Order Cycle Time Research Articles

The Effects of Industry 4.0 Technologies on Supply Chain Management Performance: A Case Study of DHL Zambia

Technological advancements related to the Fourth Industrial Revolution are causing disruptive changes that are widely felt at national, industry, and company levels. Hence this mixed methods study aimed to assess the effects of Industry 4.0 technologies on the supply chain management performance at DHL Zambia. The 50 employees selected out of 56 employees at DHL Zambia through a simple random sampling technique resulted in 50 valid responses translating into a 100% response rate. Empirical data was derived from both the Likert scale questionnaire and the interview guide distributed through Google Forms. Quantitative data was analyzed using descriptive statistics, and inferential statistics such as regression analysis, and correlation analysis while qualitative data was analyzed using a thematic approach. The study revealed that the Internet of Things, Blockchain, Big Data Analytics, Augmented Reality and Virtual Reality as Industry 4.0 technologies used at DHL Zambia. The study also found that on-time delivery, order cycle time, inventory turnover, perfect order rate, supply chain cost, lead time, and forecast accuracy, are the supply chain management metrics adopted at DHL Zambia. The multiple regression test results found a significant effect of Industry 4.0 technologies on supply chain management metrics (P>0.05). The study recommended that managers at DHL Zambia prioritize training skill development initiatives aimed at equipping the workforce with the technical competencies required for leveraging Industry 4.0 technologies effectively. This will ensure that the workforce is prepared to embrace digital transformation and adapt to the changing demands of the industry.

Stock Levels and Repair Sourcing in a Periodic Review Exchangeable Item Repair System

Background: Exchangeable item repair systems are inventory systems. A nonfunctional item is exchanged for a functional item and returns to the system after being repaired. In our periodic review setting, repair is performed either in-house or outsourced. When repair is in-house, a repaired item is returned to stock regardless of the repair status of the other items in its order. In contrast, with outsourced repair, the entire order must be repaired for it to return to stock. Methods: We develop formulas for the window fill rate (probability for a customer to be served within a given time window) to measure the system’s performance and compute it for each repair model. The cost of outsourcing is the difference between the number of spares needed to maintain a target performance level when repair is internal and when it is outsourced. Results and Conclusions: In our numerical example, we show that the window fill rate in both models is S-shaped in the number of spares and show how the graph shifts to the right when customer tolerance decreases and order cycle time increases. Further, we show that the cost of outsourcing is increasing with customer tolerance and with the target performance level.

PENERAPAN MODEL INVENTORI PROBABILISTIK FUZZY MULTIOBJEKTIF PADA SISTEM PERSEDIAAN BUAH SALAK

Inventory control is very important in production and trading activities. The purpose of inventory control is to maintain product availability. In certain cases, the products provided must be ordered from distributors outside the city and require waiting time from the time the order is placed until the product is received. The Multiobjective Probabilistic Fuzzy Inventory model can be applied to inventory optimization problems with the uncertainty of the leadtime parameter. In this study, the model was applied to the problem of supply salak fruit at one of the distributors. The first objective function is to minimize holding costs and the second is to minimize deterioration costs. The inventory model is transformed into a single objective form using a weighted method. Based on the results, the order cycle time is 3 days with the optimal total inventory of 430.1086 kg. The holding cost and deterioration costs are IDR 2,075,866 and IDR 571,034, respectively. Changes in the weight value of the objective function result in changes in the total cost value. The greater the weight for the first objective function, the smaller the total cost.

Pneumatic Control for Sustainable Compressed Air Systems: Multi-criteria Optimisation for Energy Efficient Production

Compressed air is widely utilised in manufacturing processes across all industries due to numerous advantages associated with its use. However, the energy efficiency of compressed air generation is often very low, making pneumatic systems one of the most expensive processes in a manufacturing facility. Additionally, compressed air leakages further artificially increase the demand and decrease their efficiency and productivity. This study focused on identifying the effects of pressure and flowrate regulation on the energy efficiency and productivity of a pneumatic automation system. By combining these two control strategies, a reduction in air consumption was achieved while also maintaining the system's cycle time in order to minimise productivity loss. The assessed system included various leaks of different sizes at different locations. When implementing pressure regulation, a reduction in air consumption of up to 22 per cent was achieved. As expected, this also resulted in an increase in cycle time of up to 19 per cent, which was then reduced to 7 per cent by regulating the system's flowrate at the end of use. Multi-criteria optimisation was subsequently employed to identify the optimal control parameters for a balanced effect on both air consumption and cycle time, affecting energy efficiency and productivity, respectively.

