Use of Advanced Planning and Scheduling (APS) systems to support manufacturing planning and control processes

An Advanced Planning and Scheduling (APS) system is defined as any computer program that uses advanced mathematical algorithms or logic to perform optimization and/or simulation on finite capacity scheduling, sourcing, capacity planning, resource planning, forecasting, demand planning and others. Relative the massive interest, both from academia and industry in the subject area of manufacturing planning and control, there has not been much written about the use of APS systems in practice. For academia, this means a lost opportunity of understanding benefits and problems of implementing and using advanced planning and scheduling approaches. Seeing the many algorithms developed by academia during the years that never have been put into practice this should be valuable knowledge. For practitioners, the many failed implementations should make it important to understand what could be expected when implementing an APS system and what is required to effectively use it. This thesis studies how APS systems can support manufacturing planning and control (MPC) processes in adding value to the company by focusing on the consequences of using APS system and the variables influencing the consequences of using APS systems. It is different from previous studies concerned with APS systems as special focus is given to the use, i.e. when the APS system is operated in the MPC process instead of the implementation, the phase between the software selection and going live. Four case studies and one survey have been conducted to aid in fulfilling the overall aim. The thesis found that the use of an APS system can support MPC processes by improving the decision support, simplifying planning activities, and reducing planning time and by generating feasible plans and schedules that are possible to follow. Still, the use of an APS system might make the planning activities more difficult to conduct and result in plans and schedules that are difficult to retrace or which are incorrect. It was identified that not only the use or non use of APS functionalities, but also the way the functionalities are used and the extent to which the functionalities are used influences the MPC process. The planning environment complexity, identified as the number of/and dependencies between entities and uncertainties in demand, supply and the production system of a manufacturing company, was found to influence how the APS system ‘should’ be used. Variables connected to the implementation of the APS system and to the MPC process, on the other hand, influence how the APS system is actually used. This thesis should be of interest to the subject area manufacturing planning and control. Researchers may benefit from definitions and conceptualisations of a number of constructs. For managerial usage, a number of benefits from using APS system in different MPC processes have been identified. Those may be used as a tool to assess whether the potential benefits of APS systems support the overall business objectives. Alternatively, it can be employed as an evaluation mechanism to access whether anticipated benefits were realized. A number of variables of importance in order to use an APS system in such a way so that benefits could be achieved have been identified. Those should be important when considering an APS system implementation. The thesis also contributes with a number of case descriptions in how APS systems are used in different companies and the users perceptions of using APS systems. This could be interesting knowledge for consultants and system vendors

Modeling and Controlling of an Integrated Distribution Supply Chain: Simulation Based Shipment Consolidation Heuristics

Increasing competition due to market globalization, product diversity and technological breakthroughs stimulates independent firms to collaborate in a supply chain that allows them to gain mutual benefits. This requires collective knowledge of the coordination and integration mode, including the ability to synchronize interdependent processes, to integrate information systems and to cope with distributed learning. The Integrated Supply Chain Problem (ISCP) is concerned with coordinating the supply chain tires from supplier, production, inventory and distribution delivery operations to meet customer demand with an objective to minimize the cost and maximize the supply chain service levels. In order to achieve high performance, supply chain functions must operate in an integrated and coordinated manner. Several challenging problems associated with integrated supply chain design are: (1) how to model and coordinate the supply chain business processes; (2) how to analyze the performance of an integrated supply chain network; and (3) how to evaluate the dynamic of the supply chain to obtain a comprehensive understanding of decision-making issues related to supply network configurations. These problems are most representative in the supply chain theory’s research and applications. A particular real life supply chain considered in this study involves multi echelon and multi level distribution supply chains, each echelon with its own inventory capacities and multi product types and classes. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially in a real life situation where a multitude of aspects and functions should be taken into consideration. In this dissertation, the simulation based heuristics solution method was implemented to effectively solve this integrated problem. A complex real life simulation model for managing the flow of material, transportation, and information considering multi products multi echelon inventory levels and capacities in upstream and downstream supply chain locations supported by an efficient Distribution Requirements Planning model (DRP) was modeled and developed named (LDNST) involving several sequential optimization phases. In calibration phase (0), the allocation of facilities to customers in the supply chain utilizing Add / Drop heuristics were implemented, that results in minimizing total distance traveled and maximizing the covering percentage. Several essential distribution strategies such as order fulfillment policy and order picking principle were defined in this phase. The results obtained in this phase were considered in further optimization solutions. The transportation function was modelled on pair to pair shipments in which no vehicle routing decision was considered, such an assumption generates two types of transportation trips, the first being Full Truck Load trips (FTL) and the second type being Less Truck Load trips (LTL). Three integrated shipment consolidation heuristics were developed and integrated into the developed simulation model to handle the potential inefficiency of low utilization and high transportation cost incurred by the LTL. The first consolidation heuristic considers a pure pull replenishment algorithm, the second is based on product clustering replenishments with a vendor managed inventory concept, and the last heuristic integrates the vendor managed inventory with advanced demand information to generate a new hybrid replenishment strategy. The main advantage of the latter strategy, over other approaches, is its ability to simultaneously optimize a lot of integrated and interrelated decisions for example, on the inventory and transportation operations without considering additional safety stock to improve the supply chain service levels. Eight product inventory allocation and distribution strategies considering different safety stock levels were designed and established to be considered as main benchmark experiments examined against the above developed replenishment strategies; appropriate selected supply chain performance measures were collected from the simulation results to distinguish any trading off between the proposed distribution strategies. Three supply chain network configurations were proposed: the first was a multi-echelon distribution system with an installation stock reorder policy; the second proposed configuration was Transshipment Point (TP) with a modified (s,S) inventory; and the last considered configuration was a Sub-TP, a special case from the second configuration. The results show that, depending on the structure of multi-echelon distribution systems and the service levels targets, both the echelon location with installation stock policy and advanced demand information replenishment strategy may be advantageous, and the impressive results and service level improvements bear this out. Considering the complexity of modeling the real life supply chain, the results obtained in this thesis reveal that there are significant differences in performance measures, such as activity based costs and network service levels. A supply chain network example is employed to substantiate the effectiveness of the proposed methodologies and algorithms