Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Block quote
Ordered list
Unordered list
Bold text
Emphasis
Superscript
Subscript
Integrating your demand planning and forecasting processes with other parts of the business is essential for enhancing supply chain performance. For over 20 years, companies have been striving to improve forecast accuracy by implementing toolsets, achieving varying degrees of success. The bullwhip effect, where demand signals amplify as they move through the supply chain, remains a significant challenge. While traditional forecasting plays a role in addressing this issue, it often requires a broader approach, including collaboration across different business functions, to fully resolve forecasting challenges.
Most companies choose software that can provide insight into 3 key dimensions to improve forecast accuracy: granularity, and time. Most EPM software directly addresses these aspects, and traditional forecast improvement focuses on managing seasonality and special days (e.g. holidays), promotion and markdown, uplifts, cannibalization effect, product substitutions, and inventory levels etc. Most leading solutions now also embrace machine learning and AI. Often more data granularity and detailed analysis does not produce the desired results. Why is this and what can be done?
Performance is quantified using traditional forecast metrics such as Mean Absolute Percentage Error (MAPE) and Forecast Bias etc. that sit within EPM software. With many systems now AI can be leveraged to further give insight into and automate processes and analysis supporting real time what if scenarios and pre-alerts on issues with suggested resolutions. But this will address only some of the causes of forecast inaccuracy.
To get results that add value to the business companies desire implementing EPM software will not automatically deliver the desired results. Linking supply chain to financial planning process can help focus on what aspects of your forecasting process either add or detract from your overall performance, but the real benefits will be unlocked by linking departments and focussing on shared business goals across all departments that influence the Supply chain process and outcomes.
To gain insight into the processes that contribute to inaccuracy in forecast and demand planning, EPM software can be most effective when understand the process steps in forecasting and which departments influence the forecast (statistical forecast, sales adjustments, inventory policy driving MOQ, channel decisions, market activities etc.) against defined value add performance goals. To address process, impact this needs to be captured as steps in the forecast build in EPM software and then the impact needs to be quantified. This is referred to as Forecast Value Add (FVA). When we develop a segmented demand forecast model linked to finance processes we show the input of statistical forecast, sales changes, marketing changes, decision on ramp up / down etc. We show the impact of different departments on the process steps in planning and how they impact outcomes. Forecast Value Add (FVA) is- a way to measure and manage these impacts.
This can be built in EPM software as a waterfall but if you do not combine this with a metric and do not track how Forecast Value Add evolves overtime and how this is linked to your overall goals; you may not get the desired results and may not realise all the benefits of your EPM implementation.
At Keyrus we help companies navigate EPM software choice and implementation of S&OP and IBP solutions ensuring that business goals and desired outcomes are leading and that the processes and organisational aspects of the implementation are addressed. Want to know more? I would like to hear from you.
Integrating your demand planning and forecasting processes with other parts of the business is essential for enhancing supply chain performance. For over 20 years, companies have been striving to improve forecast accuracy by implementing toolsets, achieving varying degrees of success. The bullwhip effect, where demand signals amplify as they move through the supply chain, remains a significant challenge. While traditional forecasting plays a role in addressing this issue, it often requires a broader approach, including collaboration across different business functions, to fully resolve forecasting challenges.
Most companies choose software that can provide insight into 3 key dimensions to improve forecast accuracy: granularity, and time. Most EPM software directly addresses these aspects, and traditional forecast improvement focuses on managing seasonality and special days (e.g. holidays), promotion and markdown, uplifts, cannibalization effect, product substitutions, and inventory levels etc. Most leading solutions now also embrace machine learning and AI. Often more data granularity and detailed analysis does not produce the desired results. Why is this and what can be done?
Performance is quantified using traditional forecast metrics such as Mean Absolute Percentage Error (MAPE) and Forecast Bias etc. that sit within EPM software. With many systems now AI can be leveraged to further give insight into and automate processes and analysis supporting real time what if scenarios and pre-alerts on issues with suggested resolutions. But this will address only some of the causes of forecast inaccuracy.
To get results that add value to the business companies desire implementing EPM software will not automatically deliver the desired results. Linking supply chain to financial planning process can help focus on what aspects of your forecasting process either add or detract from your overall performance, but the real benefits will be unlocked by linking departments and focussing on shared business goals across all departments that influence the Supply chain process and outcomes.
