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Author's profile photo Ashish Katyayan

Unlocking Business Efficiency: Leveraging AI-powered Corrective Actions and S/4HANA for Real-time Manufacturing Variance Analysis

In an ideal scenario, business teams should have access to reliable sources of data that provide all the necessary information for conducting a thorough root cause analysis of manufacturing variances. This includes integrating all relevant information, such as costs, vendors, bills of materials, and routing, throughout the stages of new product development and sustain phases to enable effective analysis by teams. Planning and actuals should be integrated seamlessly for a user to drilldown in different datasets. An exciting scenario involving artificial intelligence is that the system could leverage the available information to propose the next step of corrective tasks using the power of artificial intelligence/machine learning.

However, this is not the case in most large companies, which face several challenges in gathering related information. Common issues include information being compartmentalized and not connected effectively, making it nearly impossible to trace product costs from early stages to sustain phases of the product lifecycle. Teams also struggle to pull together a cohesive story on variances between planned and actual outcomes. Additionally, it can be difficult to determine how changes in costs are impacting profitability goals. When it comes to comparing plan versus actual outcomes, it is extremely challenging to identify the root causes of variances, let alone take action to rectify them. Delays in accessing crucial information can result in significant financial losses for a company, which can be easily mitigated by implementing efficient, system-driven processes.
The integrated functionalities of SAP S/4HANA offer a valuable means of assessing business performance, comparing results with the business plan, and identifying areas for improvement. Specifically, the integrated planning and manufacturing variance analysis functionality available in SAP S/4HANA Finance provides finance teams with an effective tool to utilize during period end closings.

Automated Variance Calculation with SAP S/4HANA: An Efficient Tool for Finance Managers and Controllers.
Variance analysis is an essential process that provides businesses with a benchmark to measure performance against the committed business plan and year-to-year comparisons. It allows companies to identify areas of improvement and take corrective actions to improve performance. For example, having a clearer picture of product profitability and costs can help organizations continue or discontinue non-profitable product lines. The automated calculation of manufacturing variances by SBU, Plant, and Value Stream helps identify areas of improvement and focus for the business, bringing efficiencies in the month-end closing and reporting processes.

With SAP Analytics Cloud, Production Planning, Human Resources, Asset Accounting, Cost Center Accounting, and Product Costing, businesses can achieve complete end-to-end traceability through their integrated planning processes. The functionality in SAP S/4HANA Finance provides real-time analytics for variance analysis. It enables users to perform real-time analytics on live transactional data and provides a set of built-in representations of operational data called VDM (Virtual Data Models). Users can arrive at better decisions from the available data.

Automated variance calculation using SAP provides an effective tool in the hands of Finance Managers and Controllers to support an efficient and analytical period end close. Variances are posted by Order/Product and are at the most detailed level, thereby giving an extra tool to the Costing Analyst to drill down into Variances. SAP S/4HANA integrated functionalities can calculate a variety of variances, including planning variances, production variances, production variances of the period, and total variances. Standard reports are available to do a deep dive analysis by Order/Cost Element and Variance Categories i.e. Input price variance, Resource-usage variance, Input quantity variance, Remaining input variance, Scrap variance, Mixed-price variance, Output price variance, Lot size variance, Remaining variance.

We can also use SAP S/4HANA dataset to create mid-month profit forecasts, which can help businesses track their performance against the plan and take corrective actions if needed.

In conclusion, variance analysis is a critical process for businesses that helps identify areas of improvement and take corrective actions to improve performance. The integrated functionalities of SAP S/4HANA Finance provide an effective tool for finance teams to perform real-time analytics on live transactional data and streamline month-end reporting processes. We can further improve the existing processes by introducing artificial intelligence and machine learning capabilities to identify and propose corrective actions. Excited to see the next phase of business transformation with AI and ML.

Note to readers: This article was originally written by me on my LinkedIn page

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