Demand forecasting approach inventory control for CIMS

Suesut, T.; Mongkhoin, Bancha

doi:10.1109/icarcv.2004.1469444

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Criteria like the reliability of delivery and product quality have increased importance. Suesut and Monghion (2004) suggested that inventory and demand-forecasting can be linked with automation systems, computer networks, and information technology to reduce errors and lead-time and enhance the production system's reliability. Yang and Niu (2009) concluded that stock classification techniques like ABC analysis, VED analysis, and FSN analysis are instrumental in inventory management of spare parts, capital equipment, etc.…”

Section: Introductionmentioning

confidence: 99%

Achieving cost efficiency through increased inventory leanness: Evidence from manufacturing industry

Mor

Kumar

Yadav

et al. 2021

Production Engineering Archives

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Inventory management’s fundamental problem starts with maintaining equilibrium among the operating efficiency, cost of investment, and other allied costs with extensive inventories to keep the actual conflicts at the minimum while optimizing the inventory holding levels. But, inventory management practices have not been well exploited in various manufacturing industries yet. In this study, inventory management tools, i.e., ABC and VED analysis, have been applied in the manufacturing industry, considering 146 items as raw material for an assembly. A total of 15 items under ‘AV’ class have been identified that consume 82.05 % of the total cost, and these items need strict control and frequent ordering. Sigma level of suppliers is also calculated, which comes out to be 2.36, and it must be improved to reduce the overall inventory cost.

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Section: Introductionmentioning

confidence: 99%

Achieving cost efficiency through increased inventory leanness: Evidence from manufacturing industry

Mor

Kumar

Yadav

et al. 2021

Production Engineering Archives

View full text Add to dashboard Cite

show abstract

“…Much of the work in the DC control area has focused on inventory control, including the development of forecast approaches (Suesut and Mongkhoin 2004), the use of agent-based approaches (Ito and Abadi 2002) and the study of inventory accuracy (Wayman 1995). From an operational control perspective, much of the research has been dedicated to automated warehouses, as is the case of Amato et al (2005) who developed control algorithms for the optimization of the automated systems.…”

Section: Introductionmentioning

confidence: 99%

Simulation for Predictive Control of a distribution center

Medina

Bilsel

Wysk

et al. 2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

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In this paper we present the application of Simulation for Predictive Control (SimPC) as a decision making tool for improvement of non-automated distribution centers (DCs). SimPC is focused on determining the viability of a given truckdock assignment schedule, including arrival times and dock assignments for inbound and outbound trucks. SimPC also serves to perform iterative procedures of system parameter adjustments while searching for a viable schedule. The proposed model utilizes real-time data from DC's warehouse management system (WMS) to obtain the current state of the DC, which serves as initial conditions for the simulation. The model emulates the decision rules imbedded in WMS, which include assigning tasks to system-guided resources, selecting storage locations for inbound operations and determining retrieval locations for outbound operations. The SimPC model provides insights for the identification of scheduling problems, guidance for operational and tactical solutions, and serves as a tool to verify these solutions.

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“…Management developing stock policy which can minimize operational total cost is the main purpose of planning and control. Stock management is an important factor in production process, one factor influencing stock management is demand prediction; demand fluctuation influences product stock and production activity greatly [1].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Safety Stock Using Fuzzy Time Series (FTS) and Technology of Radio Frequency Identification (RFID) for Stock Control at Vendor Managed Inventory (VMI)

2018

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This research was conducted by prediction of safety stock using Fuzzy Time Series (FTS) and technology of Radio Frequency Identification (RFID) for stock control at Vendor Managed Inventory (VMI). Well-controlled stock influenced company revenue and minimized cost. It discussed about information system of safety stock prediction developed through programming language of PHP. Input data consisted of demand got from automatic, online and real time acquisition using technology of RFID, then, sent to server and stored at online database. Furthermore, data of acquisition result was predicted by using algorithm of FTS applying universe of discourse defining and fuzzy sets determination. Fuzzy set result was continued to division process of universe of discourse in order to be to final step. Prediction result was displayed at information system dashboard developed. By using 60 data from demand data, prediction score was 450.331 and safety stock was 135.535. Prediction result was done by error deviation validation using Mean Square Percent Error of 15%. It proved that FTS was good enough in predicting demand and safety stock for stock control. For deeper analysis, researchers used data of demand and universe of discourse U varying at FTS to get various result based on test data used.

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Demand forecasting approach inventory control for CIMS

Cited by 4 publications

References 6 publications

Achieving cost efficiency through increased inventory leanness: Evidence from manufacturing industry

Achieving cost efficiency through increased inventory leanness: Evidence from manufacturing industry

Simulation for Predictive Control of a distribution center

Prediction of Safety Stock Using Fuzzy Time Series (FTS) and Technology of Radio Frequency Identification (RFID) for Stock Control at Vendor Managed Inventory (VMI)

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