As an analytics practitioner who has also taken a master's level accounting course recently, I can vouch for two things. Accounting is neither the language of the gods (because, it leaves a lot of room for interpretation for various reasons outside the scope of this blog post) nor the coolest thing to blog about unlike a topic on generative AI. Nevertheless, it is unarguably one of the essential building blocks of any business that helps answer THE most important question for it's stakeholders
" What is the overall Financial Health of my organization at any given point in time?".
That said, most organizations maintain two types of accounting systems at the minimum.
1. A financial accounting system that is established and maintained for the sole purpose of providing reliable information pertinent to investors, creditors, analysts
and regulatory authorities.
2. A management accounting system that is established to provide the necessary data and tools required to craft a competitive strategy.
Although these two are inherently different disciplines aimed at solving different problems for different facets of business, there are certain points in the decision-making process where these two disciplines intersect. Traditionally, these intersection points are the areas where the use cases for building fragmented business applications are drawn out and these are the areas where cross functional collaboration becomes vital to accomplish an optimal business outcome.
The purpose of this blog post is to explore one such intersection scenario where a comprehensive business analytics platform can make a meaningful difference in both crafting and driving a data driven strategy.
For readers who are strangers to accounting, it would help to read up on T accounts,Expense recognition principle,Costing methods and Inventory management principles to better understand the example scenario presented.
To understand how a comprehensive Business Analytics platform can help, let us consider a common scenario shown below to keep the context tangible.
The Premise
All of the transactional accounts listed in the example above are typically established to track the value flow from the perspective of Managerial Accounting in a manufacturing industry. However,depending on the industry, the transactional accounts can be structured differently;but,the goal is to track the value flow as close to reality as possible.The above example focuses on providing important cost data required to accurately calculate an important metric " Cost of Goods Sold" which is a very critical contribution from the Managerial accounting side of the house in deriving a key financial accounting metric " Gross Profit" ( Gross Profit = Total Revenue - Cost of Goods Sold).
The Problem
There are a few issues in streamlining the value flow and deriving insights as described in the diagram above. I am going to pick 3 specific business processes from this example that intersect financial and managerial accounting to explain the magnitude of the disconnect so that, one can appreciate the value a comprehensive Business Analytics platform brings in to this mix.
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Business Need |
Technology Impediment |
| 1. |
The typical workflow for value to move between step 2 & 5 described in the above example is as follows : (Bill Of Material + Operation List)->Production Order -> Job cost sheet -> Material requisition form |
Multiple source systems are involved and focused effort is needed to integrate the transactional data in near real time and provide an separate analytical view for insights. Moreover, automation of this process requires that the systems involved, talk to each other through a reliable mechanism.
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| 2. |
Manufacturing Overhead should be calculated using traditional overhead costing OR Activity based costing, depending on the complexity of the cost structure. This decision directly impacts the value of MO(5), the output of step 4. |
Calculating cost drivers and overhead variability across heterogeneous products / service lines is monumental. This requires extensive process modeling and statistical analysis. |
| 3. |
Inventory reserves such as obsolescence reserves or lower of cost or market (LCM) reserves, need to be computed to account for potential losses or declines in the value of inventory. This impacts the ending inventory in step 6, thereby impacting COGS. This also directly hits the balance sheet by reducing the value of inventory on the balance sheet. |
Estimating the inventory reserves requires predicting future market conditions that in turn requires extensive external data infusion,sophisticated predictive modeling and what-if scenario analysis |
Regardless of the industry, the challenges discussed above have a significant impact in the computation and analysis of key metrics like Contribution Margin, Break even points,Profitability of a particular product or a segment and Cost-Volume-Profit (CVP) Analysis.
In essence, this is a two prong problem. While the Financial accounting system is dependent on managerial accounting for the cost data, performance analysis, and other internal information, Managerial accounting is dependent on financial accounting for reliable historical financial data and transactional records for analysis. While the dependency in itself is not a problem, any disconnect in tracking the value flow will result in either delay or inaccuracy of critical information.
