Good Production Plan Research Articles

Perencanaan Produksi UMKM dengan Menggunakan Pendekatan Metode Transportasi

Micro, Small and Medium Enterprises (MSMEs) are an important part of the Indonesian economy. Many micro, small and medium enterprises (MSMEs) are making new innovations in the fields of handicrafts, food and drinks, among others. The development of MSMEs faces demands that change every month. To meet this demand, they must carry out optimal production planning. Production planning and control can be used as a reference for determining production quantities and forecasting product demand. By carrying out good production planning and control, MSMEs can reduce or anticipate risks, thereby increasing their profits. Coconut jelly MSMEs are one of the businesses that are also affected by poor production planning. On average, in 2023 there will be an excess production of 150 pieces per month. For small and medium businesses (MSMEs), fluctuations of this magnitude are very disruptive to company finances. Effective production planning should be able to optimize the use of all organizational resources, including raw materials, materials, labor and finance. This article carries out production planning using a transportation method approach. Initial planning is carried out following a pattern of forecasting the number of requests in the future. Forecasting is carried out using 3 methods, namely single moving average, double exponential smoothing and trend analysis. Of these three methods, the one with the smallest Mean Square Error (MSE) value will be selected. Production planning will be outlined in the Master Production Schedule (JIP). Determination of JIP is based on the results of production planning through 2 alternatives, namely permanent and changing workforce. From the results of the transportation method approach, it was found that the minimum production cost was found in the variable labor alternative, namely IDR 178,326,000, there was a cost difference from the fixed labor alternative of IDR 1,451,200. The approach method used in this article still needs a lot of improvement, especially in terms of determining the cost of each production component which can be more dynamic. The approach that can be taken can use heuristic methods such as genetic algorithms. So, it is hoped that the results obtained can be closer to reality.

Improvement intervention proposal for a small clothing company: airtable software

The basis for any industry to achieve high growth potential and, consequently, profitability, comes from organization, efficiency, standardization, quality, among other factors. Entrepreneurs are often unable to pinpoint how damaging the disorganization of a production process is, poor programming and development can suck up all the company's profits, and due to the lack of planning and production control this situation can be imperceptible in the final process. All of these points can be resolved with good company organization aligned with good production planning and control. However, every optimization process requires everyone's involvement and commitment, that is, everyone needs to be willing to donate to achieve process improvement that brings positive impacts to both the worker and the company. In this work we observed the importance of developing and implementing Production Planning, Programming and Control (PPCP) software on an online platform called Airtable. With the software it was possible to access the company's entire data history, making the information easily accessible for everyone in the company. As a result, it was possible to see a significant improvement in the company's organization, a reduction in lead time and consequently a significant growth.

Optimisasi Non Linear Programming Multivariat pada Perencanaan Produksi Industri Manufaktur Baja Lapis Seng (BjLS)

The main objective of this study was to examine the potential of production planning process optimization in Zinc Plated Steel (BjLS) manufacturing industry through Non Linear Programming (NLP) simulation. The manufacturing industry in the steel sub -sector is one of the strategic industries as it is a pillar of national development. Global competition requires companies to continuously improve the efficiency of their production activities, which is heavily influenced by the production planning process. With good production planning, it is hoped that the company can optimize the use of limited resources to achieve the company's goals. This study found that the most appropriate optimization to the real situation of the BjLS manufacturing industry is multivariate NLP with constraints on production volume, production time, raw material supply and budget to purchase raw materials. This research can also provide input on the production mix that best suits the company’s resources.

Aggregate Planning Implementation for Planning and Controlling The Materials in The Beverage Packaging Industry

The Beverage Packaging Industry is a company that produce ready-to-drink drinks that produces Box Bottle Tea (BBT) 250 slim products. This product is sent to several customers and distributors throughout Indonesia. During 2020 the demand for this product has increased. In addition, the increasing demand also fluctuated significantly so the company was unable to predict production schedule. The continuity of the production process to meet consumer demand requires good production planning. The purpose of this research is to plan and control the production process of BBT 250 slim to obtain optimal raw material requirements. This study uses the method of Linear Regression and Aggregate Planning. Based on the analysis with Linear Regression, this study resulted in a model Y=319.575+3.723X, meaning that if demand decreases, the amount of production also decreases. This model is very useful for the Beverage Packaging Industry in planning and controlling raw materials for production. Calculation of raw material requirements for BBT 250 slim products results in an efficiency of raw materials than usual forecasting.

