Planning Optimization Fit Analysis

PLANNING OPTIMIZATION FIT ANALYSIS

CONTENT Introduction Why Run a Fit Analysis? Key Results and Unsupported Features Parameters Not Considered by Planning Optimization Preparing for Migration Summary

Introduction

As organizations transition from the deprecated master planning engine in Dynamics 365 Finance and Operations to the modern Planning Optimization service, it is crucial to assess how well your current setup aligns with the new planning capabilities. Planning Optimization offers significant performance improvements and real-time insights, but there are differences in scope and functionality that need careful evaluation. The Planning Optimization Fit Analysis tool helps bridge this gap by identifying potential discrepancies, allowing businesses to make informed adjustments during the migration process.

In this article, we will explore:

• The purpose and benefits of running the Planning Optimization Fit Analysis
• How to execute the fit analysis in Dynamics 365
• Key differences identified between the legacy master planning engine and Planning Optimization
• Unsupported features and how to handle them

Why Run a Fit Analysis?

Fit analysis can be run by going to Master planning >> Setup >> Planning Optimization fit analysis. Migrating to Planning Optimization is not merely a system upgrade; it involves a fundamental shift in how master planning is executed. While Planning Optimization offers enhanced speed and scalability, it currently does not support all the features of the legacy master planning engine. The Fit Analysis helps identify where the results might differ, providing a clear picture of features or parameters that may require adjustments.

By running this analysis, you can:

• Detect unsupported features and plan accordingly
• Understand differences in parameter handling and planning logic
• Reduce the risk of unexpected planning results after migration

Streamline the migration process by making data-driven adjustments.

Running the Fit Analysis

To run the Planning Optimization Fit Analysis and review its findings, follow these steps:

1. Select the Company: From the navigation bar, choose the legal entity (company) for which you want to conduct the analysis.
2. Access Fit Analysis: Navigate to Master Planning > Setup > Planning Optimization Fit Analysis.
3. Run the Analysis: On the Action Pane, click Run Analysis. The system will execute the analysis and display the results.
4. Review the Results: If no discrepancies are detected, you will see a "No issues found" message. Otherwise, a list of unsupported features or parameters will be presented for review.

You should repeat this procedure for each company in your organization to ensure comprehensive coverage.

Key Results and Unsupported Features

The fit analysis results highlight features that Planning Optimization does not yet support. Below are some of the most notable unsupported features:

• Kanban Planning: Item coverage records with planned order type set to Kanban are not currently supported. Instead, the system will generate planned purchase orders, leading to potential discrepancies if Kanban is a critical component of your planning process.
• Sales Line Reservation Using Explosion: Planning Optimization does not automatically reserve sales lines during an explosion process. Manual reservations may be required to meet demand accurately.
• BOM/Formula Lines with Step Consumption: Step consumption in Bills of Materials (BOM) and formulas is not considered by Planning Optimization, which could impact production planning accuracy.

Microsoft continuously updates its roadmap, and these features may become supported in future releases. Regularly consulting the official documentation and working with your Dynamics partner can help you stay informed about upcoming changes.

Parameters Not Considered by Planning Optimization

During the fit analysis, you may also notice that certain parameters from the master planning engine are no longer used by Planning Optimization. Here are some key parameters that have been deprecated or modified:

• Forecast Plan Time Fence: Instead of using a forecast plan time fence, Planning Optimization requires creating a master plan that specifies a forecast model.
• Use Dynamic Negative Days: Planning Optimization always uses the dynamic negative days approach, making this parameter obsolete.
• Number of Threads: Planning Optimization manages performance and scaling automatically, eliminating the need for manual thread configuration.

Preparing for Migration

Based on the results of the fit analysis, you can take steps to adjust your setup and data for a smoother migration to Planning Optimization:

• Update Unsupported Parameters: Revise your master plan configurations, replacing deprecated parameters with supported alternatives.
• Review Feature Dependencies: Identify any processes heavily dependent on unsupported features, such as Kanban planning or sales line reservation, and plan for workarounds.
• Collaborate with Your Partner: Engage with your Dynamics partner to get expert guidance on feature gaps and configuration adjustments.

