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Five Reasons to Automate Your Demand Planning Today

By
Niki Khokale
September 21, 2022
5
min
Share this
Blog

Five Reasons to Automate Your Demand Planning Today

Share this

With the customer behavior getting complex day by day, there are just too many demand trends to keep up with. The ideal way to track these trends in real time and produce accurate predictions is by automating this process. Newer/ advanced technologies like AI and ML have taken demand planning to the next level with their highly cognitive abilities. 

Bottom line, Emerging technologies (like AI and ML) are now becoming indispensable for supply chain leaders. According to a recent survey

  • 92% of CSCOs plan to leverage predictive analytics over the next three years.
  • 89% plan to use machine learning.
  • 82% plan to run simulations and deep learning.

Over the horizon of the next three to five years, 72% of supply chain leaders expect most of their processes and workflows to be automated. 

Most forward-looking companies are using demand planning automation to move from error based low accuracy demand predictions to high accuracy demand predictions which are responsive to variations in prices, seasonality, promotions, weather, etc. 

If you are one of those businesses considering to transition from manual/home grown processes to automated demand planning, now is the right time. Here, we’ll talk about why you should consider to rethink your approach to demand planning and adopt automation. 

RETHINK DATA: From Complex Excels to Simplified Data Trends 

Oftentimes, it becomes taxing to aggregate data from multiple sources and integrate them to make plans. It only adds to the burden when there are endless spreadsheets with demand data, historical sales data and supplier data that has to be brought together manually on a daily basis. This tedious process restricts demand planners from forecasting demand more frequently resulting in poor inventory planning. 

Most companies have stuck to spreadsheets and put off the transition towards a tech driven supply chain for a long time now. They presume that it requires a great deal of time and effort to train their resources to harness these technologies. What they don’t realize is, an automated supply chain helps businesses to simplify their operations by acting as a collaborative forecasting and planning tool. 

AI and ML technologies automates demand forecasting, procurement and replenishment planning by bringing all demand drivers and supplier-side concerns together in just a few clicks. These solutions can seamlessly plug into ERPs and fetch sales data through CSV file exchanges or API integrations. The system generated forecasts are then used to make purchase/replenishment plans which then flow into Order Management Systems and Warehouse Management Systems. This essentially enables end to end supply chain automation without any hassles. 

ACCURACY MATTERS: Historical Data based Predictions to Real Time Predictions 

Traditional demand forecasting follows a top down approach and happens on a weekly/monthly basis. Though this approach helps achieve accuracy at the top level, the numbers go for a toss as we go granular in the supply chain. This leads to product unavailability and failure to meet service level targets. Also, these predictions are largely based on historical data and don’t consider current changes in the market. It results in poor accuracy and inefficient inventory planning.

Automation tools bring in a more comprehensive approach with continuous improvements. It offers unmatched accuracy by predicting demand as close to the customer as possible which is then rolled up to the top levels.This helps capture finer details and offers end to end visibility in the supply chain. The demand signals are refreshed on a daily basis by factoring in various demand drivers including historical trends, seasonal effects, cyclicity, outlier corrections, changes in pricing, promotional impact, weather, and similar SKU/store attributes.  This helps to align forecasts in real time and plan inventory accordingly. Businesses can rest assured that they will be able to hit service level targets no matter how volatile the demand is.

CUSTOMERS RULE: Out of Stock occurrences to keeping up with changing consumer preferences

 Stock outs are a common theme across businesses today. This is because demand predictions are largely based on historical sales data. So, when they record less sales due to a stock out situation and consider those numbers for future predictions, they again end up with empty shelves and lost revenue. This results in poor customer experience and higher operational costs incurred on restocking. The ideal way to get out of this loop is through active planning instead of reactive solving. 

 Intelligent demand planning tools take inventory as a signal to infer whether low sales are a result of low stock or actual low demand. If low sales are a result of low stock, then sales are calculated as if there were no availability issues. This calculation would then be used for future demand predictions. It essentially helps businesses get out of the vicious cycle of stock outs.

