Structural diversity in alkali metal and alkali metal magnesiate chemistry of the bulky 2,6-diisopropyl-N-(trimethylsilyl)anilino ligand

Bulky amido ligands are precious in s-block chemistry since they can implant complementary strong basic and weak nucleophilic properties within compounds. Recent work has shown the pivotal importance of the base structure with enhancement of basicity and extraordinary regioselectivities possible for cyclic alkali metal magnesiates containing mixed n-butyl/amido ligand sets. This work advances alkali metal and alkali metal magnesiate chemistry of the bulky aryl-silyl amido ligand [N(SiMe3)(Dipp)] (Dipp = 2,6-iPr2-C6H3). Infinite chain structures of the parent sodium and potassium amides are disclosed, adding to the few known crystallographically characterised unsolvated s-block metal amides. Solvation by PMDETA or TMEDA gives molecular variants of the lithium and sodium amides; whereas for potassium, PMDETA gives a molecular structure but TMEDA affords a novel, hemi-solvated infinite chain. Crystal structures of the first magnesiate examples of this amide in [MMg{N(SiMe3)(Dipp)}2(μ-nBu)]∞ (M = Na or K), are also revealed though these breakdown to their homometallic components in donor solvent as revealed through NMR and DOSY studies

Reducing lead time risk through multiple sourcing: the case of stochastic demand and variable lead time

This paper studies a buyer sourcing a product from multiple suppliers under stochastic demand. The buyer uses a (Q, s) continuous review, reorder point, order quantity inventory control system to determine the size and timing of orders. Lead time is assumed to be deterministic and to vary linearly with the lot size, wherefore lead time and the associated stockout risk may be influenced by varying the lot size and the number of contracted suppliers. This paper presents mathematical models for a multiple supplier single buyer integrated inventory problem with stochastic demand and variable lead time and studies the impact of the delivery structure on the risk of incurring a stockout during lead time

Controllable Production Rate and Quality Improvement in a Two-Echelon Supply Chain Model

Part 4: Collaborative NetworksInternational audienceThe flexible production plays the key role within any production system. An optimization model is developed for a production system with flexible production rate within a fixed limit, defined by the management, with quality improvement in a supply chain management. The aim of the model is to obtain the best optimum production rate with the global minimum cost. It is assumed that the lead time demand follows a normal distribution and a lead time crashing cost is used to reduce the lead time. A classical optimization technique is used to solve the supply chain model. A theorem is established to obtain the global minimum total cost. A numerical example is given to illustrate the model. Numerical studies prove that this model converges over the existing literature at the global minimum cost

The impact of monetary incentives and regulatory focus on worker productivity and learning in order picking

Purpose In manual order picking systems, temporary workers are often employed to handle demand peaks. While this increases flexibility, it may hamper productivity, as they are usually unfamiliar with the processes and may have little experience. It is important for managers to understand how quickly inexperienced workers arrive at full productivity and which factors support workers in improving their productivity. This paper aims to investigate how learning improves the performance of order pickers, and how their regulatory focus (RF) and monetary incentives, as management actions, influence learning. Design/methodology/approach Data was collected in two case studies in controlled field-lab experiments and statistically analysed. This allowed evaluating the validity of hypotheses through an ANOVA, the calculation of correlation coefficients and the application of regression models. Findings A monetary incentive based on total order picking time and pick errors has a positive influence on order picking time, but not on pick quality. The incentive influences initial productivity, but not the learning rate. A dominant promotion-oriented RF increases the effect of the incentive on initial productivity, but it does not impact worker learning. Practical implications This study contributes to behavioral and human-focused order picking management and supports managers in setting up work plans and developing incentive systems for learning and productivity enhancement, considering worker RF. Originality/value This work is among the few to empirically investigate the effect of monetary incentives on learning in interaction with RF. It is the first study to investigate these concepts in an order picking scenario