A Model for Demand Planning in Supply Chains with Congestion Effects

This paper is concerned with demand planning for internal supply chains consisting of workstations, production facilities, warehouses, and transportation links. We address the issue of how to help a supplier firmly accept orders and subsequently plan to fulfill demand. We first formulate a linear aggregate planning model for demand management that incorporates elements of order promising, recipe run constraints, and capacity limitations. Using several scenarios, we discuss the use of the model in demand planning and capacity planning to help a supplier firmly respond to requests for quotations. We extend the model to incorporate congestion effects at assembly and blending nodes using clearing functions; the resulting model is nonlinear. We develop and test two algorithms to solve the nonlinear model: one based on inner approximation and the other on outer approximation.

Why scaling up uncertain predictions to higher levels of organisation will underestimate change

Uncertainty is an irreducible part of predictive science, causing us to over- or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system components (e.g. species biomass), and combine them to generate predictions for system-level properties (e.g. ecosystem function). Here we show that this process of scaling up uncertain predictions to higher levels of organization has a surprising consequence: it will systematically underestimate the magnitude of system-level change, an effect whose significance grows with the system’s dimensionality. This stems from a geometrical observation: in high dimensions there are more ways to be more different, than ways to be more similar. This general remark applies to any complex system. Here we will focus on ecosystems thus, on ecosystem-level predictions generated from the combination of predictions at the species-level. In this setting, the ecosystem’s dimensionality is a measure of its diversity. We explain why dimensional effects do not play out when predicting change of a single linear aggregate property (e.g. total biomass), yet are revealed when predicting change of non-linear properties (e.g. absolute biomass change, stability or diversity), and when several properties are considered at once to describe the ecosystem, as in multi-functional ecology. Our findings highlight and describe the counter-intuitive effects of scaling up uncertain predictions, effects that will occur in any field of science where a reductionist approach is used to generate predictions.

Consumer Scores and Price Discrimination

We study the implications of aggregating consumers’ purchase histories into scores that proxy for unobserved willingness to pay. A long-lived consumer interacts with a sequence of firms. Each firm relies on the consumer’s current score–a linear aggregate of noisy purchase signals—to learn about her preferences and to set prices. If the consumer is strategic, she reduces her demand to manipulate her score, which reduces the average equilibrium price. Firms in turn prefer scores that overweigh past signals relative to applying Bayes’ rule with disaggregated data, as this mitigates the ratchet effect and maximizes the firms’ ability to price discriminate. Consumers with high average willingness to pay benefit from data collection, because the gains from low average prices dominate the losses from price discrimination. Finally, hidden scores—those only observed by the firms—reduce demand sensitivity, increase average prices, and reduce consumer surplus, sometimes below the naive-consumer level.

A novel amido–pyrophosphate MnIIchelate complex with the synthetic ligand O{P(O)[NHC(CH3)3]2}2(L): [Mn(L)2{OC(H)N(CH3)2}2]Cl2·2H2O

The title complex,trans-bis(dimethylformamide-κO)bis{N,N′-N′′,N′′′-tetra-tert-butyl[oxybis(phosphonic diamide-κO)]}manganese(II) dichloride dihydrate, [Mn(C16H40N4O3P2)2(C3H7NO)2]Cl2·2H2O, is the first example of a bis-chelate amido–pyrophosphate (pyrophosphoramide) complex containing an O[P(O)(NH)2]2fragment. Its asymmetric unit contains half of the complex dication, one chloride anion and one water molecule. The MnIIatom, located on an inversion centre, is octahedrally coordinated, with a slight elongation towards the monodentate dimethylformamide ligand. Structural features of the title complex, such as the P=O bond lengths and the planarity of the chelate ring, are compared with those of previously reported complexes with six-membered chelates involving the fragments C(O)NHP(O), (X)NP(O) [X = C(O), C(S), S(O)2and P(O)] and O[P(O)(N)2]2. This analysis shows that the six-membered chelate rings are less puckered in pyrophosphoramide complexes containing a P(O)OP(O) skeleton, such as the title compound. The extended structure of the title complex involves a linear aggregate mediated by N—H...O and N—H...Cl hydrogen bonds, in which the chloride anion is an acceptor in two additional O—H...Cl hydrogen bonds.

PLA‐BASED FORMALIZATION OF BUSINESS RULES AND THEIR ANALYSIS BY MEANS OF KNOWLEDGE‐BASED TECHNIQUES / PLA METODU GRĮSTAS VERSLO TAISYKLIŲ FORMALIZAVIMAS IR ANALIZĖ TAIKANT ŽINIOMIS GRĮSTAS METODIKAS

The paper presents an approach that applies the knowledge engineering techniques for representing and analysing business rules. These rules are represented by production rules using concepts of a state‐based piece‐linear aggregate (PLA) model. A knowledge base of the business rules is analysed by checking its consistency (static properties) and dynamic constraints (dynamic properties). The analysis is performed by applying methods for decision table verification and reachable state validation as well as supporting tools. The proposed approach is illustrated by an example of Userv insurance company services. Santrauka Straipsnyje pateikiamas būdas, kurio remiantis taikomos žiniomis grįstos metodikos verslo taisyklėms pavaizduoti ir analizuoti. Verslo taisyklės iliustruojamos produkcinėmis taisyklėmis, naudojant būsenomis grįsto atkarpomis tiesinių agregatų (PLA) modelio konceptus. Verslo taisyklių žinių bazė analizuojama tikrinant jos suderinamumą (statines savybes) ir dinaminius suvaržymus (dinamines savybes). Analizė atliekama taikant sprendimo lentelių verifikavimo ir pasiekiamų būsenų validavimo metodus bei šiuos metodus realizuojančias programines priemones. Siūlomas būdas iliustruojams Userv draudimo kompanijos paslaugų pavyzdžiu.

A Computer-Based Approach for Deriving and Measuring Individual and Team Knowledge Structure from Essay Questions

This proof-of-concept investigation describes a computer-based approach for deriving the knowledge structure of individuals and of groups from their written essays, and considers the convergent criterion-related validity of the computer-based scores relative to human rater essay scores and multiple-choice test scores. After completing a classroom-based course, undergraduate participants in a sophomore-level management course completed a 100-item multiple-choice final examination and then answered an extended-response essay question comparing four management theories. The essays were quantified with ALA-Reader software applying both sentence-wise and linear lexical aggregate approaches, and then analyzed with Pathfinder KNOT software. The linear aggregate approach was a better measure of essay content structure relative to the sentence-wise approach, with significant Spearman correlations of 0.60 and 0.45 with the human rater essay scores. The group network representations of low and high performing students were reasonable and straightforward to interpret, with the high group being more similar to the expert, and the low and high groups more similar to each other than to the expert. Suggestions for further research are provided.