We are excited to explore how we can add value to your demand sensing and inventory planning approach

This form uses grid for its layout. Adjust and reorganize the divs inside the Form Grid to fit 1 or 2 grid columns as needed.

Fields marked with an asterisk (*) are required.
Thank you! Your submission has been received. Our team will get back to you within 24 hours.
Oops! Something went wrong while submitting the form.

Top-Down Vs. Bottom-Up Approach for Demand Planning

By
Niki Khokale
June 23, 2022
5
min
Share this
Blog

Top-Down Vs. Bottom-Up Approach for Demand Planning

Share this

In today’s dynamic world, there are several factors that influence the demand levels for a business. It is vital to have a fresh approach to ascertain demand levels throughout the supply chain. Traditionally, demand planners analyzed the data at a top-level and broke it down into smaller quantities as they moved down the supply chain. But is it time to rethink this approach? Let’s find out.

Top-Down Approach for Demand Predictions

In the top-down approach, the predictions are made by the demand planners at the highest level, and then individual demands are calculated according to a percentage of the total for each category + warehouse + channel + store/dark store + SKU level. These forecasts fail to take into account the events at the granular levels of the supply chain. A major issue with this approach is that even if the high-level predictions are accurate, inventory operations will not improve unless granular levels of predictions are made accurately. Moreover, the proportions themselves may change, especially if one product starts to sell at a faster rate than others either because of changes in demand trends or because of promotions. 

Bottom-Up Approach for Demand Predictions

In the bottom-up strategy, the forecasts are made based on the data collected at more granular levels. For an E-commerce/Quick-Commerce company, the most granular levels will be SKU + dark store level, while the granularity for a DTC company will be SKU+ Channel + Depot levels. Demand is predicted at such granularity and is then aggregated up for overall demand forecasts. In this approach, demand is predicted as close to the customer as possible and then rolled up to the top for more accurate demand prediction numbers. A bottom-up approach is a more pragmatic approach with lesser chances of human bias or error.

Why is the Top-Down approach for Demand Predictions a failure?

Let us assume a scenario in which demand predictions are made for a beverage company named ABC Beverages Inc. with two products A and B. 

Case 1: Change in SKU demand trends

ABC Inc. is a beverage company that follows a top-down approach to demand planning. Their historical sales dictate that there is a 60-40 split between SKUs B and A and thus aggregate demand is broken down in the same ratio. In recent weeks there has been a change in the demand trends. Demand for SKU A has gone up and demand for SKU B has gone down. A top-down approach fails to capture these trends as it focuses on demand proportions derived from high-level prediction. 

As a result, there will be a gap in the predicted demand and actual demand for both SKU A and SKU B at week 0 which will result in stockouts scenarios for SKUA and excess inventory for SKU B in the coming weeks. 

Now let us explore a bottom-up approach to demand prediction. As stated earlier in this article, a bottom-up approach to demand prediction focuses on estimating demand at the most granular levels, which in this case is the SKU level, and aggregates the demand for each SKU to arrive at the aggregate demand. The illustration clearly showcases that the demand prediction factors in for changing demand trends to arrive at more precise demand predictions.

Case 2: Case of Promotions

ABC beverages regularly runs a week-long promotional campaign for its beverages category. The beverages category comprises two SKUs A and B. Their historical sales dictate that there is a 60-40 split between SKUs B and A and thus aggregate demand is broken down in the same ratio. Nevertheless, in promotional periods, the demand for SKU A exceeds that of SKU B. Now as we know that in a top-down approach the high-level demand is broken down into SKU predictions based primarily on historical proportions, the predictions for future promotional events will be incorrect. 

Now let us explore a bottom-up approach to demand prediction. As stated earlier in this article, a bottom-up approach to demand prediction focuses on estimating demand at the most granular levels. In this method, future promotional period demand predictions will factor in the previous promotional events data to arrive at a number where SKU A is higher than SKU B.

These two cases clearly showcase that the bottom-up approach for demand prediction is far superior to the top-down approach.

