Taravatsadat Nehzati
email: taravatsadat.nehzati@ntnu.no

Anita Romsdal
email: anita.romsdal@ntnu.no

Heidi C Dreyer
email: heidi.c.dreyer@ntnu.no

Jan Ola Strandhagen
email: ola.strandhagen@ntnu.no

Applicability of ERP for Production Network Planning: A Case Study

Keywords: Production planning, Production network, Enterprise resource planning, ERP system

A production network enables a company to develop capabilities to respond to diversity in national or regional demand, while at the same time integrate and coordinate their activities. Many companies have implemented enterprise resource planning (ERP) systems to overcome problems associated with coordination and planning in an organization, in recent years. In addition, advanced planning and scheduling (APS) systems have emerged to address the planning insufficiencies of ERP systems. However, due to complexity and competence dependency of these systems, advantages of them are overlooked by large organizations, and therefore ERP systems are still in use for planning. ERP systems are used in production networks for coordination of various plans and decisions across network partners. This paper aims to assess the fit and alignment between ERP functions and production network requirement for supporting production planning processes. Using a case study approach, the paper illustrates and discusses the applicability of ERP systems for planning in production networks. The case study showed that ERP systems have limited ability in coordinating order allocation in the network, detailed short term production planning and inter network distributions. It is concluded that use of ERP systems for network planning may in fact limit the network's ability to reap the full benefits associated with planning across several facilities in a network.

Introduction

Dynamic and unstable consumer markets require more agility in manufacturing companies [START_REF] Thoben | Typological issues in enterprise networks[END_REF]. Keeping proximity to customers to reduce response times to changes in demand has been a motivation for companies to spread the production sites geographically or to find industrial partners to cooperate with. This motivates the formation of production networks. Göttlich, Herty [START_REF] Göttlich | Optimal Order and Distribution Strategies in Production Networks[END_REF] defined production networks as "a set of processes utilized to efficiently integrate suppliers, manufacturers, and customers so that goods are produced and distributed in the right quantities, to the right location". A production network enables a company to develop capabilities to respond to diversity in national or regional demand, while at the same time integrate and coordinate their activities to reduce costs and improve productivity. Parameters like speed, flexibility, productivity and cost are improved, but the need for integration and coordination of processes and information are increased drastically [START_REF] Jagdev | Anatomy of enterprise collaborations[END_REF]. Many companies have implemented an Enterprise Resource Planning (ERP) system to support their internal processes. New requirements for collaboration across facility borders have led to increased demand for different functionalities in the enterprise software, such as high degree of adaptability to diverse conditions, a concise provision of comprehensive information, the capability to be applied across facilities in the network and good capabilities to support key planning processes. In order to fully exploit the advantages of the network setting, supportive information system should be capable of meeting the mentioned production network needs. ERP systems are designed to provide business benefits such as data visibility and task automation, particularly for large enterprises. ERP systems are therefore still the backbone of most companies' production planning processes. However, it has been argued that ERP systems are ill-equipped for planning in certain environments [START_REF] Van Nieuwenhuyse | Advanced resource planning as a decision support module for ERP[END_REF][START_REF] Aslan | Enterprise Resource Planning systems: An assessment of applicability to Make-To-Order companies[END_REF]. The fact that ERP systems are applied to support planning in production networks motivated us to study in more detail the particular challenges and limitations these practices impose on production planning for production networks.

The paper is structured as follows: in section 2 we explain about methodology used in this paper, section 3 briefly presents the production network concept and production planning processes in a network perspective. Section 4 focuses on the case study, describing production network characteristics and discussing how production planning tasks are executed through the use of an ERP system. Section 5 analyzes data from case study before Section 5 presents some conclusion and suggestions for further research.

Methodology

A case study is used to provide a better understanding of the applicability of ERP to support production planning in production network and to understand the practical limitations and challenges. Case studies are known as a strong means for conducting descriptive research and help to gain insights into areas that have not been explored in the literature [START_REF] Yin | Case Study Research: Design and Methods[END_REF]. This study focuses on the production network of one company and studies the consequences of applying an ERP system for managing production planning processes. Data was collected through semi-formal interviews with central and local planners. Explanation building is used as analytic technique for analyzing the case study data [START_REF] Yin | Case Study Research: Design and Methods[END_REF]. Using this method of analysis we will explain the link between phenomena and reason(s) behind that, which may be complex and difficult to measure in precise manner. After analysis, the results were discussed with other researchers and verified with key resources in the case company's supply chain department.

