Analysis of Critical Factors for Automatic Measurement of OEE

The increasing digitalization of industry provides means to automatically acquire and analyze manufacturing data. As a consequence, companies are investing in Manufacturing Execution Systems (MES) where the measurement of Overall Equipment Effectiveness (OEE) often is a central part and important reason for the investment. The purpose of this study is to identify critical factors and potential pitfalls when operating automatic measurement of OEE. It is accomplished by analyzing raw data used for OEE calculation acquired from a large data set; 23 different companies and 884 machines. The average OEE was calculated to 65%. Almost half of the recorded OEE losses could not be classified since the loss categories were either lacking or had poor descriptions. In addition, 90% of the stop time that was classified could be directly related to supporting activities performed by operators and not the automatic process itself. The findings and recommendations of this study can be incorporated to fully utilize the potential of automatic data acquisition systems and to derive accurate OEE measures that can be used to improve manufacturing performance

A model for linking shop floor improvements to manufacturing cost and profitability

Manufacturing units in the so called high-cost countries are struggling under fierce competition on the global market. In order to survive, the factory needs to generate profit to its owners. Profitability can be reached in many different ways apart from only lowering the employees' salaries. It can be improved through increased profit margins (sales in relation to costs) or with an increased capital turnover rate. Finding ways to free capacity and to improve flexibility in order to increase sales is often more interesting to the manufacturing companies than cutting the direct salary costs. A model for analysing profitability of a manufacturing unit is proposed. It is found on a production system analysis and combines in-depth production engineering analysis with economical accounting analysis of the factory. The manual work tasks are of special interest and the productivity of selected bottleneck work areas are analysed thoroughly. The model is intended for use by two industrial analysts during a one-week study. Simulation of different improvement scenarios is carried out and presented to the factory management at the end of the profitability study. A software implementation is required in order to generate the model, collect data and make simulation within the intended time. The implementation is made in spread sheet software using Visual Basic to program interfaces and automatic functions. The primary area of application is the electronics industry in Sweden where the model is used in a research project to strengthen the competitiveness of that industry

Object-oriented Modeling of Manufacturing Resources Using Work Study Inputs

Resources are the core of manufacturing models. They provide information about the people and equipment that perform activities on the shop floor. Comprehensive representations of equipment are common but human resources are often defined to a very limited extent. This paper presents how work study data can be applied as input to detailed modeling of human manufacturing resources. The purpose is to provide a valid representation of manual work tasks on a shop floor level. If implemented in manufacturing models the valid representation will contribute to improve planning, control and execution of production. It also facilitates and encourages production improvement initiatives

Productivity measurement and improvements: A theoretical model and applications from the manufacturing industry

This article concerns productivity improvement at factory floor level, i.e. at station, cell, or line level. At many companies, workers associate productivity or efficiency increase with something negative, it is interpreted as an increase in speed and the “sweat factor”. These misconceptions and lack of knowledge tend to put “a wet blanket” on all attempts to increase productivity in good times and productivity improvements are only discussed when the company is bleeding and at that time it’s often too late. For these reasons it’s important to clarify what productivity is and especially how it can be improved. In general, the productivity at shop-floor level can be improved through im-proving the method, increasing the performance, and increasing the utilization. The design of the products and the amount of scraped products also affects the productivity in both manual tasks as well as work performed by machines. These aspects of productivity will be elaborated in the theoretical model and the industrial applications presented in this article

