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The 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.

The 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.

The 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.

This research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level.

This 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.

This paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO).

The production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation.

The results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions.

This research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level.

PSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

Compares the bullwhip properties of a vendor managed inventory (VMI) supply chain with those of a traditional “serially‐linked” supply chain. The emphasis of this investigation is the comparative impact the two structures have on the “bullwhip effect” generated. Particular attention is paid to the manufacturer's production ordering activities as demonstrated using a simulation model based on difference equations. Documents and considers each of the four important sources of the bullwhip effect in turn. The analysis shows that with VMI implementation two sources of the bullwhip effect may be completely eliminated, i.e. rationing and gaming or the Houlihan effect, and the order batching effect or the Burbidge effect. VMI is also significantly better at responding to rogue changes in demand due to the promotion effect or to price induced variations. However, the effect of VMI on demand signal processing induced bullwhip or the Forrester effect is less clear cut. Concludes that on balance VMI offers a significant opportunity to reduce the bullwhip effect in real‐world supply chains.

In this paper, the joint replenishment problem is modeled for a two-level supply chain consisting of a single supplier and multiple retailers that use the vendor-managed inventory (VMI) policy for several products. This paper aims to find the optimal number of products to order in both policies, the optimal times at which each retailer orders the products in the traditional policy and the optimal times at which the supplier orders the product in the VMI policy.

The problem is first formulated into the framework of a constrained integer nonlinear programming model; then, the problem is solved using a teacher-learner based optimization algorithm. As there are no benchmarks available in the literature, a genetic algorithm is used as well to validate the results obtained.

The solutions obtained using both the algorithms for several numerical examples are compared to the ones of a random search procedure for further validation. A real case is solved at the end to demonstrate the applicability of the proposed methodology and to compare both the policies.

The paper does not have any special limitations.

The study has significant practical implications for the sellers and for the suppliers who have to get the most profit. Also, satisfying the constraints make decision more complicated.

This paper has two main originalities. The authors have developed the model of the joint replenishment problem and have contributed in the problem-solving process. They have used a new meta-heuristic and then compared it to a classic one.

This paper aims to address two questions: what kinds of benefits are realized from a vendor‐managed inventory (VMI) program (operational, i.e. efficiency related, vs strategic, i.e. sales related) and how the benefits are shared at the dyad level (suppliers vs buyers).

The paper uses an exploratory multiple case study with data from five operational VMI dyads, evaluating both buyer and supplier perspectives.

Three empirically grounded patterns of VMI are proposed. Five contextual inhibitors of VMI impacts are suggested.

The framework presented has been generated from a relatively small number of cases. Threats to external validity have been mitigated with case selection from multiple operational contexts and grounding findings in prior literature.

Using the conceptualization, potential VMI adopters can set more realistic and explicit implementation targets. The suggested contextual factors will help to design more appropriate VMI systems.

Past research on VMI can be mainly characterized by modeling/simulation approaches, focus on operational efficiency implications, and concern with impacts to buyers. In contrast, empirical studies on the actual impacts and dyad‐level reasons considering also the strategic (sales related) motivations for implementing VMI are few. This study contributes by suggesting how VMI is in some instances motivated not by bilateral interests to develop a supply chain, but by unilateral interests, with buyers searching for effortless purchasing, and suppliers for a means to lock in and secure sales.

The purpose of this paper is to examine the impact of applying two classical controller strategies, including two proportional (P) controllers with two feedback loops and one proportional–integral–derivative (PID) controller with one feedback loop, on the order and inventory performance within a production-inventory control system.

The simulation experiments of the dynamics behaviour of the production-inventory control system are conducted using a model based on control theory techniques. The Laplace transformation of an Order–Up–To (OUT) model is obtained using a state-space approach, and then the state-space representation is used to design and simulate a controlled model. The simulations of each model with two control configurations are tested by subjecting the system to a random retail sales pattern. The performance of inventory level is quantified by using the Integral of Absolute Error (IAE), whereas the bullwhip effect is measured by using the Variance ratio (Var).

