On Predicting Production Costs and Probable Learning Rates from Research and Development Investments by S-Curve/Learning Curve Relationships

1969 ◽  
Author(s):  
George V. Johnson
Keyword(s):  
Research And Development ◽  
Learning Curve ◽  
Production Costs ◽  
Learning Rates ◽  
S Curve

Whitney Johnson: applying the principles of disruptive entrepreneurship to talent management

2018 ◽  
Vol 46 (5) ◽  
pp. 2-9
Author(s):  
Brian Leavy
Keyword(s):  
Career Development ◽  
Learning Curve ◽  
Employee Engagement ◽  
Management Strategy ◽  
Talent Management ◽  
Disruptive Innovation ◽  
Harvard Business Review ◽  
Content Type ◽  
Entrepreneurial Management ◽  
S Curve

Purpose Whitney Johnson is interviewed about her latest book, Build an A Team: Play to Their Strengths and Lead Them Up the Learning Curve (Harvard Business Review Press, 2018), which extends her disruptive innovation perspective on career development into a talent management strategy for corporate leaders and their organizations. 10; 10; Design/methodology/approach In today’s exciting and volatile competitive context, leaders need to see that the skillful and entrepreneurial management of their talent will be at least as important to their organization’s future success as the skillful and entrepreneurial management of their financial resources. Findings Eager, capable employees, tackling new challenges are drivers of innovation within organizations, and the primary benefit of an S Curve talent management strategy is elevated employee engagement. Practical implications In terms of personal disruption, choosing market risk means being more entrepreneurial with your own career development and seeking out a distinctive learning curve. Originality/value Leaders will need to foster work environments that provide learning opportunities, stretch assignments, new challenging roles internally—not necessarily promotions, but also well-conceived lateral moves that can help to give employees the enhanced skillset to ultimately move ahead.

scholarly journals Changing Strategy by Turkey’s AKP: The Learning Curve Theory

2019 ◽  
Vol 10 (2) ◽  
pp. 148-166
Author(s):  
Syaza Shukri
Keyword(s):  
Learning Curve ◽  
Prime Minister ◽  
Party Government ◽  
Muslim Countries ◽  
Curve Theory ◽  
S Curve

Since 2014, Turkey has been moving towards a heightened sense of nationalism and populism especially after Recep Tayyip Erdogan became the first popularly elected President of Turkey in 2017. His nationalist rhetoric went up compared to when he became Prime Minister over a decade ago when the country was touted as a model of liberalism among Muslim countries. Rather than putting a damper on the party, government, or Erdogan himself, his conservative rhetoric has helped consolidate the government’s power, showcasing the shift in strategy by the Adalet ve Kalkınma Partisi (AKP) during uncertain times. This article borrows from behavioural psychology the concept of the learning curve theory or the S-curve theory to examine this shift in AKP strategy. It is argued that after reaching a political peak with the Gezi Park protest in the summer of 2013, Erdogan is employing a different rhetorical approach—a populist one—to gain more political traction.

Technology Evolution Modeling and Decision Making Under Uncertainty

2012 ◽  
Author(s):  
Jonathan L. Arendt ◽  
Daniel A. McAdams ◽  
Richard J. Malak
Keyword(s):  
Decision Making ◽  
Research And Development ◽  
Wind Turbine ◽  
Technology Development ◽  
Modeling Technique ◽  
Technology Evolution ◽  
The Future ◽  
S Curve ◽  
Evolution Modeling

Design is an uncertain human activity involving decisions with uncertain outcomes. Sources of uncertainty in product design include uncertainty in modeling methods, market preferences, and performance levels of subsystem technologies, among many others. The performance of a technology evolves over time, typically exhibiting improving performance. As the performance of a technology in the future is uncertain, quantifying the evolution of these technologies poses a challenge in making long-term design decisions. Here, we focus on how to make decisions using formal models of technology evolution. The scenario of a wind turbine energy company deciding which technology to invest in demonstrates a new technology evolution modeling technique and decision making method. The design of wind turbine arrays is a complex problem involving decisions such as location and turbine model selection. Wind turbines, like many other technologies, are currently evolving as the research and development efforts push the performance limits. In this research, the development of technology performance is modeled as an S-curve; slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. The S-curve model typically represents the evolution of just one performance attribute, but designers generally deal with problems involving multiple important attributes. Pareto frontiers representing the set of optimal solutions that the decision maker can select from at any point in time allow for modeling the evolution of technologies with multiple attributes. As the performance of a technology develops, the Pareto frontier shifts to a new location. The assumed S-curve form of technology development allows the designer to apply the uncertainty of technology development directly to the S-curve evolution model rather than applying the uncertainty to the future performance, giving a more focused application of uncertainty in the problem. The multi-attribute technology evolution modeling technique applied in decision-making gives designers greater insight when making long-term decisions involving technologies that evolve.

