The document discusses human factors in automated systems, optimized production technology, and modeling, optimizing, and simulating manufacturing systems. It provides details on: - Considering human factors like workload and situational awareness when designing automated systems. - Optimized Production Technology (OPT), which aims to maximize throughput through balanced flow and minimizing bottlenecks. - Modeling manufacturing systems, products, and capabilities to improve efficiency. Optimization aims to find the most cost-effective performance given constraints. - Using simulation to analyze and obtain information on manufacturing systems, such as parts production, inventory levels, and equipment utilization.

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Unit IV: Factory of the future automated systems - Human factors in automated systems, optimized
production technology, Modelling, optimizing and simulation of manufacturing systems
Human factors in automated systems
Advanced automated systems require changes in the nature of the human factors questions to be asked and
ultimately answered. If the human is to be a significant and contributing factor in the system (effectively
"in the loop"), it is critical to optimize the human and machine interface in order to keep the human aware
of system operations (and, conversely, to keep the machine aware of human operations). The advancement
and sophistication of modern technology affords increased automation of functions, while concurrently
requiring considerable ingenuity to prevent increased human workload and opportunity for errors by the
new automated system. Recent technological innovations, if judiciously chosen and implemented, may
allow for remarkable and welcomed benefits. However it must be emphasized that there may also be less
beneficial human factors consequences. Awareness and prevention of potential human factors
consequences should ultimately influence the decision-making process in automated systems design. If
human factors implications are not considered, potential benefits of progressive automation may be
unrealized.
Automation: The Human Factors Issues
When designing a system or process that will include automation it is important to also design the
human-machine (automation) interaction as well. Research into the impact of automation on the
human roles and responsibilities can support the design of automatic systems and hopefully avoid the
following issues (if you can think of any other human factors issues due to automation please let me
know):
 Changes in the roles of personnel – May result in different tasks that are harder to perform.
 Increase in complexity – Personnel will need to understand how the automation works to
enable them to monitor and supervise.
 Changes in procedures – May result in additional procedures should the automation fail.
 Machine interaction – Internal relationships and inter-dependencies between functions
results in unexpected machine behaviours.
 Poor feedback – The system does not communicate what it is doing effectively.
 Situational awareness – Personnel need to remain aware of what the automation is doing.
 Workload and skill – Workload switches to monitoring tasks, skill levels can reduce if
replaced by automation.
 Trust and complacency – As automation tends not to fail, personnel develop a trust which
can result in complacency or an over reliance on automation on the other hand operators
must have confidence in the automation.

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 Human in the loop – If humans are responsible for the outcomes, humans must be in
command and monitor the system.
 Human error – Automation brings with it different types of human errors.
 Potential change to workplace location.
 Change in workplace layout and environment.
 Increased need for asset management.
Optimized production technology
OPT (Optimized Production Technology): A computer based management system used for production
planning and scheduling, seeking the goal of throughput maximization through the
following:–
Balanced flow, not capacity
– Minimization and /or elimination of bottlenecks
– Variable lot sizes
Also know as: “Theory of Constraints”
 OPT's objective is to simultaneously raise throughput while reducing inventory and operating costs,
and achieve a smooth, continuous flow of work in process.
Optimized Production Technology (OPT) is a computerized production planning and scheduling tool
developed by Creative Output of Milford, Connecticut, USA. OPT realized that a detailed schedule and
detailed shop floor feedback where only required for the "bottleneck" processes and that "non-
bottleneck" processes can and should be slaved to these. Furthermore, OPT follows a set of principles
called the "Theory of Constraints" (TOC). This theory is focusing attention on the capacity constraints
or bottleneck parts of the operation. By identifying the location of constraints, working to remove them,
then looking for next constraints an operation always focusing on the part that critically determine the
pace of output. A constraint is defined as anything that prevents a system from achieving high
performance relative to its goal.
OPT Philosophy
The Goal: To Make Money
Measures:
1. Net Profit
2. Return on Investment
3. Cash Flow
 The Base Rules of OPT
OPT is based on a set of ten related rules, which principally focus on managing bottleneck and non-
bottleneck resources. The ten rules are as follows:-
1. Utilization and activation of a resource are not synonymous. There is no gain from running a non-
bottleneck machine if its output will only build up inventory in front of a bottleneck.

