Order Based Production Control System Research Articles

Supply chain information sharing under consideration of bullwhip effect and system robustness

Supply chain system experiences variance amplification in order replenishment and inventory level, leading to severe inefficiencies of the system. Information distortion is universally known as a fundamental reason for the variance amplification phenomenon. The purpose of this paper is to study the effect of demand information sharing in reducing bullwhip effect and improving the robustness of supply chain systems. The “automatic pipeline inventory and order-based production control system, APIOBPCS” is adopted to model supply chains with different information-sharing strategies. The stochastic factors in the supply chain system lead to poor performance in system robustness. Taguchi design is adopted to find out the optimal setting of ordering parameters in the APIOBPCS model for a robust supply chain. An extension of Taguchi design is adopted to solve the multi-response problems. The weighted signal-to-noise ratio is used as the performance index of the overall performance of the supply chain, including inventory cost, customer service level, and inventory variance amplification. The results show that full demand information transparency helps to improve the overall performance of supply chain. Furthermore, the sensitivity analysis of stochastic lead times verifies the results. This research gives some insights to improve the overall performance of supply chain via information sharing.

Analysis of the robustness of a single-tier pipeline inventory model

This paper examines the conditions necessary to specify a robust element of a supply chain using control theory and proposes a new robustness criterion clearly separating robustness from resilience...

Delivery time dynamics in an assemble-to-order inventory and order based production control system

System dynamics play a critical role in influencing supply chain performance. However, the dynamic property of the assemble-to-order (ATO) system remain unexplored. Based on control theory, the inventory and order based production control system (IOBPCS) family, can be utilized as a base framework for assessing system dynamics. However, the underlying assumption in traditional IOBPCS-based analytical studies is that the system is linear and the delivery time to end customers is negligible or backlog is used as a surrogate indicator. Our aim is to incorporate customer delivery lead-time variance as the third assessment measure alongside capacity availability and inventory variance as part of the so-called ‘performance triangle’– capacity at the supplier, the customer order decoupling point (CODP) inventory and the delivery lead-time. Using the ‘performance triangle’ and adopting non-linear control engineering techniques, we assess the dynamic behaviour of an ATO system in the electronics sector. We benchmark the ATO system dynamics model against the IOBPCS family. We exploit frequency response analysis to ensure a robust system design by considering three measures of the ‘performance triangle’. The findings suggest delivery LT variance can be minimised by maintaining the ATO system as a true Push-Pull hybrid state with sufficient CODP stock, although increased operational cost driven by bullwhip and CODP variance need to be considered. However, if the hybrid ATO system 'switches' to the pure Push state, the mean and variance of delivery LT can be significantly increased.

Interrelationship among resilience, robustness, and bullwhip effect in an inventory and order based production control system

PurposeThis study aims to examine the interrelationship between resilience, robustness and bullwhip effect using an inventory- and order-based production control system being subjected to operational disruption in the customer demand process.Design/methodology/approachControl engineering techniques and simulation are employed for the supply chain dynamics study.FindingsThe results show that resilience and robustness are two conflicting performance characteristics and therefore, a tradeoff can be established between them. It is also observed that improvement in resilience and reduction of bullwhip effect can be achieved simultaneously through a proper selection of control parameters.Originality/valueThe work establishes a relationship between the resilient behavior of a supply chain and bullwhip effect.

Dynamic Performance and Stability Research of VMI-APIOBPCS in Apparel Industry Based on Control Theory

In this article, the authors mainly investigate the dynamic performance of VMI-APIOBPCS (Vendor Manage Inventory-Automatic Pipeline Inventory and Order Based Production Control System) in the apparel industry. With the increasing importance of supply chain system integration, VMI-APIOBPCS can integrate various kinds of elements in the real operation, which is proved to be very effective in solving complex problems. Apparel supply chains have relatively more complicated dynamic behaviors because of the factors like volatile demand, the strong seasonality of sales and the large number of items with short life cycle or lacking historical data. In order to clear these behaviors, and come up with new ideas to solve present difficulties, the authors apply classic control theory to build up the model of VMI-APIOBPCS in apparel supply chain. They then analyze the dynamic performance of apparel supply chain and use Routh-Hurwitz Stability Criterion to analyze the stability on the expectation that the outcomes of our research can be useful in the practical occasions.

