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4 Types Of Demand Planners

By
Niki Khokale
October 18, 2022
5
min
Share this
Blog

4 Types Of Demand Planners

Share this

A demand planner plays a pivotal role in any organization as their work contributes to major decisions made by various departments ultimately to maintain two important things,

1. Consistent product availability across the supply chain

2. Mitigate inventory costs

And there are different types of demand planners with various approaches of their own. Let’s understand these approaches and evaluate them here to determine the best of all.

4 types of demand planners and their approaches

The Excel Power Ninja
The Excel Power Ninja

Even in today’s volatile market, a large chunk of DTC and retail companies rely on manual methods for managing their supply chain. This gives rise to the Excel Power Ninjas who use multiple spreadsheets with a myriad of calculations to plan demand. This conventional approach makes demand planning a tedious task and hence cannot be repeated frequently. While historical data plays a crucial role in preparing for future setbacks, relying only on it brings trouble. These demand planners miss out on capturing real time information and factoring in their impact while predicting demand. This leads to inefficient inventory planning and higher operational costs.

For example, the excel power ninjas would set a static safety stock level irrespective of the changing demand levels of a SKU. Eventually, they run out of stock when there is an unusually high demand and don’t have adequate safety stock to back it. This is when a dynamic safety stock approach based on real time demand comes to the rescue. 

With cutthroat competition and ever changing consumer preferences, it's important to be agile. Companies should have the ability to be cognizant of how changing demand affects inventory levels and adjust them in real time. 

Pros

  1. Suitable for limited SKUs with steady sales
  2. Helps identify demand patterns and cyclicity of various SKUs

Cons

  1. Doesn’t work for SKUs that are new and don’t have historical sales data
  2. Time extensive and tedious process

The Trend follower
The Trend follower

The trend follower is the new age demand planner that always wants to stay on top of the current market trends and derive quicker results. Sadly, these are also the ones who are overworked even during the holidays and promotional events. While it’s great to make the best use of the latest trends, being short sighted while planning demand can reflect on your bottom line numbers negatively. 

Oftentimes, trend followers are too focussed on all the current buzz that they forget history repeats too. They fail to gauge the impact of historical demand patterns and cyclicity.

The trend followers for instance, would want to make the most of a seasonal peak in demand for a SKU and procure them in large quantities. When the seasonal demand dies, they are often left with a warehouse piled with excess inventory. This results in lack of liquidity, wastage and additional costs incurred on storage. Hence, it is essential to manage risks and make informed decisions that wouldn’t affect operations in the future. 

Pros

  1. Helps to stay ahead of competition by following the trends
  2. Helps gain maximum revenue from marketing events/promotions 

Cons

  1. Failure to consider future risks leading to higher operating costs
  2. Short lived results which are unreliable

          

The Big Picture Guy       
The Big Picture Guy   

The Big Picture Guys are the ones who tend to miss out on finer details while focusing too much on the aggregated  numbers. This is a common tendency among the demand planners. They have a top down approach to demand planning and end up missing out on the demand drivers at the most granular levels. 

These demand planners predict top level demand numbers at the mother warehouse level and distribute them proportionally to the child warehouses based on past sales data. While the demand plan seems sound and highly accurate at the top level, they become inaccurate moving further down the supply chain. 

This may result in understocking/overstocking instances leading to loss of revenue and wastages. Further, this causes product unavailability and poor customer satisfaction. Hence, it's important for demand planners to predict demand closer to the customers to achieve service level targets and ensure product availability.

Pros

  1. Higher visibility for the top level nodes of the supply chain
  2. Relatively simpler process mainly relying on historical sales

 Cons

        1. Poor prediction accuracy at the granular level (SKU x store/ SKU x channel)

        2. Inefficient inventory planning across nodes leading to poor customer experience

The Accuracy Geek
The Accuracy Geek

We all know that demand planner who’s just too obsessed with accuracy. And the most common mistake these demand planners do is look at accuracy as absolutes which can often be misleading.

Accuracy geeks tend to consider any prediction accuracy less than 80% as a poor result.

However, the ideal way to look at demand prediction accuracy is in relative terms. This is because even a slight increase in prediction accuracy when compared to other traditional alternatives would mean a drastic positive impact on the top and bottom line numbers.

When it comes to demand prediction accuracy, it’s important to set credible benchmarks and look at your accuracies in relativity to them for a clear picture.

