Supply chain configurations: a model to evaluate performance in customised productions

This paper describes an approach used to evaluate the performance of different supply chain configurations in customised contexts. Based on historical data collected from the supply chain of a shoe producer, different configurations are evaluated based on a discrete-event simulation by highlighting the performance of the supply chain (in terms of supply chain order lead-time and inventory volume) when the production switched from standard production (characterised by batches of large quantities of the same product) to customised production (characterised by a small of series batches with high product variability). The simulation approach relies on experimentation through executable configurations, which enables the creation of different scenarios, and is then applied to the case of an actual firm in the footwear industry. The managerial implications of these findings are discussed.     

Robust assignment of customer orders with uncertain configurations in a production network for aircraft manufacturing

Production of multi-variant products in a network requires the assignment of customer orders to locations and periods. This is a highly complex planning task, as requirements of procurement, production, distribution, and sales have to be considered. Providing customers with the flexibility of configuring their ordered products after order assignment further increases the complexity of the planning task by taking uncertainty into account. Therefore, a robust optimisation model, using scenarios representing potential customer-specific order configurations, is introduced. By providing enough flexibility to handle maximum work overload caused by the potential order configurations at locations, a robust assignment of orders can be guaranteed in order to avoid undesirable situations causing delays and additional costs. Therefore, the mid-term adjustments of the flexibility limits are enabled by the changeability of workforce supply by making use of external workers. An industrial application of the model in manufacturing of the Airbus A320 Family of aircrafts is presented. The costs for offering configuration flexibility to customers are quantified by the expected value of perfect information. The explicit consideration of configuration uncertainty through the use of scenarios is discussed based on the value of the stochastic solution in comparison to the results attained by simplistically using the expected value.     

Simulation and performance evaluation of partially and fully integrated sales and operations planning

This article presents rolling horizon simulation models and performance analysis of partially and fully integrated sales and operations planning (S&OP) against traditional decoupled planning in a multi-site make-to-order (MTO) based manufacturing supply chain. Three simulation models are developed illustrating, respectively, the fully integrated S&OP model, which integrates cross-functional planning of sales, production, distribution, and procurement centrally; the partially integrated S&OP model, in which the joint sales and production planning is performed centrally while distribution and procurement are planned separately at each site; and the decoupled planning model, in which sales planning is carried out centrally while production, distribution, and procurement are planned separately and locally. A solution procedure is provided for each model so that a more realistic planning process can be simulated. Performances of rolling horizon simulation models are evaluated against those of the fixed horizon deterministic models. The results demonstrate that while deterministic models are important for theoretical studies, they are insufficient for decision support and performance evaluations in a real business environment. A rolling horizon simulation model is required to provide more realistic solutions. The effects of demand uncertainties and forecast inaccuracies are incorporated in the evaluation. The study is carried out based on a real industrial case of a Canadian-based oriented strand board (OSB) manufacturing company.     

MRP performance effects due to lot size and planned lead time settings

The effects of lot size and planned lead time settings are evaluated in a shop producing assembled products with common components. Material requirements planning (MRP) logic is used for production planning. A framework for the analysis of delays within the production system is presented. These delays are evaluated using spreadsheet-based MRP software linked to a simulation package that emulates shopfloor activity. Results show that by selecting the proper lot sizes and then using the planned leadtimes to control delivery performance, the required inventory levels can be minimized.     

A two-stage robust programming approach to demand-driven disassembly planning for a closed-loop supply chain system

The closed-loop supply chain system, which integrates forward and reverse logistics, is a desirable policy for retaining recoverable resources and extending the life cycles of products. In this study, we propose a methodology to contend with a demand-driven disassembly planning problem under a closed-loop supply chain system. A two-stage robust programming model is developed correspondingly, such that multiple products with a hierarchical product's structure are disassembled to satisfy uncertain demands in multiple periods. The objective of the model is to determine a robust decision for recycle volume and timing of each type of end-of-life (EOL) product, as well as recovery strategies. The results provide two-stage decisions by considering future scenarios of periodic demands at the beginning of a planning horizon. The first-stage decision is to determine a compromise solution that is close to the optimal solution for every scenario while retaining a certain level of infeasibility of constraints, such as unsatisfied demand. Afterward, when the outcome of a scenario has been realised, the second-stage decision, such as, inventory volume, is conducted to become a buffer for mitigating uncertain impacts. Furthermore, the computational results confirm the trade-off relationship between solution robustness and model robustness, which are core results of the robust model apart from expected profit. The different types of decision makers’ preferences toward risk can be accounted for to determine a compromise robust solution.     

