Are you interested in identifying usability gaps in your SAP environment? My colleagues and I have been examining repetitive processes, transactions and data that have emerged from over four years of gathering hundreds of SAP customers’ ST03N transaction data. If you are “up on” our BVA process – this ST03N data is used in producing an assessment of what processes may be optimal to improve with Winshuttle’s solutions. So now we are starting to peg these transactions and process data to a collection of data rules based on a series of “automation complexity indexes”. In this article, let’s explore one of the most critical ERP processes – Material Management – and some examples of data rules applied to the process and transaction data.
So what are some of the processes and transactions that make up the material management process? You guessed it – MM01 (create material), MM03 (display …) and MM02 (change …) and so on…
Now we normally drill into these various sub processes and transactions by obtaining data related to usage, transaction complexity, potential productivity and capacity enablement. Then we apply the learned “data patterns”. With this information, we can quickly identify suspects for transaction improvement – whether it’s a create, lookup or maintenance use case. Okay so far? Let’s take a look at some of these patterns highlighted from this sample BVA result which focused on the material management process and start to discover the “underlying stories”.
|High Complexity – GREEN||Look at overall complexity ratios and check out the 47 (green box) in the MM01 transaction. Here’s an opportunity to explore this transaction further because high complexity implies that a large set of fields is processed each time this transaction is executed. Goal: Simplify usability & streamline processes|
|High Usage – Display – YELLOW||Check out that MM03 (yellow box) – 25,509 transaction executions in a given months’ worth of work. High usage could imply bulk lookups leading to bulk changes. Goal: Simplify by reducing routine maintenance|
|High FTE Days Saved – ORANGE||Now check out the pattern associated with days savings. Look at FTE Days Saved and select the highest savings – in this case it is MM02 w. a potential of 497 days saved per annum. The pattern here reveals the impact related to capacity enablement around material maintenance, implying potential improvement in data quality related to bulk updates. Goal: Streamline mass uploads – eliminate dirty data|
|High Data Extracts – RED||Identify the Display Transaction (MD04) 3,055 executions – High Usage could imply time consuming live data extracts. Goal: Reduce Routine Maintenance|
|High Line Item Data Creates – BLUE||Identify 1-many Create Transactions (e.g. MM01, VA01, **01); 1 or 2 line item transactional data is easy to automate. Goal: Streamline transactions – enable more staff capacity and reduce data entry errors|
We can perform this same analysis for finance processes (e.g. Record to Report), supply chain processes (Procure to Pay), sales distribution (Order to Cash) and the list goes on and on…
Now comes the best part of the BVA – it’s where you can drill into specific organizations, and usage teams and perform these data patterns on groups of ERP users. I will cover this in my second blog – Transaction and Process Patterns – Part 2 “The User/Departmental View” where we demonstrate how we can examine specific areas of business operations and thread a needle in terms of identifying pockets of capacity enablement.
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