Replenishment Management Research Articles

Inventory Management Practices and Performance Of Preference Groups In Kisii County, Kenya

This study endeavored to explore on the relationship between inventory management practices and performance of preference groups in Kisii County, Kenya. The specific objectives were: stock replenishment management, just in time management, re-order level management and inventory description management. The study was anchored on various theories relevant. This study adopted descriptive survey research design. The target population was 1036 respondents’ from 210 prequalified preference groups within Kisii County and a sample size of 104 of the population was established from three stratus. The study equally distributed the sample size equally on all the constituencies of Kisii County. Reliability was tested of 10% (10) respondents using Cronbach alpha index at 0.7 while validity was tested by correlation coefficient. This was above the required before proceeding to the field for data correction. All the predictor variables displayed above average or positive relationship with performance of preference groups. Reliability test results for the variables track your activity management, daily inventory count management, manage out-of-stock product management, clear inventory description management and performance was above the threshold of >0.7. With adequate validity and reliability the instrument were clearly drafted and precisely fit to collect data. The researcher recommends that preference groups were really being affected by inventory management practices. Also the research concludes that the four specific objectives had greater impact on preference groups in Kisii County, Kenya. The study further recommends a review of Public Procurement to provide supportive regulatory framework which was establish an independent body that was to facilitate continuous capacity developments of the disadvantages groups and establish mechanism for regular compliance audit since the current regulation is not clear on action to be taken on non-compliance

An integrated connectivity diagnostics and dependency analysis framework for supporting water replenishment management

An in-depth investigation on hydrological connectivity and its relationships with water level are fundamental for regional water replenishment management. In this research, an integrated connectivity diagnostics and dependency analysis framework (ICDDAF) was developed for determining the optimal ecological water level in Baiyangdian Wetland, North China. It consisted of two core modules: hydrological connectivity diagnostics (HCD) and hydrological connectivity–water level coexistence analysis (HC–WLCA). The results were as follows. 1) In the HCD module, remote sensing coupling geostatistical connectivity function methods can accurately evaluate the hydrological connectivity considering the directional difference and the validity of distance range. For intra–annual variations, the hydrological connectivity was relatively good in March at the monthly scale, spring at the seasonal scale and the west–east direction, while relatively weak in September and summer, which were related to the periodic characteristics of vegetation growth, overall terrain and flow direction. For inter–annual variations, it was generally weak and showed a downwards, then upwards and finally downwards trend. 2) In the HC–WLCA module, Multivariate Copula Analysis Toolbox was innovatively adopted to describe the coexistence relationships of hydrological connectivity and water level. Beta and generalized Pareto distributions were selected as the optimal marginal distributions for hydrological connectivity and water level indicators respectively, and Frank copula was determined as the optimal joint distribution function; the modelling uncertainty in the coexistence relationships was focused. 3) The water level of 7 m was determined as the optimal ecological water level for maintaining good hydrological connectivity and restoring ecological environment. Considering the coexistence relationships of hydrological connectivity and water level, such an ICDDAF can provide a new perspective for efficient adjustments of water levels based on hydrological connectivity pattern variations under different water replenishment schemes, aiming to achieve ecological restoration of wetlands.

Responses of Macroinvertebrate Community Temporal Dissimilarity and Abundance to the Water Level Fluctuation Range in a Shallow Lake

Variations in the hydrological regime are among the anthropogenic pressures affecting biological assemblage structure in shallow freshwater lakes. We estimated the effects of the water level fluctuation range on the temporal dissimilarity of the macroinvertebrate community by sampling benthic macroinvertebrate assemblages monthly in 2017 and bimonthly in 2018. Then, we applied a boosted regression trees (BRT) model to quantitatively analyzing the relationship between macroinvertebrate abundance and microhabitat factors in different seasons. To distinguish differences in water level fluctuations at the sample site scale, we proposed a variable, namely, the percentage of water level fluctuation range (PWLFR). The results were as follows. (1) An increased water level fluctuation range would lead to more temporally heterogeneous macroinvertebrate communities. Temporal dissimilarity of macroinvertebrates increased linearly in response to increasing water level fluctuation range. (2) Species abundance presented seasonal characteristics, and the dominant factors affecting species abundance varied with the seasons. PWLFR was the dominant variable explaining macroinvertebrate abundance in summer. Macroinvertebrate abundance showed positive effects with increasing PWLFR. (3) The interaction between chlorophyll a and PWLFR in summer promoted an increase in macroinvertebrate abundance. These findings may provide a basis for the formulation of effective ecological water replenishment management decisions aimed at maintaining the stability of shallow lake ecosystems in arid and semi-arid regions.

Designing a fully automated and integrated inventory and replenishment system for hospitals

Surge demand shows that hospital logistics automation is unexploited given that the high pressure in work, the lack of resources, and some inventory and replenishment problems still occur. Hospitals do not fully leverage the implemented automatic systems and integrate them into large-scale logistic processes. Only some tasks/ services are automated keeping nurses in charge of the logistic labour, a large stock space, and inventory and replenishment problems. Hospitals do not adopt strategies that carefully aim for a high level of logistics automation and integration, using adequate management systems/ policies to make it cost-effective. This paper presents the design of a fully automated and integrated system for asset inventory and replenishment management in hospitals. The strategy adopted in this work starts with choosing a proactive replenishment policy operating in real-time and a suitable inventory and replenishment system (RFID-enabled two-bin system). This system is then automated using adequate and recent technologies (e.g. ‘Box-Picker’, smart gravity-flow racks, and conveyors). The proposed system is modelled and simulated in a virtual hospital under a mass casualty incident. Results show a considerable reduction of assets unavailability (99.98% in the central pharmacy and 90.47% in wards) and stock space. Besides, nurses are released from logistic tasks.