Planning in production networks

A production network consists of manufacturing plants that cooperate and share resources with each other. To describe type of production network, different elements need to be considered. Rudberg and Olhager [START_REF] Rudberg | Analysis of Series-Parallel Production Networks without Buffers[END_REF] classified types of production networks based on number of organizations in the network and number of sites in each organization. For example multiple organization in the network and single site per organization is defined as supply chain while single organization and multiple sites in that organization is described as intrafirm network. Thus, they define different types of network based on number of organization/sites involved in composition. Phillis, D'Angelo [8], on the other hand, had specific focus on configuration of the production network. They stated that plants in production network may connect to each other in series (sequential), in parallel, or in series-parallel, where in parallel networks, all stations/plants operate in the same level of value adding and can work independent to each other, while plants/works stations in serial (sequential) networks need to work at the same rate and each facility is proceeded or succeeded only by one facility [START_REF] Wiendahl | Load-oriented Manufacturing Control[END_REF].

Beside organizational characteristics, which may affect collaboration in production network, the applied control principle plays a significant role in planning for production network, as it to a large extent determines the flow of goods in the network. [START_REF] Wiendahl | Production in Networks[END_REF] have listed three control principles from literature, known as; centralized, decentralized and loadoriented control [START_REF] Wiendahl | Load-oriented Manufacturing Control[END_REF]. With having several plants in the production network, one of the key logistical challenges will be to coordinate deliveries and production plan accordingly. Therefore having an effective control principle for the network can be a challenge. In addition, Scholz-Reiter, Dashkowskiy [START_REF] Scholz-Reiter | Autonomous Decision Policies for Networks of Production Systems[END_REF] have listed scheduling of shop floor and planning [START_REF] Kopanos | Multi-Site Scheduling/Batching and Production Planning for Batch Process Industries[END_REF] (Where to produce what?) of transport operations [START_REF] Guinet | Multi-site planning: A transshipment problem[END_REF] (optimize intra network transportation), as other two planning problems in production network. Information systems are used widely for supporting coordination in production networks. Sheu, Chae [START_REF] Sheu | National differences and ERP implementation: issues and challenges[END_REF], focused on ERP application in multisite firms, and highlights the complexity of module integration among facilities. Jacobs and Whybark [START_REF] Jacobs | Why ERP?: a primer on SAP implementation[END_REF] and Markus, Tanis [START_REF] Markus | Enterprise resource planning: multisite ERP implementations[END_REF] believe that implementation of ERP system in production networks can end up in failure unless differences production methods and customer demand could be reconciled.

Regardless of production network type, [START_REF] Wiendahl | Production in Networks[END_REF] stated that intensified cooperation between manufacturing companies leads to modified tasks for production planning and control. Figure 1 shows how the core tasks of production planning and control (PPC) are oriented toward synchronization between network partners. Production program planning include synchronization between plants in the network, regarding sales, inventory and resource planning. Manufacturing requirements planning has to consider modified modes of planning for procurement, backward scheduling and capacity requirements. Thus, the collective functions of order coordination, inventory management and controlling will become more important for planning in networks than in conventional PPC environments, since every decision need to take to account complex interrelationships between processes. Control of plan in the network and planning for transportation plays significant role in the network planning. As it discussed by Kanyalkar and Adil [START_REF] Kanyalkar | Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment[END_REF] and Scholz-Reiter, Dashkowskiy [START_REF] Scholz-Reiter | Autonomous Decision Policies for Networks of Production Systems[END_REF] transport and distribution need to become an integral part of PPC processes in the network.

In general, PPC tasks in a production network are characterized by intensive communication between network partners and in broader perspective, with suppliers and customers. The goal is to get everyone in the production network into a common platform for logistics transactions and information systems [START_REF] Kuehnle | A system of models contribution to production network (PN) theory[END_REF]. This integration can result in significantly faster system responses to changes in marketplace events and patterns of demand. As a result, a highly organized network of complementary companies across the supply chain can rapidly build strategic effectiveness.

