The need to seek a balance between installed capacities and demand is an imperative for all companies. To respond to this demand, various proposals have been formulated, among which the Theory of Constraints stands out, although the application of these methodologies is always limited by financial availability. This research shows the results of the application of a methodological proposal applied in a pharmaceutical laboratory that allows managing the increase in physical or market constraints, as appropriate, also considering the incidence of capacities in supply chain management. To achieve the proposed objective, programming in MATLAB R2019a is used. The systematic application of the methodology allowed an optimal use of financial availability, an increase in the capacity utilization and an increase in the profits generated during the process.
Aguilar-EscobarV. G., Garrido-VegaP., & González-ZamoraM. D. M. (2016). Applying the theory of constraints to the logistics service of medical records of a hospital. European Research on Management and Business Economics, 22(3), 139–146. https://doi.org/10.1016/j.iedee.2015.07.001
2.
AliR., & DeifA. (2016). Assessing leanness level with demand dynamics in a multi-stage production system. Journal of Manufacturing Technology Management, 27(5), 614–639. https://doi.org/10.1108/JMTM-08-2015-0064
3.
AndradeA. M., Del RíoC. A., & AlvearD. L. (2019). A study on time and motion to increase the efficiency of a shoe manufacturing company. Información Tecnológica, 30(3), 83–94. https://doi.org/10.4067/S0718-07642019000300083
4.
ApostolovM., ScagnelliS. D., & VasileL. (2022). Global supply chains: European Union, euro area and new member states. Global Business Review, 23(4), 871–889. https://doi.org/10.1177/0972150919890216
5.
ArcelusF. J., PakkalaT. P. M., & SrinivasanG. (2018). Inventory replenishment for profit maximization over a finite horizon under one-time cost changes. Global Business Review, 19(Suppl. 3), S235–S248. https://doi.org/10.1177/0972150918758099
6.
AzadniaA. H., GhorbaniM., & ArabzadS. M. (2015). A simulation study on bullwhip effect in supply chain based on theory of constraint. In DavimJ. (Ed.), Research advances in industrial engineering. management and industrial engineering (pp. 77–87). Springer. https://doi.org/10.1007/978-3-319-17825-7_5
7.
AzzamouriA., BaptisteP., DessevreG., & PellerinR. (2021). Demand driven material requirements planning (DDMRP): A systematic review and classification. Journal of Industrial Engineering and Management, 14(3), 439–456. https://doi.org/10.3926/jiem.3331
8.
BanerjeeA., & MukhopadhyayS. K. (2016). A contemporary TOC innovative thinking process in the backdrop of leagile supply chain. Journal of Enterprise Information Management, 29(3), 400–431. https://doi.org/10.1108/JEIM-08-2014-0086
9.
ChiangW.-C., UrbanT. L., & LuoC. (2015): Balancing stochastic two-sided assembly lines. International Journal of Production Research, 54(20), 6232–6250. https://doi.org/10.1080/00207543.2015.1029084
10.
FarM. T., HaghighiM., & JafariM. A. (2022). Designing a strategic human resource planning model: A case of Export Development Bank of Iran. Global Business Review. https://doi.org/10.1177/09721509221124173
11.
FazlollahtabarH. (2017). Supply chain management models: Forward, reverse, uncertain, and intelligent foundations with case studies. CRC Press. https://doi.org/10.1201/b22492
12.
FreemanR. E. (2010). Strategic management. A stakeholder approach. Cambridge University Press.
13.
Fundora-MirandaA., Taboada RodríguezC., CuellarH., Urquiaga RodríguezA., & Sánchez LaraA. (1987). Organización y planificación de la producción II. ISPJAE.
14.
García VidalG. (2006). Constribución Epistemológica a las Ciencias Administrativas[PhD thesis]. Universidad de Oriente.
15.
GoldrattE. M., & CoxJ. (2014). The goal: A process of ongoing improvement (30th Anniversary ed.). North River Press.
16.
Gonzalez-BenitoJ., Suárez-GonzálezI., & González-SánchezD. (2022). Human resources strategy as a catalyst for the success of the competitive strategy: An analysis based on alignment. Personnel Review, 51(4), 1336–1361. https://doi.org/10.1108/PR-04-2020-0254
17.
González-SánchezD., Suárez-GonzálezI., & Gonzalez-BenitoJ. (2018). Human resources and manufacturing: Where and when should they be aligned?International Journal of Operations and Production Management, 38(7), 1498–1518. https://doi.org/10.1108/IJOPM-07-2017-0393
18.
HamtaN., Fatemi GhomiS.M.T., JolaiF., & Akbarpour ShiraziM. (2013). A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99-111. https://doi.org/10.1016/j.ijpe.2012.03.013
19.
KazemiA., & SedighiA. (2013). A cost-oriented model for balancing mixed-model assembly lines with multi-manned workstations. International Journal of Services and Operations Management, 16(3), 289–309. https://doi.org/10.1504/IJSOM.2013.056765
20.