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision’s potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments.     

Tactical production planning in a hybrid Make-to-Stock–Make-to-Order environment under supply,process and demand uncertainties: a robust optimisation model

In this paper, we consider a hybrid ‘Make-to-Stock–Make-to-Order’ environment to develop a novel optimisation model for medium-term production planning of a typical multi-product firm based on the competencies of the robust optimisation methodology. Three types of uncertainties: suppliers, processes and customers, are incorporated into the model to construct a robust practical model in an uncertain business environment. The modelling procedure is started with applying deterministic linear programming to develop a new multi-objective approach for the combination of multi-product multi-period production planning and aggregate production planning problems. Then, the proposed deterministic model is transformed into a robust optimisation framework and the solution procedure is designed according to the Lp-Metric methodology. Next, using the IBM ILOG CPLEX optimisation software, the proposed model is evaluated by applying the data collected from an industrial case study. Final results illustrate the applicability of the proposed model.     

An approach to integrate production-transportation planning and scheduling in an aluminium supply chain network

This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a time-continuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision makers.     

Inventory policies analysis under demand patterns and lead times constraints in a real supply chain

This paper reports a study on a real three-echelon supply chain operating in the beverage sector. The authors, starting from the actual supply chain configuration, propose a detailed study of the inventory systems. The test of a comprehensive set of different operative scenarios, in terms of customers’ demand intensity, customers’ demand variability and lead times, becomes a powerful tool for inventory systems analysis along the supply chain. The main objective is the comparison of the actual supply chain configuration with optimistic and pessimistic scenarios for studying the behaviour of different inventory control policies and optimizing the inventory system of each supply chain node. Due to the dynamically changing and stochastic behaviour of the supply chain variables as well as the complex interactions among its actors, the authors have been pushed to implement an advanced simulation model supported by a ‘well-planned’ experimental design. In addition the paper aims to underline the research effort for conceptualizing, modelling, validating and simulating a real stochastic supply chain.     

Simulation of capacity and cost for the planning of future process chains

One of the most promising approaches in modern microelectronics is the introduction of 3D chip micro systems with through-silicon via (TSV) interconnections. A successful transfer of this technology from the scientific level up to the level of mass production is not least of all a matter of cost-effectiveness and profit, which is directly related to high productivity. The developed technologies therefore have to be feasible for effective mass production. In this paper we introduce a method for planning and evaluating costs in future process chains. This method goes beyond usual mostly Excel-supported solutions, as it is based on a discrete event simulation system. The simulation model is generically generated out of an XML process chain definition file and includes a sophisticated state model for machines. Multiple process scenarios are created with the help of a supporting software tool. These scenarios are investigated for achieving favourable equipment and process chain configurations as well as control strategies to support manufacturing ramp-ups.     

Flexible supply network planning for hybrid shipment: a case study of memory module industry

The modern supply chain network has geographically spread out across the globe. The performance of a customer service level is highly dependent on the effectiveness of its supply chain planning. To improve the service provided to downstream customers, planners must not only decide order allocation among multiple distribution centres but also consider reducing the order-to-delivery time. Directed shipment delivery from manufacturing sites provides the flexibility of direct shipment; however, it also makes order allocation more difficult. In this study, a flexible supply network planning (FSNP) model based on integer linear programming is developed for the memory module industry. In addition to multisite order allocation planning, the FSNP model explicitly considers directed shipment from manufacturing sites for reducing the order-to-delivery time. Furthermore, the combination of characteristics of the memory module industry, such as multilevel and multisite production environments, multiple-to-multiple product structures, transportation and production lead times and capacity constraints, makes FSNP highly complicated. The results of the experiments reveal that the FSNP model improves supply chain planning regarding order due date and inventory and transportation costs.     