The simulation results show that one PID controller with one feedback loop outperforms two P controllers with two feedback loops at reducing the bullwhip effect and regulating the inventory level.

The production-inventory control system is broken down into three components, namely: the forecasting mechanism, controller strategy and production-inventory process. A state-space approach is adopted to design and simulate the different controller strategy.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This paper sets out to describe transform methods to control vendor‐managed inventory (VMI). It aims to examine the limits of modelling approaches within control‐theoretic models.

Modelling was achieved with the Simulink package using the equations developed by Disney and Towill for a two‐tier VMI system. Discrete and continuous models were considered together with two forms of production delays: a finite delay and the Forrester exponential delay. Standard control engineering analyses of these delay representations were compared to illustrate how the system response and stability depend on their formulation and to determine the permissible gains.

Response by a discrete or continuous model to step inputs in sales rates depends on the type of delay representation but the responses do not differ by more than 5 per cent if the same delay form is used in the models. The prime effect of using a finite delay is to deepen the stock‐out and increase the required order rate compared with the same response observed with the use of exponential forms of delay. Total time for recovery is similar with all models. It is shown that the continuous model with an exponential delay is always stable and when using a fixed delay the continuous model can be made stable.

The models presented here illustrate that the various forms of control‐theoretic models present similar results irrespective of technique used, provided that they have the same delay type. The range of gains for the required order rate can be computed for any VMI system, knowing that they can be designed to guarantee stable operation.

This work extends that of Disney and Towill to include different modelling representations. It allows operational gains to be safely chosen for stable operation.

The aim of this article is to provide a concise methodology for the design of a widely used class of decision supply systems (DSS) which will enable precise control of bullwhip variance and inventory variance induced within a supply chain echelon.

The study exploits recent research that derived analytical formulae for calculating these performance metrics germane to the delivery process when the demand is randomly varying about a constant mean value. These formulae have been verified via extensive simulation‐based cross‐checks.

The design methodology focuses on the specification of bullwhip variance as an input. The output is to identify combinations of parameter settings to meet this target. Hence these parameters may be mapped to provide a visual display of competing designs with their associated inventory variance.

Although the analytical solutions apply only to the case where the pipeline error and inventory error correction terms are equal, this is not a severe limitation. Both theoretical studies of dynamic response and industrial experience support this feedback gain equally as enabling good practice.

Design of this particular DSS to control bullwhip is now greatly simplified, and guaranteed via extensive verification tests. The formulae are equally sound as a means of establishing system robustness.

The methodology is unique in enabling transparency of both bullwhip variance and inventory variance computation. Not only are system design time saved and normal performance guaranteed, but considerable management insight is generated thereby.

The purpose of this paper is to seek to investigate performance outcomes of vendor managed inventory (VMI) from a buyer's perspective and enablers for its successful application.

Structural equation modelling through Partial Least Squares (PLS) is used to identify relationships between four enablers (information systems, information sharing, information quality, and relationship quality), perceived VMI success, and three outcomes (cost reductions, customer service, and supply chain control).

Buyer‐perceived VMI success is impacted by the quality of the buyer‐supplier relationship, the quality of the IT‐system and the intensity of information sharing, but not by the actual quality of the information shared. Furthermore, VMI leads to three performance outcomes: higher customer service levels, improved supply chain control and, to a lesser extent, cost reduction.

Although theory stipulates a positive impact of high quality information on the success of VMI, this study shows that the effect of information quality is limited in practice.

The results of the survey show that purchasing managers who invest in the relationship with their suppliers and a good IT infrastructure are more likely to get better results from a VMI implementation. Furthermore, this paper shows that while most managers expect major cost reductions when implementing VMI, benefits primarily come from improved service levels.

The study provides empirical evidence of why VMI in practice does not achieve all the benefits claimed in theory.