Stock Imbalance Research Articles

Effect of IMPACT team approach implementation processes on health supply chain management data use in Singida region: A quantitative study

The Government of Tanzania (GoT), in collaboration with development partners, has made significant improvements in the health supply chain since 2013 through developing of an electronic Logistics Management Information System (e-LMIS) and strengthening of the Logistics Management Services (LMS) component. Nevertheless, the use of reported data in the e-LMIS remained very low. In efforts to respond to low data use at all supply chain levels, the GoT introduced the Information Mobilized for Performance Analysis and Continuous Transformation (IMPACT) approach to reinforce data use culture for decision-making to improve the supply chain. Therefore, this study aimed to determine the effect of IMPACT team approach implementation processes on health supply chain data use in Singida region. This was a pre- and post-cross-section study whereby the quantitative data were obtained from e-LMIS stock imbalance reports. This data was processed using Microsoft Excel power query and analysed using Microsoft Excel 2019 pivot tables to get indicator results on availability, not-in-demand and overstocked health commodities. The overall pre (2018) and post (2020) intervention results for both Iramba district council (DC) and Singida municipal council (MC) were 66% and 81% for the availability of essential health commodities, 17.2% and 21% for not-in-demand health commodities and 17% and 20.7% for overstocked health commodities respectively. Iramba DC demonstrated a significant statistical increase in both indicators compared to Singida MC, as the P-value was less than 0.05 at a 95% confidence level (df=35). Conclusively, the IMPACT team approach positively influenced data use and availability of health commodities. However, it was not the case for overstocked and not-in-demand indicator results. Apart from the possible low quality of logistics data, further studies should explore other contributing factors behind the increasing trends of overstocks and not-in-demand health commodities in the region.

Global imbalances from the flow and stock perspective

The objective of the paper is to identify global payment imbalances from the flow and stock perspective in the years of 2000-2017 as well as to define the causes of differences in the development of global imbalances in both of the analysed aspects. In order to achieve the objective, current account balances and international investment positions that are systemically significant to economies were analysed with the use of descriptive statistics techniques and specific analytical indicators. From the conducted studies it arises that after the outbreak of the global financial crisis, the global flow imbalance declined, whereas the global stock imbalance rose significantly. The demonstrated divergence was caused by insufficient changes in current account balances (flow imbalances) in order to cause a decrease in net international investment positions (stock imbalances) systematically significant to economies and by a weakened impact of the transactions registered in the balance of payment (flows) on the change of an international investment position (stock).

Long-term vehicle reservations in one-way free-floating carsharing systems: A variable quality of service model

Reservations in daily services can improve user satisfaction, and give additional information about the demand patterns to the operators. However, providing reservations to carsharing clients is difficult. While carsharing is especially convenient if it is allowing one-way trips and vehicle drop-off anywhere in the service area (called free-floating), this flexibility increases management complexity because of vehicle stock imbalance. Most of the commercial providers of free-floating carsharing offer reservations under highly restrictive terms, for example only up to 30 min in advance. In this paper, we propose an innovative reservation enforcement technique that allows substantially longer reservation times while keeping the system profitable and achieving high service quality. A simple way to enforce reservations is locking vehicles until the departure time of a client. However, it comes at the cost of idling vehicles that could be used by other users and decreasing the revenue. Our approach, called relocations-based reservation enforcement method (R-BR) combines vehicle locking and relocation movements. It locks vehicles only a short time before the trip departure if a suitable vehicle is close enough due to the natural trip patterns. If no such vehicle is available, a car is relocated from another place. Further, we propose a variable quality of service (QoS) model in which the guaranteed radius around the user within which the reserved vehicle will be placed, and the maximum allowed reservation time before the departure depends on the zone of trip departure. A simulation-based optimization is used whereby the carsharing operation is simulated and optimized using an iterated local search (ILS) metaheuristic for adjustment of service level parameters. The proposed technique is tested on a set of artificial problem examples and a case study of a simulated working day in the Lisbon Municipality, Portugal. Results show that the proposed R-BR method is substantially better than the simple vehicle locking when the constant QoS approach is used and that the devised ILS metaheuristic can further increase the system performance, especially with high trip volumes.