Ship Production Planning Using Shipbuilding System Modeling and Discrete Time Process Simulation

Production planning is an important factor for production efficiency in the shipyard. However, planning is currently executed by manual operations based on the experience of field workers because of the complexity of the target shipbuilding project. This paper proposes a method of creating a realistic production plan automatically by modeling the complex shipbuilding project and using the developed process simulation. Specifically, we propose a method to represent the target shipbuilding process by four system models—Product, Workflow, Workplace, and Team. By executing the developed process simulation using system models, a Gantt chart can be created as a realistic production plan. From the case studies of a virtual shipyard manufacturing a ship hull block, it can be said that the proposed method can create a good production plan considering the complexity of the target shipbuilding project. In addition, the proposed method can evaluate the bottleneck of the target shipyard and the re-allocation plan for workers quantitatively.

MODEL PERENCANAAN DAN PENGENDALIAN PRODUKSI DI INDUSTRI PENGOLAHAN BUAH CARICA (CARICA PUBESCENS)

The competition in the carica fruit processing industry is increasing, so that efforts are needed to increase production efficiency through good production planning and control (PPC). CV XYZ is one of the carica fruit processing companies which has PPC problems. PPC activities done intuitively and not systematically causing a decision in PPC is ineffective and production process is inefficient. Those problems can be resolved by developing a PPC model in accordance with CV XYZ’s business process. So, this research objective was to develop a PPC model at CV XYZ. The model consisted of four sub-models which were related to each other. The first sub-model was Sub-model of Demand Forecasting and the second sub-model was Sub-model of Carica Fruit Availability Forecasting. Those two sub-models used the Auto-regressive Integrated Moving Average (ARIMA). The third sub-model was Sub-model of Production and Packaging Planning using Integer Linear Programming (ILP). The fourth sub-model was Sub-model of Carica Fruit Inventory Control using Material Requirement Planning (MRP). PPC model can clarify the PPC system at CV XYZ and produce optimal plans. PPC model improves efficiency of total production cost up to 39.86% and total inventory cost up to 27.56%. Keywords: carica industry, model, optimal, production planning and control

Research on Stress Distribution Characteristics for Uniquely Shaped Roadway with Hard Roof in Steeply Inclined Coal Seam and Support Technology of Reducing Vibration Impact

This paper presents a comprehensive study of the stress distribution and stability analysis of a uniquely shaped roadway having a steeply dipping hard roof. The coal seam and its roof have a certain impact tendency, which is the internal condition of rock burst. The syncline tectonic stress causes the original rock stress to reach a higher level. The large amount of coal produced in the coal mine and the large movement range of the upper strata cause the huge mining additional stress around the stope. The impact load caused by “cantilever beam” fracture of hard roof can induce and strengthen rock burst. Its engineering geological setting encompasses the mining process and surrounding rock conditions of No. 6 Coal Seam in the 2130 coal mine of Xinjiang. Numerical simulations with theoretical analysis and field measurements investigated a proposed new truss combined support scheme for implementation. A comparison was made of the differences in the state parameters of the road under the new and old support conditions. The application of the new combined support technology changed the form of the stress distribution around the road. Apart from the displacements of the two coal sidewalls, the new support system notably reduced the displacement of roof and floor by 67.8% and 83.6%, respectively. After the implementation of the new support scheme, the frequency of the original rock burst in the working face is greatly reduced, the surrounding rock control and field application effects also remained good, and personnel and equipment safety and production plan have a good guarantee.

