveness and efficiency of the demand planning process, and identify and solve the problems and issues.
2. Monitor and track the progress and performance of the demand planning process, and benchmark it against the best practices and industry standards.
3. Communicate and share the results and insights of the demand planning process, and align them with the business goals and strategies.
4. Act and improve based on the KPIs, and take the necessary actions and initiatives to optimize the demand planning process.

By using the essential and advanced KPIs, and implementing them in a practical and effective way, a business can maximize its efficiency and achieve its objectives in the demand planning process. Will you and your company be looking to implement KPIs into your demand planning structures any time soon?

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