Roberto Sala
email: roberto.sala@unibg.it

Alessandro Bertoni

Fabiana Pi- Rola

Giuditta Pezzotta

The Data-Driven Product-Service Systems Design and Delivery (4DPSS) methodology

Keywords: Product-service system, data-driven design, data-driven delivery. 1

The design of Product-Service Systems (PSS) has been approached from several perspectives like the process, innovation, engineering and operational ones providing, for each one of those, a specific view on the problem. This paper proposes a Data-Driven Product-Service System Design and Delivery (4DPSS) methodology focusing on the collection and exploitation of delivery data to feed the design phase. The logic of the methodology relies on aggregating operational data (collected in the delivery phase) to build a consistent body of knowledge to be exploited in iterative PSS design activities, thanks to better identification of customer needs, and product and service process design issues. This paper presents the 4DPSS methodology at a theoretical level, the implementations of the different methods constituting the methodology are referred to in the text, while its implementation and test as a whole are demanded to future work.

Introduction

Companies approaching the transition towards the offering of Product-Service Systems (PSS) face challenges encompassing the definition of a successful business model, the design of the efficient and sustainable product and service combinations, and the definition of operational procedures supporting the effective PSS delivery. A plethora of methods to support the transition toward PSS has been proposed in the literature. Such methods, despite approaching the challenge from different perspectives, have as a common denominator the capability to correctly capture and fulfil the needs of the customers, and use them to guide the whole PSS design [START_REF] Maussang | Product-service system design methodology: from the PSS architecture design to the products specifications[END_REF] and delivery process [START_REF] Tian | AHP, Gray Correlation, and TOPSIS Combined Approach to Green Performance Evaluation of Design Alternatives[END_REF].

The shift from a traditional product-centric perspective to the PSS perspective has brought researchers and community of practitioners to focus on the multi-disciplinary implications of such a new mindset. The "PSS transition" challenge has been approached from a process perspective, focusing on lean processes and waste reduction in service and maintenance integrating operational data [START_REF] Pezzotta | The Product Service System Design Methodology (PSSLDM): Integrating product and service components along the whole PSS lifecycle[END_REF][START_REF] Sala | A Dual Perspective Workflow to Improve Data Collection for Maintenance Delivery: An Industrial Case Study[END_REF], from a PSS innovation perspective, focusing on design space exploration and value and sustainability assessment enabled by surrogate modelling [START_REF] Bertoni | Iterative value models generation in the engineering design process[END_REF], and from an engineering practice perspective, focusing on modelling and visualization to address the cognitive limitation of engineers' when making decisions in the presence of multi-disciplinary information [START_REF] Bertoni | Model Driven Decision Arena: an areospace study[END_REF]. Another factor that should be considered as influencing the transition towards PSS is the digitalization of companies, which can be beneficial in terms of data availability for design and delivery purposes [START_REF] Ardolino | The role of digital technologies for the service transformation of industrial companies[END_REF]. All of these perspectives have been addressed singularly, without considering their integration. Thus, the paper proposes a methodology that, having roots in methodologies and tools presented in the literature, integrates the different perspectives to improve the PSS design and delivery. In the following sections, the paper introduces the main theoretical foundation of the methodology, explain the research approach, and describe and visualize the details of the proposed methodology.

2

Scientific positioning

Theoretical foundation

As an answer to need to provide guidance addressing such different dimensions, this paper presents a methodology, named Data-Driven Product-Service Systems Design and Delivery (4DPSS), developed to support the deployment of PSS solutions by creating a virtuous circle of data and information sharing between the PSS design phase and the PSS operations. In particular, the uniqueness of the methodology resides in the adoption of a four-dimensional perspective addressing both the PSS design and the PSS operations during delivery. Fig. 1 illustrates such perspectives, that consists in: (1) dealing with the process-oriented view optimizing the design process phases (i.e. the design perspective), (2) assessing the fulfilment of multi-disciplinary and multi-dimensional needs of the different stakeholders, encompassing lifecycle-value and sustainability dimensions (i.e. the value modelling perspective), (3) dealing with the engineering-human dimension of the design activity, introducing the use of boundary objects to nurture discussion and understanding of the multi-disciplinary implications of PSS design decisions (i.e. the PSS visualization perspective), (4) integrating the design process with data from the field, collected during the delivery phase as support for needs identification and effective PSS design (i.e. operational perspective).

Fig. 1. Four-dimensional perspective of the 4DPSS methodology

Different research contributions address such perspectives one at a time. The proposed methodology builds on some of the main approaches proposed in PSS research, particularly focusing on how the increasing availability of information communication technologies, combined with data science, can enable an effective and efficient PSS deployment. From a process-oriented perspective the PSS Lean Design Methodology (PSSLDM) described in [START_REF] Pezzotta | The Product Service System Design Methodology (PSSLDM): Integrating product and service components along the whole PSS lifecycle[END_REF], proposes a comprehensive approach to develop PSS along the entire lifecycle, defined by four main phases: the identification of the customer needs, the PSS conceptual design, the PSS design and the monitoring of the PSS performance when on the market. The PSSLDM introduces the adoption of specific KPIs to monitor PSS performances on the market, to facilitate the identification of PSS problems [START_REF] Mourtzis | Development of a Key Performance Indicator Assessment Methodology and Software Tool for Product-Service System Evaluation and Decision-Making Support[END_REF].