Unrelated Parallel Machines Scheduling with Sequence-Dependent Setup Times to Minimize Makespan and Tariff Charged Energy Consumption

An appropriate trade-off between total electricity costs and makespan can lead to good production planning and reduce unnecessary energy consumption. Time-of-use (TOU) electricity pricing policy has been executed in many countries which enabled industrial consumers with high energy consumption to reduce their energy costs. In this study, an unrelated parallel machines scheduling problem is considered for minimizing makespan and also energy consumption costs. Due to the importance of sequence-dependent setup times in production environments, they are considered according to the restricted duration of time periods under TOU policy. These considerations are added to the current literature. A mixed-integer bi-objective mathematical model is presented and the e-constraint method is applied to solve small and also medium-sized instances. Because the problem is shown to be NP-hard, several large-sized instances are approximately solved using Multiple Objective Particle Swarm Optimization algorithm, and Multiple Objective Simulated Annealing algorithm. Computational experiments are conducted on randomly generated data. The results show the efficiency and appropriate performance of the proposed methods.

CONTROL OF RAW MATERIAL SUPPLY WITH BILL OF MATERIALS IN PT SINAR METRINDO PERKASA

Production planning and control can be used as a reference or guideline in conducting production activities. With the existence of good production planning and control, the company can minimize or anticipate things that are detrimental to the company, so that optimal profits can be obtained by the company. The problem that occurs is PT. Sinar Metrindo Perkasa has not used the right forecasting method to predict the sales of electrical panels that continue to fluctuate. Often there is a lack of production in the company does not know the planning of raw materials for further production. Forecasting results can be used to compile production activities and operations of the company, such as in terms of raw material inventories, namely looking for safety stock or making a standard bill of material that is useful to know for further planning. Forecasting with 3 methods for evaluating forecasting using the sum of absolute errors. Mean Absolute Deviation (MAD) measures the accuracy of predictions by averaging estimated errors. MAD weight moving average is 152662.3 data, single exponential smoothing 112749.3 data and simple linear regression 83282.52 data. Then on the BKA and BKB the results obtained by the BKA in October were equal to 32,837 and the BKB value of -32,837, the average of the results of the moving range of 123,449. The graphic image on the x-axis line is the number of periods or months and the y-axis value of the number of forecasting lines BKA has uniform data because the value of the data distribution of material purchases is close to the BKA means that the percentage value of material purchases during the year is problematic and vice versa with BKB uniform data because the value of the data spread is close to the average. For this forecasting, further research can be done by finding a bill of material for each order and also looking for control of the production with safety stock.

Optimization of Production Process Time with Network/PERT Analysis Technique and SMED Method

As a company that does the production process based on orders from customers, the timeliness of completion of the product and the quality of the product is important. Companies were given a period of time to complete the order. So that the company can complete orders on time, the company must have good production planning. For scheduling was done by the method of analysis of network / PERT and SMED method. The study of literature and data collection either through observation, interviews and the collection of documentation do. Data processing is done is identify the critical path with network analysis techniques / methods PERT by collecting data while optimistic, pessimistic and then with accelerating network machine set-up time with SMED method then obtained acceleration time from 10:58 hours to 8 hours set-up compacting machine so that the time that they got 2.6 hours wane. Thus the method SMED obtain optimum results even though they can be accelerated by other methods.

Determination of Optimal Production Number by Considering the Use of Raw Materials Using Goal Programming Approach

The company produces PVC pipes in Medan city is experiencing problems in the form of a higher number of requests compared to the production number. This is because the company does not have a good production plan which is the results in not meeting consumer demand. For this reason, a good and integrated production planning is needed by using the goal programming method. Production planning carried out with goal programming aims to maximize company profits. The results shows that by using the goal programming method, the optimal production number was obtained at each type of 2-inch ECO JIS D pipe products of 42851 units, 3-inch ECO JIS D pipe products of 54445 units and 4 inches of ECO JIS D pipe products 24480 units. In addition, by using this method the company profits about Rp. 446,620,100 is greater than the actual method applied by the company.

Sensor-driven Analysis of Manual Assembly Systems

A reliable knowledge of processing times and following analysis are the basis for successful production planning. In particular, manual assembly processes are associated with great efforts to get data about processing times.That is why manual assembly processes are often not sufficiently transparent to enable good production planning, scheduling and control.This paper describes a system for automatic analysis and evaluation of manual assembly processes. It is driven by sensor and measurement technology, constructed modularly, and can be used in a wide range of assembly processes.

