Revealing the potential value of combining the Theory of Constraints (TOC) with Artificial Intelligence (AI)

AI, as a genetic name for computerized tools, capable of learning from past data for taking independent decisions, or supporting decisions, is becoming the key buzz word for the future of technology to change the world. 

However, there are quite a lot concerns regarding the ability of AI not just to improve our life, but maybe also to cause considerable damage.

I strongly believe that the Theory of Constraints (TOC) brings rational judgement and a tendency to look for the inherent simplicity, revealing the true potential of seemingly complex and uncertain situations.  Can the qualities of TOC significantly improve the potential of AI to bring value to the management of organizations?

The emphasis of TOC on identifying the right focus for achieving the best performance, which also means what not to focus on, is based on recognizing the capacity limitation of our mind.

Can AI significantly help in exploiting better the human capacity limitation?

All human beings should guide their mind to focus on what truly matters.  In managing organizations achieving more of the GOAL, now and in the future, provides the ultimate objective for assessing what to focus on right now.  No matter how cleverly we identify what truly matters some important matters might be missed.  One of the neglected areas in TOC, which no manager can afford to ignore, is to identify, as early as possible, emerging threats.

Computers are also limited with their ability to process huge amount of data – but their limitation is way above the human’s and that big gap is widening more and more.  So, can we hope that while the top objective is defined by the human manager, clever use of software, particularly AI, could constantly check the validity of the current focus and warn whenever a new critical issue emerges?

AI is widely used to replace human beings doing simple straight-forward actions, like using robots in large distribution centers.  Driving cars without a human driver is a more ambitious target, but it is also something that the vast majority of the human beings do well (when they are not under influence).   The current managerial emphasis on the use of AI is to reduce the ongoing cost of employing workers for simple enough jobs.  It’d be good to show that AI could support the substantial growth of throughput and even enhance strategic decision making.

The special power of AI is its ability to learn from huge amount of past data items.  This means it can also be trained to come up with critical decision-support information, based on observed correlations between variables, noting trends and sudden changes in the behavior of the market demand, suppliers, and flow blockages.  So, instead of making the AI module the decision maker of relatively simple decisions, it can be used for improving the performance of organizations.  A natural first target is improving the forecasting algorithms, highlight also the reasonable possible spread. The ability to identify correlation could show dependencies between various SKUs, and that would significantly improve the forecasts.  The more challenging tasks are to provide information on the potential impact of price changes, and other critical characteristics of the offerings, on the market.  Another worthy challenge is to highlight irregularities that require immediate management attention.  From the TOC BOK perspective it would be valuable to evaluate the effectiveness of the buffers better than it is done today.  Working with AI could indirectly be used to improve the intuition and even the thinking of open mind managers!  If the human manager would be able to use the AI to validate, or invalidate, assumptions and hypotheses, this will have a considerable impact on the quality of management to evaluate the ramifications of changes.

One important downside of AI, especially from a TOC perspective, is not considering logical cause-and-effect.  Being able to check cause-and-effect hypotheses is a key mission.  Another downside is the dependency on the training data, which could lead to erroneous results.  A key challenge of implementing AI is finding the way to reduce the probability of a significant mistake and be able to spot such a mistake through cause-and-effect analysis.

The process aiming at using AI most effectively starts with the GOAL, then deriving the key elements that impact it, and then deduce worthy valuable objectives.  The list of the valuable objectives, which would enhance the performance of the organization, should be analyzed to find out whether AI, maybe together with other software modules, can overcome the obstacles that currently prevent the achievement of these objectives.

A key generic idea is to recognize the potential of AI providing vital information, or even new observed insights, as an integral part of the human decision-making process. 

Setting the worthy objectives, guiding the AI to bring the supporting and necessary information, is where TOC wisdom can be so useful for drawing the most from AI.  Suddenly the title of “The Haystack Syndrome – Sifting Information out of the Data Ocean” finds wider meaning when the data ocean has grown by several orders of magnitude, but also the technology for making the best sense from it.

While computers in general, and AI in particular, are vastly superior in handling complexity, meaning many different variables that interact with each other, the tougher challenge is to face uncertainty, both the ‘noise’, the inherent common and expected variations, and the risks, which are rarer, yet highly damaging.

Here comes the opportunity of using the emerging power of AI, combined with the TOC wisdom, to support the assessment of future moves. 

Guiding the AI to observe predicted trends in the market, especially the impact of external forces, like changes in the economy, and even predicting the effect of increasing or reducing prices, could yield major value to the decision makers.  Much of the relevant data required for such missions lies in external databases.  It is possible that services for obtaining the data from various external sources would be required.  It’d be good if a cooperation between competitors to allow AI analysis of their combined data is achieved and carried by a neutral third party.  Such cooperation should ensure that no internal data of one company will ever leak to another company.  But the outcome of the analysis, highlighting issues like price sensitivity, the impact of inflation, changes in government regulations, and many others, could yield knowledge that is currently hidden, leaving the decisions to be based solely on intuition.  The key disadvantage of human intuition is being slow to adapt to changes.  Feeding the AI with huge number of similar past changes making it much better in predicting the outcomes, as long as there is enough relevant data that wasn’t made irrelevant by the change.

My current thinking about the effective use of AI for managerial decision-making, including the critical question ‘what to focus on’, is that there are two focused categories of targets for TOC-AI processes that would bring huge value to managing any organization:

1. Sensing the market demand.  This includes forecasting current trends, and predicting the potential outcomes of certain moves and changes.  Plus giving good idea of the impact of price, economy and the variety of choice.
   • Warning:  Don’t accept one-number forecasts!  The AI output should translate the result of the statistical analysis as a reasonable range, or a confidence interval, as defined by the statistical module. See my article https://elischragenheim.com/2021/04/08/forecasts-the-need-the-great-damage-and-using-it-right/
2. Pointing to an emerging threat.  The TOC wisdom could easily yield a list of potential threats that management should be aware of as early as possible.  There is a need to identify signals, observed in the recent past, that testify that a certain threat is developing.  Giving enough examples to the AI could trigger the ongoing search for enough evidence.
   • For instance, when an important supplier starts to behave erratically, it could point to problems with its management, even the possibility of bankruptcy, or that our image at a client is going down.  Similarly, a change in the quantities, and/or frequency, of the purchasing orders of a big client, could signal a change in the client’s purchasing policies.
   • One of the problems of complex environments is the accuracy of the data.  If the AI module intentionally looks for outcomes that don’t fit the data, then notifying the user to check specific data items could be meaningful.
   • An existing example is monitoring the need for maintenance of machines.  This is an Industry 4.0 related feature that identifies when the current pace and quality of the machine deviates from the norm, before it becomes critical, leaving enough time to plan the necessary maintenance activities.

Critical questions for continued discussion:

• Are there more generic organizational topics where AI, when guided by TOC, can contribute to management?
• Can we come to a generic set of insights on how TOC can impact the objectives, training, and the actual use of AI?
  • For instance, guiding the AI to thoroughly check the quality of the capacity consumption data of the constraint and the few other critical resources. Comparing them to the capacity requirements of the past and incoming demand could help in determining whether the available protective capacity is adequate.
• How can we make it happen?
  • And what training the people using the AI module should go through?