Tactical Supply Chain Planning Research Articles

A bi-objective robust model for minimization of costs and emissions of syngas supply chain

Producing syngas from forest-based biomass could facilitate the transitions in energy and forest sectors by replacing natural gas; reducing emissions and wastes; and generating revenues. Optimizing the economic and environmental impacts of the biomass supply chains can help realizing these benefits. In this study, a bi-objective robust optimization model is developed for tactical supply chain planning of forest biomass gasification at a pulp mill. It optimizes the monthly flow, inventory, and preprocessing of biomass while minimizing annual costs and emissions. Robust optimization with an adjustable risk of constraint violation is used to model the uncertainties in supply and cost of biomass. The average cost and emissions of the robust Pareto-optimal solutions are 68% and 41% higher than those in the deterministic solutions, respectively. Although, the cost and emissions and their trade-off in the deterministic case are more favorable, the robust solutions ensure no biomass shortage while avoiding over-conservatism.

Tactical supply chain planning after mergers under uncertainty with an application in oil and gas

With today's rapidly changing supply chain environment, it is essential to include uncertainty in an explicit manner in supply chain planning models. Therefore, we propose a stochastic model for tactical planning of the Crude Oil Supply Chain (COSC) under cost and demand uncertainties. The mathematical model considers a multi-echelon supply chain with multi-products and a multi-period planning horizon. It integrates inventory and backorder penalties. A Sample Average Approximation (SAA) procedure with Multiple Replications Procedure (MRP) is developed to solve the stochastic model. We illustrate how our model directly applies to supply chain planning. We present numerical results that show the impact of cost uncertainty on supply chain planning decisions and synergy gains. We also measure the value of modeling uncertainty against deterministic planning and characterize the cost/bbl after a merger under shared services cost and demand uncertainty.

Using an Adapted Zachman Framework for Enterprise Architecture in the Development of an Industry Methodology of Integrated Supply Chain Planning

Abstract Trends in the digitalization of business open up opportunities for the use of fundamental approaches to the development of enterprise architecture in the creation of appropriate methodologies. The article discusses the approach to the use of adapted Zachman framework for enterprise architecture as a basis for the systematization and structuring of the industry methodologies of integrated supply chain planning based on SCOR model. A practical example of using the proposed approach for description of one of the target processes - tactical supply chain planning is considered.

Integrated innovative product design and supply chain tactical planning within a blockchain platform

In globalised manufacturing and production environments, companies increasingly tend to exploit public contribution advantages within designing, manufacturing and marketing processes. To be benefited from outbound capabilities, this paper presents a tactical supply chain planning model to integrate the designing process in the form of open innovation within the supply chain main processes. In this regard, a fuzzy mathematical model is proposed to optimise the tactical decisions according to supply chain objectives and open innovation considerations. On the other hand, intellectual property issues and protecting the rights of innovators are significant concerns that sometimes avoid companies to be engaged in open innovation initiatives. Therefore, this paper proposes a registering mechanism in which the ideas and creative works are collected, refined and finally approved within a blockchain platform. Furthermore, in order to deal with epistemic environmental uncertainty, the fuzzy set theory is utilised. To investigate the applicability of the developed model, a case study in home appliances domain is employed. The results show that the company can achieve favourable designs by spending approximately 1% of the supply chain total cost. Additionally, benefiting the registering mechanism can decrease the cost of using non-original designs more than 41%.

Tactical supply chain planning for tyre remanufacturing considering carbon tax policy

Greenhouse gas emissions are receiving greater concern in the world due to the recent Paris climate agreement with announcement of carbon emission targets by various countries to reduce anthropogenic global climate change. Industry and their supply chains are a major source of carbon emissions. Consequently, manufacturing firms have realized the importance of adopting environmental friendly supply chain management (SCM) practices and are implementing such plans in their business operations. The study presents a tyre remanufacturing supply chain tactical planning model that integrates economic and carbon emission objectives with a carbon tax policy consideration. A modified cross-entropy solution method was employed to solve the tyre remanufacturing tactical supply chain planning model with commercial solver. Numerical experiments are completed utilizing data from Indian tyre remanufacturing industry. The analysis of the numerical results give important organizational insights for industry practitioners and policy insights for the government on (1) possible financial and emissions reduction impacts of a carbon tax rate at the tyre remanufacturing supply chain tactical planning level, (2) the use of profit/emission objective trade off analysis for making informed decisions on investments in carbon emission reduction technology, and (3) possible way to decide carbon price for maximum environmental returns achieved without substantial impacts on the economy and competitive positioning of firms. Two carbon saving indices have been proposed for evaluation of carbon scenarios. The results have been analyzed with the help of these indices. A carbon tax and reward policy (CER) has also been proposed.

