Impact of Just in Time in Manufacturing

CHAPTER I

INTRODUCTION

Introduction to the Problem

Statement of the Problem: It had been proved from time and again the positive impact of Just in Time in manufacturing. No models or methodologies have been developed to relate how predictive maintenance can have a significant effect on the performance of JIT in manufacturing and its supply chains.

In early 1950’s Toyota devised their manufacturing system called Toyota production system which streamlines the entire process of manufacturing in an organized way through continuous information sharing between supplier and customer to achieve just- in- time production. Just-in-time is one of the major pillars of Toyota production system. Implementation of lean principles gave way for various strategic advantages in manufacturing. (Lathin, 2001) stated that using lean principles, a traditional mass producer could expect a reduction of 90% in inventory, cost in quality, lead time and 50% increase in labor productivity. (Nystuen, 2002) stated that one could see a reduction of 90% in travel time, 82% in inventory and 11% in product lead time. After the success of Toyota production system,   although this production system revolutionized the entire process of manufacturing in Japan, it did not reflect the west. This is due to many reasons such as traditionally minded management (Gupta and Jain, 2013), lack of machine capability, high inventory, fluctuating markets (Golhar, Stamm, and Smith, 1990), high product variety (Cusumano, 1994) and lack of communication between processes. One of the biggest key suspects understands machine capability. This can be achieved by filling the gap between machine capability information and production planning. To achieve this system, there are 2 key elements; Real- time machine data and data processing. P.O’Donovan, K. Leahy, K. Bruton and D.T.J. O’Sullivan (2015) presented a concept called smart manufacturing where manufacturing data can be used create positive impact on the manufacturing operations

The first industrial revolution began in early 1800’s through mechanical production using steam and water. Since then, there has been two other industrial revolutions through assembly line production for mass production, increase quality, reduce cost and manufacturing time; and using technology and IT systems. Currently the manufacturing industry is in the midst of data driven revolution transforming traditional manufacturing facilities into smart manufacturing facilities (Peter O’Donovan, Kevin Leahy, Ken Bruton and Dominic T. J. O’Sullivan, 2015). Many industry Pundits today believe; we are currently undergoing fourth industrial revolution through internet technology in manufacturing.

Machine reliability has always played an important role for manufacturing. Over time machines have become smarter and are capable of collecting their performance as feedback. It has always been a challenge to fix the machines during downtime and machines technicians are also required to keep themselves updated on latest technologies. (Jay Lee, Hung-An Kao, Shanhu Yang, 2014) suggested that machines could be connected together in a cyber-workspace where, machine data could be collected and later analyzed using predictive tools for machine predictability. Connecting the machines through cyberspace enables managers to monitor every machine’s performance remotely without visiting every machine during the day.

Significance of the Research

Research Questions and Objectives

Implementation of predictive maintenance has been a buzzword for some time in Internet of Things (IoT) neighborhood. In the recent years, many companies have been implementing predictive maintenance activities it to their advantage in order to achieve machine failure free environment. There has been a lot of case studies published in the recent times on implementation of predictive maintenance activities with results closer to machine failure free operation. Most of research in predictive maintenance in recent times have focused on different methodologies and algorithms implemented in data mining, classification and prediction in order to achieve failure free operation. In the course of literature review it was found that, there has been a lack of research in studying the effect of implementation of predictive maintenance activities throughout manufacturing supply chains. This research study is conducted to answer some of the questions in an industry environment such as (1) What was effect in product flow by implementing predictive maintenance activities? (2) How were the supply chains impacted by the implementation of predictive maintenance activity (3) Was there any effect on the performance of Just-in-time manufacturing? (4) If so, what factors were affected and by how much? (5) Can a model estimate the effect on the performance of Just-in-time in manufacturing before the implementation of predictive maintenance activity?

This research study is conducted to answer these questions by collecting and mining data from current manufacturing setup and its supply chains, applying new methods to analyze it and use traditional regression models to predict the performance change in Just-in-time in manufacturing.  The objectives in this research includes

1. The development of a methodology for measuring performance variance in Just-in-time for an industry environment and throughout its supply chains by implementing Predictive maintenance activity.
2. The identification of Just-in-time performance measurement factors that would have significant effect in predicting the performance before implementation of predictive maintenance activity
3. The creation, verification and validation of a model that could estimate the performance variance in Just-in-time for future implementations throughout the supply chain

CHAPTER II

Literature review

Overview

JIT in Manufacturing

Machine Maintenance

All actions appropriate for retaining an item/part/equipment in, or restoring it to, a given condition is known as maintenance (Dhillion, 2002). Each year US manufacturing industry spends about $300 billion on plan maintenance and operations. It is also estimated that approximately 80% of the industry budget goes towards correcting chronic failures of machines, systems and peoples (Latino, 1999). There are 2 types of machine maintenance and are classified as follows. Planned maintenance is generally classified as preventive (PM) and corrective maintenance, while breakdown maintenance is considered as unplanned. Preventive maintenance can be further subdivided into fixed maintenance and predictive maintenance. (Mansor, Ohsato, Sulaiman, 2012).