Conclusion

The Planning Optimization Fit Analysis is a vital tool for assessing the compatibility of your current master planning setup with the new Planning Optimization service. By proactively identifying and addressing potential issues, you can ensure a successful transition, unlock improved performance, and take full advantage of the enhanced planning capabilities in Dynamics 365 Finance and Operations.

Planning Optimization is an evolving service, and Microsoft continues to expand its functionality based on customer feedback and its product roadmap. Regularly running fit analyses and staying informed about feature updates will help you maintain a robust and efficient planning process as your organization grows.

Next Steps: Run the fit analysis for your environment, review the results, and start planning your transition today. When you activate planning optimization, fit analysis form will no longer be visible.

How to Make Your ERP SOX Compliant? - PART 1

CONTENT   Introduction How to make your ERP sox compliant? Where does the SoD (Segregation of Duties) framework stand among the steps mentioned above? How to manage access rights? How to design application mitigating controls? Summary

INTRODUCTION

This article series acts as a comprehensive guide for SOX compliance, specifically tailored for public companies utilizing Microsoft ERP systems. The foundational knowledge provided here will then help build a SOX compliance framework. 

First article part will be theoretical, following articles will be application of the given information.

Let's get started.

The Sarbanes-Oxley Act, often referred to as SOX, is a U.S. law that sets standards for all U.S. public company boards, management, and public accounting firms. The purpose is to keep top management accountable for financial accuracy, to enhance financial disclosures, to enforce auditor independence, and to establish the Public Company Accounting Oversight Board (PCAOB). The idea is to make companies more transparent, so people feel safer when they invest their money in them.

How to make your ERP sox compliant?

To make your Enterprise Resource Planning (ERP) system SOX (Sarbanes-Oxley Act) compliant, you can follow these steps:

• Understand SOX Requirements: Familiarize with the requirements of the Sarbanes-Oxley Act, especially sections 302 and 404, which are about internal control over financial reporting.

• Assess Current Compliance Level: Evaluate your current ERP system to identify areas that do not meet SOX compliance requirements. This involves reviewing financial reporting processes, data accuracy, access controls, and audit trails.

• Implement Strong Internal Controls: Establish robust internal controls within your ERP system. This includes controls over financial data entry, processing, and reporting. Ensure that these controls are documented and tested regularly.

• Manage Access Rights: Strictly control access within the ERP system. Implement role-based access controls to ensure that only authorized personnel have access to sensitive financial information.

• Ensure Data Accuracy and Integrity: Implement measures to ensure the accuracy and integrity of financial data. This can involve validation checks, regular reconciliations, and automated data processing controls.

• Maintain an Audit Trail: Your ERP should maintain a comprehensive audit trail that logs all financial transactions and changes made within the system. This is crucial for auditors to verify the accuracy of financial reports.

• Regular Testing and Monitoring: Regularly test and monitor the effectiveness of internal controls. This can be done through internal audits and by using features within the ERP system that flag anomalies or control failures.

• Train Staff: Ensure that all staff who use the ERP system are trained in SOX compliance requirements. They should understand the importance of controls and their role in maintaining compliance.

• Continuous Improvement: SOX compliance is not a one-time task but an ongoing process. Continuously review and improve the internal controls and processes within your ERP system.

• Engage with Auditors: Work closely with external auditors to understand their expectations and get feedback on your compliance efforts. This collaboration can provide valuable insights into areas needing improvement.

As result, don't forget that each organization's needs and challenges are unique, so tailor these steps to fit your specific circumstances and consult with legal or compliance professionals if needed.

Where does the SoD (Segregation of Duties) framework stand among the steps mentioned above?

Segregation of Duties (SoD) helps you making your ERP system SOX compliant, and it intertwines with several of the steps mentioned above, particularly in implementing strong internal controls and managing access rights. Here's where SoD fits into the process:

• Implement Strong Internal Controls v2: SoD is a key aspect of internal controls. It involves dividing responsibilities and tasks among different employees to prevent fraud and errors. In an ERP system, this means ensuring that no single individual has control over all aspects of a financial transaction. For example, the same person should not be authorized to initiate, approve, and reconcile transactions.

