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The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

Military values/virtues are a subset of ethical values. The purpose of this study is to examine the effect of military virtues on job performance, either directly, or indirectly through mediation by, loyalty, patience, respect, employee engagement, job performance, military ethics, courage, self-discipline, caring, military virtue, Sri Lanka Air Force (SLAF) employee engagement.

Military virtues were conceptualized as a collective construct, consisting of loyalty, courage, patience, respect, self-discipline and caring. Using a sample of 254 military officers in the SLAF, the authors measured the effect of military virtues on job performance. The first model was a direct measurement of the influence of military virtues on job performance. The second model measured the influence of military virtues on employee engagement, followed by measurement of the influence of employee engagement on job performance. Structural equation modeling was used in data analysis.

Both direct effects and mediated effects of military virtues on job performance were significant. However, the direct effect was stronger, suggesting that military virtues in and of themselves resulted in superior performance, more effectively, than by first increasing employee engagement with the task or the organization.

This may be an initial empirical examination of the effects of military virtues on job performance.