Impact analysis of customized demand information sharing on supply chain performance

In today's dynamic market environment, rapidly changing customer preferences increase the customization of products and the diversification of demand mix. Companies should understand how the demand mix influences supply chain performance and react properly to fulfil customer satisfaction as to what the customers want. This paper investigated the impact of information sharing of the demand mix on the supply chain performance, under increasing product customization, by changing customer demand pattern and production capacity. The results of simulation analysis demonstrated that a demand mix can be determined that produces the best supply chain performance. A real multi-echelon automotive supply chain was analysed for each of six performance measures and the demand mix information significantly influenced five of them. Demand variation and production capacity also resulted in significant impact on the performance measures.     

Flexibility trade-offs in a random flexible manufacturing system: A simulation study

Flexible manufacturing systems (FMSs) are allowing firms to take advantage of diversified, low volume production of products with short life-cycles. The flexibility provided by an FMS improves the ability of a system to respond to change. Several types of manufacturing flexibility (M F) have been identified in the literature. Ideally, it is desirable to provide an FMS with all types of flexibility. However, there is a general consensus on the discordant nature of some of the flexibilities. This makes it imperative for management to recognize which flexibilities it deems critical to the manufacturing strategy of the firm. This allows the management to trade-off some of the less important flexibilities for the more important ones, as the need arises.

This study is an attempt toward an empirical examination of the flexibility trade-offs in an FMS. By using computer simulation, we study the flexibility tradeoffs in the stochastic environment of a random FMS. Various configurations of the FMS are simulated under the influence of a variety of loading and dispatching strategies. The results show that there is a trade-off among the various flexibility types under most circumstances. However, some scenarios illustrate that an appropriate combination of the variables like scheduling environment, product variety, and system configuration, can be effective in containing these trade-offs. In other cases, it is seen that the FMS can accommodate more than one flexibility type simultaneously.     

Integration of production planning,project management and logistics systems for supply chain management

The objective of this research was to develop an integrated framework to handle application modules in enterprise resource planning (ERP) and supply chain management (SCM) systems that traditionally needs to be interfaced on a case-by-case basis. This paper presents a framework integrating various modules in both systems, for planning, control and execution of materials, resources and operations. The framework enables planning and execution over a range of areas, from flow to project-based manufacturing and distribution from suppliers to customers. This document presents the conceptual framework development along with algorithms for scheduling paths, and illustrated numerical examples in the supply chain environment. The numerical evaluation of scheduling paths and planning of components, in the integrated networks, shows that the developed framework could cater for ERP/SCM application modules. The framework also facilitates simultaneous planning of many components, and eliminates the need for separate modules in ERPSCM systems. It enhances planning, control and execution, and simplifies the vendor follow-up system by integrating supplier components into the integrated network. The framework therefore becomes a new production planning (PP) module in integrated ERP/SCM systems and can provide links to other manufacturing philosophies, such as just-in-time (JIT).     

Value of information exchange and synchronization in a multi-tier supply chain

We investigate the value of various information exchange mechanisms in a four-echelon supply chain under a material requirements planning framework. In the absence of any information sharing, each echelon would develop its own forecasts and plan its inventories based on the history of actual demand from its downstream customer (or echelon). Through a simulation study, we compare this policy with policies where each echelon has access to (i) the end-user demand history and (ii) the planned order schedule of the downstream echelon. Among all the demand information exchange mechanisms, planning inventories based on the planned downstream order schedules resulted in the lowest average inventory level for the entire supply chain. However, use of end-user demand history to forecast and plan inventories at all echelons resulted in the lowest total cost. In addition to the information exchange mechanisms, a simple synchronized replenishment system was considered and evaluated in the study. In the synchronized system, the retailer determines a fixed order interval and the upper echelons replenish only at integer multiples of this interval. The study found that synchronized inventory replenishments among the echelons, even without any exchange of demand information, can bring about more benefits and cost reduction than any of the information exchange mechanisms.     

Value chain management for commodities: a case study from the chemical industry

We present a planning model for chemical commodities related to an industry case. Commodities are standard chemicals characterized by sales and supply volatility in volume and value. Increasing and volatile prices of crude oil-dependent raw materials require coordination of sales and supply decisions by volume and value throughout the value chain to ensure profitability. Contract and spot demand differentiation with volatile and uncertain spot prices, spot sales quantity flexibility, spot sales price–quantity functions and variable raw material consumption rates in production are problem specifics to be considered. Existing chemical industry planning models are limited to production and distribution decisions to minimize costs or makespan. Demand-oriented models focus on uncertainty in demand quantities not in prices. We develop an integrated model to optimize profit by coordinating sales quantity, price and supply decisions throughout the value chain. A two-phase optimization approach supports robust planning ensuring minimum profitability even in case of worst-case spot sales price scenarios. Model evaluations with industry case data demonstrate the impact of elasticities, variable raw material consumption rates and price uncertainties on planned profit and volumes.     

