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

By
Niki Khokale
June 23, 2022
5
min
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Top-Down Vs. Bottom-Up Approach for Demand Planning

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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 demand is forecasted only on the basis of historical sales and primary information available at the SKU, Category levels. Here 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 channel /category/store / Darkstore  + 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, inventor 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 like SKUs + store/ darkstore/ depot/ channel and are 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: The largest competitor for beverage A, beverage X of XYZ Beverages Inc is facing macroeconomic challenges, leading to a drop in production numbers for X and consumers adopting A as an alternative thereby causing a spike in demand. Whereas SKU B demand is constantly decreasing as a result of regulatory interventions by the government.

Top Down Prediction for ABC Beverages Inc.

In a top-down approach to demand, planners take into consideration only the high-level demand numbers i.e. aggregate demand (A+B). The demand is then broken down into individual SKUs proportionately. In the graph, we can clearly see that the demand trend of both SKU A and B is volatile and it is difficult to appropriate proportions of the high-level demand for each SKU. The sudden upward trend for SKU  A will not be reflected in the demand predictions as a top-down approach does not consider granular level data and may lead to stockouts in the coming weeks. Therefore even if the high-level demands are accurate, making accurate inventory decisions off the current demand prediction model is very difficult.

Case 2: ABC beverages want to acquire more market share for product A. To overcome the largest competitor, beverage X of XYZ, they run an aggressive promotional campaign with a leading sports celebrity. 

Top-Down Demand Production for ABC Beverages Inc.

After the end of the 4 week promotional period, the demand graph looks like this:

Actual Demand For ABC Beverages Inc.

In a top-down approach to demand, planners take into consideration only the high-level demand numbers i.e. aggregate demand (A+B). The demand is then broken down into individual SKUs proportionately. In the depicted graph, we can see that even if the high-level predictions are accurate, SKU inventory may get distorted at a granular level which may lead to stockouts and loss of revenue.

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.

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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 demand is forecasted only on the basis of historical sales and primary information available at the SKU, Category levels. Here 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 channel /category/store / Darkstore  + 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, inventor 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 like SKUs + store/ darkstore/ depot/ channel and are 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: The largest competitor for beverage A, beverage X of XYZ Beverages Inc is facing macroeconomic challenges, leading to a drop in production numbers for X and consumers adopting A as an alternative thereby causing a spike in demand. Whereas SKU B demand is constantly decreasing as a result of regulatory interventions by the government.

Top Down Prediction for ABC Beverages Inc.

In a top-down approach to demand, planners take into consideration only the high-level demand numbers i.e. aggregate demand (A+B). The demand is then broken down into individual SKUs proportionately. In the graph, we can clearly see that the demand trend of both SKU A and B is volatile and it is difficult to appropriate proportions of the high-level demand for each SKU. The sudden upward trend for SKU  A will not be reflected in the demand predictions as a top-down approach does not consider granular level data and may lead to stockouts in the coming weeks. Therefore even if the high-level demands are accurate, making accurate inventory decisions off the current demand prediction model is very difficult.

Case 2: ABC beverages want to acquire more market share for product A. To overcome the largest competitor, beverage X of XYZ, they run an aggressive promotional campaign with a leading sports celebrity. 

Top-Down Demand Production for ABC Beverages Inc.

After the end of the 4 week promotional period, the demand graph looks like this:

Actual Demand For ABC Beverages Inc.

In a top-down approach to demand, planners take into consideration only the high-level demand numbers i.e. aggregate demand (A+B). The demand is then broken down into individual SKUs proportionately. In the depicted graph, we can see that even if the high-level predictions are accurate, SKU inventory may get distorted at a granular level which may lead to stockouts and loss of revenue.

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.

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