With AI powered inventory planning, companies can actively track different SKUs that are likely to run out of stock in the near future and the revenue loss that they may incur. Users can further act upon these alerts by automating purchase plans to restock inventory. These tools also recommend dynamic safety stock levels which can come in handy to tackle stock out contingencies. This will enable businesses to make the most of their revenue opportunities and foster customer loyalty.

 

OPTIMAL PRICING: Gut based/Heuristics to data driven pricing decisions 

 Pricing is one of the most crucial factors that helps any business gain a competitive advantage in the market. However, most businesses set prices based on their judgements or prices that simply match their competition. They lack the capacity to experiment with different price points for multiple SKUs and explore how demand changes with respect to these prices. They often set prices that may not help them achieve revenue targets or desired profit margins. It has always been a trade off between the two.

With technologies like AI and ML, users can enhance baseline forecasts by running different simulations based on quantity and price.They can experiment with different price points and evaluate the demand levels at each point. This is done by establishing a price elasticity curve that captures past sales numbers of a particular SKU at multiple price points. This allows them to set the ideal prices that will help them achieve their revenue targets. Users can also edit the baseline forecasted quantities based on their judgements or other internal factors for any particular SKU from time to time. These updated numbers are then used to predict demand and plan inventory for the upcoming period. This facilitates a fine collaboration between human intuition and artificial intelligence to manage supply chains.

 

 SHUN THE SILOS: Compartmentalized planning to seamless collaboration  

A rigid supply chain does not facilitate coordination between different departments in the organization. It becomes difficult to identify and solve problems proactively when teams are functioning in silos.

Automation enables seamless collaboration between various teams through inclusive demand plans and better visibility. For instance, when an employee edits purchase order quantities due to an unexpected surge in demand they can notify other users about this change by adding comments. This will allow for quicker communication and proactive actions rather than costly reactive responses.

Moreover, these tools can even help record any promotional event  with the impact segment, event type and duration. This is then used to predict demand accurately for similar events in future. It allows the operations team to plan inventory for promotions in concert with the marketing team. Additionally, the sales and operations folks can also work together on making pricing decisions to meet sales targets by accurately determining demand levels at different price points. 

Technologies like AI and ML empower businesses with highly agile and intelligent supply chains that are a need of the hour. Our flagship product, Kronoscope is an AI-powered demand forecasting and inventory planning solution that automates procurement and replenishment planning by bringing all demand drivers and supplier-side concerns together in just two clicks. This essentially makes real time demand forecasting and inventory planning a cake walk through seamless end to end automation of your supply chains.

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

Five Reasons to Automate Your Demand Planning Today

Share this

With the customer behavior getting complex day by day, there are just too many demand trends to keep up with. The ideal way to track these trends in real time and produce accurate predictions is by automating this process. Newer/ advanced technologies like AI and ML have taken demand planning to the next level with their highly cognitive abilities. 

Bottom line, Emerging technologies (like AI and ML) are now becoming indispensable for supply chain leaders. According to a recent survey

  • 92% of CSCOs plan to leverage predictive analytics over the next three years.
  • 89% plan to use machine learning.
  • 82% plan to run simulations and deep learning.

Over the horizon of the next three to five years, 72% of supply chain leaders expect most of their processes and workflows to be automated. 

Most forward-looking companies are using demand planning automation to move from error based low accuracy demand predictions to high accuracy demand predictions which are responsive to variations in prices, seasonality, promotions, weather, etc. 

If you are one of those businesses considering to transition from manual/home grown processes to automated demand planning, now is the right time. Here, we’ll talk about why you should consider to rethink your approach to demand planning and adopt automation. 