Pembuatan Aplikasi Persediaan Barang Berbasis Web Pada PT. Bayan Resources Tbk

This research aims to design and implement a web-based Goods Inventory Information System at PT Bayan Resources Tbk. The company faces challenges in inventory management, such as stock uncertainty, variations in order cycle time, and operational efficiency levels that need to be improved. Waterfall method is used as a framework for system development, with stages of requirement analysis, design, implementation, testing, and maintenance. The system is designed to improve operational efficiency through integration of inventory data, order management, and item tracking. The result is a web-based application with features such as a secure login page, informative dashboard, master data management, efficient input of incoming and outgoing goods, and careful user control. With the implementation of dark mode, the system gives users the flexibility to customize the display according to their preferences. Users can easily access, track, and analyze inventory data, supporting faster and more accurate decision-making for the company.

MODELING STRATEGIC PLANNING FOR SUSTAINABLE COMPETITIVE ADVANTAGE IN SUPPLY CHAIN MANAGEMENT

This paper proposes a knowledge-based customer-centric strategic model to respond to environmental challenges and achieve sustainable competitive advantage (SCA) in the supply chain management (SCM) context. The proposed model strives to leverage knowledge-based distinctive core competencies (DCCs) to improve customer satisfaction and achieve SCA. An exploratory research design has been adopted. The study undertook a systematic literature review approach with 36 articles or books published between 1970 and 2020 to construct the strategic planning model. The paper presents an integrated multi-dimensional model for SCM, including value chain activities, competitiveness strategies, business process re-engineering (BPR), and SCA. The practical result of the article is to develop a theoretical strategic planning model for SCM as a holistic framework for using the company’s DCCs and creating an SCA in a highly competitive and dynamic business environment. The proposed model comprehensively covers the supply chain (SC)’s competitive advantage strategy. For businesses that tend to adapt and triumph, the proposed model helps reduce order cycle times, reduce inventory levels, increase accuracy and efficiency, reduce costs, and improve customer service and satisfaction.

An evaluation of the operational efficiency of pizza restaurants in Zimbabwe

The study sought to test the industry wide claim in the fast-food restaurant industry that they deliver pizza within 10 minutes of waiting. The study further sought to assess the extent to which the actual waiting period for pizza differs from the perceived waiting time. A sample of 500 pizza transactions had their order cycle time measured, and the corresponding perceived waiting time was also recorded from the surveyed respondents who had initiated the transactions. The study revealed that there are statistically significant differences between the prescribed standard waiting time and actual waiting time, and between the actual waiting time and perceived waiting time. This study therefore recommends that restaurateurs in the pizza industry must assess their human and equipment resources in order to reduce the variances in lead times. They must as well provide various materials and activities that occupy customers in order to reduce the perceived waiting time.

Aplikasi Supply Chain Operation Reference (SCOR) pada Produk Chocodot PT Tama Cokelat Garut Indonesia

This study applies the SCOR (Supply Chain Operations Reference) method at one of the companies engaged in the Food and Beverage (F&B) sector, namely PT. Chocolate Tama. The goal is to determine the level of importance of the company's five main activities, namely Plan, Source, Make, Deliver and Return so as to measure and improve the company's total supply chain performance. The research begins with determining KPIs and Supply Chain Management Metrics and weighting supply chain management metrics Level I, II and III using AHP. The results of the validation process there are 44 metrics that represent the performance of supply chain activities, namely 11 plan metrics, 10 source metrics, 7 make metrics, 10 deliver metrics and 6 return metrics. The plan activity has the highest weight value of 0.3188, after that successively source is 0.2254, deliver is 0.1560, make is 0.1511 and return is 0.1487. There are 21 sequences of supply chain performance metrics that must be improved so that there are no more delays in the supply of raw materials as well as recommendations for improvement for increasing the company's revenue in the future. The first three sequences that have the highest values that need to be improved are plan source (planning the source of raw materials), forecast accuracy (forecasting accuracy) and purchase order cycle time (determination of purchase order cycle time).