Investigating the learning curve in endoscopic compared to microscopic myringotomy and ventilation tube insertion

2020 ◽  
Vol 134 (6) ◽  
pp. 497-500
Author(s):  
O Denton ◽  
A Daglish ◽  
L Smallman ◽  
S Fishpool
Keyword(s):  
Learning Curve ◽  
Learning Curves ◽  
Learning Rate ◽  
Middle Ear Surgery ◽  
Ventilation Tube ◽  
Ear Surgery ◽  
Steep Learning Curve ◽  
Learning Rates ◽  
Significant Difference ◽  
Rate Of Learning

AbstractObjectiveRate of learning is often cited as a deterrent in the use of endoscopic ear surgery. This study investigated the learning curves of novice surgeons performing simulated ear surgery using either an endoscope or a microscope.MethodsA prospective multi-site clinical research study was conducted. Seventy-two medical students were randomly allocated to the endoscope or microscope group, and performed 10 myringotomy and ventilation tube insertions. Trial times were used to produce learning curves. From these, slope (learning rate) and asymptote (optimal proficiency) were ascertained.ResultsThere was no significant difference between the learning curves (p = 0.41). The learning rate value was 68.62 for the microscope group and 78.71 for the endoscope group. The optimal proficiency (seconds) was 32.83 for the microscope group and 27.87 for the endoscope group.ConclusionThe absence of a significant difference shows that the learning rates of each technique are statistically indistinguishable. This suggests that surgeons are not justified when citing ‘steep learning curve’ in arguments against the use of endoscopes in middle-ear surgery.

THE LEARNING EFFECT ON SETUP COST REDUCTION FOR MIXTURE INVENTORY MODELS WITH VARIABLE LEAD TIME

2008 ◽  
Vol 25 (04) ◽  
pp. 513-529 ◽  
Author(s):  
JASON CHAO-HSIEN PAN ◽  
MING-CHENG LO
Keyword(s):  
Learning Curve ◽  
Lead Time ◽  
Learning Effect ◽  
Total Annual Cost ◽  
Setup Cost ◽  
Demand Distribution ◽  
Solution Algorithms ◽  
Learning Rates ◽  
General Demand ◽  
The Impact

This paper investigates the impact of learning curve effect on setup cost for the continuous review inventory model involving controllable lead time with the mixture of backorder and partial lost sales. A learning curve is a well known tool which describes the relation between the performance of a task and the number of repetitions of that task. The objective of this study is to minimize the expected total annual cost by simultaneously optimizing order quantity, safety factor and lead time under different setup learning rates. There are two models considered in the paper, one with normal demand distribution and another with general demand distribution having both mean and variance known and finite. Numerical examples are presented to illustrate the procedures of the proposed solution algorithms, along with the savings on the total costs of the models with the inclusion of the learning effect on setup.

Uncertain Technology Evolution and Decision Making in Design

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Jonathan L. Arendt ◽  
Daniel A. McAdams ◽  
Richard J. Malak
Keyword(s):  
Research And Development ◽  
Pareto Frontier ◽  
Evolutionary Process ◽  
Evolutionary Model ◽  
Development Effort ◽  
Technology Evolution ◽  
Curve Model ◽  
S Curve ◽  
Over Time

The potential for engineering technology to evolve over time can be a critical consideration in design decisions that involve long-term commitments. Investments in manufacturing equipment, contractual relationships, and other factors can make it difficult for engineering firms to backtrack once they have chosen one technology over others. Although engineering technologies tend to improve in performance over time, competing technologies can evolve at different rates and details about how a technology might evolve are generally uncertain. In this article we present a general framework for modeling and making decisions about evolving technologies under uncertainty. In this research, the evolution of technology performance is modeled as an S-curve; the performance evolves slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. We extend the existing single-attribute S-curve model to the case of technologies with multiple performance attributes. By combining an S-curve evolutionary model for each attribute with a Pareto frontier representation of the optimal implementations of a technology at a particular point in time, we can project how the Pareto frontier will move over time as a technology evolves. Designer uncertainty about the precise shape of the S-curve model is considered through a Monte Carlo simulation of the evolutionary process. To demonstrate how designers can apply the framework, we consider the scenario of a green power generation company deciding between competing wind turbine technologies. Wind turbines, like many other technologies, are currently evolving as research and development efforts improve performance. The engineering example demonstrates how the multi-attribute technology evolution modeling technique provides designers with greater insight into critical uncertainties present in long-term decision problems.

scholarly journals Identifikasi Faktor-Faktor Penumpukan Realisasi Anggaran Belanja Di Akhir Tahun (Studi Kasus Pada Balai Penelitian Dan Pengembangan Lingkungan Hidup Dan Kehutanan Manado)

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Nina Sudarwati ◽  
Herman Karamoy ◽  
Winston Pontoh
Keyword(s):  
Research And Development ◽  
Financial Management ◽  
Early Years ◽  
Fiscal Year ◽  
Government Programs ◽  
Budget Planning ◽  
National Budget ◽  
Forestry Research ◽  
Development Institute ◽  
S Curve

