OBI1 (Object Business Intelligence 1) is an Information Systems Conceptual Model Development Platform that creates an all-encompassing, enterprise-wide,
Conceptual Object Model, which is then used to provide data and powerful management and analysis tools to users of different profiles in the organization,
from completely non-technical users that only consume data to highly trained programmers, analysts and software architects.
What exactly is ERP today?
Business software suites today are named ERP suites and are mainly composed of two parts:
a database containing very comprehensive information about the operations of a business
software tools to take administrative actions, actions that are sometimes based on external input (like when receiving a new Sales Order) and
some other times based on the information contained in the database about the business
Human users utilize the software tools of part 2 to perform administrative actions (like creating a Purchase Order), based on their own criteria,
mostly dictated by company methodologies and policies, but also determined many times by their common sense, applied to the information contained in the
database in part 1.
The very name ERP, “Enterprise Resource Planning”, is a euphemism that does not reflect the true nature of existing ERP suites. Resource planning is mostly
left for the human users to do, based on the data in the company database of part 1 and using the software tools available in part 2.
Actual automated resource planning done by the existing suites is very limited, if at all present, as a function that the ERP suite will perform on its own.
Planning is mostly left to the humans, while the ERP suite provides information and tools for humans to execute the planning.
Both parts of existing ERP suites have been very well refined and polished, providing eye catching user interfaces, ingenious data representation methods and
sophisticated data analysis techniques.
Several ERP software providers have even started to offer Artificial Intelligence data analysis tools, like what is now known as “big data”, tools that get
the analysis of the existing company data in part 1 to new heights. However, this is of little, if any, interest for small and medium size companies.
But no provider of ERP software has shown any interest so far in producing software that actually performs the tasks and decision making of the human users,
that are still ultimately responsible for using parts 1 and 2 to act, in an informed way, on the existing company information.
Enter Aboard Software
Many human user decisions and actions that take place when utilizing an ERP suite are as simple as noticing that some raw material is about to run out,
or about to go below a certain minimum level, and then creating a Purchase Order to replenish that resource.
While much is expected by the general public from Intelligent Robots based on Artificial Intelligence, like self driving car robots, creating a Purchase Order
when something is needed, and scheduling it to be sent to the vendor so the purchased materials will arrive “just in time” is not that complicated.
Aboard Software’s vision is that these simple actions and decisions, and some much more complex ones, currently left to human users by existing ERP suites,
can be performed by intelligent software robots based on Artificial Intelligence algorithms.
The new key business software part
At Aboard Software we have added a third part to the traditionally accepted two-part ERP software paradigm:
a team of robots that makes most of the decisions and performs most of the actions that human users did in the past suites
Robots never miss work, never introduce human errors and are always ready and vigilant, working 24/7, monitoring changing conditions and relentlessly
acting when it is needed.
And they are not paid a salary and require no benefits.
We believe the future will be one in which offices and warehouses have substantially less human workers. We believe that is a reality where goods and
services provided to everyone are delivered faster and are much more affordable.
Robots as humble clerical workers
The teams of robots must be designed to perform the myriad clerical decisions and tasks that are required daily in a normal company office.
They must report to their human team leaders and show the product of their work in a simple, graphic and intuitive way.
Human users must then supervise the work done by the robots, and eventually override it, due to special considerations when they apply.
Robots must always respect what humans changed, no human-generated or human-altered transaction should be changed or deleted by a robot.
And robots should in principle not communicate directly with external entities like vendors, customers or service providers. That should be done by the
human supervisors, upon their approval of the work the robots did, and following the optimal schedule prepared by the robots that did the analysis.
Robots are better than humans at many tasks. To give a few examples:
They work tirelessly around the clock and never complain about anything
They don’t mess things up with the introduction of human errors
They always get a full picture of the situation, they never overlook special or odd cases
They provide very fast and very good optimizing solutions to complex problems
Applicability of Robots to different ERP suites
A team of software robots can use any ERP suite, but as it’s the case with human users, it can be impaired by the limitations of the suite they use.
Embedding some members of the team of robots into the ERP suite itself provides faster response, as robot actions can be triggered instantaneously when
conditions require it.
Other applications for Robot Teams
There are other areas where robots can deliver incredible performance enhancement over their human counterparts. To give a few examples:
Data conversion into a new ERP suite
Human user support in the use of the ERP suite
Data gathering from banks, the Internet, etc. for tasks like reconciliation of accounts
Applicability to non-clerical tasks
It is intuitively clear that robots are ideal to perform repetitive clerical tasks and make simple administrative decisions, but there are other more complex
areas in which robots can do a much better job than humans:
Cash Flow Planning
Manufacturing Shop Floor Scheduling
Determination of optimal inventory levels based on existing historical data and current trends
Teams of Robots provide the ideal assistants for CFOs and COOs.