RETHINK DATA: From Complex Excels to Simplified Data Trends 

Oftentimes, it becomes taxing to aggregate data from multiple sources and integrate them to make plans. It only adds to the burden when there are endless spreadsheets with demand data, historical sales data and supplier data that has to be brought together manually on a daily basis. This tedious process restricts demand planners from forecasting demand more frequently resulting in poor inventory planning. 

Most companies have stuck to spreadsheets and put off the transition towards a tech driven supply chain for a long time now. They presume that it requires a great deal of time and effort to train their resources to harness these technologies. What they don’t realize is, an automated supply chain helps businesses to simplify their operations by acting as a collaborative forecasting and planning tool. 

AI and ML technologies automates demand forecasting, procurement and replenishment planning by bringing all demand drivers and supplier-side concerns together in just a few clicks. These solutions can seamlessly plug into ERPs and fetch sales data through CSV file exchanges or API integrations. The system generated forecasts are then used to make purchase/replenishment plans which then flow into Order Management Systems and Warehouse Management Systems. This essentially enables end to end supply chain automation without any hassles. 

ACCURACY MATTERS: Historical Data based Predictions to Real Time Predictions 

Traditional demand forecasting follows a top down approach and happens on a weekly/monthly basis. Though this approach helps achieve accuracy at the top level, the numbers go for a toss as we go granular in the supply chain. This leads to product unavailability and failure to meet service level targets. Also, these predictions are largely based on historical data and don’t consider current changes in the market. It results in poor accuracy and inefficient inventory planning.

Automation tools bring in a more comprehensive approach with continuous improvements. It offers unmatched accuracy by predicting demand as close to the customer as possible which is then rolled up to the top levels.This helps capture finer details and offers end to end visibility in the supply chain. The demand signals are refreshed on a daily basis by factoring in various demand drivers including historical trends, seasonal effects, cyclicity, outlier corrections, changes in pricing, promotional impact, weather, and similar SKU/store attributes.  This helps to align forecasts in real time and plan inventory accordingly. Businesses can rest assured that they will be able to hit service level targets no matter how volatile the demand is.

CUSTOMERS RULE: Out of Stock occurrences to keeping up with changing consumer preferences

 Stock outs are a common theme across businesses today. This is because demand predictions are largely based on historical sales data. So, when they record less sales due to a stock out situation and consider those numbers for future predictions, they again end up with empty shelves and lost revenue. This results in poor customer experience and higher operational costs incurred on restocking. The ideal way to get out of this loop is through active planning instead of reactive solving. 

 Intelligent demand planning tools take inventory as a signal to infer whether low sales are a result of low stock or actual low demand. If low sales are a result of low stock, then sales are calculated as if there were no availability issues. This calculation would then be used for future demand predictions. It essentially helps businesses get out of the vicious cycle of stock outs.

With AI powered inventory planning, companies can actively track different SKUs that are likely to run out of stock in the near future and the revenue loss that they may incur. Users can further act upon these alerts by automating purchase plans to restock inventory. These tools also recommend dynamic safety stock levels which can come in handy to tackle stock out contingencies. This will enable businesses to make the most of their revenue opportunities and foster customer loyalty.

 

OPTIMAL PRICING: Gut based/Heuristics to data driven pricing decisions 

 Pricing is one of the most crucial factors that helps any business gain a competitive advantage in the market. However, most businesses set prices based on their judgements or prices that simply match their competition. They lack the capacity to experiment with different price points for multiple SKUs and explore how demand changes with respect to these prices. They often set prices that may not help them achieve revenue targets or desired profit margins. It has always been a trade off between the two.

With technologies like AI and ML, users can enhance baseline forecasts by running different simulations based on quantity and price.They can experiment with different price points and evaluate the demand levels at each point. This is done by establishing a price elasticity curve that captures past sales numbers of a particular SKU at multiple price points. This allows them to set the ideal prices that will help them achieve their revenue targets. Users can also edit the baseline forecasted quantities based on their judgements or other internal factors for any particular SKU from time to time. These updated numbers are then used to predict demand and plan inventory for the upcoming period. This facilitates a fine collaboration between human intuition and artificial intelligence to manage supply chains.