The iterative use and evolution of value models along the PSS design process have been proposed [START_REF] Bertoni | Iterative value models generation in the engineering design process[END_REF][START_REF] Bertoni | Model Driven Decision Arena: an areospace study[END_REF]. Such an iterative process is enabled by the use of surrogate modelling and data mining to explore a wider design space [START_REF] Bertoni | Model Driven Decision Arena: an areospace study[END_REF]. This triggers design negotiations, forcing the PSS cross-functional team members to "(1) confront each other's perceptions on what the value of a system is, to (2) resolve conflicts where conclusions differ, and to [START_REF] Pezzotta | The Product Service System Design Methodology (PSSLDM): Integrating product and service components along the whole PSS lifecycle[END_REF] progressively learn what a 'good design' is" [3, p. 18]. To populate such models with reliable information, researchers have investigated how models should be built based on data collected from product operations, historical databases, sensors, and online resources, and how unexpected correlations can be discovered by the use of data mining and machine learning [START_REF] Bertoni | PSS cost engineering: A model-based approach for concept design[END_REF]. The effectiveness of using such value models to frontload engineering design activities is strongly related to their effectiveness in enhancing cross-disciplinary and cross-functional collaboration in early design, in other words to their ability to act as effective boundary objects in decision making. This has ultimately led to the proposal of a physical interactive visualization environment and interfaces to enhance the multidisciplinary awareness of decision-making teams when dealing with ill-defined design problems encompassing both a product and service dimensions [START_REF] Bertoni | Model Driven Decision Arena: an areospace study[END_REF].

From the delivery perspective, the 4DPSS methodology integrates the operational data gatherable during the PSS delivery (e.g. during maintenance services) to improve the recognition of problems at the design or process level. This activity is supported through the implementation of a structured approach, as presented in [START_REF] Sala | A Dual Perspective Workflow to Improve Data Collection for Maintenance Delivery: An Industrial Case Study[END_REF], enhancing the data collection, management and analysis, which are three central aspects for value generation from data [START_REF] Zhou | Big Data in Wisdom Manufacturing for Industry 4[END_REF].

Research approach

The definition of the research questions and objectives at the basis of the paper is the results of a research clarification activity based on an extensive literature review in the field of PSS design and delivery, combined with a participatory action research approach in several industrial contexts. The authors have directly participated in industrial research initiatives aiming to facilitate the design and operationalization of PSS, as well as contributing to the development of case studies on the topic. Four Swedish mediumlarge companies in the aerospace, construction machining, and packaging industry have been involved in the investigation of needs in terms of PSS concept design and tradeoff, regarding mainly the current PSS design process in terms of phases and data used for decision-making purposes. Similarly, three Italian manufacturing companies, producing bottling and packaging machines, balancing machines and pumps for the Oil&Gas sector, have been engaged to support the definition of the operational characteristics supporting the collection of information to be used later in the design phase. For these interviews, the attention was devoted to the data generated during the maintenance delivery and used for maintenance decision-making (e.g. technician selection and intervention scheduling). Qualitative data were gathered through semi-structured interviews, workshops and focus groups, initially to compile a comprehensive descriptive analysis of the needs and challenges of PSS design and delivery, and later with the intent to preliminary validate the different steps of the methodology.

3

The 4DPSS methodology

This section deals with the description of the 4DPSS methodology structure. As introduced in Section 1, the paper proposed a methodology covering different aspects of the PSS design and delivery, integrating different perspectives and working on different levels, from the process one (definition of the main phases) to the operative one (definition of the sub-phases and the decision-making tools).

The methodology describes how the integration of different perspectives can be concretized by making the best use of the data collection and analysis in the PSS design, implementation and monitoring phases. Fig. 1 illustrates visually the four-dimensional perspective adopted by the 4DPSS methodology, encompassing the focus on PSS design, on value modelling for design space exploration and trade-off, visualization for engineering design practice and decision making, and the integration of data from the field.

The integration of the four perspectives described in Fig. 1 results into the Data-Driven Product-Service Systems Design and Delivery (4DPSS) methodology (Fig. 2). Each one of the perspectives contributes in supporting the phases of the 4DPSS by providing different views on the design (e.g. PSS design, PSs value modelling and PSS visualization) and delivery problems (e.g. PSS operations).

The 4DPSS is composed of four consecutive phases: i) Needs identification, ii) Concept generation and design, iii) Implementation and, iv) Monitoring. Each phase can be characterized by one or more sub-phases to carry out specific tasks.

The first phase is characterized by the identification of the needs leading to the conceptualization of the PSS characteristics and components. In particular, needs can stem from three different sources: a) customer, b) product and, c) service. The customers' needs originate from the direct dialogue with the customer and are collected through different means such as interviews, surveys or analysis of customer feedback on social platforms.

Fig. 2. The 4DPSS Methodology

Instead, the product and service needs are the results of improved knowledge of the product and service thanks to the data analysis and criticalities identification activities carried out in the Monitoring phase.

The prioritization of the needs is performed according to the criticality of the problem identified, usually associated with the time or cost required to solve the problem.

The second phase, Concept generation and design, uses the information collected in the previous phase as inputs for the creation of PSS concepts. The Value Creation Strategy is updated based on the data collected in the needs identification phase, and it is used to assess the potential value of new technologies. At this stage, a number of potential design modifications of a baseline design are decided and the design space is explored by running simulation models. In case of complex and critical product design, where a set of simulations can take up to days or weeks, such as in aerospace component design, a Design of Experiment setting can be used to generate a large enough set of simulation results to create surrogate models approximating the whole design space (see [START_REF] Bertoni | Model Driven Decision Arena: an areospace study[END_REF] for more details).

