Over the years, the use and application of Neural Networks (NN) has found a “home” in the domain of industrial process control. It is also well known that NN is practically a core function in most popular data mining solutions. 
It is interesting though to note that NN algorithms have been embedded in process control solutions, yet sometimes seen or even projected as a bit of a “black box” or “magic box”. Obviously, because of the complexity involved for most process control engineers to rationalize the output of an NN algorithm, except the performance of the controller.
Root Cause Analysis (RCA) has traditionally been conducted by core statistical applications. RCA is classified based on the use or objectives as:

1. Safety-based RCA, which descends from the fields of accident analysis and occupational safety and health
2. Production-based RCA, which has its origins in the field of quality control for industrial manufacturing.
3. Process-based RCA, which is an “add-on” to production-based RCA, but with a scope that has been expanded to include business processes.
4. Failure-based RCA is rooted in the practice of failure analysis as employed in engineering and maintenance.
5. Systems-based RCA emerged as an amalgamation of the preceding uses, along with ideas taken from fields such as change management, risk management, and systems analysis.

In the course of an RCA initiative, the need to deal with substantial volumes of process data is well known. The combined dependent and independent variables can be in the range of hundreds for a single knowledge discovery problem addressed with data mining, and it is not uncommon that analyses without deep process knowledge can fail because of insufficient understanding of the process characteristics and behavior in question.  
As such, data collection and the ability of a data mining solution to interface, sometimes in near real time, with plant data bases residing in a control systems (i.e. Integrated Control and Safety System) or a Historian data base, is key to the development of an integrated solution that can be deployed for use in a dynamic environment versus performing data mining analytics offline. 
The situation in the Process Industries is that it deals with large data sets, considering that the time resolutions of such sequences can be seconds or even milliseconds.
Take the scenario of a huge data set with a long sequence of events and alarms, with thousands of triggered flags or events, logged operator actions and also changes in battery limit conditions, and then add changes that are being tracked due to heat exchange fouling or catalyst characteristics, rotary equipment, etc. , and then try to rationalize the outcome and impact on the behavior of continuous or discreet variable or a number of variables, e.g. trip events or an alarmed deviation of a safety or quality variable. Try also to visualize the endless number of dimensions (variables) involved as those related to the plant or process areas, the time dimension itself, the operators involved, the process unit operations or physical assets associated with the plant areas and units, etc., and then it becomes obvious that a data mining scenario of high complexity evolves. Yet , this is where data mining brings value and makes its money, because data can be both explored and analyzed in so many ways, but also used for predictive purposes by using the same techniques as in the retail example. It is by far a more complex situation that any other industry can offer.
In the case of the RCA, data, once extracted, transformed and loaded for mining, rules about associations or the sequences of items as they occur in a transactional database can be established and make them useful not only for addressing the RCA problem in concern, but for many other applications, including exploratory and predictive data mining, as for example predicting runaway conditions for a catalytic reactor in a plant or preventing off spec production, or even avoiding trip of critical equipment in a plant. 

The Total Cost of Ownership (TCO) for capital investments as for example an IT system, a DCS, an OTS, etc. is allocated to various cost components e.g. purchase/acquisition costs, operational costs, etc. 
Gartner and Forrester Research present some typical TCO figures as per the adjacent image. Both firms indicate a purchase cost of about 33% of the TCO. Given the asset lifespan period, the longer the lifespan, the higher the TCO in absolute figures, but the purchase cost as % of the TCO gets lower. 
A key point of course is that the purchase/acquisition costs alone do not represent the most advantageous selection, from the financial point of view. It is the TCO Index. 
Calculating the TCO Index for each competitive bid is quite analytical but worth the effort during evaluation of supplier proposals. Contact ziconNET for more information and engagement on how to calculate the TCO Index for your current or next project. 

