Growing Your Multi-Entity Business with Sage IntacctAre you adding new locations? Opening another office? Congratulations! Your business is on a growth path. While it’s exciting to cut the ribbon or open the bottle of the bubbly to celebrate, challenges await. New entities add complexities to your organization that your QuickBooks financial software isn’t built to handle.  Some of these include: decentralized payables, inter-entity transactions, multiple currencies, and global consolidations—all of which require a purpose-built financial management system such as Sage Intacct. Whether your business is global or domestic, simple or complex, now you can close the books faster and gain the visibility you need even as your operations grow and evolve. Get quick and accurate closes with automated consolidations Finance teams in distributed organizations struggle with QuickBooks’ inability to quickly consolidate information at period close or when it’s needed for decision making. Sage Intacct’s multi-entity structure allows you to consolidate multiple entities with just a couple of clicks. Now you get accelerated closes, improved accuracy, and better visibility. Plus, every global consolidation includes a detailed journal entry report for easy audibility. Easily manage multiple currencies Sage Intacct automates the management of multiple currencies, saving you time and increasing accuracy. You’ll use up-to-date exchange rates for currency conversions and revaluations, and instantly access information on currency gains and losses. You can also produce reports in your headquarters’ currency or the entity’s local currency. Save time and effort with simple setup and maintenance With Sage Intacct, all your business entities reside in one system. You can quickly set up policies, procedures, workflows, and reporting for new entities regardless of the complexity of your multi-entity structure. For example, you can easily configure each new entity with unique definitions. Or automatically “inherit” centralized definitions—workflows, charts of accounts, period definitions, and lists—across entities for a standard, organization-wide configuration. You can also opt for automated inter-entity transactions with manageable rules, making it easier to centralize payables and receivables. Give the right people the right access with permissions Sage Intacct provides permissions that can be scaled to just the location the user works in and to just the area that person needs to see. For example, you may want a person to be able to enter sales orders for their entity, but they don’t need to see everything that’s going on in purchasing. Also, you may want a manager to be able to pull a report just for their department without being able to delve into the financial information of the whole company. Take advantage of fast, flexible reporting Giving people self-service access lets them get to the information they need—when they need it—to help their part of the company grow, and you don’t become a single source for constant requests. And Sage Intacct makes it easy to quickly produce accurate financial reports, regardless of your organization’s complexity. You can get insights from real-time visibility with instant roll-up reporting (single currency) or push-button consolidations (multiple currencies). What’s more, you can easily switch between consolidated and local views for further insights into the figures. Growing businesses trust Leaf Software Solutions QuickBooks, while excellent for new businesses, wasn’t designed for the complexities of multi-entity organizations. Whether you’re expanding geographically or diversifying your business, rely on Sage Intacct to keep you in control across your company. The professionals at Leaf Software Solutions can create and implement a technology solution that meets your unique business requirements—now and as you grow. Contact us by phone or email and let us know how we can help. An example of using real data from SupplyKick as an ML experiment |
So, what exactly is Machine Learning?Machine learning is a growing concept in computer science, where scientists attempt to teach computers to learn abstract concepts just as a human would. This is a major paradigm shift from traditional programming, in which programs operate using a set of logical rules that are explicitly stated by programmers. In machine learning, the programmers instead develop a “model,” a structure that is able to learn the data that the user feeds into it. |  |
One example that illustrates the difference between traditional and machine learning is trying to read handwriting. With traditional programming a programmer could attempt to program a set of rules that determine which letter is which, but this would be very time consuming and it would be impossible to generate a set of rules that is able to identify all types of handwriting. In machine learning, a user creates a model, which can be thought of as a complex mathematical function. The programmer will feed in handwriting data for which the correct answers are known. The model will read in all of this data, attempt to make predictions on what it thinks the answers (labels) are, compares its predictions with the correct answers, and adjusts itself until its able to make accurate predictions. The machine learning model is not only much less labor intensive to produce, but also makes much more accurate predictions on many more types of data.
The model used in this project is a special type of machine learning model called a “artificial neural network.” These types of models have been around for over half a century, the first was theorized in 1958, however recent improvements in computation power has made it possible for practical use. Essentially, these are modeled off of the way brains (or “neural networks”) work, as they are an interconnected structure of “neurons” (or nodes) and “synapses” (or connections). Unlike real neural networks, which send biological compounds called “neurotransmitters” between neurons, neurons in artificialneural networks simply communicate using numbers. As it turns out, this seemingly basic structure is able understand complex concepts, similar to humans. It's worth noting that in this project a special type of neural network called a “recurrent neural network,” was applied, which is specifically designed to read sequences of data.
| The first thing we need to do was get an actual dataset to manipulate. We did so using Amazon’s Marketplace Web Services API. We pulled in the responses from this API and stored them as CSV files that are easy for our program to read. We then clean the data by filtering out repeat entries or records that don’t contain any data at all. Because the goal is to predict future sales, we ordered the rows by date and totaled all orders on the same day. Finally, through experimentation, we found that the model fits best to data for individual products; rather than for all products, so we filtered down to only rows containing a specific product. The result of all of this cleaning is plotted in blue. |  |
As we can see, the data is pretty noisy, which is not great for the model to achieve a fit. Really, we want the general trend rather than data that changes drastically day to day. In order to do so, we apply a smoothing function as illustrated in red.
| Let’s try getting some predictions! After no training, here’s how well the model fits to the training data. It’s pretty terrible, but this is what we expect without any training. This would be like walking into a calculus test without even having heard what “math” is. After training on the data 100 times we are given the following result. |  |
| It seems to be picking up the general trends in the data but doesn’t quite hit the mark. Lets try doing another 900 iterations through the data, for a total of 1000 training runs. Now the prediction is clearly fitting to the actual sales trend. Also, keep in mind that although 1000 iterations sound like a lot, the entire training was completed in about 15 seconds. However, the real challenge is fitting the model to data that the model has never seen before. Below are the predictions on the “test” dataset. Though these predictions aren’t quite as good as on the training set, this is expected, and they are still very accurate overall and likely more precise than a human’s predictions. |   |
What this could look like for you
Of course, machine learning is cool, but you probably want to see the real business applications for the technology. Here’s an example of how it can be implemented in our services.
