True, serious mistakes can be avoided early on, whether the right questions are asked and more importantly whether they are answered appropriately. Today competition derives its edge comes from competing value networks. In a globally distributed economy these networks lie as much outside the organization as much as they lie within.
A distributed value chain, means that the value created and concomitantly the risk are distributed across the network. Therefore metrics at the right granularity that combine signals within and outside the organizational boundaries hold the key to the right answers. That is the genesis of interorganizational intelligence.
A large CPG Manufacturer found it difficult track Inventory that was lying with its various suppliers and Contract Manufacturers in the absence of visibility and hence inter organizational Intelligence.
The key question then is how true inter-organizational intelligence is generated. A good example of an inter-organizational intelligence signal could be the Point of Sales (POS) information from a retailer that may drive Sales and Operations Planning across a distributed Manufacturing or Services Network. When signals from partnering organizations are morphed into cross organizational metric reporting and then evolved into predictive analytics, Inter organizational Intelligence becomes most meaningful.
Inter-organizational intelligence is a natural consequence of Collaboration.
– Visibility and Orchestration between partnering organizations. Inter- organizational intelligence is synergistic in the sense that it derives its unique value only when data streams from partnering organizations are woven together.
Inter-organization intelligence helps in arriving at optimization that is specific to the value chain rather than few value generating nodes or production centers that could be specific to a process or internal to an organization. Inter-organizational
Inter organizational Intelligence could manifest itself in the form of metrics that mitigate Supply Chain disruption, facilitate planning activities or help anticipate potential cost over-runs in the entire Value Chain. These are just a few examples. A couple of other key outputs of inter-organizational intelligence could be as follows:
not only on demand Forecasts but also inputs such as market trends and interest rates. In most of the cases examined by Y point, given the lack of information sharing among collaborating partners, it is virtually next to impossible to obtain futuristic Risk and Liability estimates in the Supply Chain.
Much like a relay game where the token is passed from one player to another, an inter organizational intelligence signal is moved from one echelon in the value chain to another upstream or downstream. The necessary metrics then form the basis of a global decision and not a locally sub optimal one.
Trust forms the basis of all forms of collaboration. A relationship based on sharing key transactional information (a fact) such as inventory/ point of sales/volume or capacity forms the basic scaffolding of an Inter organizational Intelligence network. The data shared needs to be published in a standard format for Industry exchange as well.
1. Discipline and Ease of pooling data hold the key
Alignment of key stakeholder goals across organizations to arrive at key indicators that are not only leading but also strategic in terms of providing long term industry competitiveness.
2. Technical challenges to inter-organizational intelligence.
There are various ways data can cross the organizational boundaries. Via a messaging system like MQ series or JMS or Flat files via Secure transfer or SAP PI. An inter organizational system might be challenged by the need to maintain the same data in different data formats like XML, Delimited Flat file, concatenated strings, and allow variety of data transfer mechanisms, (download over web, SFTP, connect direct, messaging etc) to appropriately disseminate the information required by partner organizations. The files either need to port through the established exchange interface or through simple flat file uploads.
Delta Master Data or Transaction Data
– Important Master Data or Transaction Data needs to be decidedly sent either in the Delta format or in the form of a completely new Time Series each time. Bandwidth challenges might force only changed data to be transferred, for example instead of transferring your complete product catalogue along with its rates; you might decide to transfer only changes to your product catalogue. Both structured and unstructured data might be transferred between the organizations. Structured data includes, invoices, quotes, inventory status for each product, and key financial data.
The abundance of vast data in a collaborating ecosystem can push collaborating partners into the realm of Big Data. The din and noise around Big Data should not deter the accurate determination of key interorganizational metrics and the appropriate dimensional models that drive the responsiveness of the value chain. Much of Big Data would make sense only when bagged at the appropriate
hierarchical levels. Structured dimensional models would pave the way for a meaningful engagement with the vast pool of data generated in the ecosystem of partnering organizations, and thus facilitate monitoring and corrective action at the appropriate level. An exercise in structured dimensional