Such information converges in a collaborative decision-making environment, featuring visualization capabilities, where models are used as boundary objects to enhance engineers' awareness decision making. In parallel with the exploration of alternative product design, the PSS design activities are also characterized by the definition of specific data collection and analysis strategies for the service and the product components, as suggested in [START_REF] Sala | A Dual Perspective Workflow to Improve Data Collection for Maintenance Delivery: An Industrial Case Study[END_REF]. The definition of proper data-related strategies is fundamental to enable profitable PSS delivery and to understand what kind of information could be collected and analyzed during the use phase to improve service delivery.

The third phase, Implementation, is focused on the collection of the data from the field supporting the decision-making phase at the operational level. The effectiveness and operational efficiency of this phase are subjected to the decisions made in the previous phase. This phase should support the operational decision making exploiting historical and aggregated data collected during the PSS delivery. Data sources are different and cover both product and service sides, involving also customers as long as their general information is involved. Data collection during PSS use is aimed at creating a comprehensive database supporting the decision-making phase in service delivery [START_REF] Sala | A Dual Perspective Workflow to Improve Data Collection for Maintenance Delivery: An Industrial Case Study[END_REF]. The cross-analysis and the service delivery decision could be supported by tools such as optimization models able to consider product and service information.

The fourth phase, Monitoring, exploits the aggregated data collected in the Implementation phase to extract information concerning the PSS performance to identify problems and criticalities. In particular, analyses are focused both on the product and service-related data. For example, the analysis of maintenance data could be used to identify problems in the product design (e.g. breakdowns and stoppages due to the frequent failure of the same component), or in the way service delivery is designed (e.g. useless activities in the process, wrong tools). Thus, this phase aims to extract information that could be used as input for the following phase, Needs identification. With the proposed approach, needs won't be identified only through the experience of the designer or the direct inputs of the customers, but also through a structured analysis of the data collected from the PSS on the field.

The 4DPSS methodology finds its roots in methodologies and methods discussed in the literature. The innovativeness and the value-added, of the 4DPSS stems from the integration of these methods and methodologies, developed to reduce the wastes along with the PSS design and delivery phases, and improve the effectiveness of the PSS delivery through data-driven design and delivery. Methods and methodologies have been selected due to their synergies concerning the inputs and outputs of each phase in the scope of improving the PSS design and increase the effectiveness of the need identification phase enlarging the pool of sources not only to feedback from customers but considering also the data from the service delivery phase.

Discussion and conclusions

The 4DPSS methodology introduces a data-driven decision-making approach exploiting aggregated operational data to identify needs and emerging PSS criticalities from the operational phase and use them in both PSS design and delivery. The 4DPSS methodology shall be seen as a bridge between the literature focusing on PSS development, PSS innovation and design exploration, and PSS visualization for decision-making. The necessity for such a methodology finds its root in the lacks identified through interviews and collaboration with the companies. Specifically, it emerged the incoherence between data collection and exploitation approaches as they are currently carried out in the companies, mainly due to the problems in data management and analysis for design and delivery purposes. For example, some of the interviewed companies had experience with the data-driven design methods part of the "Concept generation and design" phase, but no experience with what is proposed in the "Implementation" phase. Similarly, other companies were experienced with the "Implementation" and "Monitoring" phases but had no experience with the methods proposed in the "Concept generation and design" phase. In this sense, it emerged the interest from companies in introducing digital supports and perspectives able to improve the internal processes related to the design phase and then, improve the ones related to the service delivery [START_REF] Ardolino | The role of digital technologies for the service transformation of industrial companies[END_REF]. Thus, the research approach allowed identifying the blind spots related to data utilization in the various phases and propose solutions to tackle them through a mix of knowledge taken from literature, company experience and researchers' experience. The design, implementation, and monitoring of PSS rely on a data-driven approach that works both at the operational and strategic levels and that is aimed at improving, (1) the maintenance delivery through improved operational decisions, (2) the PSS composition through an analysis of the components who fails most frequently and of the service delivery process characteristics, identifying and correcting the most critical activities, (3) the PSS design process by populating value models with a constant flow of information derived from the analysis of the data collected during the PSS operations.

Finally, the proposed methodology should be considered as the results of a prescriptive study and it has not been implemented and validated in its entirety in a real case study. Nevertheless, the methods and approaches proposed in its frame have been singularly validated in the different research initiatives cited in the paper. Future work will focus on the implementation and test of the methodology in a single case study. The validation process of the 4DPSS methodology would require to apply it in its entirety to a single case study to evaluate its effectiveness and the implementation barriers. Given the long time required to collect operational data for new PSS, the identification of needs from operational data will require to identify a company who is already collecting operational data from maintenance in a suitable way, which allows applying the logic part of the 4DPSS.


Roman Ungern-Sternberg
email: roman.ungern-sternberg@ipa.fraunhofer.de

Hans-Hermann Wiendahl

Backlog Oriented Bottleneck Management -Practical Guide for Production Managers

Keywords: Bottleneck, Management Guide, Production logistics 2 Literature Review

Today's productions systems become more and more complex, comprising a multitude of different resources interacting with each other. The Theory of Constraints (TOC) describes the dilemma that the overall output of such a system is constrained by one or more resources called "bottlenecks". Identifying those bottlenecks has been subject of many research papers. Classical approaches focus typically solely on the bottleneck utilization and neglect accumulated deviations against the planned utilization. This so-called backlog is critical as it has a direct effect on the on-time-delivery. This paper therefore provides a practical approach that can be used by the operative management in order to identify, prioritize and manage bottlenecks in a backlog situation efficiently and effectively.