Pros

  1. Enables continuous improvements in accuracy numbers
  2. Team is aligned and focused on process efficiency 

Cons

  1. Portrays a misleading picture by considering accuracy an absolute term
  2. Limited visibility in the demand accuracy numbers across the various nodes of supply chain

Now that we have looked at different types of demand planners, we can see that while each one has a strength they also fall short in some aspects. The key is to find the right balance of all these aspects and arrive at comprehensive, agile demand forecasts.

Do we have a winner?

Drum Roll please! Presenting the,

The AI/ML Enthusiast
The AI/ML Enthusiast

The AI or ML enthusiasts are the ones who embrace changes by adopting highly intelligent tools like Kronoscope. They are up for exciting experimentations and are equipped with the power to tackle any uncertainties.

Their lives are made simple with Kronoscope, an AI powered demand prediction and inventory planning solution which offers,

  • Demand predictions that are comprehensive of both historical and real time events by taking into account 9 major demand signals including seasonal effects, cyclicity, promotions, weather etc.
  • Automated purchase and replenishment plans created in real time by effectively eliminating out of stock situations and inventory wastages.
  • Seamless inventory planning for events and promotions based on similar events in the past.
  • Superior demand prediction accuracies using the bottom up approach that beats AWS forecasts 8 out of 10 times.

In summary, the AI and ML enthusiasts work their magic with the combination of artificial intelligence and human intuition. This way, they are able to deliver a great customer experience no matter the change in demand levels. So today, embrace the change with our state-of-the-art AI-powered Inventory Planning solution.

Access The Case Study

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Blog

4 Types Of Demand Planners

Share this

A demand planner plays a pivotal role in any organization as their work contributes to major decisions made by various departments ultimately to maintain two important things,

1. Consistent product availability across the supply chain

2. Mitigate inventory costs

And there are different types of demand planners with various approaches of their own. Let’s understand these approaches and evaluate them here to determine the best of all.

4 types of demand planners and their approaches

The Excel Power Ninja
The Excel Power Ninja

Even in today’s volatile market, a large chunk of DTC and retail companies rely on manual methods for managing their supply chain. This gives rise to the Excel Power Ninjas who use multiple spreadsheets with a myriad of calculations to plan demand. This conventional approach makes demand planning a tedious task and hence cannot be repeated frequently. While historical data plays a crucial role in preparing for future setbacks, relying only on it brings trouble. These demand planners miss out on capturing real time information and factoring in their impact while predicting demand. This leads to inefficient inventory planning and higher operational costs.

For example, the excel power ninjas would set a static safety stock level irrespective of the changing demand levels of a SKU. Eventually, they run out of stock when there is an unusually high demand and don’t have adequate safety stock to back it. This is when a dynamic safety stock approach based on real time demand comes to the rescue. 

With cutthroat competition and ever changing consumer preferences, it's important to be agile. Companies should have the ability to be cognizant of how changing demand affects inventory levels and adjust them in real time. 

Pros

  1. Suitable for limited SKUs with steady sales
  2. Helps identify demand patterns and cyclicity of various SKUs

Cons

  1. Doesn’t work for SKUs that are new and don’t have historical sales data
  2. Time extensive and tedious process

The Trend follower
The Trend follower

The trend follower is the new age demand planner that always wants to stay on top of the current market trends and derive quicker results. Sadly, these are also the ones who are overworked even during the holidays and promotional events. While it’s great to make the best use of the latest trends, being short sighted while planning demand can reflect on your bottom line numbers negatively. 

Oftentimes, trend followers are too focussed on all the current buzz that they forget history repeats too. They fail to gauge the impact of historical demand patterns and cyclicity.

The trend followers for instance, would want to make the most of a seasonal peak in demand for a SKU and procure them in large quantities. When the seasonal demand dies, they are often left with a warehouse piled with excess inventory. This results in lack of liquidity, wastage and additional costs incurred on storage. Hence, it is essential to manage risks and make informed decisions that wouldn’t affect operations in the future. 

Pros

  1. Helps to stay ahead of competition by following the trends
  2. Helps gain maximum revenue from marketing events/promotions 

Cons

  1. Failure to consider future risks leading to higher operating costs
  2. Short lived results which are unreliable

          

The Big Picture Guy       
The Big Picture Guy   

The Big Picture Guys are the ones who tend to miss out on finer details while focusing too much on the aggregated  numbers. This is a common tendency among the demand planners. They have a top down approach to demand planning and end up missing out on the demand drivers at the most granular levels. 

