Analysis of Data-Driven Sustainability Strategy for Manufacturing

This report provides an analysis of sustainability strategies, focusing on improving energy efficiency in manufacturing systems. The report examines a data-driven approach, as proposed in a research article, which involves collecting and analyzing data on energy consumption to identify inefficiencies and optimize processes. Key factors for achieving sustainability, including automation, smart manufacturing, ICT tools, and planning/scheduling systems, are discussed. The data-driven approach involves data preparation, correlation analysis, determining efficiency frontiers, and quantifying energy efficiency potentials. The report highlights the benefits of these strategies, such as reduced resource consumption, waste minimization, and optimized operations. The report also emphasizes the role of technology and data analytics in driving future improvements in manufacturing sustainability, contributing to reduced greenhouse gas emissions and improved quality of life.

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SUSTAINABILITY STRATEGY ANALYSIS
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Table of Contents
1. Introduction.......................................................................................................................................3
2. Objective............................................................................................................................................4
3. Sustainability Factors........................................................................................................................5
3.1. Proposed data analytics approach............................................................................................5
3.1.1. Data preparation................................................................................................................5
3.1.2. Correlation analysis...........................................................................................................6
3.1.3. Determining efficiency frontiers.......................................................................................6
3.1.4. Quantifying energy efficiency potentials..........................................................................6
3.2. Automation.................................................................................................................................6
3.3. Smart manufacturing................................................................................................................7
3.4. ICT tools.....................................................................................................................................8
3.5. Planning and scheduling systems..............................................................................................8
4. Summary............................................................................................................................................9
References................................................................................................................................................11

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1. Introduction
Sustainability has become an issue of great concern in all sectors and engineering fields
over the last decade. It is now a very crucial subject for both the current and future generations
(Garetti & Taisch, 2012). In simple terms, sustainability is a concept of using less resources to
produce more products (Litos, et al., 2017). Manufacturing being a key pillar of social
development is one of the sectors that account for the largest percentage of total global energy
and materials consumption and waste production (Ocampo & Clark, 2014). As a result, the
sector contributes a large portion of greenhouse gas emissions that continue to worsen the global
climate change problem. It is estimated that manufacturing sector accounts for approximately
30% of global carbon dioxide emissions (Owodunni, 2017). Today, sustainable manufacturing is
an emerging environmental, technological, social and economic challenge to the manufacturing
industry stakeholders, government entities and academia. Manufacturers have developed
different strategies and practices to make their operations more sustainable. One of these
strategies are use of energy efficiency advances that are aimed at minimizing energy
consumption in production processes. Reducing energy consumption in manufacturing sector has
numerous environmental, economic and social benefits (Abdul-Rashid, et al., 2017); (May &
Kiritsis, 2017).
This paper analyzes an article written by Song, et al. (2018) about how data-driven
approach can be used to improve energy efficiency in manufacturing systems. Th authors are
from Singapore Institute of Manufacturing Technology and the article was presented in the 25th
CRP Life Cycle Engineering Conference held in Copenhagen, Denmark, from 30 April to 2 May
2018. According to the authors of this article, the best approach of reducing the amount of
energy consumed by a manufacturing system is to analyze the complexity, dynamics and the

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related power consumption data of various machines used in the production processes. The
authors of the article have proposed a data-driven approach comprising of the following four
steps: collecting and organizing data; carrying out correlation analysis of operations and data
consumption, performing frontier analysis of energy efficiency, and quantification of potential
energy saving (Song, et al., 2018).
Technology has a very big role to play in achieving sustainable manufacturing. The
subject area that this paper focuses on is the use of technological tools such as information and
communication technology (ICT) tools to improve energy efficiency of production processes.
This is a very essential subject in modern-day manufacturing industry because most of the
operations are going digital and are driven by data. Therefore any company aiming at increasing
sustainability of its operations should understand how to mine and use data to improve
efficiency. The improved efficiency can be in terms of reducing energy and water consumption,
and minimizing or eliminating wastage. However, this paper is only focused on improving
energy efficiency of manufacturing systems using data-driven approach.
2. Objective
The objective of this report is to critically analyze the way energy efficiency of
manufacturing systems can be improved using data-driven approach. The data-driven approach
entails collecting data on how various machines in the manufacturing systems consume energy;
using the data to design predictive models of energy consumption; and making decisions,
including upgrading existing machines, buying new ones or educating and training staff on
sustainability goals and practices, so as to reduce overall energy consumption in different levels
of production. These practices will the wider society’s future needs by optimizing design of
machine tools and their operations. As a result, the machine tools will be designed to consume

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minimal resources and avoid or minimize waste. This is important because it will mean less
extraction of natural resources, reduced greenhouse gas emissions, decreased production costs,
improved air and water quality, and improved quality of life.
3. Sustainability Factors
Below are some of the factors that are essential in achieving sustainability of manufacturing
systems
3.1. Proposed data analytics approach
The hypothesis of the article analyzed in this paper was that energy efficiency in
manufacturing systems can be significantly improved by identifying and solving energy
inefficiencies in these systems. The authors recommended a data analytics approach to
accomplish this. The four components of the proposed data analytics approach are discussed
below
3.1.1. Data preparation
The objective parameter in improving energy efficiency of manufacturing systems is the
amount of energy consumed. Sensors can be installed to measure and record digitally the amount
of energy consumed over time against the output of the machine tool (such as number of
components fabricated over that time) (Meo, et al., 2017). The input parameters of the machine,
such as operating speed, volume of materials used, utilization percentage, etc., are also recorded.
The accuracy of data collection is very essential because it will affect all other subsequent steps.
Therefore it is important to use advanced software frameworks that have been developed to
increase accuracy of collecting energy consumption data in manufacturing systems (Bauerdick,
et al., 2017).