Introduction

A bottleneck is a resource whose capacity requirement exceeds the available capacity in a period [START_REF] Cox | Apics Dictionary. 9 th ed. Amer Production & Inventory 3[END_REF]3]. If all production orders have to pass through the bottleneck, the maximum production output corresponds to that of the bottleneck [6: p. 115], resulting to the fact that an hour lost at a bottleneck is an hour lost for the overall system [3]. Classical approaches focus solely on the bottleneck utilization and do rarely provide guidelines for their management. Furthermore, accumulated deviations, which are represented in backlog, are typically neglected. Backlog however, is critical as it has a direct effect on the on-time-delivery. This paper therefore develops a practical two-step approach for analyzing and improving operations. The intended users are productions managers in need of a systematic but practical approach to select critical bottlenecks in manufacturing to improve on-time-delivery.

Bottlenecks

Definition. Within this paper, the capacity bottleneck is defined as the resource with the highest load factor (load divided by maximum capacity) during a defined period. A selection of methods for detecting and managing bottlenecks is summarized in the following section.

Management. Adams uses a graph based iteration process to detect bottlenecks based on throughput time and lateness. The Shifting Bottleneck method (SB) solves bottlenecks from iteration to iteration for all machines. In each iteration step, the machine with the largest maximum delay is selected. Then the bottleneck is solved on this machine and an optimal processing sequence of the orders is determined [START_REF] Adams | The Shifting Bottleneck Procedure for Job Shop Scheduling[END_REF].

Based on the duration a machine is active without interruption Roser develops the Active Period Method [START_REF] Roser | A practical bottleneck detection method[END_REF]. Bottlenecks are detected based on the idea that the longer a machine is working without interruption, the more likely it is constraining the overall systems performance [START_REF] Roser | Detecting Shifting Bottlenecks[END_REF].

H.-P. Wiendahl describes the Bottleneck Oriented Logistic Analysis (BOLA) as a method for identifying capacitive bottlenecks in the material flow [START_REF] Wiendahl | Load-Oriented Manufacturing Control[END_REF][START_REF] Wiendahl | Engpassorientierte Logistikanalyse. Methoden zur kurzfristigen Leistungssteigerung in Produktionsprozessen[END_REF]. This method allows to derive problem specific measures based upon analyzing the logistics behavior using the concept of the logistics operating curves [START_REF] Nyhuis | Logistische Kennlinien. Grundlagen, Werkzeuge und Anwendungen[END_REF]. The BOLA is thereby a permanent control's task and thus a continuous process for improving logistics [5: p. 170].

The existing approaches consider backlog only indirect by observing lateness as a relevant parameter. As backlog is a critical parameter in production control, it will be directly considered in the presented method. The backlog range is the time needed to reduce the backlog to zero (assuming that no new orders are planned) and calculated as the backlog divided by the planned capacity per period of the relevant resource [14: p.149]. Figure 1 shows an overall backlog situation with more late orders (too late done) than advanced orders (too early done) for one resource.

Backlog in

Management. Unmanaged backlog generally leads to longer throughput-times, which in turn leads to sequence deviations; both typically cause low on-time-delivery rates. For backlog management there are three general approaches sorted by effectiveness:

Proposed Heuristic Approach

This paper develops a two-step approach for the operative bottleneck management: (1) Critical bottlenecks are prioritized based on their backlog situation and general management guidelines are derived. [START_REF] Cox | Apics Dictionary. 9 th ed. Amer Production & Inventory 3[END_REF] The contributions of different causes to the overall backlog are described, quantified and countermeasures are suggested.

Prioritization / Resource Selection & General Management Guidelines

Real world production systems have many resources. Therefore, a practical prioritization approach is required in order to wisely use the limited management capacity.

Prioritization / Analysis. The task of factory planning is to evaluate and design static, long-term bottlenecks. For a first structured and qualitative identification of requirements for the PPC design and operation, the turbulence profile can be used [START_REF] Wiendahl | Turbulence Germs and their Impact on Planning and Control -Root Causes and Solutions for PPC Design[END_REF]. For the evaluation and prioritization of actions, short-term bottlenecks with already fixed production orders (and due dates) additionally the backlog situation should be considered for taking appropriate actions while keeping high on-time-delivery rates. To facilitate the analysis the so-called Backlog-Load-Portfolio is introduced (see figure 2): On the y-axis the current resource situation is expressed by backlog range. For benchmarks or heterogeneous backlog tolerances, a relative backlog range is recommended [START_REF] Wiendahl | Montageversorgungsanalyse in der Einzelfertigung. Ist-Situation, Potentiale und Ursachen[END_REF]. On the x-axis the potential to reduce backlog by capacity increase expressed by the load factor (or alternatively the utilization) is depicted. The bubble size reflects the operational cost of the resource, expressed in the machine-hour rate. The depicted limits are experience based and should be adjusted case-by-case.