The easy part of the answer to this question is: When you can answer difficult questions quickly and accurately. Before answering the more difficult question (how), a bit more about the terminology again. 
It has been a bit of search to come up with a term that represents what Plant Intelligence or Process Intelligence is all about. 
As noted in the July issue of ziconNEWS, Process Intelligence is used for Business Process Intelligence (or BPI). Plant Intelligence may be more of an applicable term, but this term may be too limiting, since the concept is to combine business, process and control system intelligence for related data. 
Some use the term Manufacturing Intelligence, which is more common in the discreet manufacturing sector and not too common in the Process Industries, which prefer to distinguish from discreet manufacturing. 
So, here is a suggestion. How about Process Plant Intelligence. This could cover intelligence derived from everything in any process plant field and control room, as for example Levels 0, 1, 2 systems, to the executive management financial, planning, etc. or as known Level 4 systems.
So, to answer the question fully ... one can also claim that Process Plant Intelligence is in place when you have a reasonable infrastructure in place with Level 1 - 4 type systems. These include a DCS or an ICSS (Integrated Control & Safety System) with a Historian. At Level 4, there must be an ERP system to manage business requirement and a planning system to develop production plans. There also must be a Maintenance Management System (typically part of the ERP). 
At Level 3, there should also be a Data Reconciliation System (DRS) in place for reconciled heat and material balances and possibly a LIMS system. The obvious question is do you need to have an MES? The answer is no, but it will be great of there is one, since more useful operations action - oriented data can be generated and made available. 
Now, with such systems, the plant generates lots of data at all levels. Data which can fuel knowledge. This data may be provided at different levels in the form of various reports. This gives Exploratory Intelligence, but not necessarily Predictive Intelligence. Even for Exploratory Intelligence the "drilll-down" "slice and dice" capability of the data exploitation may be limited, unless properly data management techniques are properly implemented (e.g. OLAP reports). A full Process Plant Intelligence system then that includes data mining can provide answers to difficult questions and predictive replies like:

1. During which operations shift and at which time period a certain Heater was run at its lowest possible cost and highest efficiency?
2. What is the expected load and cost of utilities over the next production time horizon.  
3. During which operations shift we had the maximum deviation between planned production and actual production?
4. What is the likelihood for a trip for a specific group of rotary process equipment.
5. What is the expected run for a furnace before decoking?

  These are some rather questions that can not be answered unless a Process Plant Intelligence initiative has been successfully deployed. The rest of the questions and answers can be endless. And endless are the possibilities that Process Plant Intelligence can offer to those who endorse it for their plants and companies and begin to look more into this domain.

Gartner reports that Consumarization is one of the "Top 10 Forces to Impact Outsourcing and IT Services Industry".
Gartner also states that "consumerization refers not only to the acceleration of consumer-oriented technology and behaviors into people's lives, but also to the introduction and expansion of these consumer-oriented technologies into enterprise IT strategies".
Reading on ahead, it comes to mind that, when the article refers to SaaS and Outsourcing Strategies, users of IT should focus on what the core business of their business is and not on how to exploit IT.
Take an oil & gas producing company, maybe they should concentrate on running their plant safely and efficiently and let IT and Automation suppliers handle their needs for the related infrqstructures. Or in some cases, for complex projects bring in subject matter experts from outside their organization who can help not only complete complex projects but also accelerate time to project completion.
Another point that comes to mind for companies is for them concentrating on their customers, whom without, business can not survive. Thus a B2B CRM initiative is justified. This is true for all business, from oil & gas, refining and and petrochemical producers to IT and Automation suppliers. Enter here social networks, or in the case of industry, professional netoworks.
Does then social media marketing in the form of a B2B Social CRM make sense?
Of course it does and it will more. Social networks comprise of customers and users of products and services. Anything, ranging from a beverage to an automobile, and from a fine chemical to a piece of IT and Automation technology, can be perceived differently, better or worse, when the social media marketing mechanism is put into operation. Over the past 2 years, we have all seen serious players, both on the product and on the technology side experimenting with social media marketing vehicles like LinkedIn, Twitter and Facebook.
Gartner predicts that by the year 2020 25% of the global GDP will be generated through digital activity, and the strong posibility is that double of this global GDP will be influenced by social media marketing.
So, the advice is for get on the Social Medial bandwagon and stay there, before your competitors do.

According to ebizQ's Business Agility Watch and its Top 10 BI Predictions for 2010, Excel will continue to provide the dominant paradigm for BI end-users. No doubt that it is also the tool of choice for process and operations engineers and managers when it comes to process intelligence.

Interesting news and developments now available in the ziconNET Tweets page.

The true cost of OTS, that is ... what OTS truly costs to customers represents a challenge for them. Much like any project or system acquired by customers, in addition to the purchase price of OTS, or cost of acquisition in TCO terms, many other costs are incurred over the OTS lifecycle.
How can customers reduce their costs?
When OTS costs are considered as part of a TCO estimation (Total Cost of Ownership), there are many cost components that come into consideration. In fact, most of the incurred costs may be during the use of the OTS, these are known as operating costs, and if a company trains for example 100 operators over a period of 5 years, the cost of OTS acquisition may  represent only 20% of the TCO.
From the Suppliers' angle, and users' in many cases, the benefits are numerous and outweigh costs. ziconNET is running a survey to get feedback of what users and suppliers think about the cost of OTS. Future surveys on the topic of OTS TCO may in fact reveal ways to reduce TCO and make OTS even more attractive for users.
Visit the OTS Cost Survey page and participate.