Unwinding External Stock Imbalances? The Case of Italy’s Net International Investment Position

This paper is a case study of an (almost complete) adjustment of Italy’s external stock imbalance. After reaching a peak of around 25 per cent of GDP in early 2014, Italy’s net external debtor position has steadily decreased, reaching less than 7 per cent of GDP at the end of 2017. The contribution of this work is twofold. First, it reviews the main developments in Italy’s net international investment position (NIIP) since 1999. Second, it reports a baseline projection of Italy’s NIIP over a medium-term horizon, as implied by current account balance forecasts. Since this projection ignores the role of valuation adjustments, the study also provides an analysis of their sensitivity to a set of potential movements in exchange rates and equity or bond markets.

An Integrated Supply-Demand Approach to Solving Optimal Relocations in Station-Based Carsharing Systems

The dominant challenge in one-way carsharing systems is the vehicle stock imbalance. Previous studies have proposed relocation approaches to handle it using optimization and simulation models. However, these models do not consider the interdependence between supply and demand in carsharing systems. In this paper, we develop an integrated optimization model to link supply and demand together. A discrete choice model that includes vehicle availability as a parameter directly affecting user’s mode choice is introduced and incorporated within the optimization formulation. In this framework, carsharing travel demand is influenced by vehicle supply. The reaction of the demand further changes vehicle availability in the system. The incorporation of a discrete choice model with the Integer Linear Programming formulation leads to a nonlinear model. We propose a linearization scheme to reformulate it. We test the model in realistic case studies representative of an Australian carsharing operator. A sensitivity analysis on total travel demand, system capacity, one-way trip price, and vehicle availability coefficient is undertaken to evaluate their impacts on system profit. The results reveal that the pattern of profit over trip price varies across scenarios with different vehicle availability coefficients and travel demand. The profit-efficiency of enlarging carsharing network is also dependent on travel demand. We conclude that the interdependence between demand and supply should be considered when setting network development plans and pricing strategies in one-way carsharing systems. If there is a strong interaction between demand and supply, the supply of carsharing vehicles has a critical impact on system profit.

Responsive pricing and stock redistribution: Implications for stock balancing and system performance

We consider two firms that order from a supplier and sell products to the markets with uncertain demand. The firms can transship stocks in between at an endogenous transfer price and set retail prices after learning actual market sizes. The relative timing of stock transshipment and retail pricing, both ex-post market size realization, gives rise to two decision models. The firms set retail prices and transship upon stock imbalance in the pro-pricing model, and, furthermore, transship stocks in between before retail pricing and demand satisfaction in the pro-transshipment model. We demonstrate that responsive pricing alone keeps the firms off stock imbalance and insulates them from the impacts of market uncertainty, and responsive stock redistribution contributes to a more efficient deployment of stocks to market selling, even absent volatility. Enhanced responsiveness in stocking and pricing benefits firms, but can hurt the supplier unless its production cost is sufficiently low.

Relocation optimization of electric cars in one-way car-sharing systems: modeling, exact solving and heuristics algorithms

ABSTRACTCar-sharing system with electric cars is a very convenient service for urban transportation: it allows users to pick up a vehicle at a station and rent it during a short time. To manage this kind of system in the best way, it is necessary to solve the critical problem of vehicle stock imbalance across the stations. Several decision levels must be considered to balance the car distribution by taking into account the quality of service and the system operation cost. To this end, a linear programming model is proposed to formalize the problem in a mathematical framework, which allows the computation of optimal vehicle distribution strategies. To make our solution time efficient and usable for solving large problems, a greedy algorithm and a tabu search algorithm are proposed. These two algorithms are applied to the Auto Bleue network in Nice and its surrounding (France) using extensive simulations. Besides, an integrated mapping method is provided within the Geographical Information System QGIS to estimate flows and their locations. Numerical results demonstrate that the tabu search algorithm is able to find near-optimal solutions and good compromises between client satisfaction, number of staff agents and vehicles used, and computing time.