Quantification & Countermeasures of Backlog for Selected Resources

Generally, backlog is caused by due-date deviation, throughput-time deviation or startdate deviation. Figure 3 depicts a more detailed hierarchy of backlog causes. This section describes definitions of the respective causes, approaches for quantification and potential countermeasures. Sequence Deviation. Sequence deviation is the deviation of the planned and the actual rank of an order [4: p.85]. Within this paper the sequence deviation is measured roughly, i.e. all orders within one planning period have the same rank, so sequence deviations within a planning period do not affect the backlog situation.

Load Factor

Sequence deviations lead to symmetrical backlog and advance situations. Thus, their effect on the overall backlog is the minimum of too late and too early orders. E.g. a resource processed 8 h of work too early and is late for 20 h, thus 8 h of the backlog is caused by sequence deviation (the overall backlog equals 12 h).

Countermeasures are disciplinary measures or technical restrictions such as blocking of sequence deviations by IT or physical setup on the shop-floor e.g. by FiFo-rail.

Planning & Control. These backlog causes can be split into three groups.

Blocked Backlog. Orders that are too late and available at the resource, but not processable cause blocked backlog. An order is not processable if it cannot be started, independently from the capacity situation at the resource [5: p. 218].

The backlog effect of blocked orders (or blocked WIP) is the sum of orders in backlog which are not (yet) processable. Delayed processability regularly leads to sequence deviations [4: p.103].

For effective countermeasures, further analysis is required. Main reasons for not processable orders are missing closures due to technical quality defects, materials (in assembly processes) or manufacturing tools [5: p.218]. Depending on the order release strategy, limiting the work-in-progress by postponement of order release is helpful to keep stable throughput times [4: p. 102].

Late Predecessor. Orders that already late, but didn't yet (physically) arrive at the resource for processing cause backlog due to late predecessor, e.g. by order creation with past due dates or late order processing, both leading to start deviation for the resource.

The backlog effect of late preceding processes is the sum of all orders in backlog at a resource, but are not yet available.

The problem should be analyzed and solved going upstream starting at the last resource, including the planning activities such as production and sales order creation and order release. To eliminate the root causes an analysis of the turbulence germs is recommended [14: p.209f].

Over planning. A resource is over-planned if the planned capacity exceeds the available capacity in a period [10: p. 677]. Planning more work content on a resource than capacity is available in that period leads to backlog in the magnitude of over planning. This is typical for the MRP approach, if the planning is assuming no capacity competition of orders (so called "planning against unlimited capacity"). The effect is calculated per period (and not per order) as the difference between the available capacity and the sum of work content for the respective period. Limited over-planning should be compensated with short-term capacity flexibility. A countermeasure could be the use of APS (advanced planning and scheduling) with limited capacity planning approaches.

Performance Deviation. This difference between planned and actual performance within a period can be caused by supply and/or demand deviations. Underperformance (because of lower capacity than promised or higher demand, i.e. standard times, then planned) leads to backlog in the magnitude of the deviation between planned and actual performance. Two cases of performance deviation can be separated within an analysis period:

1. Cases with a reduced performance compared to the planned performance within an analytical period. Here the causes are ambiguous and cannot be assigned clearly to supply or demand deviations. Thus, the guideline is to increase the sampling frequency of the time series data to reach the second case (see figure 4). 2. Cases with non-performance within an analytical period. These cases generally can be classified unambiguously as supply deviations with planned (e.g. maintenance) and unplanned non-performance (e.g. breakdowns) periods, where the latter contributes to the backlog.

The higher sampling frequency required for the second case are commonly achieved by data recording at resource level as production feedback data usually do not have such a high resolution. Thus a first action is tracking the resource status with high sampling frequency, e.g. by machine data acquisition or cyber-physical-systems. Remaining periods with less than expected performance (but not non-performance) can be caused by supply or demand deviations and need further analysis.

Countermeasures for backlog due to supply deviation (e.g. lower than planned resource performance) are generally programs for higher performance, such as preventive maintenance. Countermeasures for demand deviations (e.g. wrong standard times) are adapted demand data, e.g. by advanced calculation methods for standard times.

The distinction between supply and demand deviations in complex systems such as semi-automated assemblies with changing operator performance (supply deviations) but also learning curve effects (demand deviation) is ambiguous. The definition of distinctive criteria between the two categories as well as the required statistical analysis of these multi-causal issues is beyond the scope of this article.

Limitations

The article's approach is on backlog management of single resources. However backlog can be inherited from other resources, e.g. due to wrong transition times or late finalization of preceding operations. As shown in above not all effects can be exactly quantified or even assigned to one exact cause. The analysis focuses on the current situation and does not cover past backlog dynamics over time or the proactive detection of bottlenecks by prediction. Sufficient data quality is a prerequisite for precise and trustable analytics [11, 12: p. 8ff, 222ff]. The order creation and calculation of the due dates is not part of the approach, e.g. the scheduling procedure applied or the handling of customer requests with shorter than standard lead times (so called express orders). In addition, an effective application of the presented results requires profound logistical competence of the managers.

Conclusion & Outlook

The article follows a two-step approach for backlog management in system operations with general management recommendations and a quantification of backlog reasons for selected resources. Recommendations are given, when the applications of cyber physical systems could support a better understanding for backlog causes. This approach supports the production management in focusing on relevant resources and to take correct actions to mitigate backlog situations.

Further research should be conducted on the required data quality, the automation of the analysis and suggestion of potential countermeasures as well as the automated detection and correction of supply and demand master data.

Fig. 2 .

