The inventory system has been affected by many characteristics, among which deterioration of a food product is a critical issue. Chilled foods deteriorate during storage time, and their quality reduces over time. Indian Spiced Pulled Pork Sandwiches are very observable customer goods in India that are, in fact, unpreserved. If chilled foods’ original value reduces over time, consumers are not much likely to buy them. The retail price of chilled food maintained is strictly dependent on its quality. From the vendor’s approach, measuring quality and leftover value should be a severe commercial issue. The model aims to study deterioration together with the quality prediction of Indian Spiced Pulled Pork Sandwiches. This model measures food quality and leftover value. Deterioration rate is considered as a function of two-parameter Weibull distribution, suitable for bacterial inactivation, microbial growth, enzymes, nutrients, and pigments dreadful environments under a non-isothermal atmosphere. The dynamic structure of demand has its importance in business. The price-storage time of product-dependent demand rate is debated in this model as demand rarely remains constant. The objective is to maximize the vendor’s total profit concerning storage time and the product’s selling price. A numerical example supports the model. Sensitivity analysis is carried out to derive insights for decision-makers. The graphical result, in three dimensions, is exhibited with a supervisory decision.
Keywords: Decision support system, quality of food, performance of foods, Gompertz function, $$
@article{RO_2021__55_5_3141_0,
author = {Jani, Mrudul Y. and Chaudhari, Urmila and Sarkar, Biswajit},
title = {How does an industry control a decision support system for a long time?},
journal = {RAIRO. Operations Research},
pages = {3141--3152},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {5},
doi = {10.1051/ro/2021063},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021063/}
}
TY - JOUR AU - Jani, Mrudul Y. AU - Chaudhari, Urmila AU - Sarkar, Biswajit TI - How does an industry control a decision support system for a long time? JO - RAIRO. Operations Research PY - 2021 SP - 3141 EP - 3152 VL - 55 IS - 5 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021063/ DO - 10.1051/ro/2021063 LA - en ID - RO_2021__55_5_3141_0 ER -
%0 Journal Article %A Jani, Mrudul Y. %A Chaudhari, Urmila %A Sarkar, Biswajit %T How does an industry control a decision support system for a long time? %J RAIRO. Operations Research %D 2021 %P 3141-3152 %V 55 %N 5 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021063/ %R 10.1051/ro/2021063 %G en %F RO_2021__55_5_3141_0
Jani, Mrudul Y.; Chaudhari, Urmila; Sarkar, Biswajit. How does an industry control a decision support system for a long time?. RAIRO. Operations Research, Tome 55 (2021) no. 5, pp. 3141-3152. doi: 10.1051/ro/2021063
[1] , , and , A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains. Food Control 29 (2013) 451–460. | DOI
[2] and , Multi-item EOQ inventory model in a two-layer supply chain while demand varies with a promotional effort. Appl. Math. Model. 39 (2015) 6725–6737. | MR | DOI
[3] , and , The validity of modified Gompertz and Logistic models in predicting cell growth of Pediococcus Acidilactici H during the production of bacteriocin pediocin AcH. J. Food Eng. 80 (2007) 1171–1175. | DOI
[4] , and and , An integrated inventory model involving discrete setup cost reduction, variable safety factor, selling price dependent demand, and investment, RAIRO: OR 53, 2019 39–57. | MR | DOI
[5] , , , and , Microbiological quality of 18 °C ready-to-eat food products sold in Taiwan. Int. J. Food Microbiol. 80 (2003) 241–250. | DOI
[6] , and , The effect of sodium chloride and temperature on rate and extent of growth of clostridium botulinum type an unpasteurized pork slurry. J. Appl. Bacteriol. 62 (1987) 479–490. | DOI
[7] , and , State of the art in food: The changing face of the worldwide food industry. Elsevier Business Information (2002) 663.