To gain insight into the processes that contribute to inaccuracy in forecast and demand planning, EPM software can be most effective when understand the process steps in forecasting and which departments influence the forecast (statistical forecast, sales adjustments, inventory policy driving MOQ, channel decisions, market activities etc.) against defined value add performance goals. To address process, impact this needs to be captured as steps in the forecast build in EPM software and then the impact needs to be quantified. This is referred to as Forecast Value Add (FVA). When we develop a segmented demand forecast model linked to finance processes we show the input of statistical forecast, sales changes, marketing changes, decision on ramp up / down etc. We show the impact of different departments on the process steps in planning and how they impact outcomes. Forecast Value Add (FVA) is- a way to measure and manage these impacts.
This can be built in EPM software as a waterfall but if you do not combine this with a metric and do not track how Forecast Value Add evolves overtime and how this is linked to your overall goals; you may not get the desired results and may not realise all the benefits of your EPM implementation.
At Keyrus we help companies navigate EPM software choice and implementation of S&OP and IBP solutions ensuring that business goals and desired outcomes are leading and that the processes and organisational aspects of the implementation are addressed. Want to know more? I would like to hear from you.
Integrating your demand planning and forecasting processes with other parts of the business is essential for enhancing supply chain performance. For over 20 years, companies have been striving to improve forecast accuracy by implementing toolsets, achieving varying degrees of success. The bullwhip effect, where demand signals amplify as they move through the supply chain, remains a significant challenge. While traditional forecasting plays a role in addressing this issue, it often requires a broader approach, including collaboration across different business functions, to fully resolve forecasting challenges.
Most companies choose software that can provide insight into 3 key dimensions to improve forecast accuracy: granularity, and time. Most EPM software directly addresses these aspects, and traditional forecast improvement focuses on managing seasonality and special days (e.g. holidays), promotion and markdown, uplifts, cannibalization effect, product substitutions, and inventory levels etc. Most leading solutions now also embrace machine learning and AI. Often more data granularity and detailed analysis does not produce the desired results. Why is this and what can be done?
Performance is quantified using traditional forecast metrics such as Mean Absolute Percentage Error (MAPE) and Forecast Bias etc. that sit within EPM software. With many systems now AI can be leveraged to further give insight into and automate processes and analysis supporting real time what if scenarios and pre-alerts on issues with suggested resolutions. But this will address only some of the causes of forecast inaccuracy.
To get results that add value to the business companies desire implementing EPM software will not automatically deliver the desired results. Linking supply chain to financial planning process can help focus on what aspects of your forecasting process either add or detract from your overall performance, but the real benefits will be unlocked by linking departments and focussing on shared business goals across all departments that influence the Supply chain process and outcomes.
To gain insight into the processes that contribute to inaccuracy in forecast and demand planning, EPM software can be most effective when understand the process steps in forecasting and which departments influence the forecast (statistical forecast, sales adjustments, inventory policy driving MOQ, channel decisions, market activities etc.) against defined value add performance goals. To address process, impact this needs to be captured as steps in the forecast build in EPM software and then the impact needs to be quantified. This is referred to as Forecast Value Add (FVA). When we develop a segmented demand forecast model linked to finance processes we show the input of statistical forecast, sales changes, marketing changes, decision on ramp up / down etc. We show the impact of different departments on the process steps in planning and how they impact outcomes. Forecast Value Add (FVA) is- a way to measure and manage these impacts.
This can be built in EPM software as a waterfall but if you do not combine this with a metric and do not track how Forecast Value Add evolves overtime and how this is linked to your overall goals; you may not get the desired results and may not realise all the benefits of your EPM implementation.
At Keyrus we help companies navigate EPM software choice and implementation of S&OP and IBP solutions ensuring that business goals and desired outcomes are leading and that the processes and organisational aspects of the implementation are addressed. Want to know more? I would like to hear from you.
Integrating your demand planning and forecasting processes with other parts of the business is essential for enhancing supply chain performance. For over 20 years, companies have been striving to improve forecast accuracy by implementing toolsets, achieving varying degrees of success. The bullwhip effect, where demand signals amplify as they move through the supply chain, remains a significant challenge. While traditional forecasting plays a role in addressing this issue, it often requires a broader approach, including collaboration across different business functions, to fully resolve forecasting challenges.
Most companies choose software that can provide insight into 3 key dimensions to improve forecast accuracy: granularity, and time. Most EPM software directly addresses these aspects, and traditional forecast improvement focuses on managing seasonality and special days (e.g. holidays), promotion and markdown, uplifts, cannibalization effect, product substitutions, and inventory levels etc. Most leading solutions now also embrace machine learning and AI. Often more data granularity and detailed analysis does not produce the desired results. Why is this and what can be done?