These demand planners predict top level demand numbers at the mother warehouse level and distribute them proportionally to the child warehouses based on past sales data. While the demand plan seems sound and highly accurate at the top level, they become inaccurate moving further down the supply chain. 

This may result in understocking/overstocking instances leading to loss of revenue and wastages. Further, this causes product unavailability and poor customer satisfaction. Hence, it's important for demand planners to predict demand closer to the customers to achieve service level targets and ensure product availability.

Pros

  1. Higher visibility for the top level nodes of the supply chain
  2. Relatively simpler process mainly relying on historical sales

 Cons

        1. Poor prediction accuracy at the granular level (SKU x store/ SKU x channel)

        2. Inefficient inventory planning across nodes leading to poor customer experience

The Accuracy Geek
The Accuracy Geek

We all know that demand planner who’s just too obsessed with accuracy. And the most common mistake these demand planners do is look at accuracy as absolutes which can often be misleading.

Accuracy geeks tend to consider any prediction accuracy less than 80% as a poor result.

However, the ideal way to look at demand prediction accuracy is in relative terms. This is because even a slight increase in prediction accuracy when compared to other traditional alternatives would mean a drastic positive impact on the top and bottom line numbers.

When it comes to demand prediction accuracy, it’s important to set credible benchmarks and look at your accuracies in relativity to them for a clear picture.

Pros

  1. Enables continuous improvements in accuracy numbers
  2. Team is aligned and focused on process efficiency 

Cons

  1. Portrays a misleading picture by considering accuracy an absolute term
  2. Limited visibility in the demand accuracy numbers across the various nodes of supply chain

Now that we have looked at different types of demand planners, we can see that while each one has a strength they also fall short in some aspects. The key is to find the right balance of all these aspects and arrive at comprehensive, agile demand forecasts.

Do we have a winner?

Drum Roll please! Presenting the,

The AI/ML Enthusiast
The AI/ML Enthusiast

The AI or ML enthusiasts are the ones who embrace changes by adopting highly intelligent tools like Kronoscope. They are up for exciting experimentations and are equipped with the power to tackle any uncertainties.

Their lives are made simple with Kronoscope, an AI powered demand prediction and inventory planning solution which offers,

  • Demand predictions that are comprehensive of both historical and real time events by taking into account 9 major demand signals including seasonal effects, cyclicity, promotions, weather etc.
  • Automated purchase and replenishment plans created in real time by effectively eliminating out of stock situations and inventory wastages.
  • Seamless inventory planning for events and promotions based on similar events in the past.
  • Superior demand prediction accuracies using the bottom up approach that beats AWS forecasts 8 out of 10 times.

In summary, the AI and ML enthusiasts work their magic with the combination of artificial intelligence and human intuition. This way, they are able to deliver a great customer experience no matter the change in demand levels. So today, embrace the change with our state-of-the-art AI-powered Inventory Planning solution.

Access the

Blog

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

A demand planner plays a pivotal role in any organization as their work contributes to major decisions made by various departments ultimately to maintain two important things,

1. Consistent product availability across the supply chain

2. Mitigate inventory costs

And there are different types of demand planners with various approaches of their own. Let’s understand these approaches and evaluate them here to determine the best of all.

4 types of demand planners and their approaches

The Excel Power Ninja
The Excel Power Ninja

Even in today’s volatile market, a large chunk of DTC and retail companies rely on manual methods for managing their supply chain. This gives rise to the Excel Power Ninjas who use multiple spreadsheets with a myriad of calculations to plan demand. This conventional approach makes demand planning a tedious task and hence cannot be repeated frequently. While historical data plays a crucial role in preparing for future setbacks, relying only on it brings trouble. These demand planners miss out on capturing real time information and factoring in their impact while predicting demand. This leads to inefficient inventory planning and higher operational costs.

For example, the excel power ninjas would set a static safety stock level irrespective of the changing demand levels of a SKU. Eventually, they run out of stock when there is an unusually high demand and don’t have adequate safety stock to back it. This is when a dynamic safety stock approach based on real time demand comes to the rescue. 

With cutthroat competition and ever changing consumer preferences, it's important to be agile. Companies should have the ability to be cognizant of how changing demand affects inventory levels and adjust them in real time. 