Kronoscope with its bottom-up approach offers unmatched demand prediction accuracy by capturing real-time events that occur at the most granular level of the supply chain including SKUs and stores/dark stores. It factors in important influencing parameters including historical trends, seasonal effects, cyclicity, outlier corrections, changes in pricing, promotional events, holidays, and even weather. With more accurate predictions, businesses can proactively plan for shortcomings and have a more agile approach to their inventory planning.

Access The Case Study

Schedule A Consultation

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Blog

Top-Down Vs. Bottom-Up Approach for Demand Planning

Share this

In today’s dynamic world, there are several factors that influence the demand levels for a business. It is vital to have a fresh approach to ascertain demand levels throughout the supply chain. Traditionally, demand planners analyzed the data at a top-level and broke it down into smaller quantities as they moved down the supply chain. But is it time to rethink this approach? Let’s find out.

Top-Down Approach for Demand Predictions

In the top-down approach, the predictions are made by the demand planners at the highest level, and then individual demands are calculated according to a percentage of the total for each category + warehouse + channel + store/dark store + SKU level. These forecasts fail to take into account the events at the granular levels of the supply chain. A major issue with this approach is that even if the high-level predictions are accurate, inventory operations will not improve unless granular levels of predictions are made accurately. Moreover, the proportions themselves may change, especially if one product starts to sell at a faster rate than others either because of changes in demand trends or because of promotions. 

Bottom-Up Approach for Demand Predictions

In the bottom-up strategy, the forecasts are made based on the data collected at more granular levels. For an E-commerce/Quick-Commerce company, the most granular levels will be SKU + dark store level, while the granularity for a DTC company will be SKU+ Channel + Depot levels. Demand is predicted at such granularity and is then aggregated up for overall demand forecasts. In this approach, demand is predicted as close to the customer as possible and then rolled up to the top for more accurate demand prediction numbers. A bottom-up approach is a more pragmatic approach with lesser chances of human bias or error.

Why is the Top-Down approach for Demand Predictions a failure?

Let us assume a scenario in which demand predictions are made for a beverage company named ABC Beverages Inc. with two products A and B. 

Case 1: Change in SKU demand trends

ABC Inc. is a beverage company that follows a top-down approach to demand planning. Their historical sales dictate that there is a 60-40 split between SKUs B and A and thus aggregate demand is broken down in the same ratio. In recent weeks there has been a change in the demand trends. Demand for SKU A has gone up and demand for SKU B has gone down. A top-down approach fails to capture these trends as it focuses on demand proportions derived from high-level prediction. 

As a result, there will be a gap in the predicted demand and actual demand for both SKU A and SKU B at week 0 which will result in stockouts scenarios for SKUA and excess inventory for SKU B in the coming weeks. 

Now let us explore a bottom-up approach to demand prediction. As stated earlier in this article, a bottom-up approach to demand prediction focuses on estimating demand at the most granular levels, which in this case is the SKU level, and aggregates the demand for each SKU to arrive at the aggregate demand. The illustration clearly showcases that the demand prediction factors in for changing demand trends to arrive at more precise demand predictions.

Case 2: Case of Promotions

ABC beverages regularly runs a week-long promotional campaign for its beverages category. The beverages category comprises two SKUs A and B. Their historical sales dictate that there is a 60-40 split between SKUs B and A and thus aggregate demand is broken down in the same ratio. Nevertheless, in promotional periods, the demand for SKU A exceeds that of SKU B. Now as we know that in a top-down approach the high-level demand is broken down into SKU predictions based primarily on historical proportions, the predictions for future promotional events will be incorrect. 

Now let us explore a bottom-up approach to demand prediction. As stated earlier in this article, a bottom-up approach to demand prediction focuses on estimating demand at the most granular levels. In this method, future promotional period demand predictions will factor in the previous promotional events data to arrive at a number where SKU A is higher than SKU B.

These two cases clearly showcase that the bottom-up approach for demand prediction is far superior to the top-down approach.

Kronoscope with its bottom-up approach offers unmatched demand prediction accuracy by capturing real-time events that occur at the most granular level of the supply chain including SKUs and stores/dark stores. It factors in important influencing parameters including historical trends, seasonal effects, cyclicity, outlier corrections, changes in pricing, promotional events, holidays, and even weather. With more accurate predictions, businesses can proactively plan for shortcomings and have a more agile approach to their inventory planning.