 2 Fig.2. Backlog-Load-Portfolio, based on[13: 208f]

Fig. 3 .

 3 Fig. 3.Reasons for Backlog, based on cause-effect relationships of[START_REF] Lödding | Handbook of Manufacturing Control[END_REF]

Fig. 4 .

 4 Fig. 4. Increase of sampling frequency to identify performance deviations unambiguously.

Order Based Production Systems Definition. The

		backlog corresponds to the amount of work that a resource should have
	completed by a certain date but has not yet completed. Thus, the backlog is calculated
	as the difference between the planned and actual output [7: p. 65f, 14: p.148, 18: p.80].
	Capacity	per Period	Overload	Analyzing Point
	8h		Underload		Available Capacity
	0h
		t -2	t -1	t 0	Planning	t 1	Time
					Period
		Order Status:	Done on time	Done too early
			Open and late	Open and scheduled
	Fig. 1. Resource order schedule (assigned by due date, showing a backlog situation)


Daiki Yasuda
email: daiki.yasuda@mapse.eng.osaka-u.ac.jp

Eiji Morinaga
email: morinaga@kis.osakafu-u.ac.jp

Hidefumi Wakamatsu
email: wakamatu@mapse.eng.osaka-u.ac.jp

A Method of Distributed Production Management for Highly-Distributed Flexible Job Shops

Keywords: Distributed Production Management, Highly-Distributed Manufacturing Systems, Flexible Job Shop, Dispatching Rules, Simulated Annealing, Internet of Things

Recent developments of computer technology and information and communication technology are realizing highly-distributed manufacturing systems (HDMSs) in which each machine is computerized and can communicate with other machines. For this type of manufacturing system, a distributed method of discrete event simulation and a distributed method of job shop production scheduling were proposed, respectively. Because these methods use a common distributed sequencing algorithm for HDMSs, they can be integrated and an integrated method of distributed simulation and scheduling was also proposed for job shops. This paper describes an extension of the method to flexible job shops, in which an operation is processed by one of multiple machines. A distributed algorithm for selecting the machine which processes the next operation of an intermediate job based on a given dispatching rule was proposed. By incorporating this algorithm, the conventional method can be applied to flexible job shops. In addition, a method of optimizing the machine selection by adjusting the ratio of each of multiple dispatching rules was proposed. The feasibility of the proposed method was shown by computer experiments.

Introduction

Diversified customers' needs have caused the transition from low-mix high-volume production to high-mix low-volume production and agile manufacturing in this half century. In these styles of production, it is important to flexibly respond to fluctuations in production conditions, and it is required to develop a method for real-time production management. To achieve this flexible manufacturing, it is necessary to immediately generate, evaluate, modify and determine an operation plan which is suitable for the new condition. As for the immediate generation of an operation plan, there have been proposed many methods for reactive scheduling that utilize meta-heuristics [START_REF] Zheng | A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem[END_REF], knowledge-based systems [START_REF] Szelke | Knowledge-based reactive scheduling[END_REF], neural networks [START_REF] Garetti | Using neural networks for reactive scheduling[END_REF], multi-agent systems [START_REF] Archimede | Reactive scheduling using a multi-agent model: The SCEP framework[END_REF][START_REF] Lou | Multi-agent-based proactive-reactive scheduling for a job shop[END_REF][START_REF] Sabuncuoglu | Reactive scheduling in a dynamic and stochastic FMS environment[END_REF] and so on. As for the evaluation of an operation plan, manufacturing simulation has been discussed and many commercial simulators have been developed. However, those simulators are not used for the purpose of immediate evaluation because of the heavy load of model construction [START_REF] Fujii | Simulation of Manufacturing System under Ubiquitous Environment[END_REF].

Recent developments of computer technology and information and communication technology are realizing highly-distributed manufacturing systems (HDMSs) in which each machine is computerized and can communicate with other machines. With this type of manufacturing systems, distributed discrete event simulation can be performed by providing each machine with a function to simulate its behavior by constructing a simulation sub-model on its own computational resource and with a function to exchange information about itself for executing events in chronological order in the entire system. Based on this concept, highly-distributed manufacturing system simulation has been discussed [START_REF] Fujii | A Basic Study on A Highly Distributed Simulation of Manufacturing Systems under The Ubiquitous Environment[END_REF][START_REF] Morinaga | A Study on Highly-Distributed Manufacturing System Simulation[END_REF].

Along with such a distributed method for evaluating an operation plan, it is desirable to also develop a distributed method for generating an operation plan. As for distributed production scheduling, methods using combinatorial auction [START_REF] Kaihara | Optimization Method using Combinatorial Auction for Production Scheduling with Batch Processing[END_REF], mediator architecture [START_REF] Matsumoto | Advanced Autonomous Distributed Manufacturing System Using Active Database[END_REF] and active database [START_REF] Tönshoff | A mediator-based approach for decentralized production planning, scheduling, and monitoring[END_REF] have been proposed. However, from the point of view of performing production scheduling together with the immediate evaluation of the generated schedule by the highly-distributed manufacturing system simulation, it is desirable to develop another distributed scheduling method which is based on the same architecture that the simulation method has. For this reason, highly distributed scheduling methods utilizing the time management mechanism of the highly-distributed manufacturing system simulation have been proposed [START_REF] Morinaga | Basic study on production scheduling method for highly-distributed manufacturing systems[END_REF][START_REF] Morinaga | A Distributed Production Scheduling Method for Highly-Distributed Manufacturing Systems[END_REF][START_REF] Morinaga | A method for highly-distributed manufacturing systems[END_REF][START_REF] Morinaga | Flexible Job-Shop Scheduling Method for Highly-Distributed Manufacturing Systems[END_REF].