 

 SHUN THE SILOS: Compartmentalized planning to seamless collaboration  

A rigid supply chain does not facilitate coordination between different departments in the organization. It becomes difficult to identify and solve problems proactively when teams are functioning in silos.

Automation enables seamless collaboration between various teams through inclusive demand plans and better visibility. For instance, when an employee edits purchase order quantities due to an unexpected surge in demand they can notify other users about this change by adding comments. This will allow for quicker communication and proactive actions rather than costly reactive responses.

Moreover, these tools can even help record any promotional event  with the impact segment, event type and duration. This is then used to predict demand accurately for similar events in future. It allows the operations team to plan inventory for promotions in concert with the marketing team. Additionally, the sales and operations folks can also work together on making pricing decisions to meet sales targets by accurately determining demand levels at different price points. 

Technologies like AI and ML empower businesses with highly agile and intelligent supply chains that are a need of the hour. Our flagship product, Kronoscope is an AI-powered demand forecasting and inventory planning solution that automates procurement and replenishment planning by bringing all demand drivers and supplier-side concerns together in just two clicks. This essentially makes real time demand forecasting and inventory planning a cake walk through seamless end to end automation of your supply chains.

Access the

Blog

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

With the customer behavior getting complex day by day, there are just too many demand trends to keep up with. The ideal way to track these trends in real time and produce accurate predictions is by automating this process. Newer/ advanced technologies like AI and ML have taken demand planning to the next level with their highly cognitive abilities. 

Bottom line, Emerging technologies (like AI and ML) are now becoming indispensable for supply chain leaders. According to a recent survey

  • 92% of CSCOs plan to leverage predictive analytics over the next three years.
  • 89% plan to use machine learning.
  • 82% plan to run simulations and deep learning.

Over the horizon of the next three to five years, 72% of supply chain leaders expect most of their processes and workflows to be automated. 

Most forward-looking companies are using demand planning automation to move from error based low accuracy demand predictions to high accuracy demand predictions which are responsive to variations in prices, seasonality, promotions, weather, etc. 

If you are one of those businesses considering to transition from manual/home grown processes to automated demand planning, now is the right time. Here, we’ll talk about why you should consider to rethink your approach to demand planning and adopt automation. 

RETHINK DATA: From Complex Excels to Simplified Data Trends 

Oftentimes, it becomes taxing to aggregate data from multiple sources and integrate them to make plans. It only adds to the burden when there are endless spreadsheets with demand data, historical sales data and supplier data that has to be brought together manually on a daily basis. This tedious process restricts demand planners from forecasting demand more frequently resulting in poor inventory planning. 

Most companies have stuck to spreadsheets and put off the transition towards a tech driven supply chain for a long time now. They presume that it requires a great deal of time and effort to train their resources to harness these technologies. What they don’t realize is, an automated supply chain helps businesses to simplify their operations by acting as a collaborative forecasting and planning tool. 

AI and ML technologies automates demand forecasting, procurement and replenishment planning by bringing all demand drivers and supplier-side concerns together in just a few clicks. These solutions can seamlessly plug into ERPs and fetch sales data through CSV file exchanges or API integrations. The system generated forecasts are then used to make purchase/replenishment plans which then flow into Order Management Systems and Warehouse Management Systems. This essentially enables end to end supply chain automation without any hassles. 

ACCURACY MATTERS: Historical Data based Predictions to Real Time Predictions 

Traditional demand forecasting follows a top down approach and happens on a weekly/monthly basis. Though this approach helps achieve accuracy at the top level, the numbers go for a toss as we go granular in the supply chain. This leads to product unavailability and failure to meet service level targets. Also, these predictions are largely based on historical data and don’t consider current changes in the market. It results in poor accuracy and inefficient inventory planning.