4

Case study

This section firstly describes the case company and its key network planning processes, and secondly summarizes the key insights from the case study with regards to the applicability of ERP for network planning.

Introduction to the case company

The case company is food producer with over 40 production facilities. The plants produce three main categories of products and the case study focuses on one of these. The products in this category are produced in three facilities, totaling over 100 product variants with varying production volumes. Although the three plants produce the same category of products, the variants are partially distributed among them and each facility is partly specialized for producing a set of variants. However, some variants are produced in all three plants. The company has strategically positioned the three facilities in different geographical positions in order to keep proximity to both suppliers and customers. Based on definition given in section 3, the case company can be defined as intra-firm production network (one organization with multiple plants), with parallel structure. The company uses a combination of centralized and decentralized PPC, and uses an ERP system for production planning. In addition, the company has recently started using specialized supply chain planning software for strategic planning and design of the production network.

4.2

Production planning The company's main planning and control principle is make to stock (MTS) based on forecasts and inventory levels. The main argument for this is that in many cases the production lead time is longer than customer order lead time expectations. Therefore, not only the tactical production planning is based on forecasts, but also short term and tactical planning operations [START_REF] Romsdal | Food Supply Chains; concept, frameworks and guidlines for differentiated production planning and control[END_REF]. The planning and control process consists of both centralized and local planning performed by resources belonging to different functions. Below, the PPC processes are described using the framework in Erreur ! Source du renvoi introuvable..

Production program planning in ERP.

Production planning (including demand management, master production schedule, and rough cut resource planning) [START_REF] Vollmann | The Definitive Guide for Professionals[END_REF] is the responsibility of the supply chain management department and is performed both centrally and at the individual facilities. As mentioned, planning and control is based on forecasts. The central forecasting department updates weekly sales forecasts continuously at the stock keeping unit (SKU) level with a horizon of 104 weeks. The central planner performs the weekly production planning for all three facilities. The ERP system suggests a production plan for each facility based on the forecasts, and this is then manually adjusted by the central planner for main two reasons. Firstly, more up-to-date information about orders and changes in the market place needs to be incorporated into the forecast, and secondly, the central planner uses the forecast for subsequent weeks to bring production forward in order to level the production for each plant over the next two weeks.

A key drawback of the company's ERP system is that it is not designed to automatically adjust the sale forecasts based on actual orders. In addition, since there is no possibility to have a view of all production plants at the same time, and due to high number of product types, it is difficult to analyze the effect of plan adjustments on performance criteria such as inventory level and capacity utilization.

Manufacturing requirement planning in ERP.

Replenishment planning (including detailed material and capacity planning) is also the responsibility of the supply chain management department and performed at both the central and facility level with a planning horizon of one day up to four weeks. The company's ERP system uses closed loop MRP which includes capacity requirement planning (CPR) for evaluating the consequences of the material plan on capacity. Since ERP systems used by case company was not using advanced planning and scheduling (APS) extension, the material and production plans need several adjustments before they can be executed. The reason is that the ERP system does not have an over view of all three plants before planning, and therefore leveling of capacity has to be done for every plant, individually, without taking to account the effect of changes in order allocation, and capacity load of other plants. The problem is amplified when the capacity of the production system has high correlation with the skills and availability of human resources since these are variable and have a direct effect on the feasibility of the production plans. Further, the lack of coordinating functionality to support planning in a network of parallel production plants complicates the use of the ERP system for requirement planning.

Transportation planning in ERP. The company's distribution division is responsible for transport scheduling, which involves planning of transport between facilities on a weekly horizon. The transportation plan is not only meant to plan distribution of finished goods based on customer orders, but also transportation between plants. Since the production plan is mainly based on forecasts, inter network transportation is important when actual demand exceeds available inventory volumes at each facility. Therefor planning of transport requirements depends on both customer orders and the central planner's allocation of orders to the individual plants.

Production planning control in ERP.

As it explained earlier case company uses combination of decentralized and centralized control principle, where in, detailed planning is done internally by individual network partners and the general planning (Production program planning) is performed at the network level. Using ERP system, the central planner have a good overview of resource used in the network, and can monitor production processes which are under process at each plant in the network, however any corrective action in the plan needs excessive communication between network partners and central planners.