Dynamic economic lot size model with perishable inventory and capacity constraints

• An ELS under perishability and stationary production capacities is introduced. • NP-hardness of the problem under our assumptions is proven. • Structural properties of the optimal solution are shown. • A Dynamic Programing based heuristic for the overall problem is given. • A heuristic is proposed for the subproblem where the production periods are given. In this study, we consider a dynamic economic lot sizing problem for a single perishable item under production capacities. We aim to identify the production, inventory and backlogging decisions over the planning horizon, where (i) the parameters of the problem are deterministic but changing over time, and (ii) producer has a constant production capacity that limits the production amount at each period and is allowed to backorder the unmet demand later on. All cost functions are assumed to be concave. A similar problem without production capacities was studied in the literature and a polynomial time algorithm was suggested (Hsu, 2003 [1]). We assume age-dependent holding cost functions and the deterioration rates, which are more realistic for perishable items. Backordering cost functions are period-pair dependent. We prove the NP-hardness of the problem even with zero inventory holding and backlogging costs under our assumptions. We show the structural properties of the optimal solution and suggest a heuristic that finds a good production and distribution plan when the production periods are given. We discuss the performance of the heuristic. We also give a Dynamic Programing-based heuristic for the solution of the overall problem.

A hybrid optimisation algorithm for production scheduling problem with random orders' arriving

Production scheduling problem is a very important functional optimisation problem in the modem manufacturing system and which greatly impacts the efficiency and capacity of the manufacturing system. It has been proved to be a NP-hard problem and cannot be well solved based on traditional algorithms. Therefore, it is very necessary to develop efficiency algorithms which can obtain a good production plan with approximate optimal solution in a reasonable time. In this paper, a new method based on integrated genetic algorithm and tabu search is proposed and which can feasibly balance the exploration ability and exploitation ability of calculation. Also, the solution encoding and decoding, crossover and mutation operator, tabu search strategies have been illustrated. In order to evaluate the performance of the proposed algorithm, one experiment has been carried out and the comparisons among them are also presented. The result shows that the proposed algorithm can obtain satisfactory solutions and also it is applied to solve the production scheduling problem in a practical textile machine manufacturing enterprise.

A hybrid optimisation algorithm for production scheduling problem with random orders' arriving

Production scheduling problem is a very important functional optimisation problem in the modem manufacturing system and which greatly impacts the efficiency and capacity of the manufacturing system. It has been proved to be a NP-hard problem and cannot be well solved based on traditional algorithms. Therefore, it is very necessary to develop efficiency algorithms which can obtain a good production plan with approximate optimal solution in a reasonable time. In this paper, a new method based on integrated genetic algorithm and tabu search is proposed and which can feasibly balance the exploration ability and exploitation ability of calculation. Also, the solution encoding and decoding, crossover and mutation operator, tabu search strategies have been illustrated. In order to evaluate the performance of the proposed algorithm, one experiment has been carried out and the comparisons among them are also presented. The result shows that the proposed algorithm can obtain satisfactory solutions and also it is applied to solve the production scheduling problem in a practical textile machine manufacturing enterprise.

A two-level replenishment frequency model for TOC supply chain replenishment systems under capacity constraint

Good production planning and replenishment management are important for a firm to keep competitive in the market. The theory of constraints-supply chain replenishment system (TOC-SCRS) is a replenishment method under the TOC philosophy. In the application of the TOC-SCRS in a node of a supply chain, the replenishment frequency (RF) and the reliable replenishment time (RRT) are required parameters. Generally, the RF of a node depends on the public transportation schedule such as ship schedules or its private conveyor schedule. If this node is a plant, however, the RF depends on the setup frequency in this plant, and a higher RF (i.e., once a day) is preferred by the TOC because of lower inventory and quick response to different market requirement. Basically, the RF in a plant is determined by its sales or production quantity. When sales increase significantly, the RF in a plant requires to be elongated from higher frequency (i.e., once a day) to lower frequency (i.e., once every two or more days) due to the limited capacity. Therefore, a two-level replenishment frequency model for the TOC-SCRS under capacity constraint is proposed. This model is especially suitable to a plant in which different products have a large sales volume variation. Numerical examples are utilized to evaluate the application of the proposed method. Employing this proposed methodology will facilitate a plant or a central warehouse to implement an effective TOC-SCRS successfully.

