For a long time, companies were challenged to shift from a “functional” organization to a “process centric” operating model. This was part of a general trend to:
- Break silos in companies
- Be lean and reduce nonvalue added activities
- Focus on end to end processes efficiency and lead-time
- Become customer centric
Many companies spent millions to achieve that and a new generation of business applications, in particular ERPs, were the cornerstone of this change.
Now, the paradigm is shifting to a new world where the data is the key. In the new world, operating models are extended and structured around “data” and “events” versus the old world model in which companies were structured around functions or end-to-end processes only.
Let’s take an example: In a classic B2B company with a make-to-stock supply chain model, a sales department is in charge of sales planning and forecast, promotions, business development and lead management, pricing, taking orders, tracking of the delivery status and billing.
In this company, the sales department interacts with other departments (Logistics and Finance) through an ERP which integrates different departments together. It's likely that the company also has different applications or programme engines to optimize or accelerate specific activities such as pricing or promotions.
Now, let’s imagine the same company but Born Digital; The bulk of key activities above are executed automatically (ordering, billing, ….) without any manual intervention. They are articulated around two key master data sets:
- Product catalog: Product description and data, pricing data, manufacturing data, inventory data, cost data, … ; and
- Customer catalog: Payment terms, billing address, delivery address, credit data, … .
The responsibility of teams is largely focused on:
- Making sure that product and customer catalogs are fully up-to-date so operations (ordering, packing, shipping, transport, billing, and collection, …) are automatic and seamless without errors or disruption; and
- Managing exceptions (product description or price errors, inventory errors, address errors, customer complaints, …).
As you see, these are two different mindsets to define and set up the best target operating models. In the old world, the management focuses on streamlining and benchmarking activities, optimizing the transition from one department to another. In the new world, the focus is on errors and exceptions, aiming to reduce them to none.
Focus on process optimization and optimizing interaction cross departments
Focus on data quality
Complex activities and processes to address different cases
Processes are ultra-simplified
Sophisticated programs reflecting the complexity of processes
All activities are fully automatic (STP: Straight to process)
Processes rely on manual or human intervention
No human intervention except for errors
System data doesn't reflect the reality (wrong inventory, wrong prices, ....)
Data in systems reflect the right information
Systems updated in batch in many cases
Systems are real time
Out of system fixes for broken processes (firefighting)
Exception and errors are managed through the improvement of master data quality
Hundreds of operational KPIs (for each activity and process)
Simplified KPIs (for exceptions mainly)
Skills required: Doers and fire fighters
Skills required: Problem analyzers / solvers
Defining the right operating model and organization in the non-born digital companies would require a major shift of the paradigm in the extension of the process organizations to a data / event mindset. Failure to do this will lead them to automate the wrong habits and, ultimatlely, they will become an underdog in the fierce competition with newcomers and Born Digital companies.
If you have had similar experience, please share your experience in building effective operating models fit to business challenges in the 21st century.