Data management.

Since all plants in the production network are belong to one organization, ERP system gives good access to data for all members of the production network in the case company.

Analysis

The previous section described some of the key production planning tasks in the case company's production network, including production program planning, manufacturing requirement planning and transportation management. Based on interviews and observation of the current situation in the case company, a number of insights on the effect of using an ERP system for network planning were generated.

5.1

Production program planning The ERP system in the case company is not capable of distributing orders between network facilities and therefore a number of manual adjustments are needed before volumes are allocated to each plant. As a result, the ERP system does not provide any support in the optimization of order allocation to plants in the network based on available capacity, inventory status, or proximity to the customer.

5.2

Manufacturing requirement planning The ERP system's functionality is limited in terms of taking into consideration detailed planning parameters from each facility in the network such as: --

Coordinating material plans between all facilities --Coordinating capacity plans between all facilities This means that the local planners at the individual facilities must make decisions based on their own experience and rules of thumb, without support from the ERP system. This includes daily schedules and personnel planning. Since the ERP system used by case company did not have advanced planning and scheduling (APS) extension [START_REF] Aslan | Enterprise Resource Planning systems: An assessment of applicability to Make-To-Order companies[END_REF] details on local personnel availability is not considered in the plan, therefore the system provides little or no support for short term planning for production operations that are human resource dependent [START_REF] Dreyer | Global supply chain control systems: a conceptual framework for the global control centre[END_REF].

5.3

Transportation planning Although ERP system is used for distribution planning, however, internal transportation is not included in the planning phase as an option for resource splitting. That means, potentials of the network is not used in planning phase but considered in control of the plan as a potential for support in unscheduled events, reactively.

5.4

Production planning control Although the control principle used by the case company gives a reasonable chance to all plants in the network to have a control over production processes, there is still strong arguments in the theory for applying fully centralized planning in production network [START_REF] Kanyalkar | Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment[END_REF][START_REF] Scholz-Reiter | Intelligent Nonhierarchical Manufacturing Networks[END_REF]. The applicability of ERP system in production networks under fully centralized control principle [START_REF] Markus | Enterprise resource planning: multisite ERP implementations[END_REF] need to be studied for the case company.

5.5

Data management Being part of one organization and producing the same family of product in all three plants, and as a consequence using the same resources gives a good motive for plants in the network to share relevant data and ERP system showed to be effective in that regards. The production network context can provide advantages in terms of using network partners for controlling and reducing the effects of demand uncertainty. However, the ERP system does not support internal distribution decisions in the network and such decisions therefore have to be made manually and require considerable communications between the facilities. Moreover, the ERP system was found to have limited flexibility to cope with unforeseen events in short terms plans caused by for instance changes in demand and machine breakdowns that affect capacity.

Conclusion

This paper used a case study to investigate the applicability of ERP systems for production planning in production networks. The conclusion is that the ERP system provides limited support for the major planning activities in the production network and prevents the company from fully exploiting the network setting.

The results obtained from this study may not be generalizable as findings are verified in only one case. However, we believe that the case company is not unique and that many other companies are also attempting to use their ERP systems to support planning across network facilities. Therefore, the results from our study may provide valuable insights for managers and executives on how the use of ERP systems for network planning may in fact limit the network's ability to reap the full benefits associated with planning across several facilities in a network.

The paper contributes to theory by highlighting that although ERP systems are not designed for planning in the network context, and despite the availability of advanced planning systems, in practice, companies are still trying to use their ERP systems to support their main planning processes. This provides good motivation for future research to find solutions and tools that can be used together with ERP systems to meet production network requirements and reap more benefits of the network context.

Fig. 1 .

 1 Fig. 1. Production planning and control functions in production network [adapted from 16]

Acknowledgment

The research in this paper was financed by the Norman project (see www.sfinorman.no) and the Research Council of Norway.