Wavelet study of dynamic variations in steel and ironmaking rest gases. Potential effect on external use

The surplus energy in rest gases is an important by-product in the SSAB EMEA steel plant in Luleå. The surplus is used in a local heat and power plant. Studies have been and are being carried out to find alternative use for the high calorific gases, especially the coke oven gas. The different gas flows typically are mixtures of irregularly distributed transients and non-periodic contents of different duration. Slowly varying fluctuations can probably be parried by good production planning. For shorter wavelengths some kind of safety margin could be appropriate, perhaps in the range 0.75–1.5kNm3/h. However, the existing buffer capacity in gasholders, etc. is too small to cover the fluctuations. Good production planning can partly compensate low buffer capacity. An example from a period without gas holder is discussed.We describe analysis of the gas flows that can be used both for better production planning and for efficient external use, e.g., for fuel production. Rather than traditional Fourier analysis, we use wavelet analysis, which is better suited for analyzing the irregular and nonperiodic characteristics of the gas flows. We demonstrate such analysis on collected gas flow data from the SSAB EMEA steel plant and give some suggestions for how to make a good choice of wavelet basis for the analyzed signals.

Geological modeling of rock type domains in the Balya (Turkey) lead-zinc deposit using plurigaussian simulation

AbstractMineral resource evaluation requires defining geological rock-type domains. The traditional simulation methods have serious limitations for applications to large numbers of domains, which have complex contact relations. Plurigaussian simulation is an effective method which can be applied, in a simple way, to any number of domains, using both local and global geological information to infer the distributions of rock types. This work not only presents the application of the plurigaussian simulation method to the Balya lead-zinc deposit, but also assesses the spatially varying rock type proportions, and accounts for uncertainties between them. These parameters are extremely important for mining deposits, since the mineralizations of interest generally occur only in certain rock types. Furthermore, being able to model the different geological rock types is vital to good mine operations, production planning, and management. The results indicate that the plurigaussian method correctly reproduces the different orientations of the individual rock types, as seen in drill holes, and the proportion of each rock type, even if this varies in space.

Hierarchical Production Planning for Semiconductor Wafer Fabrication Based on Linear Programming and Discrete-Event Simulation

This paper focuses on a production planning and scheduling problem in a semiconductor wafer fabrication facility. We propose a two-level hierarchical production planning (HPP) method that employs an itmmerative procedure for production planning and operations scheduling. In the method, production plans are obtained with a linear programming (LP) model in the aggregate level, and schedules at the machines are obtained with a priority-rule-based scheduling method and evaluated with discrete-event simulation in the disaggregate level. An iterative scheme is adopted for obtaining a good and feasible production plan. The proposed method is evaluated through simulation experiments and the results show that the method works better than an existing algorithm in terms of the total production cost and total tardiness of orders.

Design of a replenishment system for a stochastic dynamic production/forecast lot-sizing problem under bullwhip effect

Inventory has the function of balancing production and demand. To shorten lead time, businesses adopt the make-to-stock approach that satisfies customer’s demand by inventory through forecast. This approach highlights the two contradictory objectives: to lower stock cost and to satisfy customer’s demand. Moreover, forecasting is one of the main causes of the bullwhip effect. Therefore, it is the policymaker’s concern to have good production planning and replenishment control through effective inventory management under such a circumstance. This paper studies a stochastic dynamic lost-sizing problem under the bullwhip effect. To solve this problem, this paper proposes a solution of two-stage ant colony optimization (TACO) and adds a mutation operation in the second-stage ACO. The experiment is mainly composed of two parts, with the first part analyzing the solution quality of the TACO, and the second part discussing the relationships between the bullwhip effect, replenishment (forecast) cycle, and the cost.