Inventory Control Performance Research Articles

The Influence of ERP-SAP Implementation and Inventory Management on Production Efficiency through Inventory Control Performance Using Partial Least Square (PLS) Method

PT XYZ is the largest manufacturing company in Indonesia that produces biscuits with the support of the SAP system, which has integrated data from each division, especially the inventory control division. Due to the accessibility of real-time data, it was observed that there were two late arrivals of raw materials, causing the company to only achieve 75% of the weekly production target. Therefore, this study aims to determine the effect of ERP-SAP implementation and inventory management on smooth production and provide recommendations for improvement through inventory control performance at PT XYZ. The method used is Partial Least Square (PLS) which uses SmartPLS. Based on the research conducted, it was found that the implementation of ERP-SAP does not have a positive impact on production flexibility, but has a positive impact on inventory management performance, and then inventory management directly positively affects production efficiency and inventory control performance

Accu Scrap Raw Material Inventorycontrol Using the Min- Max Method

This research investigates the efficacy of implementing the Min-Max method to enhance inventory control performance in a manufacturing setting converting ACCU scrap into tin metal. Focused on two product types, bullion, and ingots, the study aims to optimize inventory policies by determining safety stock, minimum and maximum stock levels, order quantities, and reorder points to mitigate stockouts and overstocks. Results demonstrate a significant improvement, with the Min-Max method enabling a 33% increase in raw material control effectiveness compared to previous methods. This research underscores the potential for the Min-Max approach to provide practical solutions for inventory management challenges in manufacturing, offering valuable insights for industry practitioners and researchers alike. Highlight : The Min-Max method improved inventory management effectiveness by 33%. This approach facilitated the determination of safe stock levels, minimal order quantities, and reorder points. Manufacturers experienced reduced operating expenses while maintaining competitive edge and responsiveness to market dynamics. Keywords: Accu scrap processing, Min-Max method, inventory control, supply chain optimization, resource allocation

Electronic inventory control systems as a competitive tool for e-retailers

Despite technological advances, some e-retail organisations are still struggling to improve their inventory control performance due to a limited application of such technology, a lack of relevant skills and inadequate supply chain management policies. This article explored how EICS can enhance the competitiveness of an online retailer. Adopting the qualitative paradigm, data was collected through semi-structured interviews. Consequently, data analysis entailed identifying common patterns within the responses and critically analysing them to achieve the research objectives. The findings revealed that with the implementation of EICS, the organisation could oversee inventory stock cycle transactions, perform a transparent inter-branch transfer and security control. Also, the vital competitive advantages, achieved through EICS implementation, include high customer satisfaction due to faster system services rendered than previously. Likewise, a rise in customer service efficiency and higher customer loyalty rates. In sum, this research supports the idea that EICS technology is an essential ingredient for e-retailers to thrive and compete. A major recommendation is for e-retailers to clearly define the attributes and metrics of their inventory control performance in line with inventory control objectives as the EICS competitiveness framework.

Forecasting of lead-time demand variance: Implications for safety stock calculations

Lead-time demand forecasting constitutes the backbone of inventory control. Although there has been a considerable amount of research on forecasting the mean lead-time demand, less has centered around forecasting lead-time demand variance, especially in the case of stochastic lead-times. This represents an important gap in the literature, given that safety stock calculations rely explicitly on the lead-time demand variance (or equivalently the variance of the lead-time demand forecast error for unbiased estimators). We bridge this gap by exploring the viability of three strategies to estimate the variance of the lead-time demand forecast error under stochastic lead-times: (1) aggregating the per period variance of forecast errors over the lead-time, which is the classical approach; (2) considering the variance of the aggregated (over the lead-time) forecast error; (3) considering the variance of the forecast errors resulting from temporally aggregated (over the lead-time length) demand. Analytical results are derived for a first order autoregressive moving average ARMA(1,1) demand process for both a single exponential smoothing and the minimum mean squared error forecasting method. A numerical investigation assesses the effects of demand autocorrelation and lead-time variability on the accuracy of each strategy, and the conditions under which one outperforms the others. The results show that the classical strategy presented in textbooks appears to be the least accurate one, except for cases with a high negative demand autocorrelation. An analysis of the inventory control performance also reveals that the classical strategy often leads to higher inventory costs and lower service levels for positive autocorrelation.

