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Record W2346928446

Pengoptimalan Sistem Pengendalian Persediaan Pipa Kecil General Market di PT Bakrie Pipe Industries

2016· article· id· W2346928446 on OpenAlex
Kadek Dwika Yundarani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJurnal Ilmiah Universitas Bakrie · 2016
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsAgricultural scienceSafety stockOperations managementEngineeringBusinessEnvironmental scienceMarketingSupply chain
DOInot available

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk membuat model sistem persediaan pipa kecil general market beserta batasan-batasannya yang menghasilkan biaya persediaan minimum. Adapun batasan-batasannya meliputi minimum order bahan baku, kapasitas pengiriman bahan baku, kapasitas penyimpanan gudang bahan baku dan barang jadi, safety stock, dan kapasitas produksi. Penelitian ini menggunakan simulasi menggunakan data historis selama tahun 2014-2015 dan kemudian digunakan sebagai acuan proyeksi untuk tahun 2016. Berdasarkan simulasi yang dijalankan ditemukan bahwa metode ini dapat menghemat biaya persediaan hingga 38% dan 64% di tahun 2014 dan 2015 berturut-turut. Kata Kunci: persediaan, minimum order, safety stock, simulasi, PT Bakrie Pipe Industries. This research aims to create a model model of inventory system of general market small pipe including the constraints involved. The constraints are minimum order of raw materials, raw material delivery capacity, warehouse capacity of raw materials and finished goods, safety stock, and production capacity. This study uses a simulation using historical data over 2014-2015 and then used as a reference projection for 2016. According to simulations, it is found that this method can save inventory costs up to 38% and 64% in 2014 and 2015 respectively. Keywords: inventory, minimum order, safety stock, simulation, PT Bakrie Pipe Industries. DAFTAR PUSTAKA Abraham, B., & Ledolter, J. (2005). Statistical Methods for Forecasting. New Jersey: John Wiley & Sons, Inc. Bedworth, D. D., & Bailey, J. E. (1982). Integrated Production Control System. Canada: John Wiley & Sons, Inc. Chopra, S., & Meindl, P. (2007). Supply Chain Management: Strategy, Planning, and Operation. New Jersey: Pearson Education, Inc. Chung, C. A. (2004). Simulation Modelling Handbook: A Practical Approcah. Danvers: CRC Press. Elsayed, E. A., & Boucher, T. O. (1994). Analysis and Control of Production System. New Jersey: Prentice-Hall. Hicks, P. E. (1994). Industrial Engineering and Management: A New Perspective. New York: McGraw-Hill, Inc. Jacobs, F. R., & Chase, R. B. (2014). Operations and Supply Chain Management. New York: McGraw-Hill Education. Liker, J. K. (2006). The Toyota Way. Jakarta: Erlangga. Tee, Y. S., & Rosetti, M. D. (2001). Using Simulation to Evaluate a Continuous Review (R, Q) Two-Echelon Inventory Model. Proc. the 6th Annual International Conference on Industrial Engineering. San Francisco. Yaffee, R. A., & McGee, M. (2000). Introduction to Time Series Analysis and Forecasting: with Applications of SAS and SPSS. New York: Academic Press.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.201
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it