Targeted transparency is a necessary component in building a hyperconnected supply chain. There are six key steps to enabling the targeted transparency that will drive supply chain optimization.
1. Put the K in KPI
How long is your list of KPIs? Do you have five? Ten?
Based on our theory of targeted transparency, less is more – at least at the outset.
Reduce your list of KPIs to those truly essential metrics that are most aligned with the business's key success factors. Usually you should start with those related to customer satisfaction and market demand fulfillment, measured with OTIF and PFR respectively.
These are the indicators that will not only assess current performance, but also serve as the foundation for future metrics, like cost to serve. That's why it's so important to get these initial KPIs right. Miscalculations here will be compounded later.
2. Defining and communicating KPIs
It is common, and even expected, that subsidiaries use different versions of a KPI – with different thresholds and bases of calculation. But the expected doesn't always live up to expectations.
KPIs need to be easy to understand, easy to calculate, and easy to communicate across all business units, functions, and subsidiaries. To that end, we recommend that KPIs meet our CPA check:
Is the KPI common? Do all parties use the exact same calculation method?
Is the KPI precise? Is the calculation method, its components, and its intent rigorously set?
Is the KPI agnostic? Are pre-calculations formulated to avoid assumptions?
These principles also apply to KPIs with third-party logistics providers and even customers themselves. To that end, include the mathematical definitions in each contract and formalize the basis of calculation as well as communication channels for every shipment with every vendor, customer, or other stakeholder.
3. Build a data warehouse
Your IT infrastructure should support the targeted transparency initiative. But, as already noted, it's not necessary to consolidate multiple ERP systems. Rather, the organization only needs a separate repository where relevant, timely, clean data can be collected in a standardized way.
Enter the data warehouse. Whether hosted on the cloud or a traditional on-prem repository, this approach splits data integration from business analytics. The result? Analysts can focus on their job without being burdened with data integration tasks.
Integration of data does not necessarily need to be done internally. In some cases, external cloud services are more appropriate, especially if data that is originating from many systems helps coordinate the activity of various entities.
4. Locate, analyze, and join data streams
Once the exact operational definitions for tier-one KPIs, such as OTIF and PFR, are validated, a cross-functional supply chain and IT team must work together to identify the required data streams. This may include customer order data at its granular level from different subsidiaries and other transactional data.
To be able to match data correctly, the team must pay special attention to master data and transactional data. If these data streams are not well maintained, the business may need to set up a separate initiative for its cleansing.
Admittedly, this is a complicated process and the organization should not expect that all data streams can be seamlessly combined. We set the benchmark of 80/20: collect the data in the data warehouse, determine the most common inconsistencies, and address them through automation. Once this is done, the data can be visualized in business intelligence tools and used for root cause analysis and decision making.
5. Standardize critical processes
Since different parts of the business have different processes that affect the customer order data streams, it's important to identify those that have the biggest impact on a KPI. The business can then launch an initiative to standardize customer order processing.
This often involves incentivizing subsidiaries to complete the process within ERP (as opposed to working on Excel extracts) and ensuring that order statuses, such as open, confirmed, or rejected, are used consistently.
Again, we recommend an 80/20 approach, rolling it out across the subsidiaries by priority and implementing automated workflows on the data streams whenever there is an obvious calculation that can be streamlined.
6. Repeat and reap
Having the proper IT architecture and the essential KPIs in place drastically increases transparency. With these key pieces in place, the business can establish a KPI roadmap to prioritize other indicators that will generate valuable insights. Once the roadmap is established, the company can apply the targeted transparency method to a new batch of KPIs.
As more KPIs are integrated, the centralized data will grow. This, in turn, enables the deployment of advanced analytics models. The more digitally mature the organization, the easier it will be to identify root causes of inefficiencies and create optimal plans for improved performance.