This work focuses on quantifying resources in an ever-increasing era of customization and erratic demand

Omar, Mohammad Milad

English

OHIO Open Library (Ohio University)

Resource Level Estimation Under Probabilistic Demand EnvironmentIndustrial and Systems Engineering. Mohammad Milad OmarYusuf ZakariaDr. Omar AlhawariIntroductionMethodologyEquationsAssumptionsData tablesSimulationResultsIndustry 3.0 is characterized by high product volume, product variety andshort delivery time. With mass customization on the rise, the ability toquickly react to response in changing customer demands, or marketconditions is crucial to the competitiveness of industries.This study is focused on formulating the best manufacturing design formeeting variable product demand for a fashion jewelry manufacturingcompany based on historical demand data collected across distributioncenters using data analysis tools.• 10  products of interest to the manufacturer were collected and analyzed on basis of their production processes.• Resource availably, reliability and efficiency are considered in determining the actual capacity required.• Resource levels (number of machines) were estimated, and design alternatives were considered.• Viability of meeting demand was simulated to corroborate the optimal design.11   Resource Level     = Ni iNii iii ii iD TDTC SCRD BTD BTC S  == → =  → →  →  01020304050607080P01 P02 P03 P04 P05 P06 P07 P08 P09 P10Number of UnitsProductsNumberCreated NumberDestroyed050100150200250300M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12Number of UnitsMachinesNumberEntered NumberExited•Based on the simulation runs, considering flowshopperspective of an individual product, bottleneckmachine with the highest processing time determinesthe throughput of each production line.Consequently, high average WIP for the finishedproduct is resulted as shown in Figure 3.•Considering jobshop perspective where each producthas a different route in the system, less average WIP isobtained for each machine department as shown inFigure 4. This proves the validity of the model for ajobshop.•On average, based on simulation results in Figure 5, themachine units utilized are closer to the ones calculatedin Table 3.i. Machines efficiency and reliability is estimated 90%.ii. Continuous processing with insignificant waiting/transfer time.iii. One shift per a day (8 hours/shift), 50 weeks per a year.iv. Capacity available per year is 2000 hours.Table 1: Products annual demand and their probability. Table 2: Operations/machines in the system studied.• 10 products were modeled proportionally from a data table.• Products were routed to appropriate nodes based on processing stepsoutlined in Table-3.• OptQuest selected the scenario with an exponential arrival time withmean of 5hrs and number of entities per arrival 3 for maximumthroughput.• Simulation was run for 24 hours.• Sample path for P-1 and P-7 shown in Figure 1.Figure 2: Job floor simulation runTable 3: Product process and capacity estimation.ConclusionThe process layout/job shop design as calculated wasselected for reduced WIP in the system. The bottleneckissue in the flowshop perspective dictates thethroughput of each line; however, the impact of thebottleneck can be minimized by duplicating machines;however, this is not a cost-effective method.ReferencesYin, Y., Stecke, K. E., & Li, D. (2017). The evolution of production systems from industry 2.0 through industry 4.0. International Journal of Production Research, 56(1-2), 848–861. https://doi.org/10.1080/00207543.2017.1403664 Alhawari, O. I., Süer, G. A., & Bhutta, M. K. (2021). Operations performance considering demand coverage scenarios for individual products and products families in supply chains. International Journal of Production Economics, 233, 108012. https://doi.org/10.1016/j.ijpe.2020.108012  Annotations  CR Capacity required (hours) 𝐷𝑖  Demand for product 𝑖 (annual)  𝑇𝑖  Processing time product 𝑖 (hours) 𝐵𝑇𝑖  Bottleneck processing time for product 𝑖  (hours) C Capacity available for machines (annually)  η Machine reliability λ System efficiency θ Number of machines considering flow shop β Number of machines considering job shop Figure 1: Supply chain networkProduct Demand ProbabilityP-1 7524 0.114P-2 1400 0.021P-3 5800 0.088P-4 1900 0.029P-5 11700 0.177P-6 9700 0.147P-7 8190 0.124P-8 4600 0.070P-9 1110 0.017P-10 14000 0.212Operation Number Operation Machine Number1 Finding M12 Deburring M23 Casting M34 Degating M45 Tumbling M56 Buffing M67 Chain Platting M78 Manual Platting M89 Barrel Platting M910 Dying M1011 Carding M1112 Packaging M12Product M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 BN CR θP-1 0.1 0.6 0 0 1.2 0 0 0 0 0 0.4 0.7 1.2 9029 6P-2 0.3 0.7 0 0 0 0 0 1 0 0 0.2 0.4 1 1400 1P-3 0.5 0.9 0.3 0.85 1.4 1.1 1.6 1.4 0 0 0 0 1.6 9280 6P-4 0.2 0.6 0.1 0.7 1.5 0.5 1.1 0.8 0 0 0 0 1.5 2850 2P-5 0.1 0.3 0.2 0.9 0 1.3 0.8 0.6 0 0 0 0 1.3 15210 9P-6 0.3 0.8 0 0 1 0 0 0 0 0 0.5 0.9 1 9700 6P-7 0 0 0 0.6 1.4 0 0 0 1 0.3 0.5 0 1.4 11466 7P-8 0 0 0 1 1.2 0 0 0 0.7 0.2 0.4 0 1.2 5520 3P-9 0 0 0 0.7 0 0 0 0 0.4 0.1 0.5 0 0.7 777 1P-10 0.3 0.8 0 0 1 0 0 0 0 0 0.2 0.8 1 14000 9Exp. D 12732 34324 4270 27081 60685 22540 20730 18060 11854 3488 17430 25757β 8 21 3 17 37 14 13 11 7 2 11 16Figure 3: Product throughputFigure 4: Machine throughputFigure 5: Capacity analysis0510152025303540M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12Capacity UnitsAverage MaximumThis study is significant in calculating the number of machines considering the flowshopand jobshop perspectives. To meet the demand for all products, jobshop perspectiverequires more machines. Generally, this study helps decision makers in such industry tominimize the investment cost which will help them compete well in their market.

Cost and Resource Level Consideration Under Probabilistic Demand Environment.

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Cost and Resource Level Consideration Under Probabilistic Demand Environment.

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