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2. The level of utilization of a non-bottleneck is not determined by its own potential, but by some other
constraint in the system. The utilization of a non-bottleneck is limited by the rate of the bottleneck
machine.
3. An hour lost at the bottleneck is an hour lost for the total system. This rule parallels and extends rule
number 1, and helps managers focus on all activities at the bottleneck.
4. An hour saved at a non-bottleneck is a mirage. Throughput will not increase with savings at a non-
bottleneck. Therefore, managers should focus improvement efforts elsewhere. The time spent by a job
at a bottleneck is compared of set-up and processing time, while the time spent at a non-bottleneck
includes set-up, processing, and idle time. Reducing the set-up time at a bottleneck saves time for the
entire system. But, reducing set-up time at a non-bottleneck may increase idle time.
5. The bottleneck governs the throughput and inventory in the system. Inventory should be used
carefully so that the bottleneck is never face a lack of parts to process.
6. The transfer batch size should not necessarily equal the production batch size. When a large batch
being run on a non-bottleneck just prior to a bottleneck then it would be desirable to get it started on
part of the batch (This part called "transfer batch"), even through the non-bottleneck is still processing
the reminder. The use of different sized transfer batches is called "lot streaming".
7. The production batch size should not be the same from stage to stage in the process. Lot sizes at
bottlenecks should, in general, be larger than at non-bottleneck, so that less time is lost to set-ups. Of
course, the small batches from the non-bottleneck need to arrive at the bottleneck in the time to be
rejoined into a large batch.
8. Capacity and priority should be considered simultaneously. Because the lead time for a given batch
depends on the priority given to it at a machine, and on the capacity of the machine, priority rules
should be determined in conjunction with the capacity of the machine. In fact, the capacities at all
constrained resources should be considered.
9. Balance flow, not capacity. The flow through the plant should equal market demand.
10.The sum of local optima is not equal to the optimum of the whole. Problems develop when
supervisors at bottleneck, supervisors at non-bottlenecks, and marketing personnel all optimize for their
own goals. Many supervisors try to run their equipment at full capacity, while many marketing
personnel try to make bigger profits by selling more at the end of the quarter.
 The Mechanism of OPT
OPT produce production plans and detailed schedules using the following four basic modules:-
1. Buildnet: This module creates a model of the manufacturing facility using data on work center
capabilities, routings, Bill of Materials, inventories, and sales forecasts. This model is in the form of
network.
2. Serve: The model of the workshop is run through an iterative process to determine bottlenecks in the
system. Serve is similar to MRP in its workings, and one of its outputs is a load profile for each of the
resources in the model. The most heavily utilized resource could produce a bottleneck in the system
and must be examined carefully. Sometimes, rescheduling work from the heavily utilized machine to
some other alternate to machine may produce satisfactory results.

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3. Split: The network model of the shop floor is divided into two parts: critical resources and non-
critical resources. The bottleneck operation and all other operations follow it in the order of
manufacturing process up to the customer orders are included in the critical resources portion of the
network. The remaining portion of the network includes non-critical resources.
4. Brain: The operations in the critical resource portion of the network are scheduled using module
called the Brain of OPT. This module determines production and transfer lot sizes and the timing of
production for each product for the bottleneck operations. Its output is fed to serve module, which then
produces the entire production plan.
 Weaknesses of OPT
Although, OPT attempts to overcome the weakness of the early MRP systems of taking no account of
the finiteness of shop floor capacity, but practically OPT also have its own disadvantages. The main
disadvantage of OPT is the concept of shifting bottlenecks. When the production volume and the mix
of products are known, we can find out the bottlenecks in a system. But practically, the "aggregate
planning" exercise is done at least over several months and the volume of the mix may change from
one week to another. Different volumes or mixes can lead to different bottlenecks when that happen,
this means that the bottleneck is shifting. It is not clear how OPT handle this dynamic situation because
it relies on a clearly identified stationary bottleneck.
From other side, OPT focuses on bottleneck machines and ignores others during the planning horizon.
Thus, OPT provides a plan for a production system that approximates the actual production system. In
order for an OPT plan to work, it is necessary to have plenty of non-bottleneck resources. When the
cost of non-bottleneck resources are not small, the OPT plan may have a high cost. This restricts the
usefulness of OPT for cases when non-bottleneck resources are expensive
Comparison