Performance is quantified using traditional forecast metrics such as Mean Absolute Percentage Error (MAPE) and Forecast Bias etc. that sit within EPM software. With many systems now AI can be leveraged to further give insight into and automate processes and analysis supporting real time what if scenarios and pre-alerts on issues with suggested resolutions. But this will address only some of the causes of forecast inaccuracy.
To get results that add value to the business companies desire implementing EPM software will not automatically deliver the desired results. Linking supply chain to financial planning process can help focus on what aspects of your forecasting process either add or detract from your overall performance, but the real benefits will be unlocked by linking departments and focussing on shared business goals across all departments that influence the Supply chain process and outcomes.
To gain insight into the processes that contribute to inaccuracy in forecast and demand planning, EPM software can be most effective when understand the process steps in forecasting and which departments influence the forecast (statistical forecast, sales adjustments, inventory policy driving MOQ, channel decisions, market activities etc.) against defined value add performance goals. To address process, impact this needs to be captured as steps in the forecast build in EPM software and then the impact needs to be quantified. This is referred to as Forecast Value Add (FVA). When we develop a segmented demand forecast model linked to finance processes we show the input of statistical forecast, sales changes, marketing changes, decision on ramp up / down etc. We show the impact of different departments on the process steps in planning and how they impact outcomes. Forecast Value Add (FVA) is- a way to measure and manage these impacts.
This can be built in EPM software as a waterfall but if you do not combine this with a metric and do not track how Forecast Value Add evolves overtime and how this is linked to your overall goals; you may not get the desired results and may not realise all the benefits of your EPM implementation.
At Keyrus we help companies navigate EPM software choice and implementation of S&OP and IBP solutions ensuring that business goals and desired outcomes are leading and that the processes and organisational aspects of the implementation are addressed. Want to know more? I would like to hear from you.
Integrating your demand planning and forecasting processes with other parts of the business is essential for enhancing supply chain performance. For over 20 years, companies have been striving to improve forecast accuracy by implementing toolsets, achieving varying degrees of success. The bullwhip effect, where demand signals amplify as they move through the supply chain, remains a significant challenge. While traditional forecasting plays a role in addressing this issue, it often requires a broader approach, including collaboration across different business functions, to fully resolve forecasting challenges.
Most companies choose software that can provide insight into 3 key dimensions to improve forecast accuracy: granularity, and time. Most EPM software directly addresses these aspects, and traditional forecast improvement focuses on managing seasonality and special days (e.g. holidays), promotion and markdown, uplifts, cannibalization effect, product substitutions, and inventory levels etc. Most leading solutions now also embrace machine learning and AI. Often more data granularity and detailed analysis does not produce the desired results. Why is this and what can be done?
Performance is quantified using traditional forecast metrics such as Mean Absolute Percentage Error (MAPE) and Forecast Bias etc. that sit within EPM software. With many systems now AI can be leveraged to further give insight into and automate processes and analysis supporting real time what if scenarios and pre-alerts on issues with suggested resolutions. But this will address only some of the causes of forecast inaccuracy.
To get results that add value to the business companies desire implementing EPM software will not automatically deliver the desired results. Linking supply chain to financial planning process can help focus on what aspects of your forecasting process either add or detract from your overall performance, but the real benefits will be unlocked by linking departments and focussing on shared business goals across all departments that influence the Supply chain process and outcomes.
To gain insight into the processes that contribute to inaccuracy in forecast and demand planning, EPM software can be most effective when understand the process steps in forecasting and which departments influence the forecast (statistical forecast, sales adjustments, inventory policy driving MOQ, channel decisions, market activities etc.) against defined value add performance goals. To address process, impact this needs to be captured as steps in the forecast build in EPM software and then the impact needs to be quantified. This is referred to as Forecast Value Add (FVA). When we develop a segmented demand forecast model linked to finance processes we show the input of statistical forecast, sales changes, marketing changes, decision on ramp up / down etc. We show the impact of different departments on the process steps in planning and how they impact outcomes. Forecast Value Add (FVA) is- a way to measure and manage these impacts.
This can be built in EPM software as a waterfall but if you do not combine this with a metric and do not track how Forecast Value Add evolves overtime and how this is linked to your overall goals; you may not get the desired results and may not realise all the benefits of your EPM implementation.
At Keyrus we help companies navigate EPM software choice and implementation of S&OP and IBP solutions ensuring that business goals and desired outcomes are leading and that the processes and organisational aspects of the implementation are addressed. Want to know more? I would like to hear from you.
Access the full details of this key play, and have it on the go. Available as a PDF.
Have questions about our EPM solutions? Our team is ready to help you find the right approach for your business.