Dynamic Optimal Vehicle Relocation in Carshare Systems

One-way carsharing, providing users with more flexibility on returning stations, has attracted larger market demand than has the traditional round-trip service. However, the main challenge faced by a one-way carsharing system is the vehicle stock imbalance due to the uneven distribution of user demand. This study attempted to address this problem by proposing an optimization model that integrated with a discrete choice model. The model accounts for the interdependent relationship between carshare demand and supply. User demand is influenced by the availability of carshare vehicles; meanwhile, conversely, the demand further changes vehicle availability as well as vehicle stock distribution. The model determines the optimal relocation decisions to maximize the profit for carshare operators that offer both one-way and round-trip services. This model was applied to the network of a carshare operator in Australia to evaluate the impacts of different pricing and capacity policies on the system profit. The results indicate that the one-way trip price has a more significant impact on system profit than does vehicle pod capacity. Further, the maximum profit occurs when the price of a one-way trip is around four times higher than that of the round trip.

Trip pricing of one-way station-based carsharing networks with zone and time of day price variations

One-way station-based carsharing systems provide short term car rentals in which users can take a car from the initial station and return it to any other station. They are more flexible than round-trip carsharing, where the vehicle can only be returned to the station where it was picked up, and can be used for daily commuting trips as well. This flexibility, however, comes at a cost of vehicle stock imbalance within the network. Several solutions and strategies have been suggested to counter this problem, one of which is variable trip pricing. By charging high prices for the trips that increase imbalance and lowering prices for trips that help improve the balance, it has been hypothesized, but never demonstrated, that the clients’ behavior could be used to balance the vehicle stocks and thus make carsharing systems more manageable and profitable. In this paper, we develop a mixed integer non-linear programming (MINLP) model, defined as the Trip Pricing Problem for One-Way Carsharing Systems (TPPOCS), which sets these prices in order to maximize profit. An iterated local search (ILS) metaheuristic is proposed for solving it. The method is applied to the theoretical case-study of a network of 75 stations distributed across the city of Lisbon (Portugal). Although the implemented metaheuristic is tuned for the Lisbon example, the generic nature of its operators makes the model applicable elsewhere. The results demonstrate that the trip pricing strategy can be used to increase profit through a more balanced system. If no price-based balancing strategies are applied, operating this service results in a daily deficit of €1161. When the trip pricing policy is applied, profits of 2068 €/day are possible. The optimal prices are on average 23% higher than the base price, and 18% less demand is served, but the enhanced performance leads to lower expenses with the fleet of vehicles and number of parking spaces.

The Added Value of Accounting For Users’ Flexibility and Information on the Potential of a Station-Based One-Way Car-Sharing System: An Application in Lisbon, Portugal

Car-sharing systems are an alternative to private transportation whereby a person may use an automobile without having to own the vehicle. The classical systems in Europe are organized in stations scattered around the city where a person may pick up a vehicle and afterward return it to the same station (round trip). Allowing a person to drop off the vehicle at any station, called one-way system, poses a significant logistics problem because it creates a significant stock imbalance at the stations, which means that there will be times when users will not have a vehicle available for their trip. Previous mathematical programming formulations have tried to overcome this limitation by optimizing trip selection and station location in a city in order to capture the best trips for balancing the system. But there was one main limitation: The users were assumed to be inflexible with respect to their choice of a station, and held to use only the one closest to their origin and destination. If the user is willing to use the second or even the third closest station the user could benefit from using real-time information on vehicle stocks at each station and be able to select the one with available capacity. In this article we extend a previous model for trip selection and station location that takes that aspect into account by considering more vehicle pick-up and drop-off station options and then apply it to a trip origin–destination matrix from the Lisbon region in Portugal. Through the extended formulation we were able to conclude that user flexibility allied with having information on vehicle stocks increases the profit of the company, as people will go directly to a station with a vehicle available, thus making the use of the fleet more efficient. Observing the size of the stations resulting from the model, we also concluded that the effect of information is enhanced by large car-sharing systems consisting of many small stations.