Dynamics analysis of a production-inventory control system with two pipelines feedback

PurposeThe purpose of this study is to propose a new dynamic model of a production-inventory control system. The objective of the new model is to maximise the flexibility of the system so that it can be used by decision makers to design inventory systems that adopt various strategies that provide a balance between reducing the bullwhip effect and improving the responsiveness of inventory performance.Design/methodology/approachThe proposed production-inventory control system is modelled and analysed via control theory and simulations. The production-inventory feedback control system is modelled through continuous time differential equations. The simulation experiments design is conducted by using the state-space model of the system. The Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS) model is used as a benchmark production-inventory control system.FindingsThe results showed that the Two Automatic Pipelines, Inventory and Order-Based Production Control System (2APIOBPCS) model outperforms APIOBPCS in terms of reducing the bullwhip effect. However, the 2APIOBPCS model has a negative impact on Customer Service Level. Therefore, with careful parameter setting, it is possible to design control decisions to be suitably responsive while generating smooth order patterns and obtain the best trade-off of the two objectives.Research limitations/implicationsThis research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level.Originality/valueThis present model is an extension and improvement to Towill’s (1982) and John et al.’s (1994) work, since it presents a new dynamic model of a production-inventory control system which utilises an additional flow of information to improve the efficiency of order rate decisions.

Dynamic analysis and design of a semiconductor supply chain: a control engineering approach

The combined make-to-stock and make-to-order (MTS-MTO) supply chain is well-recognised in the semiconductor industry in order to find a competitive balance between agility, including customer responsiveness and minimum reasonable inventory, to achieve cost efficiency while maintaining customer service levels. Such a hybrid MTS-MTO supply chain may suffer from the bullwhip effect, but few researchers have attempted to understand the dynamic properties of such a hybrid system. We utilise a model of the Intel supply chain to analytically explore the underlying mechanisms of bullwhip generation and compare its dynamic performance to the well-known Inventory and Order-Based Production Control System (IOBPCS) archetype. Adopting a control engineering approach, we find that the feedforward forecasting compensation in the MTO element plays a major role in the degree of bullwhip and the Customer Order Decoupling Point (CODP) profoundly impacts both the bullwhip effect and the inventory variance in the MTS part. Thus, managers should carefully tune the CODP inventory correction and balance the benefit between CODP inventory and bullwhip costs in hybrid MTS-MTO supply chains.

Behavioral causes of the bullwhip effect: An analysis using linear control theory

It has long been recognized that the bullwhip effect in real life depends on a behavioral component. However, non-experimental research typically considers only structural causes in its analysis. In this article, we study the impact of behavioral biases on the performance of inventory/production systems modeled through an APVIOBPCS (Automatic Pipeline, Variable Inventory, Order-Based Production Control System) design using linear control theory. To explicitly model managerial behavior, we allow independent adjustments to inventory and pipeline feedback loops. We consider the biases of smoothing/over-reaction to inventory and pipeline mismatches and the under-/over-estimation of the pipeline. To quantify the performance of the system, we first develop a new procedure to determine the exact stability region of the system and we derive an asymptotic stability region that is independent of the lead time. Afterwards, we analyze the effect of different demand signals on order and inventory variations. Our findings suggest that normative policy recommendations must take demand structure explicitly into account. Finally, through extensive numerical experiments, we find that the performance of the system depends on the combination of the behavioral biases and the structure of the demand stream.

IOBPCS based models and decoupling thinking

The inventory and order based production control system (IOBPCS) is mainly a model of a forecast driven production system where the production decision is based on the forecast in combination with the deviation between target inventory and actual inventory. The model has been extended in various directions by including e.g. WIP feedback but also by interpreting the inventory as an order book and hence representing a customer order driven system. In practice a system usually consists of one forecast driven subsystem in tandem with a customer order driven subsystem and the interface between the two subsystems is represented by information flows and a stock point associated with the customer order decoupling point (CODP). The CODP may be positioned late in the flow, as in make to stock systems, or early, as in make to order systems, but in any case the model should be able to capture the properties of both subsystems in combination. A challenge in separating forecast driven from customer order driven is that neither the inventory nor the order book should be allowed to take on negative values, and hence non-linearities are introduced making the model more difficult to solve analytically unless the model is first linearized. In summary the model presented here is based on two derivatives of IOBPCS that are in tandem, and interfaces between them related to where the demand information flow is decoupled and the position of the CODP.

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

In 1994, through classic control theory, John, Naim and Towill developed the ‘Automatic Pipeline, Inventory and Order-based Production Control System’ (APIOBPCS) which extended the original IOBPCS archetype developed by Towill in 1982 ─ well-recognised as a base framework for a production planning and control system. Due to the prevalence of the two original models in the last three decades in the academic and industrial communities, this paper aims to systematically review how the IOBPCS archetypes have been adopted, exploited and adapted to study the dynamics of individual production planning and control systems and whole supply chains. Using various databases such as Scopus, Web of Science, Google Scholar (113 papers), we found that the IOBPCS archetypes have been studied regarding the a) modification of four inherent policies related to forecasting, inventory, lead-time and pipeline to create a ‘family’ of models, b) adoption of the IOBPCS ‘family’ to reduce supply chain dynamics, and in particular bullwhip, c) extension of the IOBPCS family to represent different supply chain scenarios such as order-book based production control and closed-loop processes. Simulation is the most popular method adopted by researchers and the number of works based on discrete time based methods is greater than those utilising continuous time approaches. Most studies are conceptual with limited practical applications described. Future research needs to focus on cost, flexibility and sustainability in the context of supply chain dynamics and, although there are a few existing studies, more analytical approaches are required to gain robust insights into the influence of nonlinear elements on supply chain behaviour. Also, empirical exploitation of the existing models is recommended.