• Manage Access Rights v2: SoD is closely linked to managing access rights within the ERP system. By controlling who has access to perform certain tasks or view certain data, you can enforce SoD effectively. For instance, different roles and permissions can be set up in the ERP system to ensure that conflicting tasks are not performed by the same person.

• Regular Testing and Monitoring v2: Part of regular testing and monitoring should include reviewing the effectiveness of SoD controls. This might involve checking whether the roles and responsibilities assigned in the ERP still align with SoD principles and making adjustments as needed.

• Continuous Improvement v2: The SoD framework should be reviewed regularly to adapt to changes in the organization, such as new business processes, changes in staff roles, or updates to the ERP system itself.

Incorporating SoD into your ERP system is essential for mitigating risks related to fraud, errors, and financial misstatements, making it a vital element of SOX compliance.

How to manage access rights?

Managing access rights in an ERP system is a critical component of maintaining security and compliance, particularly with frameworks like SOX and in ensuring proper Segregation of Duties (SoD). Here's a detailed approach to managing access rights effectively:

• Least Privilege Principle: This principle dictates that users should be granted only the access rights that are absolutely necessary for them to perform their job functions. This minimizes the risk of unauthorized access or actions within the system. Regularly review user permissions to ensure they align with current job responsibilities.

• Role-Based Access Control (RBAC): Define roles within your organization and assign access rights based on these roles. For example, a financial officer would have different access rights compared to a sales manager. This makes managing and auditing access rights more efficient.

• Implementing Segregation of Duties (SoD): SoD is vital for preventing fraud and errors. Ensure that conflicting tasks, such as creating a vendor and approving invoices, are not assigned to the same person. Design roles in the ERP system in a way that these duties are segregated.

• Regular Audits and Reviews: Periodically audit access rights to ensure they are still appropriate. Changes in employee roles, departures, or new hires often necessitate updates in access permissions. This process helps in identifying and rectifying any inappropriate access rights.

• User Access Reviews: Conduct regular user access reviews where managers verify and confirm the appropriateness of their team members’ access. This practice helps in identifying any discrepancies or unnecessary access privileges.

• Strong Authentication and Authorization Procedures: Implement strong authentication methods, like multi-factor authentication (MFA), to ensure that access to the ERP system is secure. Also, ensure that authorization procedures are robust and that any elevation in access rights is properly vetted and approved.

• Training and Awareness: Educate employees about the importance of access control and the risks associated with improper access. This includes training on how to handle access credentials securely.

• Use of Automated Tools: Consider using automated tools for managing access rights. These tools can help in efficiently assigning roles, tracking changes, and conducting regular audits.

• Documenting Policies and Procedures: Document your access control policies and procedures. This documentation should include details on how roles are defined, how access is granted, reviewed, and revoked, and the procedures for auditing and compliance checks.

• Incident Response Plan: Have a plan in place for responding to access-related security incidents. This should include steps for immediate action, investigation, and remediation to minimize potential damage.

How to design application mitigating controls?

Application mitigating controls are an essential part of the framework for ensuring the security and compliance of an ERP system, particularly in the context of SOX compliance and effective management of access rights. These controls are specific to the ERP application and are designed to ensure the integrity, accuracy, and confidentiality of the data and processes within the application. Here’s how application controls fit into the overall framework:

• Data Input Controls: These ensure that the data entered into the ERP system is accurate, complete, and authorized. This can include validation checks, field format restrictions, and mandatory fields to prevent incomplete entries.

• Data Processing Controls: These controls ensure that data is processed correctly within the ERP system. They can include workflow approvals, automated calculations, and checks that transactions are processed as intended.

• Data Output Controls: These controls ensure the integrity of data outputs, such as reports and exports from the ERP system. They ensure that data is accurately and appropriately presented and can only be accessed by authorized individuals.

• Integration with Access Rights Management: Application controls work hand-in-hand with access rights management. They help enforce the principles of least privilege and SoD by controlling what actions users can perform within the application based on their assigned roles and permissions.

• Audit Trails and Logs: Application controls often include creating and maintaining detailed audit trails and logs that record transactions and changes within the ERP system. These logs are crucial for audits and for monitoring and investigating suspicious activities.

