Abstract Paper aims The objectives of this paper are: (i) to analyze the impacts of Inventory Record Inaccuracy (IRI) on picking productivity (PP), lost sales (LS), and warehouse capacity utilization (WCU) for different warehouses; (ii) verify if the Cycle Counting (CC) implementation is sufficient to reduce IRI and (iii) the number of CC operators to maintain warehouse performance. Originality A causal loop diagram in detail of the IRI and CC variable's effects in receiving and picking processes is developed. IRI is separated into ghost and hidden inventory to represent the actual effects in WCU, LS, and PP. Research method A dynamic system model is built. Simulations are performed for the five categories of warehouse performance with different numbers of CC operators. Main findings IRI impacts in all warehouses simulated, so managers should monitor IRI besides employee productivity. CC can reduce IRI in all types of warehouses. The number of CC operators to be implemented is different for each warehouse. Implications for theory and practice Several implications for theory and practice are discussed. For example, IRI impacts more ADV and BIC warehouses without CC implementation. In TYP, ADV, and BIC warehouses CC is sufficient to reduce IRI. In MO and DIS warehouses, other improvements are needed.

Abstract Paper aims This paper proposes a roadmap framework based on a literature review to choose the most proper indicators and targets for a given logistics process derived from the organization's strategic goals. Originality One of the many benefits from the introduction of technologies and the digitization of logistics processes is the acquisition of a great amount of data, which is a valuable asset for an efficient management. In order to transform this data into useful information, most companies rely on performance indicators. In this sense, this paper proposes a roadmap framework based on previous works to guide managers on how to choose a set of indicators that are linked to the company’s targets. To the best of our knowledge, no previous research has proposed a framework dealing with target definitions based on historical data and previous standards or benchmarking, as well as the logistics indicators choice based on the representation theory. Research method In order to elaborate the framework, a two-step structured literature review was carried out, combined with a case application. The literature review included steps based on the works of Govindan & Bouzon (2018) and Moher et al. (2009). The Scopus and the Web of Science databases were selected to gather material to base this study. The framework was applied in a Brazilian construction enterprise, located in Joinville city, Southern Brazil. Main findings The framework application showed that the methodology can facilitate the selection of indicators linked with the company’s strategic goals. Additionally, the indicators legitimation process with managers, a step from the framework, showed that managers' knowledge about the company’s processes is essential for a successful logistics performance system implementation. However, the managers can be resistant to changes for new indicators, and this situation should be avoided during the legitimation process. Future studies may expand the methodology application to other areas than logistics, and future applications of the framework in a Logistics 4.0 environment should provide more insights for the model. Implications for theory and practice From a theoretical perspective, a complete table of logistics performance indicators is provided, as well as the framework for indicators and target definition. From a practical panorama, the framework application shows that practitioners can use this study as a guide to develop more effective logistics performance measurement systems. Moreover, concluding remarks on the relationship between digital transformation and performance measurement systems are provided.

Resumo As informações do mercado, quando interpretadas pelo especialista e incorporadas na previsão quantitativa de forma adequada, aumentam a acuracidade da previsão final. Entretanto, o julgamento humano pode inserir vieses na previsão e uma forma de evitá-1os é estruturar a incorporação do julgamento. Este artigo apresenta um procedimento estruturado para que as empresas possam implantar um sistema de previsão de demanda considerando a incorporação de julgamento à saída de previsões estatísticas. A utilização do procedimento em uma pequena empresa demonstra a aplicação do método. Na análise dos resultados verificou-se que os ajustes por julgamento reduziram os erros das previsões quantitativas em média em 5%. Além disso, o melhor desempenho dos ajustes ocorreu para o produto com maior variabilidade na série histórica de dados e os maiores ajustes trouxeram os melhores resultados.

Abstract When marketing information is well interpreted and incorporated into a quantitative forecast by an expert, forecast accuracy may be enhanced. However, human judgment might introduce biases into the forecast. One way to avoid these biases is to use structured adjustment approaches. This article presents a procedure to help companies implement a demand forecasting system with a judgmental adjustment of statistical forecasts. The use of this procedure in a small company shows its implementation. The results demonstrated that judgmental adjustments improved quantitative forecast accuracy by an average of 5%. The results also showed that the product with the greatest variability in a time series had the best adjustment performance and that the best outcomes came from the larger adjustments.

Todas as empresas em desenvolvimento passam pelo momento de decidir se há ou não necessidade de realizar novos investimentos para suprir uma demanda crescente. Para tomar tal decisão é imprescindível conhecer se o processo atual tem capacidade de produzir a nova demanda. Porém, são raras as empresas que têm a percepção de que os refugos e retrabalhos também consomem recursos da produção e, portanto, devem ser considerados no cálculo da capacidade produtiva. A proposta deste trabalho consiste em incluir esses fatores na análise de capacidade da fábrica, utilizando uma matriz de transição estocástica da cadeia absorvente de Markov como ferramenta para obtenção do fator de capacidade. Este fator, aliado ao índice de eficiência e a demanda desejada ao fim do processo, resulta na capacidade real necessária. Um estudo de caso exemplifica a metodologia, apresentando resultados que permitem o cálculo do índice de ocupação real de cada centro produtivo. O cálculo desse índice demonstrou que alguns centros de trabalho necessitam de análises sobre investimentos em capacitação, pois ultrapassaram 90% de ocupação.

All developing companies must decide once in a while whether it is required to perform new investments to handle a growing demand. In order to make this decision, it is essential to know whether the current productive capacity is able to supply the new demand. However, just few companies realize that refuse and rework use production resources, which must be taken into account in the productive capacity calculation. The aim of this work was to include these factors in factory capacity analysis, using Markov chain stochastic transition matrix as a tool to obtain the capacity factor. This factor - used together with the efficiency index and the required demand in the end of the process - results in the necessary real capacity. A case study exemplifies the proposed methodology, presenting results that allow for the calculation of the real occupation index for each production cell. The calculation of this index demonstrated that some production cells presented more than 90% of occupation and needed investments in their productive capacity.