Emrah Arica
email: emrah.arica@ntnu.no

Jan Ola Strandhagen

Hans Henrik Hvolby

Designing a decision support system for production scheduling task in complex and uncertain manufacturing environments

Keywords: production scheduling and control, complex and uncertain manufacturing environments, decision support system

The production planning and control process is performed within complex and dynamic organizations made up of equipment, people, information, IT systems, and influenced by a multitude of external factors. How to effectively schedule in uncertain and complex manufacturing environments, still remains a central question to academics and practitioners. In this paper, we propose a framework that can be utilized to design/enhance decision support systems for scheduling activities in complex and uncertain manufacturing environments. The framework is based on the analysis of the relevant literature that addressed human, organizational, and technological aspects of the production planning and scheduling.

Introduction

Today, a rich amount of scientific literature exists, addressing the planning and scheduling task in uncertain manufacturing environments by proposing advanced methods, techniques, and algorithms that can predict and/or react on unscheduled events [see for example the recent literature reviews provided by 1, 2-5]. Majority of the theoretical studies view this task from a technical perspective with well-defined, simplified and objective-driven analytical problems [START_REF] Higgins | Interaction in hybrid intelligent scheduling[END_REF][START_REF] Crawford | Investigating the work of industrial schedulers through field study[END_REF], incorporating a limited number of events and their estimated or identified time impact on single schedules [START_REF] Vieira | Rescheduling manufacturing systems: a framework of strategies, policies, and methods[END_REF][START_REF] Aytug | Executing production schedules in the face of uncertainties: A review and some future directions[END_REF][START_REF] Hozak | Issues and opportunities regarding replanning and rescheduling frequencies[END_REF].

In stable and reliable environments, these analytical tools and computerized systems can yield expected performance results, especially for the high level material and capacity planning decisions [START_REF] Mula | Models for production planning under uncertainty: A review[END_REF]. However, shop types with high uncertainty and complexity where many factors affect the decision making process can be substantially challenging [START_REF] Wiers | A framework for decision support in production scheduling tasks[END_REF][START_REF] Mckay | The application of computerized production control systems in job shop environments[END_REF][START_REF] Mckay | Integrated decision support for planning, scheduling, and dispatching tasks in a focused factory[END_REF][START_REF] Wiers | The relationship between shop floor autonomy and APS implementation success: evidence from two cases[END_REF][START_REF] Ivert | Shop floor characteristics influencing the use of advanced planning and scheduling systems[END_REF]. As such, very few of the advanced models and systems that address dynamic scheduling/ re-scheduling decisions have influenced and provided guidelines in the industrial practice [START_REF] Aytug | Executing production schedules in the face of uncertainties: A review and some future directions[END_REF][START_REF] Kreipl | Planning and scheduling in supply chains: an overview of issues in practice[END_REF][START_REF] Georgiadis | Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis[END_REF]. This results in substantial efforts for customizing the scheduling systems to the actual needs of the production environment and decision makers [START_REF] Pinedo | Planning and Scheduling in Manufacturing and Services[END_REF].

Thus, the basic reason behind this theory and practice gap can be attributed to the inconsistency between the theoretical definition of the dynamic scheduling process and the complexity of this process in practice. Several field-based studies investigated and captured the important characteristics of the scheduling and control practice in order to reduce this gap and improve the applicability and implementation of the decision support models and tools [e.g. 9, [START_REF] Jackson | A new model of scheduling in manufacturing: tasks, roles, and monitoring[END_REF][START_REF] Mckay | Planning, scheduling and dispatching tasks in production control[END_REF][START_REF] Berglund | Human, technological and organizational aspects influencing the production scheduling process[END_REF][START_REF] Cegarra | A cognitive typology of scheduling situations: A contribution to laboratory and field studies[END_REF][START_REF] Karltun | Contextual conditions influencing the scheduler's work at a sawmill[END_REF][START_REF] De Snoo | Coordination activities of human planners during rescheduling: Case analysis and event handling procedure[END_REF][START_REF] Webster | A case study of scheduling practice at a machine tool manufacturer[END_REF]. These practice-based studies showed that the scheduling activity is driven by unscheduled events in practice; involving a multitude of aspects (e.g. human, organizational, and technological -HTO) in the decision making process to identify and solve the problems effectively, besides the technical aspect. These upcoming insights might flesh out a holistic view on important requirements of designing decision support systems for effective scheduling/re-scheduling decisions. This view is not clear as these field-based reports and contributions addressed different elements of this process in isolation, based on different objectives, methodologies and methods (e.g. hierarchical task analysis, cognitive task analysis). Depending on the objective and the chosen methodology, they pointed out various characteristics and requirements of the decision making process.