KiranD. R. (2019). Theory of constraints. In KiranD. R. (Ed.), Production planning and control (pp. 399–407). Butterworth-Heinemann. https://doi.org/10.1016/C2018-0-03856-6
21.
KomalavalliD., & PadmaT. (2021). Swarm intelligence-based task scheduling algorithm for load balancing in cloud system. International Journal of Enterprise Network Management, 12, 1–16. https://doi.org/10.1504/IJENM.2021.112669
22.
KovačevićS. (2005). Work and time study in operating several looms. Melliand International, 11(5), 112–116.
23.
KucukkocI., LiZ., KaraoglanA. D., & ZhangD. Z. (2018). Balancing of mixed model two-sided assembly lines with underground workstations: A mathematical model and ant colony optimization algorithm. International Journal of Production Economics, 205, 228–243. https://doi.org/10.1016/j.ijpe.2018.08.009
24.
LandM. J., ThürerM., StevensonM., FredendallL. D., & ScholtenK. (2021). Inventory diagnosis for flow improvement—A design science approach. Journal of Operations Management, 67(5), 560–587. https://doi.org/10.1002/joom.1133
25.
LyonnetB., PralusM., PilletM., & HabchiG. (2016). A new way to identify critical resources by combining human and machine data. International Journal of Business Excellence, 9(3), 271–292. https://doi.org/10.1504/IJBEX.2016.075591
26.
MarkovK. M. (2009). Functional-value management (FVM) of business processes of textile production using marketing indicators. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 3, 5–7.
27.
MezentsevaL. V., KurkovK. N., ArsakhanovaZ. A., MartynenkoM. V., TakmakovaE. V., & GusevaT.G. (2018). Human resource management strategy: Implementation features, prerequisites, and effective results. Espacios, 39(31), 10.
28.
MousavipourS., FarughiH., & AhmadizarF. (2019). A job shop scheduling problem with sequence-dependent setup times considering position-based learning effects and availability constraints. International Journal of Industrial Engineering and Production Research, 30(3), 329–340. https://doi.org/10.22068/ijiepr.30.3.329
29.
NegahbanA., & SmithJ. S. (2016). The effect of supply and demand uncertainties on the optimal production and sales plans for new products. International Journal of Production Research, 54(13), 3852–3869. https://doi.org/10.1080/00207543.2016.1157274
30.
NiebelB.W., & FreivaldsA. (2001). Métodos, estándares y diseño del trabajo (10ma ed.). Alfaomega.
31.
PanizzoloR. (2016). Theory of constraints (TOC) production and manufacturing performance. International Journal of Industrial Engineering and Management, 7(1), 15–23.
32.
PrigogineI. (1997a). El fin de las certidumbres. Santillana, S.A.
33.
PrigogineI. (1997b). ¿Tan solo una ilusión? Una exploración del caos al orden. Tusquets Editores, S.A.
34.
PucheJ., PonteB., CostasJ., PinoR., & de la FuenteD. (2016). Systemic approach to supply chain management through the viable system model and the theory of constraints. Production Planning and Control, 27(5), 421–430. https://doi.org/10.1080/09537287.2015.1132349
35.
RakshitB. (2021). Evaluating profitability and marketability efficiency: A case of Indian commercial banks. Global Business Review, 22(4), 977–995. https://doi.org/10.1177/0972150918822569
36.
RamosB., AlvesC., & Valério de CarvalhoJ. (2022). An arc flow formulation to the multitrip production, inventory, distribution, and routing problem with time windows. International Transactions in Operational Research, 29(1), 526–553. https://doi.org/10.1111/itor.12765
37.
Rodriguez SernaL., NakandalaD., & BowyerD. (2022). Stakeholder identification and prioritization: The attribute of dependency. Journal of Business Research, 148, 444–455. https://doi.org/10.1016/j.jbusres.2022.04.062
38.
Romero-RojasJ. D., Ortiz-TrianaV. K., & Caicedo-RolónÁ.Jr. (2019). Theory of constraints and optimization as management tools for production scheduling. An application in furniture industry. Revista de Metodos Cuantitativos para la Economia y la Empresa, 27, 74–90.
39.
SanguriK., & MukherjeeK. (2021). Forecasting of intermittent demands under the risk of inventory obsolescence. Journal of Forecasting, 40(6), 1054–1069. https://doi.org/10.1002/for.2761
40.
SchragenheimE., DettmerH. W., & PattersonJ. W. (2009). Supply chain management at warp speed: Integrating the system from end to end. Auerbach Publications. https://doi.org/10.1201/9781420073362
41.
ShafieeF., KazemiA., Jafarnejad ChaghooshiA., SazvarZ., & Amoozad MahdirajiH. (2021). A robust multi-objective optimization model for inventory and production management with environmental and social consideration: A real case of dairy industry. Journal of Cleaner Production, 294, 126230https://doi.org/10.1016/j.jclepro.2021.126230
42.
SimonH. A., & MarchJ. G. (2009). Teoría de la organización. Ariel.
43.
