Waterfall chart

One of the best chart to explain reason for changes is the waterfall chart.

Type of analysis from one period to another to give some examples include

• Profitability explaining sales price and sales quantity variance, cost of sales variances, fixed costs components, etc.
• Sales revenue explaining quantity, prices, geographic, segmentation changes
• Fixed cost explaining main expenses components that contributed the most to the fluctuations
• Logistic cost explaining the quantity of shipments, cost per location, product types, etc.
• Variable manufacturing cost deliberating on specific consumption variance, material cost variance, efficiency variance, production volume variance, etc.
• Working capital movements highlighting accounts payable, accounts receivable and inventory movements with further insights into days outstanding.

Enhancing the data into possibilities of additional dimensions such as manufacturing sites, region, business area, profit centers, and functional areas will provide more insights of possible intervention for improvements.

Power BI tool can really support to make this level of analysis.

INFORMATION MANAGEMENT of SECONDARY DATA

The recommended approach for Accountants with limited IT support to access SAP tables or direct connectivity data is to manage secondary data using folders.

Secondary data are information extracted from another location such as SAP, bank statement downloads, accounting software or websites.

SAP transaction that helps in bringing data efficiently includes GD12 formerly known as 2KEE and FBL3N. Just to name a few as there are more SAP t-code that can help in building your data set for analysis.

On periodic basis, new data can be added to the same folder as long as the format are consistent.

You power query editor can be setup to allow seamless refresh of new data, making sure the effort spent during original setup represent 90% of final build.

Wish you good luck in using folders as your data warehouse.

How to handle data before building a simple analysis

 In building a simple analysis two major types of data can be segregated accordingly

1. Primary data - sometimes described as dimensions or filters
2. Secondary data - sometime described as facts or source data to be analyzed 

Distinctions of these will allow clearer path in building analysis.

Example:

• Primary data | cost center numbers with all its information and relevant groupings e.g. region, site, plant, functional area, etc.
• Secondary data | SAP data download from cost center report for monthly spendings

Relationship connections between the primary and secondary can create a lot of insights.

Investing in adding further references in primary data add more dimensions into the overall pictures such as adding hierarchy, region of the cost centers, grouping of the cost centers to represent a set of locations or assets, etc.

While secondary data is where the final results yield, building the appropriate dimensions using elaborated primary data will allow more visibility than you can imagine.