The Next Frontier of Analytics Inter-organization Intelligence

Transcription

1 M76 Analytics The Next Frontier of Analytics Inter-organization Intelligence The most serious mistakes are not being made as a result of wrong answers. The truly dangerous thing is asking the wrong question Peter Drucker Do we know the right metrics and that too today? 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 inter-organizational intelligence.

2 Genesis of Inter-organizational 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 interorganizational 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 metrics also assess the entropy of your entire value chain. In a nutshell Inter-organizational indicators drive the sense and response ability of an entire value chain and not just a few operational centers. 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: A. Activity Based Costing or Cost of Goods Sold estimates in a distributed Manufacturing Network that can help the brand owner estimate Working Capital requirements and liability to partners across the network. Importantly it can also help the Brand Owner take key decisions across the network such as production distribution or re-routing of services. Many a times the Cost of Goods 2

3 B.Risk and Liability Calculations in toll manufacturing are based not only on demand Forecasts but also inputs such as market trends and interest rates. In most of the cases examined by M76, 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. C. Ability to respond to capacity constraints across manufacturing partners. Storage of inventory and production capacities could be visualized across the Supply Chain instead of just within the organization. Often this means that we ascertain the Ability to respond to capacity constraints across manufacturing partners. capacity to ramp up or ramp down production and inventory across a Supply Network in response to the Demand. This is critically important to organizations with globally distributed manufacturing networks as they cannot commit to their customers without having updated intelligence about their own Supply Chain. D. Measuring the impact of external events or disruptions such as shortages in the supply Network, Demand spikes or natural disasters on the Supply Chain. Collaboration, the cornerstone of Inter- organizational intelligence. 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 Interorganizational Intelligence network. The data shared needs to be published in a standard format for Industry exchange as well. 3

4 Future scope How do structured dimensional models augur for the era of Big 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 Key Challenges to collaborative systems 1. Discipline and Ease of pooling data hold the key A CPG company recently implemented a portal solution for the purpose of collaboration. The brand owner was a toll manufacturer with substantial amounts of inventory lying with its Contract Manufacturers. And the calculations pertaining to inventory risk and liability were paramount to the overall performance of its Supply Chain. Inventory collaboration was thus the key to managing Liability and Working Capital. However the collaboration system failed because a large number of suppliers did not take make a discipline out of sharing Inventory information. The key points to drive home are: Inter-organizational intelligence systems are contingent upon availability of clean data and thus also the data governance and sharing disciplines within the contributing organizations. -Measurement and collection capabilities matter here. Only organizations that are mature in measurement, collection and reporting could be good contributors to an interorganizational intelligence reporting. This also means that clearly defined integration standards must be followed. 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. a) Ability to pool data in standardized formats from heterogeneous systems and per a discipline. b) An inter-organizational system might need to maintain all dimensions pertaining to a fact. Having a common dimensional model that facilitates all forms of necessary reporting, and facilitates navigating dimensional hierarchies in a structured fashion. Only when the dimensional model is comprehensive would a robust inter-organizational system take birth with the right metrics being churned out. For exam- 4

5 c) In general the best practice is to use the industry standard data exchange standards if they are available. Industry standards based exchange would ensure that all the necessary dimensions are comprehensively accounted for at the beginning of the exercise. d) Importantly the solution needs to have the ability to write back the necessary metrics to the native applications /ERP s after they have been processed for inter-organizational intelligence. Engineering the change Managing Data Transfer 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 allowvariety 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. 5

6 M76 Analytics M76 is founded by IIT-Bombay Alumni. Together our founders bring 45 years of experience in business consulting. They have successfully delivered strategy and implementation projects related to Analytics, Business Intelligence, Optimization, Data Architecture, Data Integration, Data Migration, Corporate Performance Management, Solution Architecture and Business Strategy. / Planning, Corporate Performance Management. They have been involved in Manufacturing, Government, Telecommunication, Health Insurance, Mortgage, Student Aid / Loan, Logistics, Retail, Media and Banking Industries, CPG, Hi-Tech, Media and Entertainment, CPG, Logistics and Transportation to Electoral Analysis. M76 Analytics M76 Analytics M-07, SINE, CSRE Building, IIT Bombay, Powai, Mumbai Phone jai@m76analytics.com We're on the web!