SudtachatK. (2020). Dynamic programming model for cellular manufacturing layout under demand uncertainty[Conference Presentation]. 2020 9th International Conference on Industrial Technology and Management (ICITM), Oxford, 11–13 February 2020. https://doi.org/10.1109/ICITM48982.2020.9080361
44.
TambeP. P. (2021). A genetic algorithm approach for scheduling jobs on identical parallel machines[Conference Presentation]. Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management, Singapore, 7–11 March 2021.
45.
TanhaieF., RabbaniM., & ManavizadehN. (2020). Simultaneous balancing and worker assignment problem for mixed-model assembly lines in a make-to-order environment considering control points and assignment restrictions. Journal of Modelling in Management, 15(1), 1–34. https://doi.org/10.1108/JM2-04-2018-0052
46.
Tibben-LembkeR. S. (2009). Theory of constraints at UniCo: Analysing the goal as a fictional case study. International Journal of Production Research, 47(7), 1815–1834. https://doi.org/10.1080/00207540802624003
47.
ToumehA. A. (2022). The influence of enterprise resource planning system implementation on accrual-based earnings management: Empirical evidence from Jordan. Global Business Review. https://doi.org/10.1177/09721509221116692
48.
TripathiS., & TalukderB. (2020). Supply chain performance and profitability in Indian automobile industry: Evidence of segmental difference. Global Business Review. https://doi.org/10.1177/0972150919898302
49.
TrojanowskaJ., & DostatniE. (2017). Application of the theory of constraints for project management. Management and Production Engineering Review, 8(3), 87–95. https://doi.org/10.1515/mper-2017-0031
50.
TsaiF.-S., CabriloS., ChouH.-H., HuF., & TangA. D. (2022). Open innovation and SME performance: The roles of reverse knowledge sharing and stakeholder relationships. Journal of Business Research, 148, 433–443. https://doi.org/10.1016/j.jbusres.2022.03.051
51.
TuY.-M., & ChenH.-N. (2009). Capacity planning with sequential two-level time constraints in the back-end process of wafer fabrication. International Journal of Production Research, 47(24), 6967–6979. https://doi.org/10.1080/00207540802415568
52.
TustinD.H., & StrydomJ.W. (2003). Measuring the impact of advertising and promotion expenditure at national, industry and company levels in South Africa. SAJEMS, 6(3), 627.
53.
VoronkovaO. V., SemenovaY. E., LukinaO. V., PanovaA. Y., & OstrovskayaE. N. (2018). Assessment of the influence of human factor on the working process effectiveness as a factor for improving the efficiency of production management at industrial enterprises. Espacios, 39(48), 25.
54.
VottoR. G., & FernandesF. C. F. (2014). Lean production and theory of constraints: Proposal of a method for joint implementation in the Engineer-to-Order Capital Goods Industry. Gestao e Producao, 21(1), 45–63. https://doi.org/10.1590/S0104-530X2013005000009
55.
VrontisD., ThrassouA., & CzinkotaM. R. (2011). Wine marketing: A framework for consumer-centred planning. Journal of Brand Management, 18(4-5), 245–263. https://doi.org/10.1057/bm.2010.39
56.
WangJ., HouK., & ZhuX. (2022). Does sticky inventory management improve productivity?Journal of Manufacturing Technology Management, 33(2), 355–377. https://doi.org/10.1108/JMTM-05-2021-0184
57.
WilsonJ. M. (2016). The origin of material requirements planning in Frederick W. Taylors planning office. International Journal of Production Research, 54(5), 1535–1553. https://doi.org/10.1080/00207543.2015.1092616
58.
WolniakR. (2021). The concept of operation and production control. Production Engineering Archives, 27(2), 100–107. https://doi.org/10.30657/pea.2021.27.12
59.
WuY. M., DaiL.Z., & LuoL. F. (2018). Evolution balancing of mixed model-assembly line considering task reassignment. Journal of Advanced Manufacturing Systems, 17(1), 3–21. https://doi.org/10.1142/S0219686718500014
60.
XuX., & PottersJ. (2018). An experiment on cooperation in ongoing organizations. Journal of Economic Behavior & Organization, 147, 28–40. https://doi.org/10.1016/j.jebo.2017.12.023
61.
ZhaJ., & ZengX. (2013). Mixed-model U-line balancing and sequencing in lean production. International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings. https://doi.org/10.1007/978-3-642-38445-5_49
62.
ZhangZ., TangQ., HanD., & QianX. (2020). An enhanced multi-objective JAYA algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios, International Journal of Production Research, 59, 6146–6165. https://doi.org/10.1080/00207543.2020.1804639
63.
ZhengX., ZhouS., & ChenH. (2018). Ant colony optimisation algorithms for two-stage permutation flow shop with batch processing machines and nonidentical job sizes. International Journal of Production Research, 57(10), 3060–3079. https://doi.org/10.1080/00207543.2018.1529445
64.
ZhuX., YuanQ., & ZhangW. (2018). Inventory leanness, risk taking, environmental complexity, and productivity: A mediated moderation model. Journal of Manufacturing Technology Management, 29(7), 1211–1232. https://doi.org/10.1108/JMTM-03-2018-0082