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3.1.2. Correlation analysis
After data collection and preparation, relevant statistical tools are used to establish the
relationship between input parameters, output parameters and energy consumed. The tools make
it possible to create power prediction models and power-usage optimization models. This
information is used to determine the machine characteristics that will provide the most efficient
output.
3.1.3. Determining efficiency frontiers
This process entails use of statistical tools to determine the efficiency of various machine
tools by comparing their design specifications and objective parameter. Various graphs showing
the relationship between energy consumed and the amount of work done by the machines can
also be plotted. In simpler terms, determining energy efficiency frontiers involves establishing
the maximum energy efficiency of each machine tool used in the production process. The
efficiency frontier basically represents the highest energy efficiency that a machine tool can
attain.
3.1.4. Quantifying energy efficiency potentials
The maximum energy saving potential is determined by calculating the difference between
energy consumption at efficiency frontier and current energy consumption. The feasibility of the
proposed data analytics approach in the article analyzed in this paper was demonstrated using a
case of chiller system. Using this approach, the authors of the article found that the chiller system
has the potential of saving 8.6% energy (Song, et al., 2018).
3.2. Automation
Automation is another very essential factor of sustainability. Automation systems can be
used to monitor and control different parameters of production such as temperature, humidity,

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pressure, pH level, etc. There are three main benefits of automation systems: they reduce
resource consumption, minimize wastage, and prevent damage of equipment. These benefits
results to reduction of overall operation and maintenance costs of manufacturing systems. The
automation must be considered during design stage of the manufacturing system. The design
automation can be machine tool-oriented, system-oriented or process-oriented (Lindholm &
Johansen, 2018). Through automation, machines operate optimally and experience minimal
downtime. Automated machines are also less susceptible to errors and in case of errors, they are
very quick to error diagnosis thus avoiding misuse of energy. As a result, maximum production
is attained at the lowest cost making the process sustainable (Arcot, 2017).
3.3. Smart manufacturing
Smart manufacturing in also a factor to consider when aiming to achieve sustainability in
manufacturing. The transformation that the manufacturing industry has undergone over the past
few years cannot be overemphasized. Manufacturing processes have become more computerized,
sophisticated and automated (Kusiak, 2018). This is achieved by use of sensors, computing
platforms, data intensive modelling, simulation, control, communication technology and
predictive engineering. Smart manufacturing has been referred to as the manufacturing industry’s
fourth revolution that involves integration and use of cutting-edge information and
communication technology (ICT) tools to optimize manufacturing processes (Kang, et al., 2016).
The main characteristics of smart manufacturing include: context awareness, modularity,
heterogeneity, compositionality and interoperability. Technologies of smart manufacturing
include: intelligent control, energy efficiency, cyber security, visual technology, data analytics,
cloud manufacturing, internet of things, advanced manufacturing, additive manufacturing or 3D
printing, smart materials/parts/product and IT-based production management. Enabling factors of

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smart manufacturing include: laws and regulations, innovative training and education, and data
sharing standards and systems (Mittal, et al., 2017). In general, smart manufacturing uses real-
time data and information to make decisions automatically thus maintaining and improving
performance of manufacturing systems. This also enables less consumption of energy and other
resources.
3.4. ICT tools
Manufacturing ICT tools can be divided into four main categories: automation of the
manufacturing process, controlling production using ICT-based tools, monitoring and decision
making, and integrating information flow and production processes (May, et al., 2017). This kind
of digital tools enables proper planning of manufacturing activities thus reducing delivery time,
which helps to minimize energy consumption and resource wastage.
3.5. Planning and scheduling systems
Planning and scheduling is also very important in achieving sustainability of manufacturing
systems. Manufacturing systems are characterized by a set of individual processes. Energy
efficiency can only be achieved if these processes are integrated into a seamless production flow.
This means that all production processes have to be coordinated from start to finish. The
planning and scheduling is enabled by different advanced algorithms specially developed for this
purpose. The algorithms help in finding the best sequence of production processes that will
provide the least carbon emissions by minimizing energy consumption (Gong, et al., 2016). One
of the major advantages of planning and scheduling algorithms is reducing and increasing
production activities during peak and off-peak energy demand and usage respectively. This
significantly reduces energy costs of the production processes. The planning and scheduling is
also essential in optimizing the layout of the manufacturing facility.