These distributed methods of scheduling and simulation for HDMSs can be integrated because they are based on the same time management mechanism. The integration makes it possible to generate better operational plans efficiently. It is also possible to obtain good plans by incorporating an optimization method in the integrated method. For this aim, a method to integrate them and perform optimization was given for job shops [START_REF] Yasuda | Integrated production scheduling and simulation for highly-distributed manufacturing systems[END_REF]. In actual production, it is common that there are multiple machines which can process a certain operation [START_REF] Kopp | Flexible job-shop scheduling with extended route flexibility for semiconductor manufacturing[END_REF][START_REF] Alvarez-Valdes | A heuristic to schedule flexible job-shop in a glass factory[END_REF]. This paper describes an extension of the integrated method to flexible job shops.

2

Highly-Distributed Manufacturing Management System for Job Shops [START_REF] Yasuda | Integrated production scheduling and simulation for highly-distributed manufacturing systems[END_REF] This paper considers a manufacturing system composed of an automated storage/retrieval system (AS/RS) and machining centers (MCs). Each job (product) is initially stored in the AS/RS, transported to MCs which process its required operations, and finally returned to the AS/RS. Each MC has a buffer of finite capacity for storing pending jobs and processed jobs, and processes the pending jobs stored in it one by one. The processed job is passed to the MC which is responsible for the next operation by automated guided vehicles (AGVs). Each job is temporarily returned to the AS/RS when the buffer of the recipient MC is full. Production ends when the final operation of all jobs is completed and the AS/RS receives all finished products. Each of the AS/RS and MCs, which is a component of this production system, has a calculation function and a communication function, and can perform autonomous decision-making about itself and exchange information by communication with other components. In transport using AGVs, it is important to avoid collision between AGVs. In production sites, a practical method for avoiding the collision is adopted. In the method, the transport path is divided into plural sections (called zones in this paper) with a switch for controlling an incoming AGV based on the presence or absence of an AGV in the section. In this paper, the zones are dealt with components of the production system. Each zone is assumed to have a calculation function and a communication function also as well as the AS/RS and MCs. The role of a zone is to simulate transport of intermediate products and finished products between machines by passing information about an AGV between zones by communication. There is a constraint that an AGV cannot enter a zone with another AGV until the preceding AGV goes out from the zone. To deal with uncertainty caused by this constraint, it is necessary to perform simulation.

The highly-distributed manufacturing system simulation [START_REF] Fujii | A Basic Study on A Highly Distributed Simulation of Manufacturing Systems under The Ubiquitous Environment[END_REF][START_REF] Morinaga | A Study on Highly-Distributed Manufacturing System Simulation[END_REF] executes a discrete event simulation in the highly distributed environment described above. Events that cause a change in the system status occur at discrete points in time. The execution of the simulation is realized by repeating the following steps. Step 1: Advance the simulation time (simulation clock) to the next discrete time point; Step 2: Extract all events that can occur at the new time; Step 3: The process associated with the occurrence of each event is executed to change the state of the system. In order to execute this in a distributed environment, it is necessary to generate events that occur one after another in various places in the system, while maintaining temporal consistency. For this purpose, the machine is ordered based on the earliest event occurrence time of each machine, and the right to execute simulation is given to the machine with the earliest time.

There are four types of events to be executed on the machine which has acquired the right for simulation: job reception, starting processing, finishing processing, and a transport. Each process is as follows: In the case of an event of job reception, the events of starting processing and finishing processing for the next process of the received job are generated. If the machine is idle, the occurrence time of the generated event of starting processing is determined to the time when the job is received. If the machine is processing another job, the occurrence time of the generated event is determined to the time at which the current process is finished.

In the case of an event of starting processing, first, the event list is checked whether there are plural earliest events of starting processing of stored jobs having the same occurrence time. If there are such events, one of the jobs is selected using pregiven dispatching rules (SPT, LWKR, etc.), the weights of which are optimized using similar technique as that for flexible job shops described in Section 3.2. The event of starting processing for the selected job is executed, and then the occurrence time of the event of finishing processing of the job is determined. The occurrence time of the events of starting processing of the unselected jobs are changed to the time at which the processing of the selected job finishes. If there are not plural earliest events of starting processing with the same occurrence time, the earliest event of starting processing is executed, and the occurrence time of the event of finishing processing is determined.

In the case of an event of finishing processing, a request to transport the job to the machine that processes the next operation of the job is assigned to an AGV.

In the case of an event of transport, information of the zone having the right for simulation is passed to the zone in the direction of travel of the AGV and vice versa, and the information about the AGV and the object being transported is transmitted to the recipient zone. The zone receiving the AGV generates an event of transport within itself.

In this system, sequencing is performed using two types of algorithms. One is that called initial sorting algorithm, which is mainly used in the initial stage of simulation. This sorting algorithm orders the priorities of all machines by having each machine inform all other machines of its time and adjust its priority based on received information. The other is that called re-sorting algorithm, which is mainly used for performing sequencing in intermediate stages of simulation. This algorithm reorders the priority of all machines by having the machine the time of which has changed inform all other machines of the change and the related machines adjust their priorities based on received information.

