Welcome to Module 4.

In this module, we'll be discussing the importance of measuring

the performance of supply trends.

We will also discuss decision analysis methods and techniques

that facilitate decision-making through the entire product life cycle.

Briefly, we will introduce new manufacturing paradigms originated from data analytics

and discuss the inter-security concerns in advanced manufacturing environments.

Finally, we will give an overview of potential solutions to address these concerns.

Upon completion of this module, we will be able

to participate in a discussion about the need for developing decision analysis tools

to measure the performance of supply chain.

State at least one challenge of our advancement in manufacturing systems

and state at least one solution to data security concerns.

In the first lesson,

we discuss the importance of measuring the performance of an integrated enterprise

and highlight the need to have a set of decision making tools

that use data collected in different enterprise levels throughout the life cycle.

We will discuss the importance of measuring the performance

of an integrated enterprise using decision analysis tools and life cycle data.

We will start this lesson with asking this question

- why do we need to measure the performance of an enterprise?

Measures affect actions and a strategist within an enterprise.

It is said that what you measure is what you get or the third becomes what it measures.

Measuring the performance from different perspectives

gives insight about different aspects of the business.

Just measuring the financial performance of the value chain particularly

if only from a single perspective lacks the required valued import

that decision makers need to properly manage an integrated enterprise.

Flexible performance measurement - I'm not saying

that short performance measurement can have positive affects on advanced manufacturing.

For example, customer satisfaction measurements help develop better services for consumers

and more likely resource and it's driving a sustainable business.

Lifecycle and environmental assessments

can help develop more environmentally conscious products and services.

Different measures evaluate various aspects of an enterprise.

Measuring multiple viewpoints of different stakeholders evaluates different dimensions

of an enterprise.

Success of an integrated enterprise requires good performance of all integrated units

and therefore intense performance measurement from different perspectives.

In order to effectively evaluate the enterprise performance,

it is necessarily to collect life cycle data.

Life cycle data covers all stages of the life cycle

and the needs of different stakeholders.

What should be measured?

In the past, leadership of companies focus on measuring cost as an indicator of success.

The company's investments, were decided often based on the final cost of the product

and related cost consequences.

If an option was expected to lower the cost imposed to the consumer,

that was considered a good choice in itself.

If the consideration increased cost to consumers

often the option would be taken off the table.

As opposed to reducing the price tag of a product,

this cost reduction mindset later turns them into reducing the total cost of ownership.

This allows for a consideration of all aspect of a product life cycle not just price.

The total cost of ownership,

or TCO, was introduced to address all direct and indirect costs of a product or a system.

The total cost of ownership considered the maintenance

or usage cost of a product for consumers.

For instance, not only is the purchase of a car an expense

but the required fuel and other operational costs are necessarily

and should be considered when making an investment in buying a car.

This type of total cost of ownership consideration may not always be a sufficient metric.

The cost reduction measuring system may not be the best option even

by measuring the total cost of ownership.

Let's consider telecommunications cost.

The cost of telecommunication for end users

has been significantly increased over the years.

Therefore, a cost reduction perspective would suggest they use old school,

land line forms instead of using the smartphones.

However, new technologies have offered numerous new scopes

and features such as texting, video communications, location services, and ride sharing.

This would indicate that cost reduction is not the best measure for a company's success.

What is?

Most recently successful enterprises focus on value creation rather than cost reduction.

Value creation is focused on actions

that increase the worth of a product, service, or company.

Innovation can occur from a value creation perspective

which would not be logical from an economic cost reduction perspective.

Let's think about what value should be measured.

You have stock market value of the company, balance sheets, cash flow.

The choice of the appropriate measurement depends on situations.

Like considering how rare is that the value should consider

the needs of all the stakeholders.

Let's shift to talking specifically about the impact on advanced manufacturing.

Advanced manufacturing systems are equipped with hundreds of sensors.

This censors facilitate the collection of a huge amount of data known as Big Data

and provide opportunities for improving the performance of manufacturing processes.

We should know that Big Data Technologies

are shifting from data collection to data analysis and outcome.

The value of data collection and analysis depends on the amount of decision support

that they provide to various stakeholders involved in the supply chain.

Also, the progress in data collection technologies on certain devices is impressive.

The progress in decision making using data collected

from those devices is less significant.

While data analysis techniques, such as statistical methods,

facilitate identification of patterns and trends in data,

decision making tools must be used to harness the full potential of available knowledge.

Data analysis matters how a lot of application in, in housing manufacturing operations.

Enterprises can develop a manufacturing performance dashboard

to utilize data collected from their processes to make improvements.

For example, data analysis can be used in warehouses to have good inventory management.

Companies can optimize their product pricing based on data mining

from consumer preferences or predict product consumption

of manufacturing plants based on production volume.

They may also use data mining techniques to optimize supplier selection decisions.

Currently manufacturing systems are dealing with several data related challenges.

They include poor quality data, the lack of a central system that combines operational

and decision making data, and the lack of data analysis techniques.

Let's conclude with talking about the importance of visualization of data.

It has been reported that visual representation of data

and knowledge is an important approach that can be used to software decision making.

3D displays, immersive completing technologies, and augmented reality

are examples of tools and techniques

that have been developed to support data visualization

Infrastructure Performance

Advanced Manufacturing Enterprise

Meet the Instructors