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Modelling, optimizing and simulation of manufacturing systems
Modelling
A model serves as a container of information for communication between a sender and receiver.
The purpose of modelling and simulation of manufacturing sequences is to improve efficiency in
product design and process planning. This will lead to the economic manufacturing of high-quality
products with the greatest possible productivity.
 Modelling manufacturing systems
A manufacturing system can be modelled as a product since the difference is determined of the usage
whereby the type of information is the same. Modelling a manufacturing system involves a relation to a
product domain. It is also stated that there is a third domain connected – the process domain. Modelling
a manufacturing system can consequently be divided into three domains:
 Product domain.
 Process domain (a manufacturing process)
 Resource domain (a manufacturing system)
 Modelling products
Since manufacturing systems from a modelling aspect, are closely related to products it is of interest to
evaluate different types of product models. A product model can be classified into four different types
of models.
Structure-oriented product models: Emphasizing the structure of a product.For example an assembly
structure or variant structure.
Geometry-oriented product models: Emphasizing the geometrical representation of a product. For
example a solid model in a CAD system.
Feature-oriented product models: Emphasizing the semantic signification of a product. A feature-
oriented product model works as an extension to a geometry-oriented model where the functions of the
geometric model can be described. For example maximum load capacity of a vehicle, where the
maximum load volume is related to the geometry of the vehicle.
Knowledge-oriented product models: Emphasizing the human knowl- edge of the product in terms of
constraints or guidance. For example how a product within a product family is allowed to be composed
with regards to a set of rules.
Modelling manufacturing system capability embraces all described models in order to be related to the
process and product domain.
Optimization
Finding an alternative with the most cost effective or highest achievable performance under the given
constraints, by maximizing desired factors and minimizing undesired ones

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Production optimization is the practice of making changes or adjustments to a product to make it
more desirable. A product has a number of attributes. For example, a soda bottle can have different
packaging variations, flavors, nutritional values. It is possible to optimize a product by making minor
adjustments. Typically, the goal is to make the product more desirable and to increase marketing
metrics such as Purchase Intent, Believability, Frequency of Purchase, etc.
Five factors to optimize their complex manufacturing operations:
1. Taking advantage of revenue opportunities
2. Tuning up operations and processes optimizing
3. Utilizing ERP across the enterprise
4. Finding harmony among diverse applications
5. Coming to grips with this complexity.
Simulation in manufacturing systems
Simulation in manufacturing systems is the use of software to make computer models of
manufacturing systems, so to analyze them and thereby obtain important information. It has been
syndicated as the second most popular management science among manufacturing managers. However,
its use has been limited due to the complexity of some software packages, and to the lack of preparation
some users have in the fields of probability and statistics.
Simulation is an important technique used by an analyst to validate or verify
suggested improvements for manufacturing processes and to verify that suggested conditions or
configurations satisfy design specifications.
This technique represents a valuable tool used by engineers when evaluating the effect of capital
investment in equipment and physical facilities like factory plants, warehouses, and distribution
centers. Simulation can be used to predict the performance of an existing or planned system and to
compare alternative solutions for a particular design problem.
The most important objective of simulation in manufacturing is the understanding of the change to the
whole system because of some local changes. It is easy to understand the difference made by changes
in the local system but it is very difficult or impossible to assess the impact of this change in the overall
system. Simulation gives us some measure of this impact. Measures which can be obtained by a
simulation analysis are:
 Parts produced per unit time
 Time spent in system by parts
 Time spent by parts in queue

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 Time spent during transportation from one place to another
 In time deliveries made
 Build up of the inventory
 Inventory in process
 Percent utilization of machines and workers.
Some other benefits include Just-in-time manufacturing, calculation of optimal resources required;
validation of the proposed operation logic for controlling the system, and data collected during
modelling that may be used elsewhere.
The following is an example: In a manufacturing plant one machine processes 100 parts in 10 hours but
the parts coming to the machine in 10 hours is 150. So there is a buildup of inventory. This inventory
can be reduced by employing another machine occasionally. Thus we understand the reduction in local
inventory buildup. But now this machine produces 150 parts in 10 hours which might not be processed
by the next machine and thus we have just shifted the in-process inventory from one machine to
another without having any impact on overall production
Simulation is used to address some issues in manufacturing as follows: In workshop to see the ability of
system to meet the requirement, to have optimal inventory to cover for machine failures.
The following is a list of popular simulation techniques:[10]
1. Discrete event simulation (DES)
2. System dynamics (SD)
3. Agent-based modelling (ABM)
4. Intelligent simulation: based on an integration of simulation and artificial intelligence (AI)
techniques
5. Petri net
6. Monte Carlo simulation (MCS)
7. Virtual simulation: allows the user to model the system in a 3D immersive environment
8. Hybrid techniques: combination of different simulation techniques.
The concept of organizing the factory into sub-factories with the capability to produce a
technology group is called cellular manufacturing.
Group technology is the analysis of processing operations with the goal of determining the
similarity of the processing functions and, hence, the grouping of the associated parts for
production purposes.