Even the more complex tasks described above do not remotely address the “entrepreneurial magic touch” that creates opportunities for companies to make a
profit or even to have a reason to exist.
But as much as teams of robots can provide the ideal assistants to the CFOs and the COOs, they can also provide the ideal assistant to the entrepreneur.
While this might seem farfetched, it’s already happening since the early 1990s in the securities trading industry. Since then the “quants”, ex mathematicians
and physicists, started creating robotic algorithms for identifying opportunities for the profitable trading of securities.
Those algorithms focus only on making money, and they bypass the traditional sources of “fundamentals” information for determining what is a safe and fast
money making investment.
We believe that in the future the same will be accomplished for the commercial trade and manufacturing industries.
At Aboard Software we are already moving in that direction.
The new Active ERP Suites
The new generation of ERP Suites, called Active ERP Suites, incorporates Artificial Intelligence (AI) and Intelligent Robotics (IR) into the traditional
Aboard Software’s aERP is the first such ERP Suite integrating AI and IR into a state of the art traditional ERP Suite. It is deployed and in use by
But why? What are the advantages of using AI and IR in ERP software? What do AI and IR do?
How do AI and IR work in the ERP arena?
What is a software robot?
An algorithm that determines what tasks need to be done and performs those tasks, like creating Work Orders or Purchase Orders is such a robot.
While a software robot is not manifested physically, it is no less of a robot. It acts following certain rules to perform certain expected tasks,
creating new data structures like Work Orders or Purchase Orders for example.
What is Artificial Intelligence as applied to software?
Consider a problem that an algorithm can't solve, like optimally (or even acceptably) scheduling Work Orders. The number of possibilities is far too big
(like in exponential problems or combinatorial problems); they can’t all be examined to choose the best as to do so might take the life of the universe.
However, an algorithm that uses heuristic criteria and certain AI methods can produce a solution that is very good, even if not optimal in the strict sense.
The achievement of AI is to be able to produce a human-quality solution for problems for which an algorithm would take a forbiddingly long amount of time
to find the best solution. A typical case of AI applied to software is our Operations Scheduler.
Some problems of extremely high complexity, which are impossible to solve by implementing an algorithm for them, also benefit from AI methods, because the
AI solution is much simpler to implement in those cases.
Humans can produce solutions by methods that do not involve looking at all the possible solutions and choosing the best. AI tries to do the same, and in
many cases it does it even better than humans.
What is an intelligent robot?
When AI is used to define complex rules for the tasks a robot performs you get an Intelligent Robot. Good examples are our robotic users, that create
Work Orders and Purchase Orders and optimize merchandise utilization by applying complex heuristic rules that do not depend only on the mechanics of
building Work Orders or Purchase Orders, but that also incorporate experience gathered through learning and summarized in the heuristic rules that guide
the robot in order to optimize the inventory and the operations.
This is the case when a simple robotic algorithm is guided by heuristic information that provides rules that transcend the definition of the task to perform.
This heuristic information could be compared to the experience that a good plant manager or shipping manager brings in when he is hired by a company. He has
no specific experience with the new plant, the new products, the new warehouses and the new manufacturing processes, but he looks for efficiency and
effectiveness applying variations of rules he learned to apply to other similar problems in other companies.
Refining heuristic criteria initially loaded from prior knowledge databases by adding new criteria created from the robot’s own accumulated experience in the
field in order to perform a task better is more than robotics, because the robots don't follow simple predetermined rules that come directly from the
definition of the task to perform, and because the robots learn how to perform the tasks better as they do their daily work.
So that is more than robotics, it is what we call Intelligent Robotics, because the robots are guided by more than the simple rules directly derived from
the task to perform.
In the case of creating Work Orders or Packing Lists, for example, the key is to look for the best assignment of the available merchandise to delay
acquisitions as much as possible, but still to ship the merchandise on time as expected by the customer, breaking down big production Work Orders to fit the
working shifts, placing Purchase Orders at the right times, etc.
There are many ways in which those tasks can be achieved to always ship on time and delay acquisitions as much as possible.
Our Supply Chain Optimizer is a perfect example of Intelligent Robotics, it does what needs to be done, but it's guided by heuristic rules that we refined
over many years of experience.
A comparative between the two methodologies
Below we detail the advantages of AI and IR by using a typical food manufacturing company as an example, and comparing what the humans had to do when
using a traditional powerful ERP Suite with what the humans had to do when using a new generation Active ERP Suite.