Pros

  1. Suitable for limited SKUs with steady sales
  2. Helps identify demand patterns and cyclicity of various SKUs

Cons

  1. Doesn’t work for SKUs that are new and don’t have historical sales data
  2. Time extensive and tedious process

The Trend follower
The Trend follower

The trend follower is the new age demand planner that always wants to stay on top of the current market trends and derive quicker results. Sadly, these are also the ones who are overworked even during the holidays and promotional events. While it’s great to make the best use of the latest trends, being short sighted while planning demand can reflect on your bottom line numbers negatively. 

Oftentimes, trend followers are too focussed on all the current buzz that they forget history repeats too. They fail to gauge the impact of historical demand patterns and cyclicity.

The trend followers for instance, would want to make the most of a seasonal peak in demand for a SKU and procure them in large quantities. When the seasonal demand dies, they are often left with a warehouse piled with excess inventory. This results in lack of liquidity, wastage and additional costs incurred on storage. Hence, it is essential to manage risks and make informed decisions that wouldn’t affect operations in the future. 

Pros

  1. Helps to stay ahead of competition by following the trends
  2. Helps gain maximum revenue from marketing events/promotions 

Cons

  1. Failure to consider future risks leading to higher operating costs
  2. Short lived results which are unreliable

          

The Big Picture Guy       
The Big Picture Guy   

The Big Picture Guys are the ones who tend to miss out on finer details while focusing too much on the aggregated  numbers. This is a common tendency among the demand planners. They have a top down approach to demand planning and end up missing out on the demand drivers at the most granular levels. 

These demand planners predict top level demand numbers at the mother warehouse level and distribute them proportionally to the child warehouses based on past sales data. While the demand plan seems sound and highly accurate at the top level, they become inaccurate moving further down the supply chain. 

This may result in understocking/overstocking instances leading to loss of revenue and wastages. Further, this causes product unavailability and poor customer satisfaction. Hence, it's important for demand planners to predict demand closer to the customers to achieve service level targets and ensure product availability.

Pros

  1. Higher visibility for the top level nodes of the supply chain
  2. Relatively simpler process mainly relying on historical sales

 Cons

        1. Poor prediction accuracy at the granular level (SKU x store/ SKU x channel)

        2. Inefficient inventory planning across nodes leading to poor customer experience

The Accuracy Geek
The Accuracy Geek

We all know that demand planner who’s just too obsessed with accuracy. And the most common mistake these demand planners do is look at accuracy as absolutes which can often be misleading.

Accuracy geeks tend to consider any prediction accuracy less than 80% as a poor result.

However, the ideal way to look at demand prediction accuracy is in relative terms. This is because even a slight increase in prediction accuracy when compared to other traditional alternatives would mean a drastic positive impact on the top and bottom line numbers.

When it comes to demand prediction accuracy, it’s important to set credible benchmarks and look at your accuracies in relativity to them for a clear picture.

Pros

  1. Enables continuous improvements in accuracy numbers
  2. Team is aligned and focused on process efficiency 

Cons

  1. Portrays a misleading picture by considering accuracy an absolute term
  2. Limited visibility in the demand accuracy numbers across the various nodes of supply chain

Now that we have looked at different types of demand planners, we can see that while each one has a strength they also fall short in some aspects. The key is to find the right balance of all these aspects and arrive at comprehensive, agile demand forecasts.

Do we have a winner?

Drum Roll please! Presenting the,

The AI/ML Enthusiast
The AI/ML Enthusiast

The AI or ML enthusiasts are the ones who embrace changes by adopting highly intelligent tools like Kronoscope. They are up for exciting experimentations and are equipped with the power to tackle any uncertainties.

Their lives are made simple with Kronoscope, an AI powered demand prediction and inventory planning solution which offers,

  • Demand predictions that are comprehensive of both historical and real time events by taking into account 9 major demand signals including seasonal effects, cyclicity, promotions, weather etc.
  • Automated purchase and replenishment plans created in real time by effectively eliminating out of stock situations and inventory wastages.
  • Seamless inventory planning for events and promotions based on similar events in the past.
  • Superior demand prediction accuracies using the bottom up approach that beats AWS forecasts 8 out of 10 times.

In summary, the AI and ML enthusiasts work their magic with the combination of artificial intelligence and human intuition. This way, they are able to deliver a great customer experience no matter the change in demand levels. So today, embrace the change with our state-of-the-art AI-powered Inventory Planning solution.

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