Purpose This study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement). Design/methodology/approach Simulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist. Findings The demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research. Originality/value This study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

No abstract

The liability in inventory models decide of "how much or how many" of inventory items to order. The economic order quantity (EOQ) varies from one model to another, depending on assumptions and variables. This paper has developed the EOQ in deterministic inventory model of multi-item when the objective function is subjected to investment and capacity of shortage space constraints. The constraints are assumed to be active if the left hand side does not satisfy the right hand side condition. Thus, Lagrange method is used to find the new multi-item EOQ in each four models with each constraint to achieve the new formula of EOQ.

No abstract

Globalization and advances in information and production technologies make inventory management can be very difficult even for organizations with simple structures. The complexities of inventory management increase in multi-stage networks, where inventory appears in multiple tiers of locations. Due to massive practical applications in the reality of the world, an efficient inventory system policy whether single location or multi-stage location will avoid falling into overstock inventory or under stock inventory. However, the optimality of inventory and allocation policies in a supply chain is still unknown for most types of multi-stage systems. Hence, this paper aims to determine the probability distribution function of demand during lead-time by using a simulation model when the demand distributed normal and the lead-time distributed gamma. The simulation model showed a new probability distribution function of demand during lead-time in the considered inventory system, which is, Generalized Gamma distribution with 4 parameters. This probability distribution function makes the mathematical expression more difficult to build the inventory model especially in multistage or multi-echelon inventory model.

The obligation or the traditional question in inventory models is how much or how many of inventory items to order. In inventory control, the economic order quantity, EOQ is varies from model to another based on the variables and assumptions. This paper develops the EOQ using two deterministic inventory models (Purchases model and production model with shortage) of multi-items when the objective function is subjected to a constrained number of items quantities. The constraints are supposed to be active if the left-hand side does not satisfy the right-hand side condition. Thus, to achieve the new formula of multi-item EOQ in each of the two models with the constraint, Lagrange method have been used. Finally, to provide the proposed model a numerical example has been used.Keywords: deterministic inventory models, economic order quantity and lagrange method

Demand forecasting is a fundamental element in industrial problems. Forecasts are crucial for accurately estimating intermittent demand to establish inventory measurements. The demand estimation by the Croston method gives less accurate values, which increases the standard deviation value. This increase indicates that the forecasted method is an inappropriate method of intermittent demand data because of the zero values. However, real data were adopted in an industrial sector for three years with constant lead-time. Furthermore, an integration of Bernoulli distribution and geometric distribution has been done to establish the new formulation, then extracting the mean equation and the standard deviation equation of intermittent demand during lead-time. Relying on it, the optimal quantity of safety stock and reorder levels have been obtained. Furthermore, the proposed modified forecasting method was evaluated based on the criteria of CV and the results that obtained gives a less ratio dispersion of data thus accurate results. These procedures are very important to the industrial sectors in drawing future inventory policies.

The responsibility in inventory models decides of how much or how many of inventory items to order. An economic order quantity (EOQ) differs from a model to another according to the assumptions and the variables. This paper has developed the EOQ in two deterministic inventory model (Purchases with shortage and production with shortage) of multi-item when the objective function is subjected to annual number of orders. The constraints are assumed to be active if the left hand side does not satisfy the right hand side condition. Thus, Lagrange method is used to find the new multi-item EOQ in each two models with the constraint to achieve the new formula of EOQ. Finally, a numerical example is provided to justify the proposed model.