The overall aim of this paper is to develop a framework for guiding the design/enhancement of a decision support system for dynamic scheduling decisions, utilizing the contributions and linkages in related literature. While the design of current scheduling systems are mainly focusing on generating the initial schedules with a top-down approach, this framework implies that the design/enhancement of decision support system should start with a bottom-up approach. More specifically, we focus on the dynamics of the event handling and rescheduling process and emerging HTO aspects around the information needs to make effective informed decisions. The impact of the characteristics of this process on the design of a decision support system is examined and reflected. As such, the applicability and practicality of the system can be enhanced, addressing the needs of the production control situation and decision makers.

2

Dealing with unscheduled events in a typical IT configuration

The majority of the modern manufacturing firms use Enterprise Resource Planning (ERP) systems for production planning and control activities [START_REF] De Snoo | Coordination activities of human planners during rescheduling: Case analysis and event handling procedure[END_REF]. The fundamental benefits of the ERP systems come from their abilities to process and record transactions efficiently, rather than their inherent planning and control capabilities [START_REF] Powell | Lean production and ERP systems in smalland medium-sized enterprises: ERP support for pull production[END_REF].

When used for production planning and control, ERP systems deploy the Material Requirements Planning (MRP) logic [START_REF] Koh | A holistic approach to diagnose uncertainty in ERP-controlled manufacturing shop floor[END_REF]. The MRP translates the MPS into timephased net material requirements and calculates the Planned Order Release (POR) for the detail scheduling and execution use on the manufacturing shop floor. At the shopfloor, the generated POR schedule is exposed to uncertainties that the system is not accountable for. This creates many problems for the production execution later on the shop-floor, such as varying workloads and changing bottlenecks [START_REF] Chen | A mixed integer programming model for advanced planning and scheduling (APS)[END_REF].

Over the past two decades, APS systems have emerged to supplement the ERP systems and eliminate some of the major MRP assumptions [START_REF] Steger-Jensen | Advanced planning and scheduling technology[END_REF]. APS has by far out-performed the planning and scheduling functionality of the ERP system by for example considering the limited resource capacities simultaneously with the material planning function. A strong feature in APS is the ability to simulate different planning scenarios before plan release [START_REF] Hvolby | Technical and industrial issues of Advanced Planning and Scheduling (APS) systems[END_REF], which leads to meeting the customer order deadlines with most efficient capacity planning solutions. By doing that, they prevent some of the shop floor uncertainties that ERP systems are bound to encounter on the shop floor. APS is recently standardized and presented as an advanced scheduling technology and how this technology can handle uncertainties is still an open research topic [START_REF] Ivert | Shop floor characteristics influencing the use of advanced planning and scheduling systems[END_REF].

Manufacturing Execution Systems (MES) have been evolved to aid production execution, monitoring, and control activities on the shop floor, closing the drawbacks of the ERP systems in real-time detailed information exploitation from the shop floor. MES can be described as shop floor information and communication systems that provide feedback on a real-time basis [START_REF] Manetti | How technology is transforming manufacturing[END_REF]. The functionalities of MES cover the manufacturing execution and control tasks in an enterprise, making the shop floor data available and measuring the real time performance indicators (e.g. equipment utilization, inventory availability, quality status). This makes it a powerful tool to detect and identify the disturbances. However, MES especially lacks decision making capabilities, more specifically in evaluation and diagnosis of what re-planning and rescheduling actions should be taken [START_REF] De Ugarte | Manufacturing execution system-a literature review[END_REF].