Tactical supply chain planning under uncertainty with an application in the wind turbines industry

With today's rapidly changing global market place, it is essential to explicitly include uncertainty in supply chain planning models. In this paper, we develop a two-stage stochastic programming model for the comprehensive tactical planning of supply chains under demand and supply uncertainty. The model handles multi-period, multi-product, and multi-echelon supply chains. It incorporates backorder penalties with general cost structures. The considered supply uncertainty combines supplier random yield and stochastic lead times, and is thus the most general form of such uncertainty to date. We illustrate how our model directly applies to the planning of the supply chain of one of the world's biggest manufacturers of wind turbines. We illustrate theoretical and numerical results that show the impact of supplier uncertainty/unreliability on optimal procurement decisions. We also quantify the value of modeling uncertainty versus deterministic planning.

On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty

In this work a modeling framework and a solution approach have been presented for a multi-period stochastic mixed 0–1 problem arising in tactical supply chain planning (TSCP). A multistage scenario tree based scheme is used to represent the parameters’ uncertainty and develop the related Deterministic Equivalent Model. A cost risk reduction is performed by using a new time-consistent risk averse measure. Given the dimensions of this problem in real-life applications, a decomposition approach is proposed. It is based on stochastic dynamic programming (SDP). The computational experience is twofold, a comparison is performed between the plain use of a current state-of-the-art mixed integer optimization solver and the proposed SDP decomposition approach considering the risk neutral version of the model as the subject for the benchmarking. The add-value of the new risk averse strategy is confirmed by the computational results that are obtained using SDP for both versions of the TSCP model, namely, risk neutral and risk averse.

Impact of the integration of tactical supply chain planning determinants on performance

In fulfilling customers’ orders, one of the goals of tactical supply chain planning is to satisfy the customers in terms of delivery efficiency, delivery quantity accuracy and on-time delivery. These performance objectives can be impacted by the way firms plan each of the three phases of the supply chain: procurement, production and distribution. Though the link between each of these phases and supply chain performance has been studied in extant literature, very few authors have considered all three phases at the same time. By adopting an integrated approach, this paper therefore aims to study the manner in which, taken together in one model, the planning determinants of the different phases impact on supply chain performance. It is important for managers to understand, from a holistic and integrated perspective, how a given combination of the planning determinants of the supply chain functions impacts positively or negatively on the performance of the supply chain. To carry out this study, this paper starts by proposing an integrated framework that is based on the SCOR model and the customer order decoupling point (CODP), followed by a five-step methodology for tactical supply chain planning. Then, using an analytical model and simulations, and based on a numerical example, it shows how the proposed methodology can be applied in a given decision-making situation. Our results enabled to identify the worst and the best combinations of planning determinants.

A hybrid multi-stage stochastic programming-robust optimization model for maximizing the supply chain of a forest-based biomass power plant considering uncertainties