A state-space model of a three tier APVIOBPCS supply chain

Purpose – The purpose of this paper is to investigate a control engineering-based system model that allows for any value of production delay for a three-tier supply chain with information delays between tiers or systems with epos. Design/methodology/approach – A discrete z transform model of automatic pipeline, variable inventory and order based production control system three-tier supply chain is obtained using a state-space model using a reachable control formulation. This model provides a discrete time state-space model conversion using an exponential production delay with no loss of generality. Findings – This work allows a three-tier supply chain model to be computed via a spreadsheet using any production delay and can be modified to include different sales smoothing procedures. The model is fully controllable and observable. Stability of these models is obtained from the system eigenvalues and agrees with our previously published stability boundaries. Practical implications – The system is described by a linear control model of the production process and does not include production limits or other resource limitations, including history of sales demand and response. Originality/value – This present model is an extension of the model devised by White and Censlive, in that it allows accurate modelling of multi-tier inventory production systems by permitting flexible selection of delay parameter values for real systems.

Simulation of Three-Tier Supply Chains with Product Shelf-Life Effects

Simulation models are used to examine the effects of shelf life on the performance of three-tier automatic pipeline, variable-inventory, and order-based production control system (APVIOBPCS) supply chains with different information delays between tiers with a fixed or variable inventory constructed using SIMULINK, which provides a response to a sudden demand sales rate and a finite shelf life. This incorporates the effects of product shelf life for each tier. It is assumed that for a finite shelf life, all parties practise a first-in, first-out operation.We show a substantial reduction in the number of discards, reducing carbon footprint and wasted resources. Fixed-inventory targets produce more discards than variable inventory, and vendor-managed inventory or electronic data transmission with discard feedback produce substantial reductions in discard.

The impact of cooperation mechanism on the chaotic behaviours in nonlinear supply chains

The chaotic behaviours of nonlinear inventory systems are investigated via control theory, nonlinear system theory, and chaos theory. Two well-established production and distribution scheduling algorithms, automatic pipeline, variable inventory and order-based production control system (APVIOBPCS) with demand information sharing (DIS) and vendor managed inventory (VMI) supply chains are adopted and analysed by bifurcation diagram and Lyapunov exponent spectrum. Simulation results show that with the target inventory parameter ([2) changes, behaviours of the DIS and VMI systems are different from that of traditional inventory system. The Lyapunov exponent confirms that behaviours of DIS supply chain members shift between chaotic and periodic movement with [2 increases. The system shows periodic movement when [2 is set between 1 and 1.5. For VMI system, the system is not sensitive to [2 and keeps periodic movement. Therefore, the DIS and VMI supply chain members can effectively reduce inventory, and keep periodic movement simultaneously. [Received 13 July 2013; Revised 13 January 2014; Revised 10 June 2014; Accepted 14 June 2014]

Triple Feed-forward APIOBPCS Based Resilience and Operational Costs Assessment of Supply Chain

In order to trade off between supply chain resilience and operational cost, a sort of triple feed-forward automatic pipeline, inventory and order-based production control system(TFF-APIOBPCS) is presented. A first order differential feedforward unit, introduced to production control model APIOBPCS, enables the model to mitigate the impact of fluctuations in demand on actual inventory. Aiming at different pole distribution of the production control system, analysis of the relationship between the parameters of first order differential feedforward and resilience are conducted under zero steady-state error. Supply chain operational cost model is constructed by comprehensive consideration of inventory cost and production regulation cost. Supply chain system simulations with a unit step signal and a stochastic signal as the customer demand evaluate the effect of the first order differential feedforward parameters on supply chain resilience and operational cost, and reveal the validity of TFF-APIOBPCS. The results show that, for different degree of volatility of demand, setting the parameters of first order differential feedforward unit reasonably, can obtain better equilibrium of elasticity and operation cost.

Using control theory to optimise profit in APVIOBPCS inventory systems

This paper describes an investigation of the effects on the variation of profitability with time caused by the choice of control formulation in a model for an Automatic Pipeline, Variable Inventory and Order Based Production Control System (APVIOBPCS) production system. The improvements are demonstrated using an example of electronic RAM production used by Deif and ElMaraghy [26]. The investigation using a SIMULINK™ model, includes varying the “order up to inventory” values, and “work in progress” values (WIP).Using Proportional, Integral and Derivative (PID) control elements to illustrate the profit gain from using control methods, the numerical results were optimised using MATLAB™ routines.The results show that using a range of PID gains for a controlled production system will increase profitability.The desired level of inventory also can increase profitability with a definite optimum value. The model shows increased production delays result in lower overall profit and cumulative profit is increased with increased PID gains up to a limit.Using the data published by Deif and Elmaraghy the model shows improvements in cumulative profitability by up to 30%.

