Before you attempt to import data in ABC Analyzer, read this tech article on how to export your data from your ERP.
If you're new to ABC, we recommend you forward this tech guide to your IT department.
The data structure is important. Make sure each line in your data correspond to one unique product ID (SKUs). See the example below.
If each line in your data set represents a sale, you have transaction data (and each product ID may appear several times) and you’ll need ask your IT department to create an appropriate data set for you. Give them this tech guide.
Add meaningful headings for each column
The headings you choose will appear in all your analysis. Therefore, be accurate: “Stock” might refer to “Stock value 12 months” or “pieces in stock, 3 months”. You can name each data column as you please. The software does not restrict you in any way, and you do not have to name the columns as in the example below.
Tech guide - for tech and IT responsible
ABC Analyzer supports the following file formats:
- .csv and .txt: Semicolon, tab, comma or space separated
- .xlsx (Microsoft Excel 2007)
- .xls (Microsoft Excel 97 - 2003)
Please note: For .cvs, .txt and xml Please enclose your field values with either single (‘) or double (“) quotes. Avoid line breaks within all fields.
Empty rows in all formats should be avoided.
XML files must have one single root element where corresponding child-nodes refer to each row in the data.
Date formats supported by ABC Analyzer
Data files can only be imported if they contain an ID column and at least one column with numbers.
Sanity check your data before import
All columns containing numbers must have:
- The same decimal separator
- The same thousand separator
- Number columns may not contain special symbols – like £, $, %, # etc.
For date & number columns:
- Empty cells will trigger an error message in the import guide and each time a template is loaded. You can still import the data & open the template, but your colleagues will not approve of it as it looks as is all data is flawed
- Dates: All columns containing dates must have the same date format.
We recommend using CSV/text files. As these contain raw data with the smallest overhead in size and/or processing needed. Furthermore files from Excel use an order of magnitude more memory and require additional components for importing.
Although ABC Analyer can help perform simple data aggregation on files containing multiple rows with the same ID, it is strongly advised that this fact is not utilized in any production environment - it is perfectly fine for standalone or one-off analysis - but ABC Viewer is not able to do this aggregation and therefore setups with such files will be unusable in environments with both ABC Analyzer and ABC Viewer.
Both ABC Analyzer and ABC Viewer can insert default values when they encounter parsing errors - this will occur with missing or malformed dates and numbers - commonly are blank fields and/or invalid default values from the original data. These values represent no obstacle in the import but please be advised that they do result in an initial transformation of the data and relying on values present in the input files can be impossible. Our general advisory is to contruct data files where such transformation is not needed.
Data accumulation and extraction intervals
The data period determines in which time window data are accumulated. The typical recommendation is 12 months. Using less than 12 months will introduce a risk of excluding seasonal differences (e.g. Christmas sales). The data period should be considered on the basis of the life cycles of the data in question. If the life cycles generally are less than 12 months (e.g. fast changes in fashion or technology leaps) you should consider a shorter data period.
The chosen data period should be taken into account when analyzing the data. If you choose a data period which is too short, you might be missing some highly relevant data. If you on the other hand, choose a data period which is too long, part of the data might be obsolete or simply “blank” (life cycles shorter than data period).
Analyzing with ABC can be subdivided into two main categories:
- Continual analysis
- Ad hoc analysis
Continual analysis (most common) is done multiple times using a fixed interval of re-categorizing the items. The fixed interval could be a month, a quarter, half a year or other custom intervals. This type of analysis is necessary when establishing a corporate ABC, e.g. an official ABC of your company’s products or customers. The re-categorization task can be done using ABC Analyzer or by hard-coding the ERP-system.
Ad hoc analysis does not require any re-categorization. This analysis is done once, or from time to time, to get specific insight on a specific subject.