Three essays on transport CBA uncertainty

Cost Benefit Analysis (CBA) has for a long time been used in transport planning, but it is often questioned. One main argument against CBA is that the results depend largely on assumptions regarding one or a few input factors, as for example the future fuel price or valuation of CO2 emissions. The three papers included in this thesis investigate some aspects of uncertainty in transport CBA calculations. The two first papers explore how changes in input data assumptions affect the CBA ranking of six rail and road investments in Stockholm. The first paper deals with the effect of different land-use assumptions while the second deals with the influence of economic growth, driving cost and public transport fare. The third paper investigates how alternative formulations of the public transport mode choice and route choice affect travel flows, ticket revenues and consumer surplus. These are important factors previously known to affect CBA results. The findings of the first two papers suggest that CBA results are robust concerning different land-use scenarios and single input factors. No change in rank between a road and a rail object is observed in the performed model calculations, and only one change between two road objects. The fact that CBA results seem robust regarding input assumptions supports the use CBA as a tool for selecting transport investments. The results in the third paper indicate that if there is detailed interest in, for example, number of boardings and ticket income from a certain transit line, or the total benefit of a price change, a more detailed formulation of the public transport mode choice and route choice will provide more reliable results. On the other hand, this formulation requires substantially more data on the transit line and price structure than the conventional formulation used in Swedish transport planning, especially in areas with many different pricing systems.QC 20150414</p

Three essays on transport CBA uncertainty

Cost Benefit Analysis (CBA) has for a long time been used in transport planning, but it is often questioned. One main argument against CBA is that the results depend largely on assumptions regarding one or a few input factors, as for example the future fuel price or valuation of CO2 emissions. The three papers included in this thesis investigate some aspects of uncertainty in transport CBA calculations. The two first papers explore how changes in input data assumptions affect the CBA ranking of six rail and road investments in Stockholm. The first paper deals with the effect of different land-use assumptions while the second deals with the influence of economic growth, driving cost and public transport fare. The third paper investigates how alternative formulations of the public transport mode choice and route choice affect travel flows, ticket revenues and consumer surplus. These are important factors previously known to affect CBA results. The findings of the first two papers suggest that CBA results are robust concerning different land-use scenarios and single input factors. No change in rank between a road and a rail object is observed in the performed model calculations, and only one change between two road objects. The fact that CBA results seem robust regarding input assumptions supports the use CBA as a tool for selecting transport investments. The results in the third paper indicate that if there is detailed interest in, for example, number of boardings and ticket income from a certain transit line, or the total benefit of a price change, a more detailed formulation of the public transport mode choice and route choice will provide more reliable results. On the other hand, this formulation requires substantially more data on the transit line and price structure than the conventional formulation used in Swedish transport planning, especially in areas with many different pricing systems.QC 20150414</p

Syntetisk befolkning med hushållsinformation – som markanvändningsdata i transportmodellerna

Dagens svenska transportmodeller bygger på individdata från skattning till tillämpning. Samtidigt är det väl känt att vissa beslut av stor betydelse för ett hushålls transporter styrs av dess sammansättning, inkomster och boende. Exempel på sådana beslut är bilinnehav och antal resor som utförs av individer som tillhör hushållet. Det finns därmed skäl att utveckla indata till våra modeller så att hushållsinformation finns med i modellernas tillämpningsdata. I rapporten redogörs för hur man kan göra en syntetisk befolkning med hushållsinformation knuten till individer. I hushållsdimensionen hanteras hushållen med avseende på antal vuxna och antal personer <20 år i hushållet, med totalt 15 hushållstyper. Uppdelningen görs separat för småhus och flerfamiljshus. I ett andra steg skapas en syntetisk befolkning. Här utgör hushållstyp ett villkor så att varje zon fylls av individer som bygger upp den hushållsstruktur som beräknats i föregående steg. Andra fördelningar som metoden tar hänsyn till är efter kön, ålder, inkomst och antal förvärvsarbetande. Allt är implementerat i prototypprogramvara för att göra arbetsgången repeterbar och spårbar.Hushållsprognoser och syntetisk befolkning som indata till Samper

Why is there a mismatch between operation times in the planning systems and the times in reality?

There is often a substantial difference between operation times in reality on the factory shop-floor and in the company’s planning and control system. This difference has several severe consequences for the company’s daily operation in terms of meeting delivery dates and utilizing the available resources in an efficient way, as well as more long term and strategic consequences for the company’s business. Offers to customers and investment decisions are based on the operation times in the planning system. There are three principal causes for the gap: Operation times are not set in a correct way from the outset, extra allowance time to handle temporary disturbances tends to become permanent and accumulate, and the fact that operation times once set in the planning system seldom are updated. The root cause for these three deficiencies is quite likely the management’s unawareness of the situation