• Error Detection and Correction Mechanisms: Implement controls to detect errors in data processing and provide mechanisms for their correction. This could include alert systems for unusual transactions or discrepancies.

• Segregation of Duties within the Application: Ensure that application controls help enforce SoD by restricting the ability to perform conflicting tasks within the application to different users or roles.

• Compliance and Regular Audits: Use application controls to facilitate compliance with relevant regulations and standards. Regular audits of these controls help ensure they are functioning correctly and remain aligned with compliance requirements.

• User Authentication and Authorization: Incorporate controls within the application for strong user authentication and for ensuring that authorization procedures are followed before granting access or approving transactions.

• Change Management Controls: Implement controls around the modification of the ERP system itself, including updates or changes to application settings, to ensure that they are authorized, tested, and documented.

In summary, application mitigating controls are a vital component of a secure and compliant ERP system. They work in conjunction with other measures like access rights management to provide a comprehensive approach to data integrity, security, and regulatory compliance.

Summary

Here's a table that combines the aspects of SOX compliance, Segregation of Duties (SoD), Access Rights Management, and Application Controls, highlighting how they interact with each other:

This framework illustrates the interconnectedness of these aspects in creating a secure, compliant, and efficient ERP system. Each aspect supports and reinforces the others, ensuring a holistic approach to compliance and security.

Exploring the Top Microsoft Technology Trends for 2023: An Interview with Microsoft Certified Solution Architect, Dogan Adiyaman

Check out my latest interview with dynamicssmartz by clicking the link below. We had an in-depth discussion about the hottest Microsoft technology trends you need to keep an eye on in 2023.

Influencer insight with Dogan Adiyaman 

As more and more companies seek ways to optimize their operations, Dynamics 365 has emerged as a top solution for streamlining processes and increasing efficiency. This shift towards cloud-based, modular, and scalable solutions tailored to meet a company's specific needs is evident in the growing popularity of Dynamics 365.

In the Influencer Insights segment hosted by dynamicssmartz, industry experts come together to discuss the latest technological advancements, peer-to-peer relationships, and Microsoft Business Solutions.

In our recent conversation with dynamicssmartz, we delved into Microsoft's game-changing impact on the industry and explored the technology trends that are set to shape the future in 2023.

Click here to read the full interview.

Demand Forecasting with Azure Machine Learning (AML)

This article helps you to understand the components of Demand Forecasting with Azure Machine Learning (AML).

CONTENT   Introduction Demand Forecasting Demand Forecasting Parameters Considerations to Avoid Flat Forecasting

INTRODUCTION

Dynamics 365 Supply Chain Management provides the end-to-end planning capabilities that manufacturers, distributors, and retailers require to meet their supply chain needs. Functionality ranges from demand forecasting, demand planning, master planning, and order processing. 

The focus of this article is Demand Forecasting. 

Let’s get started.

DEMAND FORECASTING

Demand forecasting is to predict future demand for products and services. This information is required to estimate revenue and drive strategic and operational business planning. Benefits include reducing cost by minimizing the buffer on-hand inventory, reducing lead times by ordering long lead time items ahead of time, increasing revenue for providing availability, and helping strategic business decisions on global capacity planning with data-driven knowledge.

The ultimate Purpose is to provide time to plan resources, plant expansion, capital equipment purchase, and anything requiring a long lead time to purchase.

The logic is to Gather transactional historical data and use machine learning to generate anticipated (expected) demand with Azure Machine Learning.

After the demand is created, forecast planning is run to calculate gross requirements for materials and capacity, and to generate planned orders.

Demand Patterns

When historical data for demand are plotted against a time scale, they will show shapes or consistent patterns. A pattern is the general shape of a time series that shows the actual demand varies from period to period.

Dynamics 365 Supply Chain Management generates time series forecasting by using Azure Machine Learning. Time series forecasting refers to models that use previous demand values to predict future demand. Time series forecasting is affected by 3 elements (Demand Patterns): Trend, seasonality, and variation.

Trend: General direction in which demand is moving. The longer the window, the smoother the trend will be.

Seasonality: In time series data, seasonality, is the presence of variations that occur at specific regular intervals, which refers to periodic fluctuations. For example, electricity consumption is high during the day and low during the night, or online sales increase during Christmas before slowing down again.