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

A case study on improving productivity in paint booth assembly

Currently, all automotive industries are faced with the need to respond to rapidly changing customer needs, their desires and the fluctuations in the market demand. During the plant visit and subsequent discussion made with the production department and also according to the operation process chart put up in the process study, it was noticed that the efficient time involved in the sub assembly process is less. Hence, this project work is conducted live to study the existing cycle time of a single model sub assembly line and suggesting improved methods to reduce the non-value-added activities, thereby reducing the cycle time in order to improve the efficiency of the sub assembly line and increase overall productivity. This case study is conducted in a leading garage equipment manufacturing industry in south India, which engages in manufacturing, designing and servicing garage equipment’s. Currently, this plant is manufacturing 25 paint booths (1.4 paint booth assembly per shift) which the company foresees a growth in demand for paint booth assembly and gearing to scale up its production rate to 40 paint booths (2.5 paint booth assembly in a shift) including Sub assembly 6 which has been newly added in the paint booth assembly to meet out its monthly requirement and achieved.

Optimization of Inventory Level Using Fuzzy Probabilistic Exponential Two Parameters Model

Inventory control is an important factor in trading activities. Inventory control aims to ensure product availability. Several factors affect the level of inventory including the level of demand factor, maximum inventory, and the level of deterioration. If the influencing factors cannot be defined with certainty and follow a certain statistic distribution then the fuzzy probabilistic approach can be applied. This research discusses the problem of optimizing the inventory of red chillies at the retail level. The level of deterioration is assumed to follow an exponential distribution and demand follows a Pareto distribution. Statistical parameters are estimated using the Maximum likelihood method and cost parameters are expressed by triangular fuzzy numbers. Based on the calculation results for several beta values, the highest total cost is 405143.6 rupiah, a maximum inventory level of 15 kg, and an order cycle time of 0.923 days.

Stochastic customer order scheduling on heterogeneous parallel machines with resource allocation consideration

This study considers a stochastic customer order scheduling and resource allocation problem in an unrelated parallel machine environment. Customer orders dynamically arrive at a machine station, and each consists of multiple product types with random workloads. Speeds of the machines are controllable through the allocation of limited resources such as overtime or dedicated manpower. The objective is to minimize the long run expected order cycle time by optimizing workload schedule and resource allocation. The impacts of workload variance, product similarity and machine speed are evaluated, and several optimal properties are explored. Three heuristic algorithms are proposed based on the theoretical results developed. The effectiveness of the proposed algorithms is demonstrated through extensive numerical experiments. This study brings new perspectives to resource allocation problems in stochastic environment, and provides insights into the relationship between resource allocation decisions and overall production efficiency.

Multi-Item Production Lot Sizing with Postponement, External Source for Common Parts, and Adjustable Rate for End Products

This study considers a multi-item production lot-size problem incorporating postponement, an external source for common parts, and an adjustable-rate for end products. Dealing with product variety, timely requirements, and limited in-house capacity has led production managers to seek manufacturing schemes and utilization-reduction strategies that can help them meet customer needs, smoothen fabrication schedules, and lower overall manufacturing expenses. We propose a two-stage manufacturing scheme. The first stage produces common parts for multiproduct incorporating a partial supply from an outside contractor to reduce utilization/uptime. Stage two fabricates all end products using an adjustable-rate to reduce the uptime further. We build a model to characterize the problem’s features and use optimization methods to derive the optimal rotation cycle time in order to help managers make cost-effective lot-size decisions and allow manufacturers to gain competitive advantages. A numerical illustration validates the model’s capability and applicability. This study makes two important contributions: (1) It offers a decision-support model for studying such a particular batch-size problem and deciding the optimal rotation cycle time, and (2) it identifies the individual/collective influence of dual uptime-reduction strategies on the operating policy and various performance indexes to help facilitate managerial decision-making.