This is a screenshot of the dashboard interface we have built for the client. As you can see it already provides them with lots of useful metrics to influence their business decisions. The end goal is to have the model running on the server, constantly pulling new data from Amazon and retraining itself. Then it would be able to show a projection of future sales in order to aid the client in reordering products.
Resources
For the purposes of this post the concepts mentioned are heavily abstracted. However, if you’d like to learn more about the field here are some other resources you may want to look into:
- 3Blue1Brown- YouTube channel with amazing visual representations of abstract mathematical concepts
- Two Minute Papers- YouTube channel that distills recent machine learning research papers down to 2-minute videos
- Siraj Rival- YouTube channel with tutorials on lots of machine learning concepts
- Google Machine Learning Crash Course- Web tutorial series on machine learning (has more of a business/real-world focus)
- Machine Learning Mastery- Website with tons of machine learning tutorials and resources
- Colah's Blog- In depth explanation of ML concepts by Google Brain Scientist
- Distill- Explains abstract concepts through interactive machine learning widgets
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If you have questions or are interested in learning how Machine Learning could impact your business, please get in touch with us.
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How to Enable ‘Doc Date Verify’ using Professional Services Tools Library
By Samantha Higdon, Microsoft Dynamics GP Consultant
We’ve all seen it (and probably done it but we won’t talk about that) – a transposed transaction/document date. And, of course, we rarely catch the transposition before we post. Thus begins the long, arduous process of correcting a lousy posting for 2081 instead of 2018. Wouldn’t it be great if there was a way to prevent that?
The good news is that thanks to Professional Services Tools’ Library (“PSTL”), there is! The answer to our 2081 problem is a function within PSTL called Doc Date Verify. This function prevents a user from entering a transaction date that does not exist in the company’s fiscal period set up.
PSTL is now available free to all supported versions of GP (Click here for a list of supported versions and life cycle information). PSTL should be installed on all workstations/servers to ensure that PSTL’s processes can run and validate all users’ data entry, validation, etc. Additionally, some tools within PSTL do include the modification of data so we strongly encourage users to coordinate with their internal GP administrator or contact Leaf with questions before making any changes with those particular tools and when in doubt, back up the database!
Once PSTL is installed, configuring Doc Date Verify is a simple process
 | 1.) Navigate to PSTL in your Navigation Pane.  2.) Click on “PSTL Main Setup” – if this is the first time entering, it may take a few minutes to open. |
3.) Under the “Misc. Tools” column on the far right, there is an option for Doc Date Verify. To enable, select the radio button and check the box next to Doc Date Verify. Once checked, you can click the “X” in the upper right corner to close the screen. Doc Date Verify is officially enabled.
Now assuming you’ve read through the post and ensured all workstations and remote servers, have PSTL installed, users won't be able to enter transaction dates where the corresponding fiscal period is not set up. Here is the Payables Transaction Entry screen to demonstrate.
You’ll see below that my Doc. Date is set to 04/21/2220 which isn’t set up on my system (and hopefully isn’t on yours either!).
As soon as I attempt to move away from the Doc Date field to the next data entry, I receive the error message below.
Though the button option says “Continue,” all it does is revert me to the Doc Date entry. I cannot continue with data entry until I put in a valid date (i.e., a date that is set up in your company’s fiscal period set up).
Entering a valid date does not prevent a user from keying a document date as 8/1/2018 instead of 7/1/2018 if both periods exist and are open in GP, but it is a great tool to capture those transpositions of numbers that put dates in nonexistent fiscal periods which are much harder to correct!
Entering a valid date does not prevent a user from keying a document date as 8/1/2018 instead of 7/1/2018 if both periods exist and are open in GP, but it is a great tool to capture those transpositions of numbers that put dates in nonexistent fiscal periods which are much harder to correct!
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For more information on Doc Date Verify, other PSTL tools or Microsoft Dynamics GP, please contact Leaf Software solutions:
317.814.8000 or gpsupport@leafsoftwaresolutions.com |
How are Microsoft 365 ENTERPRISE and Microsoft 365 BUSINESS, Different?
By Justin Kruse, Microsoft Dynamics 365 Senior Consultant
Microsoft 365 is a complete, intelligent solution, including Office 365, Windows 10, and Enterprise Mobility + Security, that empowers everyone to be creative and work together, securely.
Microsoft 365 Enterprise is designed for large organizations and integrates Office 365 Enterprise, Windows 10 Enterprise and Enterprise Mobility + Security.
 | Microsoft 365 Enterprise:
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Microsoft 365 Business is designed for small- to medium-sized businesses with up to 300 users and integrates Office 365 Business Premium with tailored security and management features from Windows 10 and Enterprise Mobility + Security. It offers services to empower employees, safeguard the business and simplify IT management.
 | Microsoft 365 Business:
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