Automation tools bring in a more comprehensive approach with continuous improvements. It offers unmatched accuracy by predicting demand as close to the customer as possible which is then rolled up to the top levels.This helps capture finer details and offers end to end visibility in the supply chain. The demand signals are refreshed on a daily basis by factoring in various demand drivers including historical trends, seasonal effects, cyclicity, outlier corrections, changes in pricing, promotional impact, weather, and similar SKU/store attributes.  This helps to align forecasts in real time and plan inventory accordingly. Businesses can rest assured that they will be able to hit service level targets no matter how volatile the demand is.

CUSTOMERS RULE: Out of Stock occurrences to keeping up with changing consumer preferences

 Stock outs are a common theme across businesses today. This is because demand predictions are largely based on historical sales data. So, when they record less sales due to a stock out situation and consider those numbers for future predictions, they again end up with empty shelves and lost revenue. This results in poor customer experience and higher operational costs incurred on restocking. The ideal way to get out of this loop is through active planning instead of reactive solving. 

 Intelligent demand planning tools take inventory as a signal to infer whether low sales are a result of low stock or actual low demand. If low sales are a result of low stock, then sales are calculated as if there were no availability issues. This calculation would then be used for future demand predictions. It essentially helps businesses get out of the vicious cycle of stock outs.

With AI powered inventory planning, companies can actively track different SKUs that are likely to run out of stock in the near future and the revenue loss that they may incur. Users can further act upon these alerts by automating purchase plans to restock inventory. These tools also recommend dynamic safety stock levels which can come in handy to tackle stock out contingencies. This will enable businesses to make the most of their revenue opportunities and foster customer loyalty.

 

OPTIMAL PRICING: Gut based/Heuristics to data driven pricing decisions 

 Pricing is one of the most crucial factors that helps any business gain a competitive advantage in the market. However, most businesses set prices based on their judgements or prices that simply match their competition. They lack the capacity to experiment with different price points for multiple SKUs and explore how demand changes with respect to these prices. They often set prices that may not help them achieve revenue targets or desired profit margins. It has always been a trade off between the two.

With technologies like AI and ML, users can enhance baseline forecasts by running different simulations based on quantity and price.They can experiment with different price points and evaluate the demand levels at each point. This is done by establishing a price elasticity curve that captures past sales numbers of a particular SKU at multiple price points. This allows them to set the ideal prices that will help them achieve their revenue targets. Users can also edit the baseline forecasted quantities based on their judgements or other internal factors for any particular SKU from time to time. These updated numbers are then used to predict demand and plan inventory for the upcoming period. This facilitates a fine collaboration between human intuition and artificial intelligence to manage supply chains.

 

 SHUN THE SILOS: Compartmentalized planning to seamless collaboration  

A rigid supply chain does not facilitate coordination between different departments in the organization. It becomes difficult to identify and solve problems proactively when teams are functioning in silos.

Automation enables seamless collaboration between various teams through inclusive demand plans and better visibility. For instance, when an employee edits purchase order quantities due to an unexpected surge in demand they can notify other users about this change by adding comments. This will allow for quicker communication and proactive actions rather than costly reactive responses.

Moreover, these tools can even help record any promotional event  with the impact segment, event type and duration. This is then used to predict demand accurately for similar events in future. It allows the operations team to plan inventory for promotions in concert with the marketing team. Additionally, the sales and operations folks can also work together on making pricing decisions to meet sales targets by accurately determining demand levels at different price points. 

Technologies like AI and ML empower businesses with highly agile and intelligent supply chains that are a need of the hour. Our flagship product, Kronoscope is an AI-powered demand forecasting and inventory planning solution that automates procurement and replenishment planning by bringing all demand drivers and supplier-side concerns together in just two clicks. This essentially makes real time demand forecasting and inventory planning a cake walk through seamless end to end automation of your supply chains.

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