scholarly journals Inventory Sharing Strategy for Disposable Medical Items between Two Hospitals

2019 ◽  
Vol 11 (22) ◽  
pp. 6428
Author(s):  
Ping Zhang ◽  
Hong Yan ◽  
King Wah Pang
Keyword(s):  
Traffic Accidents ◽  
Disease Outbreaks ◽  
Inventory Level ◽  
Demand Forecast ◽  
Patient Behavior ◽  
Inventory Sharing ◽  
Request Rate ◽  
Inventory Decisions ◽  
Optimal Inventory Level ◽  
Cost Parameters

When urgent situations occur (e.g., inaccurate demand forecast, traffic accidents, or infectious disease outbreaks), the stock of rescue medical items in the hospital might not be enough to cater to the drastically increased demand. Comparing with placing an expensive emergent replenishment order with dealers, requesting inventory sharing from another hospital with excessive stocks could save time and cost. This paper investigates the operation of the inventory sharing mechanism between two independent hospitals with the consideration of patient behavior. We first identified the inventory decisions when hospitals are under a no-sharing scenario, and derive hospitals’ sharing policies and inventory policies under the sharing scenario. Through numerical experiments, we found that the inventory sharing option is profitable for hospitals compared to emergent replenishment. Furthermore, we investigated the effects of patient behavior, safety inventory level of the hospital, and other cost parameters on inventory decisions. Under the sharing policy, the increase of hospital j’s emergent request rate and safety inventory level increased the optimal initial inventory level of hospital i, while the increase of hospital j’s initial inventory level decreased the optimal inventory level of hospital i. This paper provides more practical suggestions for hospitals’ inventory sharing operation.

scholarly journals A Fuzzy EPQ Model for Non-Instantaneous Deteriorating Items where Production Depends on Demand which is Proportional to Population, Selling Price as well as Advertisement

2019 ◽  
Vol 10 (5) ◽  
pp. 1679 ◽  
Author(s):  
Abhishek Kanti Biswas ◽  
Sahidul Islam
Keyword(s):  
Optimal Solution ◽  
Inventory Level ◽  
Selling Price ◽  
Distance Method ◽  
Defective Items ◽  
Holding Cost ◽  
Epq Model ◽  
Optimal Inventory Level ◽  
Cost Parameters ◽  
Total Average

The inventory system has been drawing more intrigue because this system deals with the decision that minimizes the total average cost or maximizes the total average profit. For any farm, the demand for any items depends upon population, selling price and frequency of advertisement etc. Most of the model, it is assumed that deterioration of any item in inventory starts from the beginning of their production. But in reality, many goods are maintaining their good quality or original condition for some time. So, price discount is availed for defective items. Our target is to calculate the total optimal cost and the optimal inventory level for this inventory model in a crisp and fuzzy environment. Here Holding cost taken as constant and no-shortages are allowed. The cost parameters are considered as Triangular Fuzzy Numbers and to defuzzify the model Signed Distance Method is applied. A numerical example of the optimal solution is given to clarify the model. The changes of different parameters effect on the optimal total cost are presented and sensitivity analysis is given.JEL Classification: C44, Y80, C61Mathematics Subject Classification: 90B05

Determination of Optimal Reserve between Two Machines in Series with the Repair Time has Change of Distribution after the Truncation Point

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 577-585
Author(s):  
T. Vivekanandan ◽  
S. Sachithanantham
Keyword(s):  
Real Life ◽  
Random Variable ◽  
Repair Time ◽  
Inventory Level ◽  
Optimal Size ◽  
Truncation Point ◽  
In Series ◽  
Optimal Reserve ◽  
Optimal Inventory Level ◽  
Two Machines

In inventory control, suitable models for various real life systems are constructed with the objective of determining the optimal inventory level.  A new type of inventory model using the so-called change of distribution property is analyzed in this paper. There are two machines M1 and M2  in series and the output of M1 is the input of M2. Hence a reserve inventory between M1 and M2 is to be maintained. The method of obtaining the optimal size of reserve inventory, assuming cost of excess inventory, cost of shortage and when the rate of consumption of M2  is a constant, has already been attempted.  In this paper, it is assumed that the repair time of M1  is a random variable and the distribution of the same undergoes a change of distribution  after the truncation point X0 , which is taken to be a random variable.  The optimal size of the reserve inventory is obtained under the above said  assumption . Numerical illustrations are also provided.