Access the

Blog

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

In today’s dynamic world, there are several factors that influence the demand levels for a business. It is vital to have a fresh approach to ascertain demand levels throughout the supply chain. Traditionally, demand planners analyzed the data at a top-level and broke it down into smaller quantities as they moved down the supply chain. But is it time to rethink this approach? Let’s find out.

Top-Down Approach for Demand Predictions

In the top-down approach, the predictions are made by the demand planners at the highest level, and then individual demands are calculated according to a percentage of the total for each category + warehouse + channel + store/dark store + SKU level. These forecasts fail to take into account the events at the granular levels of the supply chain. A major issue with this approach is that even if the high-level predictions are accurate, inventory operations will not improve unless granular levels of predictions are made accurately. Moreover, the proportions themselves may change, especially if one product starts to sell at a faster rate than others either because of changes in demand trends or because of promotions. 

Bottom-Up Approach for Demand Predictions

In the bottom-up strategy, the forecasts are made based on the data collected at more granular levels. For an E-commerce/Quick-Commerce company, the most granular levels will be SKU + dark store level, while the granularity for a DTC company will be SKU+ Channel + Depot levels. Demand is predicted at such granularity and is then aggregated up for overall demand forecasts. In this approach, demand is predicted as close to the customer as possible and then rolled up to the top for more accurate demand prediction numbers. A bottom-up approach is a more pragmatic approach with lesser chances of human bias or error.

Why is the Top-Down approach for Demand Predictions a failure?

Let us assume a scenario in which demand predictions are made for a beverage company named ABC Beverages Inc. with two products A and B. 

Case 1: Change in SKU demand trends

ABC Inc. is a beverage company that follows a top-down approach to demand planning. Their historical sales dictate that there is a 60-40 split between SKUs B and A and thus aggregate demand is broken down in the same ratio. In recent weeks there has been a change in the demand trends. Demand for SKU A has gone up and demand for SKU B has gone down. A top-down approach fails to capture these trends as it focuses on demand proportions derived from high-level prediction. 

As a result, there will be a gap in the predicted demand and actual demand for both SKU A and SKU B at week 0 which will result in stockouts scenarios for SKUA and excess inventory for SKU B in the coming weeks. 

Now let us explore a bottom-up approach to demand prediction. As stated earlier in this article, a bottom-up approach to demand prediction focuses on estimating demand at the most granular levels, which in this case is the SKU level, and aggregates the demand for each SKU to arrive at the aggregate demand. The illustration clearly showcases that the demand prediction factors in for changing demand trends to arrive at more precise demand predictions.

Case 2: Case of Promotions

ABC beverages regularly runs a week-long promotional campaign for its beverages category. The beverages category comprises two SKUs A and B. Their historical sales dictate that there is a 60-40 split between SKUs B and A and thus aggregate demand is broken down in the same ratio. Nevertheless, in promotional periods, the demand for SKU A exceeds that of SKU B. Now as we know that in a top-down approach the high-level demand is broken down into SKU predictions based primarily on historical proportions, the predictions for future promotional events will be incorrect. 

Now let us explore a bottom-up approach to demand prediction. As stated earlier in this article, a bottom-up approach to demand prediction focuses on estimating demand at the most granular levels. In this method, future promotional period demand predictions will factor in the previous promotional events data to arrive at a number where SKU A is higher than SKU B.

These two cases clearly showcase that the bottom-up approach for demand prediction is far superior to the top-down approach.

Kronoscope with its bottom-up approach offers unmatched demand prediction accuracy by capturing real-time events that occur at the most granular level of the supply chain including SKUs and stores/dark stores. It factors in important influencing parameters including historical trends, seasonal effects, cyclicity, outlier corrections, changes in pricing, promotional events, holidays, and even weather. With more accurate predictions, businesses can proactively plan for shortcomings and have a more agile approach to their inventory planning.

Like what you read?

Subscribe to receive a monthly digest of our most valuable resources like blog posts, whitepapers and much more

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Related Resources