Application of a hybrid selective inventory control technique in a hospital: a precursor for inventory reduction through lean thinking

Unleashing an ML-based selection criteria for economic lot sizing in a smart batch-type production system

Purpose

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

Design/methodology/approach

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

Findings

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

Originality/value

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

Point-of-use hospital inventory management with inaccurate usage capture

Many hospital supply chains in the US follow a “stockless” structure, often implemented with the acquisition of new systems promising improved efficiencies and responsiveness. Despite vendor promises, supply chain gains from new technology are often unfulfilled or result in a reduction of performance. A critical component of achieving promised gains is the hospital’s ability to accurately and consistently capture hospital inventory use. In practice, recording demand with perfect, 100% accuracy is infeasible, so our models condition on the level of accuracy in a particular hospital department, or point-of-use (POU) inventory location. Similar to previous literature, we consider actual net inventory and recorded net inventory in developing the system performance measures. We develop two models, optimizing either cost or service level, and we assume a periodic-review, base-stock (or par-level) inventory policy with full backordering. In addition to choosing the optimal order-up-to level, we seek the optimal frequency of inventory counts to reconcile inaccurate records. Results from both models provide insights for supply chain managers in the hospital setting, as well as hospital administrators considering the adoption of similar technologies or systems.

Kanban system applications in healthcare services: A literature review

Lean Management method has been applied in several fields before its implementation in healthcare area. Among the different techniques associated with Lean, in this paper, we are focused on Kanban system, which is a method that presents several benefits such as inventory holding reduction or improvement of the employees' satisfaction. Our main objective is to carry out a literature review focused on Kanban methodology applied to health care. In this sense, this study can serve as a script to improve hospital management in pandemic periods, such as the one currently lived on COVID-19. We carry out a literature review searching in four different databases. We combine several terms to achieve our objective. We identify several articles which describe Kanban methodology applied to health field. More specifically, we present in what areas (nursing or pharmacy, among others) this method has been applied. In addition, we show all the barriers as well as benefits caused by the implementation of this system. There are a few studies focused on analysing how Kanban is applied to health care. Therefore, we can affirm that this topic is still recent.