Why is Process Data Collection Important and Challenges

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    I once did a crude study, at our local library, of the Average Financial Performance of Public companies in various Industry codes. I discovered that out of every dollar earned by companies from Manufacturing to Service Industries they were spending an average of 40 Cents to 80 Cents on Operating Expenses. Of course, the Service Industries had more of  their Sales revenue dollar spent on Operating Expenses and Manufacturing was on the other end. Net profit percentages were about 0 or negative to about 6% or 8%.


    My guess is also that a large percentage of Operating Expenses are spent on Internal Business Processes, especially in Service Industries. On an average Operating Expenses budget of 60%, if you were to improve your business processes and reduce your operating expenses by 10%, many of these companies stand to become Doubly Profitable if you were to save an additional 6%!


    It’s no wonder that companies like Toyota that use the Toyota Production System (TPS) to continually look for waste and inefficiences and eliminate them systematically. They are able to reduce their costs constantly while increasing quality at the same time. TPS has not been applied as much outside Manufacturing but the basic idea of elimination of waste is a universally applicable!


    That’s powerful motivation to look at Continuous Process Improvement! Many Continuous Process Improvement efforts are hampered by availability of good data. Some of them are natural to the problem at hand, but none of them are insurmountable! Especially when there is so much to be gained!


    Typical data characteristics or problems are:


    a. Disparate Data Sources – An Order to Cash Process may be using a variety of backend software systems -  Order Processing, Sales Accounting, Production Planning, Manufacturing and Production, Warehousing, Shipping, Billing, and Financial Accounting Systems. These may be from the same software company like SAP or Oracle or different functions may have software from different companies. End to end collection of data becomes a task of Extracting, Transforming and Loading (ETL) data to a single repository.  I know Business Process Orchestration tools can collect this kind of information but what about the other 95% of companies that don’t use those currently or plan to use them in the near future?


    b. Multiple Data Cubes – For the same end to end process, analysis may have to be different in each stage of the process. While processing orders you may want to analyze by regions or zones where the orders are coming from.When it is being manufactured, KPIs may need to slice and dice process data down to the manufacturing shop or teams within. When a product or service is being supported over phone or other media, customer support center metrics may be the way process is analyzed.


    c. Data Availability and Integrity – If parts of an end to end process are outsourced, then the vendors systems  may be involved and you may need to get data from the vendors’ systems! Data Integrity is always brought up as a key issue.


    None of the above problems are insurmountable but looking at the potential for Continuous Process Improvement to improve the bottom lines of companies significantly, it should be motivation enough for companies to hunker down and address all of these individually and collect the data needed for analysis and focused Process Improvement. 


    It is a capital mistake to theorize before one has data – Arthur Conan Doyle. Sr.

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