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4. Summary
Sustainability is a critical issue in manufacturing sector considering the amount of energy
consumed in the industry and its contribution to greenhouse gas emissions. This report has
discussed various approaches that can be used to improve energy efficiency of production
processes so as to make manufacturing systems sustainable. The main focus was on the article
written by a group of researchers about how data-driven approach can be used to reduce energy
consumption in a manufacturing factory. In this article, the proposed data-driven approach could
reduce energy consumption of a chiller system by 8.6% thus proving to be effective in
manufacturing energy saving.
Achieving manufacturing sustainability requires combination of a variety of approaches at
different levels of the manufacturing chain. Besides data analytics approach, some of the other
factors or strategies that can be used to improve energy efficiency of manufacturing systems
include: automation, smart manufacturing, ICT tools and planning and scheduling systems.
These systems are essential in optimizing design of machine tools, operation processes of
production, facilitating seamless integration of various production processes, monitoring and
controlling production processes, and ensuring that the manufacturing systems operate optimally.
One of the common characteristics of the resource efficiency approaches is that they heavily rely
on data – they are data-driven, both in their design and operation. This means that data analytics
is very critical in improving efficiency of manufacturing systems. Even though this paper is
focused on energy efficiency, the techniques and approaches discussed can also be used to
minimize use of other resources such as water and also reduce wastage.
The various factors discussed in this paper are very essential in meeting the future sustainable
needs of wider society because they are technology-based. Modern technology has made it easier

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to collect data and use it to optimize design of machine tools and operations of manufacturing
processes. Therefore advances in technology will continue to play a big role in improving
efficiency of manufacturing systems in the future (Gershwin, 2018). These cutting-edge
technological tools will mainly use data analytics to optimize design and operation of machine
tools so as to increase resource efficiency and minimize waste.
References

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Abdul-Rashid, S., Sakundarini, N., Ghazilla, R. & Thurasamy, R., 2017. The impact of sustainable
manufacturing practices on sustainability performance: Empirical evidence from Malaysia. International
Journal of Operations & Production Management, 37(2), pp. 182-204.
Arcot, R., 2017. Automation helps manufacturing to become sustainable and energy efficient. [Online]
Available at: https://www.automationindiaexpo.com/single-post/2017/01/31/Automation-helps-
manufacturing-to-become-sustainable-and-energy-efficient
[Accessed 28 January 2019].
Bauerdick, C., Helfert, M., Menz, B. & Abele, E., 2017. A Common Software Framework for Energy Data
Based Monitoring and Controlling for Machine Power Peak Reduction and Workpiece Quality
Improvements. Procedia CIRP, 61(1), pp. 359-364.
Garetti, M. & Taisch, M., 2012. Sustainable manufacturing: trends and research challenges. Production
Planning & Control, 23(2-3), pp. 83-104.
Gershwin, S., 2018. The future of manufacturing systems engineering. International Journal of
Production Research, 56(1-2), pp. 224-237.
Gong, X., De Pessemier, T., Joseph, W. & Martens, L., 2016. A generic method for energy-efficient and
energy-cost-effective production at the unit process level. Journal of Cleaner Production, 113(1), pp.
508-522.
Kang, H. et al., 2016. Smart manufacturing: Past research, present findings, and future directions.
International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), pp. 111-128.
Kusiak, A., 2018. Smart manufacturing. Internatonal Journal of Production Research, 56(1-2), pp. 508-
517.
Lindholm, J. & Johansen, K., 2018. Is Design Automation a Feasible Tool for Improving Efficiency in
Production Planning and Manufacturing Processes?. Procedia Manufacturing, 25(1), pp. 194-201.
Litos, L. et al., 2017. A Maturity-based Improvement Method for Eco-efficiency in Manufacturing
Systems. Procedia Manufactruing, 8(1), pp. 160-167.
May, G. & Kiritsis, D., 2017. Business Model for Energy Efficiency in Manufacturing. Procedia CIRP, 61(1),
pp. 410-415.
May, G., Stahl, B., Taisch, M. & Kiritsis, D., 2017. Energy management in manufacturing: From literature
review to a conceptual framework. Journal of Cleaner Production, 167(1), pp. 1464-1489.
Meo, I., Papetti, A., Gregori, F. & Germani, M., 2017. Optimization of energy efficiency of a production
site: a method to support data acquisition for effective action plans. Procedia Manufacturing, 11(1), pp.
760-767.
Mittal, S., Khan, M., Romero, D. & Wuest, T., 2017. Smart manufacturing: Characteristics, technologies
and enabling factors. Journal of Engineering Manufacture, 1(1), pp. 1-19.
Mousavi, S. et al., 2016. An integrated approach for improving energy efficiency of manufacturing
process chains. International Journal of Sustainable Engineering, 9(1), pp. 11-24.

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Ocampo, L. & Clark, E., 2014. Developing a framework for sustainable manufacturing strategies
selection. DLSU Business & Economics Review, 23(2), pp. 115-131.
Owodunni, O., 2017. Awareness of Energy Consumption in Manufacturing processes. Procedia
Manufacturing, 8(1), pp. 152-159.
Song, B., Ao, Y., Xiang, L. & Lionel, K., 2018. Data-driven Approach for Discovery of Energy Saving
Potentials in Manufacturing Factory. Procedia CIRP, 69(1), pp. 330-335.