3

The decision making process and associated systems As such, the basic decision making process to handle unscheduled events is to detect, identify, evaluate, and response (see figure 1). Once the cause of the event is detected via automated systems or humans [START_REF] Cowling | Using real time information for effective dynamic scheduling[END_REF], the decision maker(s) should identify the significance of the problem(s) within given situation in order to classify what needs an immediate attention and who should be involved [START_REF] Mckay | The application of computerized production control systems in job shop environments[END_REF]. At the next stage, the alternative re-scheduling solutions are evaluated in order to find an appropriate one in the given situation and constraints. This evaluation stage also involves communication and information sharing efforts across the organization (e.g. operators, schedulers, planners, and even the sales department in some cases) in order to see what constraints can be relaxed, such as what jobs can be delayed [START_REF] De Snoo | Coordination activities of human planners during rescheduling: Case analysis and event handling procedure[END_REF]. Once the appropriate solution is chosen and actions are taken, schedulers then deal with the consequences. This post-analysis stage may also result in further planning adjustments (e.g. changes in executional priorities of jobs).

Hence, the decision making process involves multiple functional roles (e.g. PPC, maintenance, quality, sales) and IT systems in the problem identification and solving process. There can be many different configurations, applications, and functionality of ERP, APS, and MES systems, supporting different functional processes and roles in an organization in varying extents and contents. But, in an abstract level, possible support of these systems in the event handling and rescheduling process can be given based on the previous section where we discussed a typical setting. However, the realization of these benefits still suffers from the complexity of the re-scheduling process in practice for different reasons, in terms of an effective decision making process. Many of these challenges can be gathered around the paradigm of information needs which may encompass several issues explained in the following framework. The framework also indicates the HTO aspects involved to acquire the required information in the decision making process.

Framework

As mentioned above, we have formulated the requirements of the decision support systems around the information needs that emerge when unscheduled events occur. Many of the HTO aspects are involved in the problem identification and solution process because of the efforts to incorporate the right information into the decisions.

Information representation/modelling

This issue relates to the information introduced and represented in the modelled version of scheduling/re-scheduling task in the APS system. With varying degrees, mathematical models make assumptions and define the problem space with lesser complexity than actually it is [START_REF] Mckay | Integrated decision support for planning, scheduling, and dispatching tasks in a focused factory[END_REF]. This results in part of the internal or external constraint information to be left out of the model, which leads to a perceived problem space. When changes are detected, the adjustments are still done in the perceived problem space which leads to a new perceived problem. However, depending on the type of event and the situation, these changes causes some of the "un-represented" constraints (i.e. internal or external) to be considered in the decision making process. As such, new information is needed to identify the problem and solve in given situation. Let's consider a simple fictional example of machine breakdown that disturbs a local schedule. To take a re-allocation action, the decision maker need to get additional information about the situation such as status of the alternative machines that can run the disturbed jobs, status of the required additional resources to run the new operation (e.g. operator, tools, other consumables), internal and external status of other jobs sharing the alternative machine (e.g. their schedule, inventory levels, and priority status). This example can be widened with much more situational information to identify the problem (constraints) and find an appropriate solution. This issue brings up different interrelated HTO aspects in the problem identification and solution process. The human schedulers deal with this ill-defined situation by experience, knowledge, and communication and feedback efforts to reach at informed decisions [START_REF] Cegarra | A cognitive typology of scheduling situations: A contribution to laboratory and field studies[END_REF][START_REF] De Snoo | Coordination activities of human planners during rescheduling: Case analysis and event handling procedure[END_REF][START_REF] Mckay | The human factor in planning and scheduling[END_REF]. Furthermore, during this communication and feedback process, they acquire part of the required information from the existing IT systems [START_REF] Karltun | Contextual conditions influencing the scheduler's work at a sawmill[END_REF]. A related aspect is the division of the decision making autonomy. This aspect can be considered in two dimensions; (i) division of autonomy between shop floor personnel and schedulers [START_REF] Wiers | The relationship between shop floor autonomy and APS implementation success: evidence from two cases[END_REF] (ii) division of autonomy between humans and tools [START_REF] Van Wezel | Allocating Functions to Human and Algorithm in Scheduling[END_REF]. It is very difficult to implement the advanced scheduling rules and systems, if there is no agreement on the division of autonomy, namely who should decide what [START_REF] Wiers | The relationship between shop floor autonomy and APS implementation success: evidence from two cases[END_REF]. The multiple case study of Ivert [START_REF] Ivert | Shop floor characteristics influencing the use of advanced planning and scheduling systems[END_REF] also confirms the importance of this issue for successful implementation of the APS in the type of shop floors where uncertainty is high and many options exist to deal with them.