Testing the Validity of the MIP Approach for Locating Carsharing Stations in One-way Systems

The most relevant problem to manage one-way carsharing systems is the vehicle stock imbalance across the stations. Previous research proposed a mathematical model for choosing the stations’ location as an approach to solve it. However, it does not allow including relocation operations and trip uncertainty. In this paper we develop a simulation model that considers demand variability and one vehicle relocation policy and test the solutions provided by the previous MIP model. We have concluded that these factors influence significantly the company profit and should be considered in future research in one-way carsharing systems planning.

Optimization approach to depot location and trip selection in one-way carsharing systems

In this paper, we present an optimization approach to depot location in one-way carsharing systems where vehicle stock imbalance issues are addressed under three trip selection schemes. The approach is based on mixed-integer programming models whose objective is to maximize the profits of a carsharing organization considering all the revenues and costs involved. The practical usefulness of the approach is illustrated with a case study involving the municipality of Lisbon, Portugal. The results we have obtained from this study provided a clear insight into the impact of depot location and trip selection schemes on the profitability of such systems.

Customer-Driven vs. Retailer-Driven Search: Channel Performance and Implications

A common phenomenon that occurs in any decentralized multilocation system is stock imbalance, whereby some locations have unsatisfied demands while others are overstocked. The system can be rebalanced by using a search process that is driven by either the customers or the retailers. In a customer-driven search (CDS), the customer with unmet demand may search for the product at another location and, if it is available, complete the purchase. In a retailer-driven search (RDS), the retailer with unsatisfied demand searches for product and schedules transshipment to fulfill the unmet demand at his location. Of course, the revenues generated through search in RDS need to be shared between the parties according to a transfer pricing scheme. In a setting of one manufacturer and two retailers with price-dependent and random demand, we explore the impact of the search method and the transfer price scheme used on the preferences of the manufacturer, the retailers, and the customers. With endogenous retail prices, we find that both the manufacturer and the retailers prefer RDS over CDS when they can design the transfer pricing scheme in RDS. Interestingly, neither party prefers the fixed transfer pricing scheme commonly assumed in the literature. Instead, transfer price that is proportional to the price of the retailer with either excess stock or excess demand is preferred. However, although both parties favor an RDS system when they can design the transfer pricing scheme in RDS, they may prefer RDS or CDS when the other party designs the RDS. Thus, the interests of the manufacturer and the retailers are rarely aligned. Customers benefit from a lower price in an RDS but at the expense of lower availability (as measured by the level of safety stock).

A Dynamic Equilibrium Model of Real Exchange Rates with General Transaction Costs

We study the behavior of real exchange rates in a two-country dynamic equilibrium model. In this model, consumers can only consume domestic goods but can invest costlessly in capital stocks of both countries. Nevertheless, transporting goods between the two countries is costly and, hence, the rebalancing of the capital stock can only happen finitely often. We propose a realistic cost structure for goods transportation, wherein the total cost increases with the amount of shipment but the unit cost decreases with it due to economies of scale. Given such a cost structure, the optimal decisions on when and how much to transfer need to be determined jointly. The dual decision depends upon the magnitude of economies of scale, the production technology specifications, and the consumer preferences. The model can reconcile the observed large short-term volatility of the real exchange rate with its slow convergence to parity. Further, the drift and diffusion of the real exchange rate are not uniquely determined by the real exchange rate level. The dynamics of the real exchange rate can only be determined by a joint analysis of the real exchange rate and the underlying economic fundamentals such as the capital stock imbalance between the two countries.

Production—demand adjustment in Norwegian manufacturing: A quarterly error correction model

In this paper, we specify a general error correction equation for seasonally unadjusted data, with an arbitrary number of regressors. Special attention is paid to the specification of trend and seasonal filters, which have proved to be of importance when using seasonally unadjusted quarterly data. Within this framework, the adjustment of production to demand in a stockbuilding sector is analysed. We have specified an output decision equation with three explanatory variables, capacity, demand and stock imbalance. The model is estimated on Norwegian quarterly national accounts data by means of a non-linear least squares algorithm. Experiences from simulation exercises are also reported, illustrating the dynamic tracking as well as the dynamic behaviour of the model.