What is required to enable the information transparency and strengthen the intersection of the two disciplines of accounting?
A viable solution would entail 5 major components
1. A Business Analytics platform that can connect to heterogeneous data sources
2. A Business Analytics platform that has both business intelligence and planning & forecasting capabilities integrated
3. A Business Analytics platform that has advanced predictive analytics features infused
4. A Business Analytics platform that has comprehensive metadata modeling capabilities to model complex business processes
5. A Business Analytics platform that has extensive self service capabilities to empower business users to consume analytics
What could a hypothetical solution look like ?
The above diagram is meant to emphasize the importance of having an integrated Business analytics platform. Hence, details pertaining to Data Management, Data Governance, Data Integration, Business Automation,API strategy and other platforming aspects are excluded to stay on topic. But, those items do play a critical role in the effectiveness of the proposed solution.
Steps 1 &2 : Data is integrated from heterogeneous data sources. Both Cognos Analytics and Planning Analytics create their own metadata layer.
Steps 3 &4 : Each system produces outputs designed for specific purposes; but,they can be consumed by both Financial Accounting users and Managerial
accounting users. It is important to note that aside from many visualizations and reporting objects being available on both systems,Planning Analytic
cubes can be imported as a source for Cognos Analytics and data extracted from Cognos Analytics can be used as a source for Planning Analytics
models.
Informed decisions are made when both the systems are matured enough to feed off of the output of each other.
How does the proposed solution using IBM Business Analytics platform address the 3 business needs outlined in the problem statement ?
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Business Need |
How can IBM Business Analytics platform help
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| 1. |
The typical workflow for value to move between step 2 & 5 described in the above example is as follows : (Bill Of Material + Operation List)->Production Order -> Job cost sheet -> Material requisition form |
While this is more of a system integration problem, Cognos Analytics can provide valuable information about the time lag between each component of the business process.This information regarding the average time taken for different products can then be used in managerial accounting tasks that involve padding total labor hrs needed to get a realistic estimate of actual job/process time. With additional process information, it is also possible to perform root cause analysis to improve the overall throughput time.
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| 2. |
Manufacturing Overhead should be calculated using traditional overhead costing OR Activity based costing, depending on the complexity of the cost structure. This decision directly impacts the value of MO(5), the output of step 4. |
Both Planning and Cognos Analytics have sophisticated charts such as decision trees and driver charts that can provide a visual representation of cost drivers based on the statistical correlation between the Manufacturing Overhead and the possible components that contribute to the overhead. This can be used to augment or substantiate the assumptions made in Managerial accounting regarding cost drivers.
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| 3. |
Inventory reserves such as obsolescence reserves or lower of cost or market (LCM) reserves, need to be computed to account for potential losses or declines in the value of inventory. This impacts the ending inventory in step 6, thereby impacting COGS. This also directly hits the balance sheet by reducing the value of inventory on the balance sheet. |
While Planning Analytics provides powerful What-if scenarios to model future conditions, Cognos analytics provides the ability to create predictive models in an integrated Jupyter notebook environment that can be leveraged in an analysis as well.
Also, With all the powerful analytics functions available in Cognos Analytics, it is very easy to create a guided analytics dashboard / interactive report that can help plug in assumptions and verify the impact it would have on the financial statement.
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Conclusion
When IBM Cognos Analytics and Planning Analytics are leveraged as an Integrated Business Analytics platform, it takes Accounting analytics to a new level by providing insights into key financial metrics' dependency on a strategic business decision while simultaneously providing historical data & complex analytical perspectives to create an effective Managerial accounting system. Whether it is a CFO looking to get an estimate of warranty cost to determine an allowance account or a product engineer trying to wrap his head around a cost of failure to make design decisions or a senior level manager trying to understand the impact of a tactical decision to a financial metric , the ability to view business process related data, financial accounting data and managerial accounting data in a unified perspective, opens up the possibilities for a variety of accounting analytics related use cases.
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