• Unplanned Downtime

The unscheduled maintenance or repair to return items/equipment to a defined state and carried out because maintenance persons or users perceived deficiencies or failures is known as corrective maintenance (Dhillion, 2002).

• Planned Downtime

There are many definitions to preventive maintenance. All actions carried out on a planned, periodic, and specific schedule to keep an item/equipment in stated working condition through the process of checking and reconditioning is known as preventive maintenance (Dhillion, 2002). In the recent years, PM has been one of the most sought techniques in industries across different areas. One of the main objectives of PM is to keep the machine in running condition through standard inspection methods and correction methods at early deficiency stages. Performing PM activities has some of the advantages such as increasing equipment availability, reduction of overtime, reduction in inventory, improve safety, improve quality, reduces time and cost (Levitt, 1997). Some of the disadvantages of PM are it increases initial cost, damaging equipment, reduces life of parts and using more number of newer parts (Patton, 1983).

• Fixed maintenance
• Predictive maintenance

Similar to preventive maintenance, predictive maintenance have several definitions. To some workers, predictive maintenance is monitoring the vibration of rotating machinery in an attempt to detect incipient problems and to prevent catastrophic failure or it is monitoring the infrared image of electrical switchgear, motors or other electrical equipment to detect developing problem (Mobley, 2002). According to Dhillion (2002), predictive maintenance is a method of using modern measurement and signal processing methods to accurately diagnose item/ equipment condition during operation. It would not be wrong to say, Predictive maintenance is a complement of preventive maintenance which uses various testing and measuring methods to monitor the equipment status and predict the machine failures.

According to Mobley (2002), there are five nondestructive techniques used for predictive maintenance management: vibration monitoring, process parameter monitoring, thermography, tribology, and visual inspection.

Predictive maintenance not just limited to manufacturing sectors used various other industry such as water and wastewater utility solutions (Severn Trent Services), Transportation – railway (Finnish railway VR Group), Power grids (Israel Electric corporation), Oil and gas industry, wind power (Roland Berger Strategy Consultants, 2014), Airline industry (IBM, 2014), Biotech industry (Cypress Envirosystems, 2008) and many more. Some of the case studies related to manufacturing would be discussed in later part of this report.

Case Studies

1. KALYPSO: Predictive analytics and Improved Product design with machine learning
2. Daimler: Automotive manufacturer increases productivity for cylinder-head production by 25 percent
3. IBM Asset Analytics for Manufacturing Equipment in Automotive
4. Israel Electric Corporation moves towards smarter maintenance
5. Fluke Corporation: White Paper: Thermography
6. Roland Berger: Oil and gas – Reducing breakdowns and increasing production of highly critical assets
7. Roland Berger: Wind Power – Reducing maintenance costs and improving uptime in a challenging operational environment
8. ABB Group: Predictive Maintenance for Heavy Industry

Data collection, Data mining and Predictive maintenance methodologies

1. Data Collection
2. Sensor data
3. Historical data
4. Data mining Techniques
5. Signal Processing and Feature Extraction

Principle Component Analysis (PCA) based fault detection

1. Predictive Maintenance methodologies
2. Health Assessment

Self-organizing map (SOM)

2. Performance Prediction
3. Health Diagnosis

Self-organizing map (SOM)

References

• Latino, C.J., Hidden Treasure: Eliminating Chronic Failures Can Cut Maintenance Costs up to 60%, Report, Reliability Center, Hopewell, Virginia, 1999
• M.A. Mansor, A. Ohsato and S. Sulaiman, KNOWLEDGE MANAGEMENT FOR MAINTENANCE ACTIVITIES IN THE MANUFACTURING SECTOR, International Journal of Automotive and Mechanical Engineering, SSN: 2229-8649 (Print); ISSN: 2180-1606 (Online); Volume 5, pp. 612-621, January-June 2012
• Levitt, J., Managing preventive maintenance, Maintenance technology, February 1997, 20-30.
• Mobley, R Keith, An Introduction to predictive maintenance, 2002, 2^nd ed, ISBN 0-7506-7531-4