When asked, business users will grudgingly concede that they have realized some value from their enterprise resource planning (ERP) investments. They have done so by standardizing global processes such as order-to-cash and procure-to-pay, and facilitating cost reductions through arduous business re-engineering efforts and major reorganization around shared services.
However, these same business users get much more animated about the ability to analyze their business performance globally. In fact, the ability to analyze is always at or near the top priority of virtually every spending survey AMR Research conducts. Being able to quickly show how they can "slice and dice" all the data captured in their ERP systems leads to a much better understanding of myriad metrics such as which products are selling in each part of the world, average selling price, best- performing salespeople, contribution of each product to profitability, and other sales and financial performance measures.
Beyond The Executive Branch
The Top-Down Approach
Setting Your IT Priorities
Most Business Intelligence Looks In The Rearview Mirror
Running today's global manufacturing enterprise on ERP and traditionally implemented BI is like driving down the New Jersey Turnpike in reverse at 65 mph using only the rearview mirror. ERP architectures are optimized for transactional efficiencies rather than real-time reporting. In many cases, BI platforms must go through a time-consuming "extract, transform, load" process to turn ERP data into intelligence that can readily generate standard reports and facilitate the slicing and dicing that allows business users to analyze performance rather than spend 70% to 80% of their time collecting data, as they used to before ERP and BI.
If you are always looking in the rearview mirror, you miss what is going on around you right now. That is a recipe for disaster in today's highly dynamic business environment. By the time you detect an operational problem in BI, you have already made poor-quality products and missed orders.
Operations Intelligence Required For Demand-Driven Networks
| Top Five Best-Of-Breed Revenue Leaders In Business Intelligence And Performance Management |
This resurgence in spending has been spurred by the need for real-time visibility of manufacturing performance. While real-time dashboards for plant operators based on supervisory control and data-acquisition systems have been around for a long time, the data presented to operators for safe control of the manufacturing process have little meaning to a supply chain manager, customer service representative or the CFO. Real-time manufacturing data need to be put into many different contexts for other roles in the organization that are contributing to the optimal performance of the real-time enterprise.
Understanding Cause And Effect
After many years of tolerating a lack of visibility into manufacturing performance, the need to understand the impact of manufacturing performance on business is finally taking center stage. In another 2006 AMR Research analysis of more than 200 manufacturers on manufacturing performance gaps, the following issues were rated as extremely important:
- Multisite visibility of production key performance indicators (75% of respondents)
- Enterprise visibility into production site financial performance (76%)
- Information integration between plant-floor applications and ERP (81%)
Having identified the need for something beyond traditional BI, AMR Research published two reports on enterprise manufacturing intelligence (EMI) -- a term we have used since 2001 -- and predicted that future cross-enterprise PM architectures would need to incorporate new frameworks that would aggregate, contextualize, analyze, visualize and propagate data from the real-time domain, as well as integrate with BI platforms. This year, we have redefined the next generation of EMI solution as "operations intelligence" (OI) to eliminate any perceived silos between manufacturing, maintenance or supply chain data.
Early Operations Intelligence Pioneers
In 2003 software application providers IndX and Lighthammer were already pioneering and evangelizing frameworks that allowed manufacturers to construct their own OI solutions to provide contextualized visibility of manufacturing performance non-intrusively, for example, on top of existing investments in manufacturing systems, data warehouses and ERP. The OI market opportunity was recognized early by Siemens with its acquisition of IndX late in 2003, and SAP gave the OI market added credibility and momentum with its acquisition of Lighthammer in 2005.
SAP has subsequently built an entire partner ecosystem around what they now call SAP xMII (xApp for Manufacturing Integration and Intelligence). These partners are sorely needed to augment their OI framework with critical architectural components, such as operational data stores, high-fidelity manufacturing data models and analytical models for predictive intelligence.
Meanwhile, Oracle has been following a lower profile and very different path to addressing the demand for OI. By tackling the problem head-on and enhancing the fidelity of their manufacturing data models with integrated manufacturing execution system (MES) functionality, Oracle has reduced the architectural complexity for their E-Business suite customers.
First Generation -- Core Capabilities: The first generation of EMI resembled the early years of the BI market, with a preponderance of "real-time" ETL (extract, transform, load) tools available for early pioneers to build their own operational data stores, standard reports and dashboards. The core capabilities of the first generation of EMI include the ability to aggregate, contextualize, analyze, visualize and propagate information.
Second Generation -- Adding BI discipline to operations data: Just as the maturation of the BI market brought with it a landscape of standard data models and marts, data-mining tools and a disciplined approach to data lifecycle management, the early adopters of EMI frameworks are now looking for similar capabilities from their next generation of OI. It's time to evaluate vendors' applications and frameworks against the following additional criteria: data models, data lifecycle management, data mining and discovery tools, process modeling, and simulation and scenario analysis.
The New Breed Of Operations Intelligence
While a new generation of OI frameworks from Activplant, Acumence, Incuity and Informance have broadened their coverage of the core capabilities, their integration with ERP and BI infrastructure is still lacking. This is an area where we anticipate a lot of vendor activity in the upcoming 24 to 36 months as global producers push for convergence of manufacturing, asset and supply chain intelligence with their enterprisewide PM platform.
| Leading Operations |
When rigorous process simulation techniques cannot be applied, Aegis Analytic and Pertinence are providing data discovery tools to provide empirical insight into the dynamics of manufacturing processes. pVelocity is evangelizing the capability to evaluate the most profitable product mix that should be run through manufacturing assets, leveraging detailed costing and production activity data.
Checklist: How to decide if you need a business intelligence/operations intelligence solution.
- Are critical business and operational data available to all rather than a select few?
- Do you know with certainty which customers/products/channels are most profitable?
- Do you know which suppliers have the best on-time delivery performance?
- Does your firm have complete visibility of manufacturing key performance indicators across the full enterprise?
- Can you effectively sense and respond to dynamic changes in demand and/or supply?
- Is there a definitive source of business and operational data that everyone uses?
- Can you use information today to predict performance tomorrow?
- Is the right data available at the right place at the right time?
- Are your real-time data needs being met today?
If you answered "no" to any of these, then it would make sense for you to evaluate a business intelligence/operations intelligence solution.
Colin Masson, research director at AMR Research Inc., is focused on mid-market ERP and lean manufacturing. He is based in Boston.
John Hagerty, vice president of research and research fellow at AMR Research Inc., has more than 25 years of experience across enterprise applications, performance management and compliance. He is based in Boston.