Balancing earliness and tardiness within workload control order release: an assessment by simulation

Most Workload Control literature assumes that delivery performance is determined by tardiness related performance measures only. While this may be true for companies that directly deliver to end-customers, for make-to-stock companies or firms that are part of supply chains, producing early often means large inventories in the finished goods warehouse or penalties incurred by companies downstream in the supply chain. Some earlier Workload Control studies used a so-called time limit, which constrains the set of jobs that can be considered for order release, to reduce earliness. However, recent literature largely abandoned the time limit since it negatively impacts tardiness performance. This study revisits the time limit, assessing the use of different adaptive policies that restrict its use to periods of either low or high load. By using a simulation model of a pure job shop, the study shows that an adaptive policy allows to balance the contradictory objectives of delaying the release of orders to reduce earliness and to release orders early to respond to periods of high load as quick as possible. Meanwhile, only using a time limit in periods of high load was found to be the best policy.

Pendekatan Metode ABC Pada Toko X untuk Pengendalian Persediaan Barang

Persaingan industri retail begitu ketat, salah satu nya yaitu dengan efisiensi biaya, yang dapat dilakukan dengan mengendalikan persediaan. Toko X merupakan salah satu toko retail modern yang ada di Indonesia. Toko X menjual berbagai macam jenis barang fast moving consumer goods (FMCG). Perusahaan menerapkan sistem make to stock untuk memenuhi permintaan konsumen. Jumlah persediaan barang di gudang, tidak sesuai dengan permintaan konsumen sehingga terjadi penumpukan barang di gudang yang mengakibatkan tingginya biaya persediaan. Selain itu beberapa barang yang dijual mempunyai stok menipis, padahal permintaan konsumen banyak. Kondisi seperti ini akan mengakibatkan kerugian bagi perusahaan. Kategori A 30% jumlah barang sebesar Rp. 38.235.557,-. Klasifikasi B 20% jumlah barang senilai Rp. 7.748.157,-. Kategori C 50%. nilai Rp. 4.552.842,- Pemesanan ekonomis pada toko X, untuk persediaan barang A yaitu air mineral A PET 600 ml, pemesanan dilakukan saat persediaan 7 pcs, persediaan pengaman 46 pcs. Air mineral A PET 1500 ML pemesanan persediaan 7 pcs, persediaan pengaman 52 pcs. Susu bear brand 12 ml dengan pemesanan saat persediaan 11 pcs, persediaan pengaman 39 pcsKata kunci: persediaan barang, metode ABC, pemesanan ekonomis (ROP).

Analysis of make-to-stock queues with general processing times and start-up and lost sales costs

We consider a make-to-stock environment with a single production unit that corresponds to a single machine or a line. Production and hence inventory are controlled by the two-critical-number policy. Production times are independent and identically distributed general random variables and demands are generated according to a stationary Poisson process. We model this production-inventory system as an M/G/1 make-to-stock queue. The main contribution of the study is to extend the control of make-to-stock literature by considering general production times, lost sales and fixed production costs at the same time. We characterize the long-run behaviour of the system and also propose a simple but very effective approximation to calculate the control parameters of the two-critical-number policy. An extensive numerical study exhibits the effects of the production time distribution and the system parameters on the policy control levels and average system cost.

Push/Pull Parts Production Policy Optimization in the ATO Environment

Within the Assembly to Order (ATO) production strategy, the common approach is to produce the parts to assemble with a Push-Make to Stock policy.In recent decades, the effects of the modern Just in Time (JIT) moved to a Pull-Make to Order policy. Assembled parts characterized by wide variety and huge storage space utilization are critical, and a proper Push/Pull production policy definition is required. An appropriate balance of storage space utilization and setup times leads to the optimization of the production policy. The aim of this paper is to define a bi-objective mathematical optimization model to assign the most suitable production policy to the parts within the production mix in an ATO industrial context. A numerical simulation and an operative case study showcases the model application, proving the industrial relevance of this research.

Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.