Measuring supply chain performance: the triple E model

PurposeThis work aims to measure the performance of an electric motors supply chain (SC) by using the Triple E performance measurement model to assess three performance dimensions simultaneously: efficiency, efficacy and environmental impact.Design/methodology/approachThis research presents a real case of a Brazilian company as an example for applying the Triple E performance measurement model and the discrete-event simulation to assess the performance of an SC.FindingsPerformance measurements for the three dimensions were the following: (1) for efficiency, a high inventory cost in the SC, driven by the distribution center (DC) (49.7% total cost); an order cycle time operating with an average of 21.7 days and a logistic channel transport capacity of 88%; (2) for efficacy, a service level of 98% for all channels; (3) for environmental impact, the SC emits a total of 395,733 kg of CO2 annually, with the DC and the regional distribution center (RDC2) being the largest emitters.Originality/valueThis work allowed to measure the performance of an SC interface (manufacturing process, a DC and three regional distribution centers). It was possible to measure the inventory holding cost and out-of-stock inventory costs, order cycle time, SC service level, transport capacity utilization and CO2 emissions in the transportation process of each SC stage and the whole SC. This study can be used as a decision support guide for academics and practitioners to measure and improve the SC performance.

Modelling and Scheduling for Tier-to-Tier Multi-Shuttle Warehousing System

Tier-to-tier Multi-Shuttle Systems (TT-MSS) become increasingly adopted due to their high flexibility and efficiency. In the Tier-to-tier MSS, shuttles work across different tiers with the help of lifts. How to schedule these devices matters a lot in terms of lowering the expected order cycle time. In this paper, we propose a time sequence task scheduling model considering the movements of shuttles and lifts. The model describes the task scheduling problem between shuttles and lifts, which is generated in a specified time window. An improved genetic algorithm is proposed to solve the objective optimization function in the task scheduling problem. Finally, we illustrate the advantages of the Tier-to-tier MSS by conducting experiments, and the results indicate that with the increase of retrieval tasks, the efficiency of Tier-to-tier MSS is approaching that of TC-MSS with fewer shuttles used.

Assessing construction material manufacturers’ warehouse processes from a customer satisfaction perspective

Background: The objective of warehouse processes is to satisfy customer’s desires and requirements whilst using house, equipment and labour effectively. However, in Nigeria studies have revealed operational problems in warehousing and a lack of customer satisfaction in the delivery of construction materials. Objectives: The aim of this study was to evaluate the level of customer satisfaction with the delivery of construction material from the manufacturers’ warehouses to customers or other terminals, with a view to improving the operations. Method: This article adopted a case study research design method in which quantitative data were collected and analysed. The target population was the North-Central geo-political zone of Nigeria. A total of 32 construction material manufacturers were purposively selected from the zone. The observation and measurement approaches were adopted for data collection. A total of 72 customers’ orders were observed and recorded to be representative of deliveries from the sampled ( n = 32) manufacturers’ warehouses to other terminals. A customer quintile benchmark metric was also adopted for analysis and for comparing field results with best practices. Results: The findings revealed that the involved construction material manufacturers’ warehouse processes, were suboptimal and ineffective in terms of perfect order completion and total order cycle time. These results indicated major opportunities for improvement. Conclusion: This article concludes with providing construction material manufacturers with information about their warehouse processes that might help to ensure that the construction material arrives at its final destination in optimal quality, time and cost.

Optimal robot task scheduling based on adaptive neuro-fuzzy system and genetic algorithms

Industrial manipulators should be able to execute difficult tasks in the minimum cycle time in order to increase performance in a robotic work cell. This paper is focused on determining the near optimum route of a manipulator’s end-effector which is requested to reach a predefined set of demand points in a robotic work cell. Two subproblems are related with this goal: the motion planning problem and the task scheduling problem. A new approach is presented in this paper for simultaneously planning collision-free motion and scheduling time near optimum route along the demand points. A combination of a geometrical approach and an adaptive neuro-fuzzy system is employed to consider the multiple manipulator’s configurations, while a special genetic algorithm is designed to solve the derived optimization problem. The experiments show that the proposed method has the capacity to determine both the near optimum manipulator configurations and the near optimum sequence of demand points.