Here’s a summary of the activity volume of a typical food manufacturing company we will use for this comparative analysis, for just one month:
|Sales Orders in the typical month
|Purchase Orders in the typical month
|Work Orders for packaging Finished Products in the typical month
|Work Orders for baking
|Work Orders for WIP products to produce the product to be baked
|Total Work Orders
And here’s a table of things that humans had to do for the same transactions in the same typical company when running each of the two technologies:
Reception of 53 Sales Orders
Consultation with plant manager to determine if the order can be delivered by the date requested
Plant manager must review the production schedule and find the earliest possible Shipping Date
Ask the system if and when can the order be produced
|Acceptance of 53 Sales Orders
||Input of 53 Sales Orders
||Input of 53 SOs
Review 53 Sales Orders to create 53 Finished Goods WOs
Split 53 FG WOs in shift-sizes For a total of 79 WOs
Create 84 baking WOs for all FG WOs in all shifts
Create 194 WIP WOs for all The Baking WOs in all the Shifts
Schedule all 79 FG WOs
Schedule all 84 Baking WOs
Schedule all 194 WIP WOs
|Purchasing Raw Materials
Analyze the needs of Packaging Materials and Raw Materials for the 357 WOs
Create 762 individual Purchase Orders with expected reception dates on or before the day they are needed for production
|Monitoring of Operations
Review the production schedule to make sure Packaging and Raw Materials were ordered and will arrive on time
Adjust schedules for plant contingencies
Is the saved work all the advantage that AI and IR bring?
In the comparative above we have assumed that humans would perform impeccably, with no human errors introduced, and thus with no costs generated by having
to identify (suffer) and correct human errors that are naturally introduced when operations are run by human users.
This is not a complete picture, far from it.
Shipping late to an important customer can affect the future sales of a company in a drastic way, orders can be lost, and much worse, customers can be lost
due to late shipments.
Failure to order needed raw materials in turn can delay the execution of work orders, which in turn can lead to late shipments and idle personnel.
And so on.
There are many actions taken, or not taken, by human users that determine that the operations of the company are far from optimal. This means lost business,
late shipments, late collections, impacts in the cash flow, more difficult repeat sales and a huge number of situations that CEOs had to live with when
operations were carried out by humans supported by a traditional ERP Suite.
Robots, on the other hand, never forget a shipment that will be due, never forget to update the files, never forget to check for the availability of raw
That is the biggest advantage of all, far bigger than the saved work, that is in itself already huge.
Traditional ERP Suites will become unacceptable once CEOs and COOs start to see, in trade shows and through their colleagues, the capabilities of the new
Active ERP Suites.
An exceptional example of the effectiveness of AI applied to ERP
An experience we had with a customer is a very good example of what AI can do to produce money for the end customers.
In a food processing plant in South Florida we were given a schedule built manually for a full week of production at the plant, starting on Monday morning
and ending at the end of the day on Friday.
The version of our Shop Floor Scheduler that we were running at the time (shown in the screen shot below) was able to produce a schedule that finished the
week’s workload by Tuesday at 4 PM.
In short, the scheduler was able to do the same production in less than half the time, which is equivalent to more than duplicating the plant resources and
personnel costs. Such a substantial expansion of the plant, in many cases is not even feasible, but could be achieved just by using the existing resources
in a better way.
Alternately, you could look at the benefit as having cut to less than half the most important cost components of the Items being manufactured that week.
The advantage came from using non-trivial schedules that allow more work in parallel, utilizing resources for several different Work Orders simultaneously,
while making sure that the resources will always be available when they are needed, but avoiding having “idle time” when they could be used for a different
Think of the Work Orders as puzzle pieces that need to be placed on the scheduling timetable without overlapping, because no resource can be used for more
than one Work Order at a time, but matching their jagged edges in a way that minimizes the total length of the time it takes to process all the Work Orders.
That is something that humans are not very good at, if the problem has to be solved fast and the solution reviewed frequently when conditions at the plant
change or contingencies occur.
Humans will tend to produce clear cut schedule solutions and will naturally avoid going into the complexity of trying different scheduling options where
Work Orders are processed simultaneously. Even if some combination of the puzzle pieces could be found that has great efficiency, the typical plant manager
will not look for them, and the simple reason for that is that looking for complex but optimal solutions would take an inordinate amount of time of the human
Exploring the many alternatives for placing the Work Orders is a combinatorial problem that humans are slow to solve. A plant manager would need to spend
several hours, or even days, trying many solutions that would work, to find the best one, but that time is not compatible with running the plant in real time.
Plant managers don’t have the luxury of such time availability for the scheduling task.
But as this example shows, the impact of proper scheduling goes straight to the company’s bottom line.