Agile production planning and control with advance notification to change schedule

An agile production planning and control system (APPCS) is proposed that allows the agile rescheduling upon unexpected events. It executes schedule-based planning in the sense that scheduling is tightly connected with net-requirements planning and capacity requirements planning. APPCS does precise scheduling with so-called advanced planning system for the set of demands that are accepted in the planning cycle. It produces a feasible production plan, using production data. After releasing the production or purchase orders, notification to change the schedule may arrive from suppliers or customers in advance of due date. Based on the notification, APPCS reschedules in accordance with the then situation. Safety lead time in purchase orders and safety stock in raw material are used in APPCS to absorb the effects caused by sudden changes. As an example of implementation, it is applied to a fictitious but typical factory with a variety of products. The simulation result shows that planning with notification is effective, and that notification has the similar effect with safety lead time.     

Decentralised capacitated planning with minimal-information sharing in a 2-echelon supply chain

This paper aims at modelling decentralised planning at the tactical level, with minimal-information sharing coordination, in a 2-echelon supply chain with multiple actors at each echelon. Suppliers manage production and storage at the upstream echelon, while retailers manage transportation and storage at the downstream echelon. The main features of the planning process are (1) decentralisation and coordination using contracts and sharing of only order/supply proposals, and (2) iteration on a rolling horizon. Actor planning is modelled as a capacitated lot-sizing problem on a finite horizon, with the focus on quality of service. The objective is to minimise costs, with a high lost sales penalty if demand is not met. Two other decision problems are pointed out and modelled with Mixed Integer Programming: (1) lost sales allocation between the retailers when their demands cannot be satisfied; and (2) allocation of orders between the suppliers. A multi-agent system combines simulation of the planning process and optimisation of the local decision processes. Several strategies, including retailers’ beliefs about suppliers’ production capacity are proposed and experimentally tested, with two patterns of production capacities. The results compare the proposed allocation strategies and highlight the relevance of the proposed framework for the studied planning problem.     

Dynamic planned safety stocks in supply networks

Safety stocks are commonly used in inventory management for tactically planning against uncertainty in demand and/or supply. The usual approach is to plan a single safety stock value for the entire planning horizon. More advanced methods allow for dynamically updating this value. We introduce a new line of research in inventory management: the notion of planning time-phased safety stocks. We assert that planning a time-phased set of safety stocks over a planning horizon makes sense because larger safety stocks are appropriate in times of greater uncertainty while lower safety stocks are more appropriate when demand and/or supply are more predictable. Projecting a vector of safety stock values is necessary to assure upstream members in the supply network have advanced warning of changes. We perform an empirical study of U.S. industry, which demonstrates that significant savings can be achieved by employing dynamic planned safety stocks, confirming recent case study reports. We provide a simple optimisation model for the problem of minimising inventory given a vector of safety stock targets. We propose a computationally efficient solution procedure and demonstrate its implementation in an MRP/ERP system. We then illustrate an MRP/ERP planning system feature, which employs a dynamic planned safety stock module that supports a production planner by showing the inventory implications of safety stock plans.     

TOC-based performance measures and five focusing steps in a job-shop manufacturing environment

Theory of constraints (TOC) views a company as a set of interdependent processes working in harmony to achieve the profit goal of the company as a whole, and thus it emphasizes total system performance over localized measures to guide operational decisions. This paper demonstrates the usefulness of employing TOCbased global performance measures to make operational-decisions (e.g. product mix, continuous improvement, inventory management, production planning and scheduling) to strengthen the internal supply chain in a relatively complex manufacturing environment, i.e. a job shop. An ARENA-based simulation model is presented and a number of scenarios are discussed that provide insights regarding the characteristic features of TOC, such as goals and necessary conditions, performance measures, five-focusing steps for continuous improvement, and drumbuffer-rope scheduling. These insights will assist managers in making important decisions regarding approaches to successful TOC implementations, and will provide academics with a broad range of future research opportunities.