Variation: Unexplained or random variation in the time series. This will be discussed in tracking the forecast.

By using the 3 elements above, different forecasting tools use different statistical forecasting algorithms in different ways and predict demand.

Dynamics 365 Supply Chain Management and Azure Machine Learning allow businesses to compare different forecasting models and select the best predicting demand.

Mathematical Formula Components

An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are "pivoted" to become additional dimensions for the regressor together with other predictors. This approach, unlike classical time series methods, has the advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. Automated ML learns a single, but often internally branched model for all items in the dataset and prediction horizons. More data is thus available to estimate model parameters and generalization to unseen series becomes possible.

A subset of data is used to train the forecasting model, and it’s possible to specify what type of model validation to perform. Automated ML performs model validation as part of training. That is, automated ML uses validation data to tune model hyperparameters based on the applied algorithm to find the combination that best fits the training data. 

D365 parameters are forecasting settings and are used to make decisions on how to use the dataset and predict demand. 

Forecast accuracy is represented with MAPE (Mean Absolute Percentage Error), the estimated accuracy of the forecasting model that is used to generate the predictions.

MAPE compares the actual vs forecasted value and gives the distance of how close the forecasted values are to actual demand. A good forecast’s maximum MAPE value is 20.

You can view the accuracy percentage under Model details - MAPE on the Demand forecast details page

DEMAND FORECASTING PARAMETERS

General: All items must have a unit conversion setup that relates to the demand forecasting unit.

Select which types of transactions should be included in the historical data.

Select forecast generation strategy Azure Machine Learning vs Copy over historical demand. Before version 10.0.24, the forecast generation strategy for Azure was the Azure Machine Learning Classic which is a service that’s going to be deprecated.

Forecast dimensions: In addition to company, site, and allocation key, it is possible to include more information as the forecast dimension.

Each row of the generated forecast is a unique combination of the enabled values aka granularity attribute. The forecast will be running for each granularity attribute that is based on forecast dimensions.

Item allocation keys: Item allocation keys are groups of items used to generate a forecast together and allow the system to improve the performance of forecast generation (Similar to batch threading where different allocation keys are in parallel to generate a forecast). Additionally, transaction types can be chosen along with different forecast algorithm parameters for each item allocation key. An item must be a part of an item allocation key in order to be included in the forecast generation process.

Outliner removal: It’s a standalone parameter. Outliner removal helps to exclude outliers (exceptional data or extreme values) from the historical data that is used to calculate a demand forecast. Master planning > Setup > Demand forecasting > Outlier removal. 

Reduce forecast requirements: It’s a master plan parameter. When the master plan is run, planned orders are generated based on the forecast. But the forecast needs to be adjusted when real sales orders (actual demand) start coming for the forecasted period. Forecast reduction keys are used for that purpose. If a reduction key is not used, the forecast requirements aren't reduced during master scheduling. In this case, master planning creates planned orders to supply the forecasted demand (forecast requirements). These planned orders maintain the suggested quantity, regardless of other types of demand. For example, if sales orders are placed, master planning creates additional planned orders to supply the sales orders. The quantity of the forecast requirements isn't reduced. Master planning > Setup > Plans > Master plans > General fast tab.

Forecast algorithm parameters are used by Azure and R (a language and environment for statistical computing and graphics) to create the forecast model. The R package forecast provides methods and tools for displaying and analyzing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modeling.

Time series models: ARIMA, ETS, STL, ETS + ARIMA, ETS + STL, and ALL. When ALL is used, the system runs through all of the models and picks the model that has the lowest MAPE value. Use this model if there is no data scientist available to see what model is chosen and to analyze the data. This model consumes more time than others, in other words, has the worst performance.

Minimum and Maximum forecasted value: Forced minimum or maximum forecast values. The system doesn’t generate any forecast when the estimated forecast value is ZERO instead, this parameter value is used. On the other side, the allowed maximum value can be limited, as well.

Missing value substitution: This is about what happens when there is a gap in the historical data. Missing value can be replaced with a number or average of previous and following data or previous value or linear or polynomial interpolation. The most common use is to use ZERO since there may not be real data or demand on that date.

