Building resilience in SMEs of perishable product supply chains: enablers,barriers and risks

SMEs of perishable product supply chains (PPSCs) are critical to sustaining fresh food supply and global economy. However, they face immense challenges given the nature of their operations. Surprisingly, the literature lacks research on the distinct intra- and inter-organisational factors intertwined to build resilience in these firms. From the semi-structured interviews conducted with 30 managers of a PPSC in Australia, we developed a holistic framework to build resilience in SMEs of PPSCs. The findings offer novel insights into research and practice including an optimal portfolio of enablers, barriers and risks towards building resilience. The analysis reveals that most risks stem from the factors that are internal to the supply chain, thus necessitating a strong supply chain collaboration. The level of resilience, barriers and risks varies considerably within SMEs depending on their size (small vs. medium) and location of nodes in the supply chain. Interestingly, the SMEs with consortium support demonstrate better resilience and performance.     

A remark on estimating the mean of a normal distribution with known coefficient of variation

Let X₁, X₂, …, X_n be iid N(μ, aμ²) (a>0) random variables with an unknown mean μ>0 and known coefficient of variation (CV) √a. The estimation of μ is revisited and it is shown that a modified version of an unbiased estimator of μ [cf. Khan RA. A note on estimating the mean of a normal distribution with known CV. J Am Stat Assoc. 1968;63:1039–1041] is more efficient. A certain linear minimum mean square estimator of Gleser and Healy [Estimating the mean of a normal distribution with known CV. J Am Stat Assoc. 1976;71:977–981] is also modified and improved. These improved estimators are being compared with the maximum likelihood estimator under squared-error loss function. Based on asymptotic consideration, a large sample confidence interval is also mentioned.     

TEAM INCENTIVES: EVIDENCE FROM A FIRM LEVEL EXPERIMENT

Many organizations rely on teamwork, and yet field evidence on the impacts of team‐based incentives remains scarce. Compared to individual incentives, team incentives can affect productivity by changing both workers’ effort and team composition. We present evidence from a field experiment designed to evaluate the impact of rank incentives and tournaments on the productivity and composition of teams. Strengthening incentives, either through rankings or tournaments, makes workers more likely to form teams with others of similar ability instead of with their friends. Introducing rank incentives however reduces average productivity by 14%, whereas introducing a tournament increases it by 24%. Both effects are heterogeneous: rank incentives only reduce the productivity of teams at the bottom of the productivity distribution, and monetary prize tournaments only increase the productivity of teams at the top. We interpret these results through a theoretical framework that makes precise when the provision of team‐based incentives crowds out the productivity‐enhancing effect of social connections under team production.     

Multidimensional Poverty in Mountainous Regions: Shan and Chin in Myanmar

Poverty is complex and multidimensional. People living in mountainous regions are vulnerable and more likely to experience multiple deprivation. However, few studies have addressed multidimensional poverty in mountainous regions. Using data from 4290 households of poverty and vulnerability assessment survey and the Alkire–Foster methodology, this paper estimate and decompose multidimensional poverty in the states of Shan and Chin in Myanmar. The multidimensional poverty is measured in five dimensions and a set of twelve indicators. Nearly half of the population in Shan and three-quarters in Chin were multidimensionally poor. The average intensity of poverty was 44% in Chin and 38% in Shan. The multidimensional poverty index was 0.33 in Chin and 0.19 in Shan. The level of multidimensional poverty in Chin was similar to that in of Sub-Saharan Africa. In Chin, 60% of the population was both multidimensionally poor and consumption poor, but in Shan, it was 20%. About 28% of the population in Shan and 15% in Chin were multidimensionally poor but not consumption poor. Deprivation in education accounts for one-third of the multidimensional poverty in Shan; while deprivation in health accounts for one-third of the multidimensional poverty in Chin. A higher proportion of multidimensionally poor had experienced shocks such as the death of a household member, agricultural loss, or death of livestock compared to the multidimensional non-poor. Multidimensional poverty was significantly higher for rural household, households with lower educational attainment, consumption poor and among those who lived in Chin. Poverty reduction programs require a holistic understanding of poverty and its different dimensions as well as the main contributing factors for effective planning and program implementation. Geographical targeting of poverty reduction program and larger investment in food, health, water, energy and education can reduce the extent of multidimensional poverty in Shan and Chin.     