The Main Critical Risk in the Supply Chain of Component Automotive Industry: A Case Study

In automotive component company that produce electric components their supply activities have the opportunity to arise risks. Therefore, is necessary to identify and analyze critical risks or disturbances that are likely to arise and mitigate risks in operational activities in the component automotive supply chain. This research was conducted with using a house of risk model consisting of 2 phases. The first phase is identifying risk and agent risk, which then measure the severity and insurance level and calculate the aggregate risk priority (ARP) value. The second phase is handling risk. After doing research risk mitigations which were prioritized to be realized were review the current SOP and control the implementation, make punishment policy that does not to according to the SOP, maximize supervision to subordinates, discussions with customers for specific policy change in each issue, improve production and inventory control performance, arrange engine maintenance programs according to production capacity, discussion to review contracts with suppliers, strengthen implementation TQM in supplier and look forward to more long term potential supplier.

The Role of The Electronic Operating System in Raising the Efficiency and Effectiveness of the Performance of Inventory Control the Application of A Proposed Electronic Program in A Sample of Al-muthanna University Stores

the Role of The Electronic Operating System in Raising the Efficiency and Effectiveness of the Performance of Inventory Control the Application of A Proposed Electronic Program in A Sample of Al-muthanna University Stores

On the δ-service level for demand substitution in inventory control

In retail, it is usual to measure the performance of inventory control by a so-called service level which measures the number of days (probability) that demand is fulfilled for a certain product, i.e. the product is not out of stock. Part of the customers that do not encounter the product in stock will look for a substitute. To measure the performance, we introduce a so-called δ-service level which measures the availability of the substitute. We investigate the effect of safety levels and substitution probability on the δ-service level and illustrate them based on the case of poisson demand.

An empirical investigation of comparative performance of approximate and exact corrections of the bias in Croston's method in forecasting lumpy demand

A positive bias in Croston's method, which had been developed to forecast intermittent demand, was reported by Syntetos and Boylan (2001). They proposed an approximate correction. Subsequently, Shale et al. (2006) proposed an 'exact' correction. Both corrections were derived analytically. The mathematical analysis establishes the superiority of the exact correction over both Croston's method and the approximate correction. We empirically investigate whether or not there are significant improvements in statistical forecast accuracy as well as inventory control performance obtained by applying the approximate or exact correction when forecasting lumpy demand. Using extensive simulation experiments, we find overall superior forecast accuracy of the bias correction methods over both simple exponential smoothing and Croston's methods. However, the exact correction yielded the same or only marginally better accuracy measures compared with the approximate correction. Moreover, in terms of inventory control performance, we observe marginal differences in inventory on hand and backlogs.

Benefits of leadtime information and of its combination with demand forecast information

This paper studies both the benefits of leadtime information and the interrelationships among demand forecast information, leadtime uncertainty, and leadtime information, for improving inventory control performance. To this end, it explicitly incorporates both leadtime information and demand forecast information into an inventory model although they have conventionally been dealt with individually in the literature. Specifically, it considers a periodic-review inventory model with uncertain leadtimes, in which both leadtime information and demand forecast information are available. The leadtime information is modeled as the information of the arrival of outstanding orders and the demand forecast information as the Martingale Model of Forecast Evolution (MMFE).The paper analyzes the way to use both leadtime information and demand forecast information for inventory control operations. Specifically, under certain conditions, the optimality of the state-dependent base-stock policy is shown, and an effective heuristic policy is developed.It also derives some managerial insights for exploiting both leadtime information and demand forecast information in the case where leadtimes are uncertain. For instance, while leadtime information itself significantly improves inventory control performance, it also enhances the benefits of demand forecast information. Specifically, the benefits of demand forecast information may significantly decrease as leadtime uncertainty increases. However, the decrease of the benefits of demand forecast information may be alleviated with leadtime information.

Providing decision support for replenishment operations using a genetic algorithms based fuzzy system

AbstractOwing to the shockwaves brought by the recent financial tsunami, most enterprises are facing tremendous challenges in maintaining the good liquidity of their own companies. In order to sustain a desirable level of cash flow for expanding business, inventory needs to be well organized because unnecessary inventory that ties up the capital in the business would prevent the enterprises from making investments. Because the existing approaches to replenishment are inflexible and unsophisticated, a new customer‐based responsive replenishment system embracing online analytical processing, fuzzy logic and genetic algorithm is proposed in this paper. This system could determine accurate and realistic order quantities based on all possible and relevant variables that affect the order quantity for each item that needs to be replenished. Once the quantity has been accurately identified, the company can increase the level of customer satisfaction while minimizing stocks. Furthermore, rather than static rule repositioning, the proposed dynamic rule refining ability makes the replenishment system self‐ameliorating by using genetic algorithm to investigate the possible fuzzy rule candidates for a more accurate inventory management model. A study has been conducted in a case company for the validation of the feasibility of the proposed system. After performing a spatial analysis, the results obtained indicate that the proposed responsive replenishment system is capable of ensuring improved inventory control performance in the case company.