[8] , and , Perishable inventory management with dynamic pricing using time-temperature indicators linked to automatic detecting devices. Int. J. Prod. Econ. 147 (2014) 605–613. | DOI
[9] and , Recycling of lifetime dependent deteriorated products through different supply chains. RAIRO: OR 53 (2019) 129–156. | MR | Zbl | Numdam | DOI
[10] and , Quality changes of ready-to-eat ginseng chicken porridge during storage at 25 ∘C. Meat Sci. 92 (2012) 469–473. | DOI
[11] , and , Environmental effect for a complex green supply-chain management to control waste: a sustainable approach. J. Clean. Prod. 277 (2020) 122919. | DOI
[12] , , , and , The effect of advance payment with discount facility on supply decisions of deteriorating products whose demand is both price and stock dependent. Int. Trans. Oper. Res. 27 (2020) 1343–1367. | MR | DOI
[13] , , and , Use of a modified Gompertz equation to model nonlinear survival curves for Listeria Monocytogenes Scott A. J. Food Prot. 58 (1995) 946–954. | DOI
[14] , , , and , Development of a fuzzy economic order quantity model of deteriorating items with promotional effort and learning in fuzziness with a finite time horizon. Inventions 4 (2019) 36. | DOI
[15] and , Optimal replenishment and credit policy in an inventory model for deteriorating items under two-levels of trade credit policy when demand depends on both time and credit period involving default risk. RAIRO: OR 52 (2018) 1175–1200. | MR | Zbl | Numdam | DOI
[16] , and , Joint pricing and ordering policy for a deteriorating inventory. Comput. Ind. Eng. 47 (2004) 339–349. | DOI
[17] , and , Joint pricing and inventory control for fresh produce and foods with quality and physical quantity deteriorating simultaneously. Int. J. Prod. Econ. 152 (2014) 42–48. | DOI
[18] , and , Coordinated replenishment and marketing policies for non-instantaneous stock deterioration problems. Comput. Ind. Eng. 88 (2015) 49–62. | DOI
[19] , and , Joint optimal inventory, dynamic pricing, and advertisement policies for non-instantaneous deteriorating items. RAIRO: OR 51 (2017) 1251–1267. | MR | Zbl | Numdam | DOI
[20] , and , Flexible setup cost and deterioration of products in a supply chain model. Int. J. Appl. Comput. Math. 2 (2016) 25–40. | MR | DOI
[21] , , , , and , Optimal replenishment decision for retailers with variable demand for deteriorating products under a trade-credit policy. RAIRO Oper. Res. 54 (2020) 1685–1701. | MR | Numdam | DOI
[22] , , and , Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. Int. J. Prod. Econ. 231 (2021) 107867. | DOI
[23] , and , Selection of remanufacturing/production cycles with an alternative market: a perspective on waste management. J. Clean. Prod. 245 (2020) 118935. | DOI
[24] and , Optimal policies for three players with fixed life time and two-level trade credit for time and credit dependent demand. Adv. Ind. Eng. Manage. 4 (2015) 89–100.
[25] and , Optimal ordering for deteriorating items of fixed-life with quadratic demand and two-level trade credit. In: Optimal Inventory Control and Management Techniques. IGI Global (2016) 1–16.
[26] and , Economic order quantity model for non-instantaneously deteriorating items under order-size-dependent trade credit for price-sensitive quadratic demand. AMSE J. 37 (2016) 1–19.
[27] , and , Economic order quantity model under trade credit and customer returns for price-sensitive quadratic demand. Rev. Invest. Oper. 36 (2015) 240–248. | MR
[28] , and , Optimal down-stream credit period and replenishment time for deteriorating inventory in a supply chain. J. Basic Appl. Res. Int. 14 (2015) 101–115.
[29] , and , Impact of future price increase on ordering policies for deteriorating items under quadratic demand. Int. J. Ind. Eng. Comput. 7 (2016) 423–436.
[30] , and , Integrating credit and replenishment policies for deteriorating items under quadratic demand in a three echelon supply chain. Int. J. Syst. Sci. Oper. Logist. 7 (2020) 34–45.
[31] , , and , An inventory model of a three-parameter Weibull distributed deteriorating item with variable demand dependent on price and frequency of advertisement under trade credit. RAIRO: OR 53 (2019) 903–916. | MR | Zbl | Numdam | DOI
[32] , and , A two-warehouse inventory model for non-instantaneous deteriorating items with interval valued inventory costs and stock dependent demand under inflationary conditions. Neural Comput. App. 31 (2019) 1931–1948. | DOI
[33] and , Predictive modeling of microbial behavior in food. Foods 8 (2019) 654. | DOI
[34] and , An inventory control problem for deteriorating items with back-ordering and financial considerations. Appl. Math. Model. 38 (2014) 93–109. | MR | DOI
[35] , , , , and , Application of a predictive growth model of pseudomonas spp. for estimating shelf life of fresh Agaricus bisporus. J. Food Prot. 80 (2017) 1676–1681. | DOI
[36] and , A classification of models in predictive microbiology. Food Microbiol. 10 (1993) 175–177.
[37] and , Deteriorating inventory model for chilled food. Math. Prob. Eng. 2015 (2015) 816876. | MR
[38] , , and , Coordinating a supply chain for deteriorating items with a revenue sharing and cooperative investment contract. Omega 56 (2015) 37–49. | DOI
[39] , and , Temperature monitoring for quality prediction and inventory control in cold chain: A case of 18 °Cready-to-eat food in Taiwan. 7 th International European Forum (Igls-Forum) on System Dynamics and Innovation in Food Networks, Innsbruck, Austria (2013) 593–600.
Cité par Sources :