Modelling and design for a shuttle-based storage and retrieval system

The shuttle-based storage and retrieval system (SBS/RS) is a relatively new part-to-picker order picking system. We have developed a performance estimation and design algorithm for the SBS/RS. The performance estimation is based on a queuing model. The design algorithm aims to find the minimum cost configurations in terms of number of tiers, aisles, lifts and workstations with given throughput, tote capacity and order cycle time requirements. We used simulation driven by parameters abstracted from an actual SBS/RS to verify the performance estimation, and applied the design algorithm in the case study. The results indicate that: (1) compared to simulation results, the throughput of the performance estimation is nearly identical when the arrival rate is below the maximum capacity; (2) the design algorithm yields a configuration with 28.1% cost reduction in the current system. In addition, we also compared the shuttle system with the competing robotic order fulfilment system (robotic system in short) in terms of facility cost, building cost and order cycle time. We found that the shuttle system is a better choice if large storage capacity and high throughput are required whereas the robotic order fulfilment system performs better if small storage capacity and low throughput are required.

“Monday Effect” on Performance Variations in Supply Chain Fulfillment: How Information Technology–Enabled Procurement May Help

If labor productivity varies in unknown ways over time, this could lead to unforeseen fluctuations in production or delivery rates, leading to stockouts or inventory build-up in a supply chain, while contributing to higher operating costs and to lower satisfaction levels for customers. The “Monday Effect,” that is lower productivity and higher error rates on Mondays compared with other days of the week, has been demonstrated in the finance and industrial relations literature. Using a database of 12 months of purchases from the General Services Administration (GSA) of the U.S. government (a key distributor of goods to government purchasers), we show that, indeed, performance is lower on Mondays compared with other weekdays in terms of order fulfillment and order cycle time. However, when purchasers use the GSA’s internet-based electronic market instead of using manual ordering procedures, the fulfillment gap between Mondays and other weekdays narrows considerably, demonstrating the value of information technology in smoothing distribution flows. By acknowledging the Monday Effect, distributors can thus plan for these weekly fluctuations and use information technology to improve service levels.

Minimizing Expected Cycle Time of Stochastic Customer Orders Through Bounded Multi-Fidelity Simulations

This paper considers the scheduling of stochastic customer orders to minimize expected cycle time. Customer orders dynamically arrive at a machine station, and each order consists of multiple product types. Random workloads are required by each product type, and the workloads are assigned to a set of unrelated parallel machines in the station to be processed. The objective is to obtain the minimal long-run expected order cycle time through proper workload assignments. In view of the difficulty in evaluating the objective function, this paper models the targeted problem as a simulation optimization problem and proposes to solve it under the multifidelity model framework. To improve the efficiency in evaluating candidate solutions, a low-fidelity model is constructed to select solutions with better performances for high-fidelity simulations. The effectiveness of this low-fidelity model is demonstrated through a series of theoretical evidences. A simulation optimization algorithm, named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MO2TOS), is developed by taking advantage of the properties of the low-fidelity model. Numerical experiments are conducted to evaluate the performance of the proposed algorithm against three other well-known simulation optimization algorithms in the literature. Results indicate that the Bound-MO2TOS outperforms all the other tested algorithms, and its performance is robust against changes in problem scale. Note to Practitioners —Customer order scheduling models have wide applications in industries where every single order may contain multiple product types and the entire order requires one shipment delivery. In many applications, such as pigments dyes manufacturing, auto repair, and grocery, consideration of customer orders rather than individual jobs is often preferred. Given the stochastic nature and the synchronization constraints of the problem, a multifidelity simulation algorithm named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MO2TOS) is proposed. Experimental results suggest that the proposed algorithm outperforms three other popular simulation optimization algorithms under a variety of scenarios. It is admitted that further improvement can be made in refining the computation resource distribution process of Bound-MO2TOS. In the future research, such an improvement will be more thoroughly elaborated so that practitioners can utilize it to coordinate their productions better.