Integrated cooperative inventory models with one vendor and multiple buyers in the supply chain

Nowadays, supply chain management (SCM) is a popular practice in manufacturing systems. The concept of SCM has forced members to find ways to cut costs and bring performance benefits to the firms. In the creation of long-term partnerships in the supply chain, the collaboration between supply chain partners for improving the performance of inventory control is of special interest. This paper develops a single-vendor multiple-buyer integrated inventory model for minimising the total relevant annual costs incurred by the vendor and the buyers while the probability distribution of the lead time demand is normal, and the option of investing in process quality improvement is included.

On the choice of a demand distribution for inventory management models

An inventory system containing uncertainty, e.g., in demand, in costs, in lead time, or in supplied quantity or quality, needs a probability distribution of demand for reorder point models. In the literature on inventory control, many times reference is made to the Normal or Gamma distribution for describing the demand in the lead time. The Poisson distribution has been found to provide a reasonable fit when the demand is very low. However, information about the functional form of the probability distribution is often incomplete in practice. For example, it might be that only the first moments of the probability distribution are known. This incomplete information is a problem as the shape of the distribution is important in terms of the performance of inventory control. A procedure is described to determine shape characteristics when only the first two moments of the distribution of demand during the lead time are known, using a compound Poisson distribution and the Pearson chart. [Received 18 August 2006; Revised 15 November 2007; Accepted 11 February 2008]

Evaluating inventory management performance using a turnover curve

Managing inventory levels in the aggregate is a common concern of senior management. A generalized formula (turnover curve) developed in previous research that mimics practical inventory control is used to audit inventory control performance of inventories in the aggregate and at multiple stocking points. The same turnover curve is used to estimate the impact of changing the inventory control procedures or to set new targets for inventory levels. It is a simple yet powerful tool for evaluating inventory managerial performance that can be developed from readily available company data. This research provides additional examples to further validate the practical usefulness of the turnover curve.

Tracking signals in inventory control systems A simulation study

The purpose of this analysis is to evaluate three different tracking signal models. From the point of view of inventory control, we are interested in examining the tracking signal's impact on inventory control performance as measured by the resulting costs — including holding costs, order costs, and shortage costs. In this analysis we are dealing with an inventory control problem for thousands of different items (typical class B items) with independent demand. It is therefore assumed that inventory is controlled using a simple and in practical inventory control often used ( s, Q) system, where the order quantity is calculated using the EOQ formula, and the order point is determined using a well-known stochastic inventory model. The method used will be stochastic simulation, by which it is possible to simulate the practical use of the tracking signal models together with theoretical inventory models. The simulations are carried out for different items where the probability distribution of demand is a dynamic normal distribution, with means describing different time-dependent demand patterns, including trends and different seasons. The conclusion, based on the simulation results, leads to some guidelines on implementing tracking signal methods in inventory control systems.

Evaluation of forecast models used for inventory control during a product's life cycle: A simulation study

This article analyses the consequences of using different exponential smoothing models in inventory control. The evaluation of forecast models has received much attention in the literature. In most articles, forecast models have been evaluated using statistical criteria, such as average forecast error and mean square error. From the point of view of inventory control, however, it is more interesting to examine the forecast model's impact on inventory control performance as measured by, for example, the resulting costs - including holding costs, order costs and shortage costs. The method used is stochastic simulation, with which it is possible to simulate the practical application of forecast models together with theoretical inventory models. The simulations are carried out for a product whose probability distribution of demand is the normal distribution with - a mean described by a traditional life cycle, to which both 6- and 12-month seasons have been added, and - both constant and time-dependent standard deviations. In this analysis, it is assumed that inventory is controlled by an ( s,Q) system, where the order quantity is calculated using the EOQ formula, and the reorder point is determined using an inventory model based on the minimisation of order costs, holding costs and shortage costs.

Hospital Pharmacy Indexes: A Tool for Assessing Purchasing and Inventory Control Performance

The development and use of price and value indexes that differentiate between the effects of price and volume changes on a hospital's cost of drug products and related supplies are discussed. To construct a price index it is necessary to (1) compile a general list of drug products representative of a major portion of the institution's annual drug expenditure, (2) assign appropriate weights (quantity determinants) to these listed products, (3) select a base period that reflects normal hospital operations, and (4) select a suitable equation, the authors' choice being a fixed-weight aggregates method. The value index, which measures the combined effects of acquisition cost changes and volume changes, can be calculated when the acquisition cost change for the period is known. Information derived from the indexes can be used in (1) cost analysis and operational forecasts, (2) measurement and assessment of purchasing and inventory control, and (3) simplification of procurement and inventory processes.