On the stability and bullwhip effect of a production and inventory control system

This paper focuses on the discrete-time automatic pipeline, inventory and order-based production control system (APIOBPCS), a well-established production and inventory control model. The feedback mechanism within the replenishment rule enables the model to mitigate the bullwhip effect, but introduces a stability problem. In this research, a comprehensive stability analysis is conducted for arbitrary lead times using difference equation theory. On the basis of stability, a state space approach is advocated to analyse the impact of replenishment parameters, demand processes, and lead times on the robustness of the bullwhip effect. The stability results demonstrate that the production control system can easily be destabilised without incorporating the work-in-progress (WIP) feedback loop. Furthermore, it reveals that the stability problem for long lead times can be simplified with the stability condition independent of the lead time. The results obtained in this study provide useful guidelines for the selection of replenishment parameters to guarantee stability and mitigate the bullwhip effect.

Multi-objective design of fuzzy logic controller in supply chain

Unlike commonly used methods, in this paper, we have introduced a new approach for designing fuzzy controllers. In this approach, we have simultaneously optimized both objective functions of a supply chain over a two-dimensional space. Then, we have obtained a spectrum of optimized points, each of which represents a set of optimal parameters which can be chosen by the manager according to the importance of objective functions. Our used supply chain model is a member of inventory and order-based production control system family, a generalization of the periodic review which is termed ‘Order-Up-To policy.’ An auto rule maker, based on non-dominated sorting genetic algorithm-II, has been applied to the experimental initial fuzzy rules. According to performance measurement, our results indicate the efficiency of the proposed approach.

Stability analysis of constrained inventory systems with transportation delay

Stability is a fundamental design property of inventory systems. However, the often exploited linearity assumptions in the current literature create a major gap between theory and practice. In this paper the stability of a constrained production and inventory system with a Forbidden Returns constraint (that is, a non-negative order rate) is studied via a piecewise linear model, an eigenvalue analysis and a simulation investigation. The APVIOBPCS (Automatic Pipeline, Variable Inventory and Order Based Production Control System) and EPVIOBPCS (Estimated Pipeline, Variable Inventory and Order Based Production Control System) replenishment policies are adopted. Surprisingly, all kinds of non-linear dynamical behaviours of systems can be observed in these simple models. Exact expressions of the asymptotic stability boundaries and Lyapunovian stability boundaries are derived when actual and perceived transportation lead-time is 1 and 2 periods long respectively. Asymptotically stable regions in the non-linear Forbidden Return systems are identical to the stable regions in its unconstrained counterpart. However, regions of bounded fluctuations that continue forever, including both periodicity and chaos, exist in the parametrical plane outside the asymptotically stable region. Simulation shows a complex and delicate structure in these regions. The results suggest that accurate lead-time information is essential to eliminate inventory drift and instability and that ordering policies have to be designed properly in accordance with the actual lead-time to avoid these fluctuations and divergence.

Measuring Bullwhip Effect in a Single Echelon SupplyChain Using Fuzzy Approach

This paper studies the well-established automatic pipeline feedback compensated inventory and order-based production control system (APIOBPCS) for a single echelon supply chain, in which the uncertain demand is modelled using the fuzzy numbers. The bullwhip effect is therefore measured as the possibility variance amplification in replenishment orders. It has been found that the bullwhip effect measured using fuzzy approach has similar features as the one measured using probability theories, and the sharper the fuzzy demand forecast, the smaller the bullwhip effect.

Service levels, system cost and stability of production–inventory control systems

Production-inventory control system models have been analysed in the literature either in terms of their stability against demand fluctuations or in terms of their service level and cost performance under uncertain demand. This article analyses the production-inventory system performance in terms of service level (i.e. order fill rate) and average system costs, under stable settings of the control parameters. The classical automatic pipeline variable inventory and order-based production control system has been modified by explicitly modelling safety stock to help achieve higher services levels in the face of random demand. The stability of the system is affected by the control parameters: fractional rates of adjustment of work-in-process and inventory. However, the service level and average cost are affected by the control parameters as well as the smoothing factor in demand forecasting. This article puts forward five propositions which give light to general system performance based on the parameters selection. Intensive simulation experiments have also been carried out to reveal the performance variations within the stable region, leading to further insights on the system behaviour. The managerial insights which can assist proper tuning of systems to help achieve the desired performances have been discussed.