Electricity generated from forest-based biomass is an attractive source of renewable energy. However, the cost of generating heat and/or electricity from it is relatively high due to the low energy density of wood, high moisture content and variations in its quality and availability. Models have been developed to optimize the supply chain and reduce the cost per kilowatt hour generated. This paper focuses on incorporating uncertainty in the supply chain of such a model. The model considers the tactical supply chain planning of a power plant over a one-year time horizon with monthly time steps. Uncertain parameters which impact the net profit of the power plant include ‘biomass quality,’ namely moisture content and higher heating value, and ‘monthly available biomass’ from different suppliers. Robust optimization is used to model uncertainty in the quality of biomass. Then a hybrid, multi-stage, ‘stochastic programming-robust optimization’ model is presented in order to simultaneously include uncertainty in biomass quality and biomass availability. It is demonstrated that the hybrid model takes advantage of both modelling approaches to balance the profit estimates and the tractability to various circumstances. The model provides solution considering all instances of the uncertain parameters within the defined sets and scenario tree. The results revealed a major trade-off between profit and range of biomass quality. Profit decreased by up to 23% when there was ±13% variation in moisture content and ±5% change in higher heating value. The model achieved a biomass purchase cost that was lower than the current commercial costs at the power plant. Implementing the model could prevent production curtailment and undesirable fluctuation in storage levels which occurred in the past due to variations in biomass availability and quality.

A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis

This article presents a tactical supply chain planning model that can be used to investigate tradeoffs between cost and environmental degradation including carbon emissions, energy consumption and waste generation. The proposed model also incorporates other aspects of real world supply chains such as multiple transport lot sizing and flexible holding capacity of warehouses. A solution methodology, the Nested Integrated Cross-Entropy (NICE) method, is developed to solve the proposed mixed-integer nonlinear mathematical model. The application of the model and solution method is investigated in an actual case problem. Analysis of the numerical results focuses on investigating the relationship between lean practices and green outcomes. We find that (1) not all lean interventions at the tactical supply chain planning level result in green benefits, and (2) a flexible supply chain is the greenest and most efficient alternative when compared to strictly lean and centralized situations.

Tactical supply chain planning under a carbon tax policy scheme: A case study

Greenhouse gas emissions are receiving greater scrutiny in many countries due to international forces to reduce anthropogenic global climate change. Industry and their supply chains represent a major source of these emissions. This paper presents a tactical supply chain planning model that integrates economic and carbon emission objectives under a carbon tax policy scheme. A modified Cross-Entropy solution method is adopted to solve the proposed nonlinear supply chain planning model. Numerical experiments are completed utilizing data from an actual organization in Australia where a carbon tax is in operation. The analyses of the numerical results provide important organizational and policy insights on (1) the financial and emissions reduction impacts of a carbon tax at the tactical planning level, (2) the use of cost/emission tradeoff analysis for making informed decisions on investments, (3) the way to price carbon for maximum environmental returns per dollar increase in supply chain cost.

A tactical supply chain planning model with multiple flexibility options: an empirical evaluation

Supply chain flexibility is widely recognized as an approach to manage uncertainty. Uncertainty in the supply chain may arise from a number of sources such as demand and supply interruptions and lead time variability. A tactical supply chain planning model with multiple flexibility options incorporated in sourcing, manufacturing and logistics functions can be used for the analysis of flexibility adjustment in an existing supply chain. This paper develops such a tactical supply chain planning model incorporating a realistic range of flexibility options. A novel solution method is designed to solve the developed mixed integer nonlinear programming model. The utility of the proposed model and solution method is evaluated using data from an empirical case study. Analysis of the numerical results in different flexibility adjustment scenarios provides various managerial insights and practical implications.

Tactical supply chain planning models with inherent flexibility: definition and review

Supply chains (SCs) can be managed at many levels. The use of tactical SC planning models with multiple flexibility options can help manage the usual operations efficiently and effectively, whilst improve the SC resiliency in response to inherent environmental uncertainties. This paper defines tactical SC flexibility and identifies tactical flexibility measures and options for development of flexible SC planning models. A classification of the existing literature of SC planning is introduced that highlights the characteristics of published flexibility inclusive models. Additional classifications from the reviewed literature are presented based on the integration of flexibility options used, solution methods utilized, and real world applications presented. These classifications are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.