Hospitals Automatic Replenishment System Under Surge Demand: a Proactive Priority Management Policy of Replenishment Orders

Recently, hospitals utilize RFID (Radio-frequency identification) enabled Two-Bin system with conveyors for an effective and automatic inventory and replenishment management. In this framework, replenishment orders are typically managed according to continuous review. In case of surge in demand due to special-events and disasters, the resulted big number of replenishment orders is managed in real time according to First-In First-Out (the first order is replenished first) with Priority of Out-of-Stock Medicines (FIFO-POSM) policy. However, this policy lacks flexibility with random increase in medicines consumption and lacks anticipation, leading to stock-outs occurrence. So far, there is not a flexible and anticipative policy for orders priority management in this case. This paper proposes a flexible and anticipative policy based on stock coverage indicator and anticipative system of random variations in medicines demand, to considerably reduce or eliminate stock-outs. RFID enabled Two-Bin system with conveyors is modeled using Colored Timed Petri Nets, first, considering FIFO-POSM policy then considering the proposed policy. Simulation results prove the efficiency of the proposed policy; medicines stock-outs and out-of-stock medicines unavailability durations are respectively reduced by 54.24% and 52.06% during ten days of a public protest.

Centralised grocery supply chain planning: improved exception management

Purpose – The purpose of this paper is to present an operations model for retail replenishment collaboration and identifies its expected benefits and limitations for the members of a grocery supply chain. Design/methodology/approach – A case study is conducted on a development project between a grocery wholesaler and two grocery product suppliers. Data are collected through semi-structured interviews with key respondents from four different companies. Findings – Despite advances in collaborative practices in grocery supply chains, retail store replenishment management faces challenges. In particular, demand exceptions management is a challenge in the grocery industry. A replenishment model called Collaborative Buyer-Managed Forecasting (CBMF) creates a proactive planning approach and a platform for close collaboration in the supply chain. The centralised forecasting transforms retailer sales data into a plan which serves the whole supply chain by creating one-order forecast. The CBMF model facilitates efficient demand management, improves demand responsiveness and promotes better availability of products in retail stores. Research limitations/implications – CBMF provides a replenishment planning model for the whole supply chain. It is tested to a limited extent in one supply chain. Practical implications – The study provides managers with a better understanding of the benefits of centralised forecasting and closer replenishment collaboration, especially during periods of exceptional demand. Originality/value – A new approach for managing demand in grocery supply chains with centralised forecasting is provided.

A two-level replenishment frequency model for TOC supply chain replenishment systems under capacity constraint

Good production planning and replenishment management are important for a firm to keep competitive in the market. The theory of constraints-supply chain replenishment system (TOC-SCRS) is a replenishment method under the TOC philosophy. In the application of the TOC-SCRS in a node of a supply chain, the replenishment frequency (RF) and the reliable replenishment time (RRT) are required parameters. Generally, the RF of a node depends on the public transportation schedule such as ship schedules or its private conveyor schedule. If this node is a plant, however, the RF depends on the setup frequency in this plant, and a higher RF (i.e., once a day) is preferred by the TOC because of lower inventory and quick response to different market requirement. Basically, the RF in a plant is determined by its sales or production quantity. When sales increase significantly, the RF in a plant requires to be elongated from higher frequency (i.e., once a day) to lower frequency (i.e., once every two or more days) due to the limited capacity. Therefore, a two-level replenishment frequency model for the TOC-SCRS under capacity constraint is proposed. This model is especially suitable to a plant in which different products have a large sales volume variation. Numerical examples are utilized to evaluate the application of the proposed method. Employing this proposed methodology will facilitate a plant or a central warehouse to implement an effective TOC-SCRS successfully.

Innovative replenishment management for perishable items using logistic regression and grey analysis

In this paper, an innovative decision support system is proposed, by consolidating the newsboy model, logistic regression, and grey relation analysis, to develop an efficient replenishment policy, which maximises the total profit of perishable items in a convenience store. First, the basic order quantity of the overall meal-box is determined by the newsboy model. Next, we develop a wastage-free system by employing logistic regression to adjust the overall basic order quantity, which may deviate from the real demand due to the effect of uncertain factors such as the weather and the number of customers. Finally, grey relation analysis is conducted to allocate the order quantity of each kind of meal-box efficiently. Based on actual data from a convenience store of the President Chain Store Corporation in Taiwan, the superiority of the decision support system was evaluated. The experimental findings reveal that the proposed policy can outperform the traditional replenishment policy. Since customers’ tastes can be precisely monitored through this system, daily needs can be estimated and controlled more accurately and the quantities of shortage and wastage can be reduced. This system is believed to raise customer satisfaction and increase the profit of the store.

Bringing order [replenishment process control

The importance of replenishment management in make to stock businesses is often overlooked in demand management studies in favour of forecast management. The real issue in replenishment strategy is addressing the fundamental challenge between holding enough inventory to protect future sales versus reducing inventory to reduce costs and enhance productivity. It is likely that replenishment lead-time will be greater than is desirable, vendors will be unreliable and forecasts will in most cases be wrong. Thus, in this paper, the author urges manufacturers to take control of the replenishment process.