Information availability

This issue relates to the availability of the required information in the IT systems to identify and solve the problem. As the occurrence of the event entails a need for additional information to solve the episode in a given context effectively, the availability of the information becomes a salient characteristic of this process [START_REF] Framinan | Guidelines for the deployment and implementation of manufacturing scheduling systems[END_REF]. Most of the information might be available in the systems, however, in this respect, the use of IT systems may be challenged with the availability of the information in an updated form. In a periodic control decision, this prerequisite wouldn't be a problem to put in place as the aggregated information (e.g. inventory levels at the end of the week) is captured and used. However, the event-driven decision making is a demanding process in terms of the need for detailed updated information about both the primary (e.g. the current inventory levels of the disturbed jobs with the machine breakdown) as well as the secondary constraints (e.g. the current inventory levels of the other jobs sharing the alternative machine) of the problem. Jonsson and Mattson [START_REF] Jonsson | Inventory management practices and their implications on perceived planning performance[END_REF] argue that accurate lead times and safety stocks are two of the most critical parameters for achieving high planning and control performance. Furthermore, poor data quality is one of the main causes of the use of specific tools and spreadsheets to complement the scheduling tools [START_REF] Framinan | Guidelines for the deployment and implementation of manufacturing scheduling systems[END_REF].

Information accessibility

This issue relates to the integration of the other systems with the scheduling/rescheduling system in order to retrieve and exploit the required information in the decision making process. A key factor to implement such an event-driven control mech-anism is providing the information at the time of perceived information need [START_REF] Tang | The impact of information transparency on the dynamic behaviour of a hybrid manufacturing/remanufacturing system[END_REF]. In many cases the information may be available in different systems and formats, which makes it difficult to access it when needed [START_REF] Leung | A real-time hybrid information-sharing and decision support system for the mould industry[END_REF]. In this respect, some of the authors proposed automated solutions that utilizes the real time information from other systems and technologies [e.g. 14]. Authors that follow the practice-based research stream mainly favored the human-computer interactive solutions [e.g. 32]. Whether it will be processed by the algorithm or visualized as a supportive function, the integration of the information sources to the scheduling system becomes a relevant issue. In this context, Framinan and Ruiz [START_REF] Framinan | Guidelines for the deployment and implementation of manufacturing scheduling systems[END_REF] emphasize that integration of systems may become too complex and impossible and suggests simple and modular approaches such as using data exchange protocols for information exchange.

Information structuring and visualization

This issue relates to how information is presented to the decision maker. The studies show that most of the IT systems have drawbacks to take into consideration of the cognitive skills of the people. As this is an information-driven process, the effectiveness of the decisions also relies on how quick the decision maker can make the sense of the situation from the available information. Today a great deal of real time information is available with recent technological advancements [START_REF] Cowling | Using real time information for effective dynamic scheduling[END_REF]. However, besides the advantage of improved possibility for decision making with more available and accurate information, a challenge of dealing with the increased data in a practical way arises. Hence, visualization also plays an important role in the successful implementation of systems. The use of planning support computer programs is device dependent with psychological relevance but yet event independent [START_REF] Wezel | Allocating Functions to Human and Algorithm in Scheduling[END_REF]. In general, the person who needs to interact with the program and the real time information are disregarded.

Having a better perception of the planning system for the scheduler needs a better availability and visibility of the required information, based on the cognitive analysis of the human scheduler [START_REF] Riezebos | Planner-oriented design of algorithms for train shunting scheduling[END_REF]. The limits of human decision making skills should be well understood when designing the display content.

Conclusions

This study provided indications to the design of the decision support systems for production scheduling task in complex and uncertain manufacturing environments. The motivation behind this study was to build a framework that can facilitate operationalization of the important HTO aspects derived from field-based studies addressing the gap between scheduling theory and practice. There is need for a further study to explicitly reflect the outlined requirements on the design and architecture of a scheduling system and test it in a real life case study for a validity check.

Fig. 1 .

 1 Fig. 1. Decision-making loop for responding unscheduled events and major support areas of the systems

Acknowledgements

This study was financed by the research center called The Norwegian Manufacturing Future (SFI NORMAN).