Model Predictive Control of a Forecasting Production System with Deteriorating Items

2015 ◽  
Vol 6 (4) ◽  
pp. 19-37 ◽  
Author(s):  
Abdelhak Mezghiche ◽  
Mustapha Moulaï ◽  
Lotfi Tadj
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Production System ◽  
Objective Function Value ◽  
Inventory Level ◽  
Optimal Production ◽  
Control Approach ◽  
System Parameters ◽  
Demand Rate ◽  
Optimal Inventory Level

The authors consider in this paper an integrated forecasting production system of the tracking type. The demand rate during a certain period depends on the demand rate of the previous period. Also, the demand rate depends on the inventory level. Items on the shelves are subject to deterioration. Using a model predictive control approach, the authors obtain the optimal production rate, the optimal inventory level, the optimal demand rate, and the optimal objective function value, explicitly in terms of the system parameters. A numerical example is presented.

scholarly journals Convergence of estimated optimal inventory levels in models with probabilistic demands

2003 ◽  
Vol 13 (2) ◽  
pp. 217-227
Author(s):  
Carlos Bouza
Keyword(s):  
Monte Carlo ◽  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Search Algorithms ◽  
Inventory Level ◽  
Density Functions ◽  
Monte Carlo Experiments ◽  
Unknown Probability ◽  
Optimal Inventory Level

The behavior of estimations of the optimal inventory level is analyzed. Two models are studied. The demands follow unknown probability distribution function. The included density functions are estimated and a plug-in rule is suggested for computing estimates of the optimal levels. Two search algorithms are proposed and compared using Monte Carlo experiments. .

scholarly journals The Determination Of Optimal Inventory Using Markov Chain

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Novie Novie ◽  
Haryadi Sarjono
Keyword(s):  
Markov Chain ◽  
Inventory Control ◽  
Spare Parts ◽  
Inventory Level ◽  
Research Results ◽  
Markov Chain Method ◽  
Crucial Problem ◽  
Optimal Inventory Level ◽  
A Company

The complication of inventory is a crucial problem for the company because inventory is one of the valuable assets for the company. The existence of inventory control is needed so the inventory levels that exist within the company is not in very large (optimal) therefore the costs incurred by the company can be suppressed as minimum as possible. PT NM is a company engaged in spare parts, especially branded cars in West Jakarta. The level of inventories of goods owned by  these  companies is high because The company did not want to experience stock out of stock but on one side the stock is large enough so the costs incurred  by  companies  such as storage costs of goods becomes even greater. In order to control inventory levels, this study uses a Markov Chain method that can identify optimal inventory level and the expectation of profits earned per month by making estimates of the future demand a previous demand. From the research results shows expectation of profit of each state starting from state 2 to state 10 is Rp 13.146,98921; Rp 12.246,94064; Rp 11.346,61466; Rp 10.444,64569; Rp 9.534,074035; Rp 8.584,408534; Rp 7.484,413248; Rp 5.913,288143; Rp 3.211,609986,-. While the profit expectations that earned by the company per month is Rp 694.233,333, and the optimal inventory level is 50.

scholarly journals Inventory Sharing and Demand-Side Underweighting

2020 ◽  
Author(s):  
Hui Zhao ◽  
Liang Xu ◽  
Enno Siemsen
Keyword(s):  
Problem Definition ◽  
Decision Makers ◽  
Supply Side ◽  
Systematic Bias ◽  
Demand Side ◽  
Systems Methodology ◽  
Risk Pooling ◽  
Decision Bias ◽  
Inventory Sharing ◽  
Inventory Decisions

Problem definition: Transshipment/inventory sharing has been used in practice because of its risk-pooling potential. However, human decision makers play a critical role in making inventory decisions in an inventory sharing system, which may affect its benefits. We investigate whether the opportunity to transship inventory influences decision makers’ inventory decisions and whether, as a result, the intended risk-pooling benefits materialize. Academic/practical relevance: Previous research in transshipment, which is focused on finding optimal stocking and sharing decisions, assumes rational decision making without any systematic bias. As one of the first to study inventory sharing from a behavioral perspective, we demonstrate a persistent stocking-decision bias relevant for inventory sharing systems. Methodology: We develop a behavioral model of a multilocation inventory system with transshipments. Using four behavioral studies, we identify, test, estimate, and mitigate a demand-side underweighting bias: although inventory sharing brings both a supply-side benefit and a demand-side benefit, players underestimate the latter. We show analytically that such bias leads to underordering. We also explore whether reframing the inventory sharing decision reduces this bias. Results: Our results show that subjects persistently reduce their order quantities when transshipments are allowed. This underordering, which persists even when a decision-support system suggests optimal quantities, causes insufficient inventory in the system, in turn reducing the risk-pooling benefits of inventory sharing. Underordering is evidently caused by an underweighting bias; although players correctly estimate the supply-side potential from transshipment, they only estimate 20% of the demand-side potential. Managerial implications: Although inventory sharing can profitably reduce inventory, too much underordering undermines its intended risk-pooling benefits. The demand-side benefits of transshipment need to be emphasized when implementing inventory sharing systems.

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