8. Select Technologies

Current methods of solving the problem or controlling the process provide an important window into both the challenges with the use case and the best way to solve it. In this module, you'll learn to identify promising solution elements from the current control methods and match challenges to key design elements.

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Learning Objectives

By the end of this module, you will be able to:

• Analyze current control methods and their strengths and weaknesses

• Identify challenges that make the process difficult to control and their implications for solution design

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Video Lesson

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Practice Activity

Instructions

Read the enzyme reactor case study.

In your SME interview, you learned a few new facts about the process. Consider these facts and then answer the questions. Read on to check your answers.

Questions:

• What are the current methods for controlling the process?

• What challenging phenomena is the SME describing?

• What solution design elements could help with those phenomena?

SME:

"We have a few different guys who work on this and they all have their own things that they do. There are PID controllers that are doing the actual adjustments, but my guys are setting the set points."

"There are a few actions that all impact the system in different ways, that's part of why it's tricky to control. We have the flow rate into the tank, and then we can blow in oxygen, which also affects the temperature, and we can send in acid or base which changes the pH but also affects the level in the tank. Did I mention that it's a lot to keep track of? Some operators are a lot better than others and we've never been able to really automate this."

"We have a bunch of sensors that we're always watching, especially the temperature, pH, oxygen, and the total level in the tank. One thing we don't know is . . . we know how many microbes are in the tank from the level. But some of them are alive and some are dead at any given point and we don't have a way to know that until we test the batch later in the lab."

Current Methods

Challenges

Solutions

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Check Your Understanding

Read the production scheduling use case and answer the following questions.

For questions 1-2: Currently, the bakery is scheduled by an algorithm that prioritizes first cakes, then cupcakes, and then cookies up to a set number of each product based on the day of the week.

1. What control method does this represent?

   1. Control theory

   2. Optimization

   3. Heuristic rules

   4. Human operator

Check your answer

The answers is C. The rule “make cakes first, then cupcakes, then cookies” is a classic example of a rules-based or heuristic control system. It encodes human expertise into simple if/then logic.

1. What are likely limitations of this control method as a means to maximize profit?

   1. It can’t respond to dynamic changes in demand

   2. It doesn’t produce equal numbers of each product

   3. It ignores the bakers’ preferences and individual skills

   4. It trades off against high stakes events

Check your answer

The answer is A. Because heuristic rules are fixed, they can’t adjust to shifts in conditions. For example, if demand for cookies suddenly spikes during the holidays, the algorithm will still produce the same proportions based on its preset rules. This lack of adaptability is one of the key weaknesses of rules-based systems: they are effective for stable environments but fail in dynamic, high-variability systems.

1. Which of these represents the challenging phenomenon of different scenarios in the production scheduling use case?

   1. b. A mixer that is slower than the others because of a broken part

   2. The design of the atmospheric distillation unit

   3. The three phases of the production process (mix, bake, decorate)

   4. High demand for cookies during the holiday season

Check your answer

The answers is D. Different scenarios occur when conditions fundamentally change, requiring the system to operate under different rules or priorities. In this case, seasonal demand spikes represent distinct operating modes. The bakery’s control system must be able to recognize and adapt to these scenarios, a key motivation for orchestration strategies where different agents specialize in different conditions

1. One of the bakers is known to occasionally eat one of the cookies she is decorating, leading to a smaller batch size than expected. Which challenging phenomenon does this represent?

   1. Incomplete information

   2. Changing conditions

   3. Noise

   4. High-stakes events

Check your answer

The answer is C. Noise refers to random or unpredictable variations in system signals or performance, such as inaccurate sensor readings or inconsistent human actions. The missing cookies create data noise because the actual output doesn’t match the expected result. Systems designed with learning-based control can filter or adapt to noisy input, recognizing that occasional inconsistencies shouldn’t trigger overreactions

1. Which of these would be a high-stakes event in the context of autonomous control of bakery scheduling?

   1. A pipe bursts, flooding the production floor

   2. A post about their cupcakes goes viral and everyone has to have one

   3. A baker discovers a way to speed up the decorating process

   4. The cost of an ingredient doubles

Check your answer

The answer is B. A high-stakes event is one with large consequences that require immediate and accurate response that differs from standard control strategy. In this example, a viral trend creates a surge in demand that could make or break the day’s profitability. The system must detect and adapt to such events quickly to avoid missed revenue or reputation damage.

1. What design element could be included to address demand spikes as a high-stakes event?

   1. A benchmark that puts fluctuations in a broader context

   2. A learning algorithm that optimizes for the KPI

   3. An orchestration pattern that includes high-stakes events

   4. A perception module that makes predictions based on dynamic data

Check your answer

The answer is D. Perception modules trained to interpret dynamic data can help address high-stakes events. In this case, a machine learning model could analyze social media activity, online orders, or sales trends to predict demand surges before they happen. The perception agent would then inform decision-making agents to adjust production scheduling accordingly.

1. One of the challenges facing the bakery is that some human schedulers are much more skilled than others. What deployment method would address this by helping the novice operators to upskill?

   1. Decision-support

   2. Closed-loop autonomous control

   3. Optimization algorithms

   4. Funnel states

Check your answer

The answer is A. Decision-support deployment keeps humans “in the loop” rather than fully automating the process. In this setup, the AI system provides recommendations or insights that guide human schedulers, allowing less experienced operators to learn from expert-like feedback over time.