Abstract. Implementation of the budget is a very important stage in the realization of government programs that have been organized in the National budget. The implementation sometime found difficulties, including the realization of the budget tends to be low in the early years and accumulated at the end of the year. According to Rusqiayati (2014), ideally the realization of the government's budget follows the "S-curve" which is tends to be stable at the beginning of the year, then increased in the mid and stabilized towards the end of the fiscal year. This research aims to identify factors that causing the accumutaed of budget realization in the end of the year at Environment and Forestry Research and Development Institute of Manado. This study is a qualitative case study. The key informants in this research are  officers or employees directly involved in financial management, such as the Budget Authority (KPA), Committing Officer (CO), official signing of Warrant Pay (PPSPM), Treasurer, Assistant Treasurer, and Executive Technical Officer ( PTK).The results showed that  factors that causing the accumulated of budget realization in the end of the year at Environment and Forestry Research and Development Institute of Manado  were budget planning, budget execution, procurement of goods/services, and human resources. Keywords: Accumulation, Budget realization, Thematic Analysis Abstrak Pelaksanaan anggaran merupakan tahap yang sangat peting dalam merealisasikan program dan kegiatan pemerintah yang telah disusun dalam APBN. Dalam pelaksanaan mengalami berbagai kendala, diantaranya realisasi anggaran yang cenderung rendah di awal tahun dan menumpuk di akhir tahun. Menurut Rusqiayati (2014) idealnya, realisasi anggaran pemerintah mengikuti “Kurva S” yaitu cenderung stabil di awal tahun, kemudian meningkat pada pertengahan, dan kembali stabil menjelang akhir tahun anggaran. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang menyebabkan penumpukan realisasi anggaran di akhir tahun pada Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manado. Penelitian ini menggunakan metode kualitatif studi kasus. Informan kunci penelitian ini adalah pejabat atau pegawai yang terlibat langsung dalam pengelolaan keuangan, seperti Kuasa Pengguna Anggaran (KPA), Pejabat Pembuat Komitmen (PPK), Pejabat Penandatangan Surat Perintah Bayar (PPSPM), Bendahara Pengeluaran, Pembantu Bendahara, dan Pelaksana Teknis Kegiatan (PTK). Hasil penelitian menunjukkan bahwa faktor-faktor yang menyebabkan penumpukan realisasi anggaran belanja di akhir tahun pada Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manado yaitu perencanaan anggaran, pelaksanaan anggaran, pengadaan barang/jasa, dan sumber daya manusia. Kata Kunci : Penumpukan, Realisasi Anggaran Belanja, Analisis Tematik

Life Cycle Costing for Sustainability

2016 ◽  
pp. 96-107 ◽  
Author(s):  
Mustafa Üç ◽  
Cemal Elitaş
Keyword(s):  
Life Cycle ◽  
Research And Development ◽  
Transaction Costs ◽  
Life Cycle Costing ◽  
Production Costs ◽  
Three Dimensions ◽  
Maintenance Costs ◽  
User Costs ◽  
Development Costs ◽  
Definitions Of Life

Life cycle costing supports sustainability by stressing the costs not only in the production and selling phases but also considering the costs which are incurred after the sale of the products. This chapter reveals main characteristics of life cycle costing as follows: Life cycle costing also includes production costs which are; (a) research and development costs, (b) marketing, selling, distribution and design costs, (c) and also user costs. User costs have three dimensions: transaction costs, maintenance costs, and disposal costs. Therefore one can simply assert that life cycle costing has a broader approach in the calculation of the costs compared to other conventional costing systems. After introducing the main characteristics and basic definitions of life cycle costing, this chapter will discuss the implementation of life cycle costing in comparison with other conventional costing systems. Following this, we will analyze the link between life cycle costing, waste management and sustainability. Overall review will be done in the conclusion.

A pragmatic approach to using resource loading, production, and learning curves on construction projects

1994 ◽  
Vol 21 (6) ◽  
pp. 939-953 ◽  
Author(s):  
R. Max Wideman
Keyword(s):  
Learning Curve ◽  
Construction Projects ◽  
Learning Curves ◽  
Construction Work ◽  
Productivity Improvement ◽  
Resource Loading ◽  
New Construction ◽  
The Difference ◽  
S Curve ◽  
Resource Input

The purpose of this paper is to present some rules of thumb, based on experience, for the early planning of new civil and building construction work. In such construction, resource input (men, materials, equipment, etc.) is varied according to the planned timing and availability of the work. On a well-run site, this resource loading as well as its consequent output follows a distinctive pattern within relatively narrow limits for the whole of the job. Practical considerations of why this should be so are presented.Based on experience, this paper suggests first approximation profiles for both typical resource loading and progress S-curves and shows that the difference could be due to the effects of learning. The basis for calculating the shape of the learning curve and how the application of this concept is limited on a construction site are described. The manner in which an alternative learning curve calculation can be more useful in tracking progress is demonstrated. The significance of these profiles and their relationships for improved planning and tracking of new construction work is suggested. An example of the output from a less well managed project as compared to the planned S-curve is also included. Key words: learning curve, productivity improvement, progress (production) curve, resource loading.

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