Integrative retail logistics: An exploratory study

Grocery retail companies have gone through a transformational change in the past by heavily investing in distribution centers of their own and by expanding their logistics activities. As a result, many retailers are now in the process of better adjusting their logistics operations to their specific requirements against the backdrop of raising pressure in a highly competitive environment. In this light, we provide an exploratory study based on semi-structured face-to-face interviews with 28 leading European grocery retailers. First we examine the current strategic designs of grocery retailers’ internal logistics networks. Next, we shift our focus to the resulting interdependencies in tactical supply chain planning between instore operations and upstream logistics processes. We have identified five interdependent planning issues: order packaging unit, store delivery pattern, store replenishment lead time, store delivery arrival times and arrival time windows, as well as roll-cage sequencing and loading carriers. Each of these mid-term planning interdependencies is evaluated with regard to implications in the stores, in transportation and in the distribution centers. The mid-term operations planning issues in the grocery retail industry considered in this paper have remained practically unexplored up to now. The outcome of this empirical research study therefore has substantial relevance for future retail research and practice.

Decision-Making and Reactive Tactical Planning Approach

This paper addresses the problem of tactical supply chain planning under uncertainty and perturbation. Uncertainty is considered on customer demand. Perturbation is seen as the result of one or more events that may affect the production process and modify already taken decisions. We develop an approach based on successive planning steps (rolling horizon procedure). For each planning step, data is updated and real implemented decisions are taken into account. To deal with uncertainty and perturbation, a detailed analysis of the different stages in decision making is done. This leads us to an original modeling of the decision process. A linear programming model is then proposed. Numerical experiments show the benefits of our approach to face uncertain and disrupted environment.

A Fuzzy Optimization Model For Supply Chain Production Planning With Total Aspect Of Decision Making

This paper models supply chain uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-stage with different methods of manufacturing in each stage, multi-distribution centre and multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to achieve the best use of the available resources and the best method of manufacturing at each stage for a product along the time horizon so that customer demands are met at a minimum cost. The fuzzy model provides the decision maker with alternative decision plans with different degrees of satisfaction.

A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-level, multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to centralize multi-node decisions simultaneously to achieve the best use of the available resources along the time horizon so that customer demands are met at a minimum cost. This proposal is tested by using data from a real automobile SC. The fuzzy model provides the decision maker (DM) with alternative decision plans with different degrees of satisfaction.

Centralised supply chain master planning employing advanced planning systems

Higher expectations on supply chain performance force organisations to reinvent themselves in order to cut costs and increase customer service, all to gain competitive advantage. Pursuing the best network of manufacturing, supply and distribution facilities relative to the marketplace is therefore on top of many managers’ ‘most wanted’ list concerning supply chain management. Supply chain planners are thus in need of decision support to be able to establish feasible and sufficient plans. This article discusses how decision support through advanced planning systems (APS) can assist tactical supply chain planning. A case study is presented showing how APS can act as an enabler in adapting logistics and supply chain principles, as well as reducing costs through streamlining the supply chain. The purpose of this article is primarily to present findings from a case study regarding supply chain planning with the aid of a master planning APS module. The case study emphasises that APS in the scope of logistics management have several positive effects on supply chain performance.

Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis

A mixed integer programming model that aims at supporting the tactical wood procurement decisions of a multifacility company is presented. This model allows for wood exchanges between companies. Furthermore, the material flow through the supply chain is driven by both a demand to satisfy ("pull" strategy) and a market mechanism ("push" strategy), enabling the planner to take into consideration both wood freshness and the notion of quality linked to the age of harvested wood into log, chips, and end-product demands. An inability to consider alternative plans for implementation, and the difficulty of assessing the performance of these plans in an uncertain environment, are two shortcomings of the manual planning process. A planning process, based on human planner – decision support system interactions that allows a company to overcome these shortcomings is therefore presented. The process combines Monte Carlo methods and an anticipation mechanism that will, in the long term, enable the company to take into account equipment transportation costs. The proposed planning process leads to a multicriteria decision-making problem where the human planner has to select a plan to implement from a set of candidate plans. A hypothetical test case shows that it is possible to manage the wood flow from stump to end market in such a way as to preserve freshness and extract higher value from the logs processed in the mills. The test case also shows that the proposed planning process achieves an average profitability increase of 8.8% compared with an approach based on a deterministic model using average parameter values. Finally, a sensitivity analysis reveals that the accuracy of standing inventory on harvest blocks and the anticipated market conditions are the most important parameters to consider in selecting a good wood procurement plan.