Missing value substitution scope: This is about what happens when there is a gap in the historical data. Substitution rule can be applied to the whole history, or to a specific granularity attribute. The advised value is to apply the substitution to all of the data.

Confidence level: It is the range of possible future demand values that the system generates.

Seasonality hint: It is the number of seasonal patterns that data scientist think of. For example; if data has a quarterly seasonality pattern, and forecasting is in monthly buckets, the Seasonality value should be 3.

Force seasonality: For the models using seasonality, it is possible to specify what the relationship between trend and seasonality is.

The test set size percentage: It is the data set percentage for the testing accuracy of the forecast vs how much should be used to generate the forecast.

In general, the forecast model needs to have enough data to generate a good forecast.

CONSIDERATIONS TO AVOID FLAT FORECASTING

Test, test, and retest: Use different parameters and compare the forecast results and MAPE to arrive at the best configuration for YOUR specific data.

Involve a data scientist: Helps business to dive into the detailed areas of configuration and keeps business from getting stuck with a flat forecast.

Use R package parameters: Dynamics 365 parameters are not the full set of parameters, there are more in the R forecast package. If the results with basic configurations are not working well enough, the next step should be to review the additional parameters in the R package that cannot be changed through Dynamics 365 interface.

Use correct parameters: Correct parameters including both D365 and R need to be used to get a good MAPE measurement.

Have enough size data: Enough data needs to be used to get a good MAPE measurement. Data history needs to be twice the forecast horizon.

The ultimate purpose is to predict demand with the least error. Errors cannot be eliminated unless you are able to see to the future. The fact is as below!

Follow up Removed or deprecated features

Removed or Deprecated Features

This article focuses on two main areas. 

❕Removed or deprecated features in finance and operations apps

❕Deprecations in the Microsoft Power Platform

Let's start with finance and operations apps.

Removed or deprecated features in finance and operations apps

Removed or deprecated features page describes features that have been removed, or that are planned for removal from finance and operations apps.

• A removed feature is no longer available in the product.
• A deprecated feature is not in active development and may be removed in a future update.

This list is intended to help you consider these removals and deprecations for your own planning.

Detailed information about objects in finance and operations apps can be found in the Technical reference reports. You can compare the different versions of these reports to learn about objects that have changed or been removed in each version of finance and operations apps.

Deprecations in the Microsoft Power Platform

Important changes (deprecations) coming in Power Apps and Power Automate page provides information about deprecations in the Microsoft Power Platform. The announcements and deprecations described in this website apply to Power Apps and Power Automate. Admins and IT professionals can use this information to prepare for future releases. This page was first published on June 27, 2017.

• Deprecated means Microsoft intends to remove the feature or capability from a future release. The feature or capability will continue to work and is fully supported until it is officially removed. This deprecation notification can span a few months or years. After removal, the feature or capability will no longer work. This notice is to allow you sufficient time to plan and update your code before the feature or capability is removed.

For deprecation information of other products, see Other deprecation articles later in this article.

Follow up Released Versions of Dataverse

Released Versions of Microsoft Dataverse

Microsoft is continuously enhancing the platform and services that support Finance and Operations apps with new capabilities to enable businesses everywhere to accelerate their digital transformation. The key driver for all of the new, core capabilities is to increase productivity and return on investment.

Dataverse release notes that contain details on the most recent update or incremental release can be found in the links below:

• This page includes an overview of the current version and next version of Microsoft Dataverse. 
• This page also includes all Dataverse service updates. 

The new features and enhancements demonstrate Microsoft's continued investment to power digital transformation. 

Check these pages regularly to have helpful and transparent information that helps your business prepare for updates. 

Follow up Warehouse Management mobile app changes

What's new or changed in the Warehouse Management mobile app

There is a special Microsoft web page for announcing changes specifically for Warehouse Management mobile app. 

This page lists new features, fixes, improvements, and known issues for each released version of the Warehouse Management mobile app for Microsoft Dynamics 365 Supply Chain Management.

All announcements can be found in historical order.

Released version numbers may not be aligned with Service updates version numbers.

This special page can be reached by clicking HERE.