On the readability of monotone Boolean formulae

Golumbic et al. (Discrete Appl. Math. 154:1465–1477, 2006) defined the readability of a monotone Boolean function f to be the minimum integer k such that there exists an ∧−∨-formula equivalent to f in which each variable appears at most k times. They asked whether there exists a polynomial-time algorithm, which given a monotone Boolean function f, in CNF or DNF form, checks whether f is a read-k function, for a fixed k. In this paper, we partially answer this question already for k=2 by showing that it is NP-hard to decide if a given monotone formula represents a read-twice function. It follows also from our reduction that it is NP-hard to approximate the readability of a given monotone Boolean function f:{0,1} ⁿ →{0,1} within a factor of O(n)\mathcal{O}(n) . We also give tight sublinear upper bounds on the readability of a monotone Boolean function given in CNF (or DNF) form, parameterized by the number of terms in the CNF and the maximum size in each term, or more generally the maximum number of variables in the intersection of any constant number of terms. When the variables of the DNF can be ordered so that each term consists of a set of consecutive variables, we give much tighter logarithmic bounds on the readability.     

A note on the asymptotic efficiency of the sobel–wald test

Let X₁,X₂, … be iid random variables with the pdf f(x,θ)=exp(θx?b(θ)) relative to a σ-finite measure μ, and consider the problem of deciding among three simple hypotheses H_i:θ=θ_i (1?i?3) subject to P(acceptH_i|θ_i)=1?α (1?i?3). A procedure similar to Sobel–Wald procedure is discussed and its asymptotic efficiency as compared with the best nonsequential test is obtained by finding the limit lim_a→0(E_iN(a)/n(a)), where N (a) is the stopping time of the proposed procedure and n(a) is the sample size of the best non-sequential test. It is shown that the same asymptotic limit holds for the original Sobel–Wald procedure. Specializing to N(θ,1) distribution it is found that lim_a→0$\text{(E}{}_{\mathrm{i}}\text{N(α)/n(α))=}\text{1}\text{4}$ (i=1,2) and lim_a→0 (E₃N(α)n(α))=δ²₁/4δ, where δ_i=(θ_i+1?θ_i) with 0<δ₁?δ₂. Also, the asymptotic efficiency evaluated when the X's have an exponential distribution.     

A note on the admissibility of hammersley's estimator of an integer mean

Let X₁, X₂, …, X_n be identically, independently distributed N(i,1) random variables, where i = 0, ±1, ±2, … Hammersley (1950) showed that d = [X?_n], the nearest integer to the sample mean, is the maximum likelihood estimator of i. Khan (1973) showed that d is minimax and admissible with respect to zero-one loss. This note now proves a conjecture of Stein to the effect that in the class of integer-valued estimators d is minimax and admissible under squared-error loss.     

Circular balanced repeated measurement designs in periods of three different sizes

Abstract

Repeated measurement designs are widely used in medicine, pharmacology, animal sciences and psychology. If there is a restriction on the total number of treatments, some experimental units can receive on the total length of time while some experimental units can remain in the trial, then repeated measurements designs with unequal period sizes should be used. In this article, some infinite series are developed to generate the minimal balanced repeated measurement designs in periods of three different sizes p₁, p₂ and p₃, where 2?≤?p₃?<?p₂ ≤ 10 and p₂?<?p₁.     

Influences of landscape structure on butterfly diversity in urban private gardens using a citizen science approach

Urban Ecosystems - Although urbanization is increasing worldwide, private gardens may help mitigate the impact of urbanization on butterfly diversity. We investigated how local and landscape...