Are you Stuck at a Local Maximum?

WolframAlpha defines “Local Maximum” as a relative maximum somewhere in the neighborhood of the global maximum. Are you or your organization spending more and more resources and time on Advanced Analytics but you are not happy with the quality of the insights? This might be a sign that you have hit a local maximum.

How do you get out of this struggle zone? In this post let us look at couple of reasons.

Stuck

Advanced Analytical Tools from your BI or Database Vendor : Your existing set of Database and BI tools are successful in transaction processing and Reporting. When this vendor provides advanced analytical tools which fit easily into your existing workflow, in general, these tools fail to deliver the expected insights. These tools are not the first choice for data scientist because they run inside the database, geared towards transactional processing and ELT. On top of it, they are expensive.

Most vendors provide Advanced Analytical tools in the database, but these are not specialized for data science. Their primary motivation is to prevent you from moving the data outside its data store. Embrace R and Python eco-systems. These tools are free, open source with a vibrant and active data science community. Support from an active community of experts is the key to success in Advance analytics.

Next, using Engineers to do analytical work: Your natural impulse is to use existing engineers or BI experts for analytics. Also, some data science tools deceptively look similar to engineering tools, and some even offer drag and drop workflows and configuration of hyperparameters.  But the similarity end there. The world of engineers is deterministic, but data science embraces randomness. The engineer’s view of optimization is in terms of  big-O-notation, but a scientist thinks in terms of optimizing the cost function. Both these projects have different lifecycles.

If you want deep insights, look beyond ETL, beyond heuristically describing the data and avoid focus on making the visualizations pretty. Try to find your answers using statistical analysis, discover the hidden networks and patterns in your data, augment the features from the structural data with the features from Text data, use NLP and Machine Learning.

Without understanding these difference some organization treat analytical projects as engineering projects and go to the extent of using not only engineers to do the analysis but also employ engineering project managers and the infamous agile methodologies. Ideally, it should be a combination of data scientists and engineers. Engineers help to operationalize and scale the model. Data science experts handle the modeling part of it. 

To break away from the local maximum use tools specialized in data science like R and Python from Anaconda. Create a team comprising of both engineers and data scientists. Think about these project differently and start them differently than the traditional engineering projects.

We at Neocortex specialize in finding insights using advanced data analytics. We can help you get out of your local maximum. We can supplement your existing data science team or work with your engineering team for all your data science needs.

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Approval Hell, Using Approvals as key business process control, Is it working for you?

In our auditing of various business process for our clients, it is a commonplace to find the use of Human Approval as key process control. Approval Process gives the assurance that a responsible human would act as a gatekeeper. It makes the engineering of the business process simple. Responsibility gets delegated to the Human, and it simplifies the software design. Our analysis shows approval system is often overused. Practically there is no transactional application that does not need an approval.

Few unsung heroes in the organization end up with a lot of these Approval emails. With limited information about the approval in the email and a large volume creates what we call “Approval Hell.” Word Cloud (1)

We found on “Worst Approval Days” few executives end up approving or viewing more than 100 critical notifications. Another important measure we found was, those executives who have  these Worst Approval Days are also most likely have high “Approval Decision Per Year Per Person.” Indication that they sustain high volume of approvals.

When faced to deal with a large amount of information, it is natural to have Information Blindness; Approvers go for the easy choice, just “Approve” the damn thing and get the people off the back.  In our studies, we found rejections are often less than 3% of the volume of the approval notifications. Around 2% of the notifications are forwarded. 95% of the notifications gets approved. When a busy approver receives a notification and people are chasing him, they are frantically swiping on the small screen to find that “Green” Button. If there are few rejections, isn’t it a sign, that it is either redundant or unreliable?

It brings us to an important question, Is your current Approval System working as an effective control? Are you living with Approval Hell? 

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Oracle Apps Resource launched: 300+ SQL queries, case studies, whitepapers etc.

We have launched our Resources website with free access to more than 300 SQL queries, case studies, whitepapers etc.

Check it out today at https://resource.neocortex.com

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Oracle projects executed locally for a fraction of the TCO!

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We’re looking for Oracle EBS interviewers!

We are expanding! We are looking for Oracle EBS interviewers who are experts in their respective areas to conduct interviews for us on a part-time basis.

You could be based anywhere in the world as long as you have a good Internet connection or a VoIP phone line.

You should be able to take interviews for an average of one hour a day / 5 hours a week during US timings.

This role will require excellent communication skills and a mastery of any technical or functional area within Oracle EBS.

If you’re interested, and would like to discuss more, please send us an email at apply@neocortex.com

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What it takes to integrate OPM with ASCP

Before R12, customers on Oracle Process Manufacturing (OPM) usually used OPM MRP as the planning engine though there are customers who have successfully integrated OPM with Advanced Supply Chain Planning (ASCP). With R12, OPM MRP is now obsolete and is replaced by unconstrained ASCP in a centralized setup. Another significant change has been the inventory convergence for Discrete Manufacturing and Process Manufacturing. In 11i, OPM organizations are different from Discrete organizations and hence for integration with other modules, a 1:1 mapping needs to be done for OPM and Discrete Organizations. With R12, this is now not required. Read More…

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Cost Manager Concepts in Oracle Inventory

Cost Manager Concepts in Oracle Inventory

Cost Manager is a Process that scheduled to run by default after every 5 minutes. At schedule interval, it runs and spawns ‘Actual Cost Worker’ process for each Inventory Organization in which there is a transaction in past 5 minutes. The process ‘Actual Cost worker’ processes Material Transactions and creates accounting entries for the material transactions.
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Accounting Entries for Invoices

We will discuss here how accounting entries are generated with accounts affected in Payables.
For example, there is a business case
We do an arrangement with a supplier that we will buy some services from him at the cost of $100.

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Duplicate Suppliers, How to avoid and get rid of, if they are already there

Oracle maintains uniqueness among suppliers with Supplier Numbers. It is possible that one supplier exist in the system with two similar names. For example, the Supplier name ‘ABC and Co’ can also be entered as ‘ABC & Co.’ Special care should be taken to maintain suppliers so that there should be no duplicate.
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Thinking of R12? Go for an Enhanced Upgrade

Call it the bane or boon of an ERP system, companies do not have a choice but to upgrade to a higher version. There are various factors which drive the decision to upgrade namely de-support of the current system by the ERP vendor, availability of new features in higher version which have the potential to give a strategic edge. Typically companies choose the upgrade path to avoid the de-support problem and hence what it finally does is a pure as-is technical upgrade. This has multiple benefits in the sense that it is least risky and also can be done in a shorter timeframe. But once in a while it makes sense to have a look at the new features which can bring efficiencies in the process.
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