3

Highly-Distributed Manufacturing Management System for Flexible Job Shops

Processing Machine Selection and Prioritization

In job shops, an operation of a job is processed by a pre-specified machine. But, in flexible job shops, there are a plurality of machines that can process the operation. In job shop scheduling, the MC responsible for each operation of each job is determined in advance, so the transfer of jobs between operations can be simple processing. However, in flexible job shop scheduling, before processing a job for delivery, it is necessary to select an MC to be in charge of the next operation of the job. A common dispatching rule for performing this selection is PT rule that gives priority to a high-performance MC, and a highly distributed scheduling method using this rule has been proposed [START_REF] Morinaga | Flexible Job-Shop Scheduling Method for Highly-Distributed Manufacturing Systems[END_REF].

In this approach, before assigning a job, each machine autonomously determines the initial value of the priority of the machine in accordance with the PT rule that prioritizes high-performance MC. This process is called initial sorting. Each machine broadcasts the required processing time 𝑃𝑇 and the ID number 𝐼𝐷 to other machines. Thereafter, each machine receives a message from another machine (𝐼𝐷 ) and analyzes the magnitude relationship with the required time 𝑃𝑇 of the sender machine. If the required time 𝑃𝑇 is larger, the value of 𝑆 (initial value is 1), which represents the own priority of the machine for each product, is incremented by one to lower the priority. If they are equal, the one with the smaller ID number has priority. By performing this set of processes, the correct priority sequence of all machine is obtained.

An example of processing is shown below. First, AS/RS assigns each job to one of the MCs that can take charge of processing its first operation. At this time, the priorities of the MCs based on the PT rule is calculated and the job is assigned to the MC with the highest priority. A transportation event and a job reception event are then generated by job allocation. Next, by the initial sorting algorithm, a machine (AS/RS, MC or zone) with the earliest event is determined, and the machine executes its earliest event. After that, the priorities of the generated events are ordered by the re-sorting algorithm, so that a machine with the earliest event is determined, and the machine executes its earliest event. When the job arrives at the MC, the MC executes the event of starting processing for the job. After that, when the event of finishing processing for the job is executed, the MC responsible for the next operation of the job is determined by performing the machine selection based on the PT rule. If the selected MC has multiple jobs in its buffer when it reaches the idle time, the priorities of those jobs is calculated and the event of starting processing for the job with the highest priority is executed.

Machine Selection Optimization

If only the PT rules is used, jobs may be excessively concentrated on high-performance machine, resulting in a poor schedule. Thus, if only one dispatching rule is adopted, the shortcomings of the rule will have a significant effect on the schedule. Therefore, it can be expected that the shortcomings of each rule can be compensated by using some dispatching rules together. There are various rules for machine selection other than the PT rule. In this study, the excessive concentration of jobs to a machine is suppressed by using the NINQ rule, which can be expected to reduce the machines that become idle, together with the PT rule. The NINQ rule is a rule that, when assigning jobs, assigns to the MC that has the least number of jobs in the buffer among the assignable MCs. The evaluation value of the PT rule is time, and the evaluation value of the NINQ rule is the number of jobs. Therefore, when these are used together, it is necessary to perform normalization to make the value range uniform. The following shows how to normalize the PT rule and the NINQ rule respectively.

𝑉

Since each 𝑉 is defined by the rule that a small value is good, the MC with the smallest 𝑃 value is preferentially selected. Using the weight 𝑊 of each rule in this equation as a solution, the weight is optimized by simulated annealing (SA). Since 𝑊 is a continuous variable, it generates a neighborhood solution using normal random numbers. Normal random numbers are generated by the Box-Muller method. If the random variables X and Y are independent of each other and both follow a uniform distribution on [0,1], then, z i1 defined by the following formula becomes independent random variables according to the standard normal distribution N(0,1) with mean 0 and variance 1. 𝑧 𝑖1 2log𝑋 * cos2π𝑌 (4) The random variable x following the normal distribution N(μ,σ) is expressed as follows.

𝑥 𝜇 𝜎𝑧 (5) Since the neighborhood solution 𝑊 of the solution 𝑊 is a normal random number according to N(𝑊 ,σ), the neighborhood solution is obtained by the following equation.

𝑊 𝑊 𝜎𝑧 (6) Weight adjustment is performed by the above method.

Numerical Example

Processing flows based on the proposed algorithms and concept were coded in the C language and the proposed method was implemented on a generic workstation in semblance by interprocess communication with sockets due to machine constraints. The method was applied to an example of flexible job shops of 10 jobs which require 10 operations using 3 machining centers (MCs), 1 automated storage/retrieval system (AS/RS) and 2 AGVs shown in Fig. 1.

As a result of optimizing the makespan with the number of iterations in SA set to 200, a schedule with a makespan of 281 was obtained. Table 1 shows the numbers of message exchange which occurred in the simulation. Assuming that the number of machines is n, n(n-1) times of communication are required for the initial sorting algorithm, and 2(n-1) times of communication are required for the re-sorting algorithm. When the number of jobs is o, the number of operations is p and the maximum transport distance is q zone, the maximum number of events from the start to the end of production is op(q+3). By these characteristics of the communication algorithms, the number of message exchange tends to be large. Assuming that a message is sent based on TCP and its size is 80byte, the total size is about 792MB. If the communication is performed using a standard of wireless LAN (IEEE 802.11n), it can be completed in about 9.9sec. This result shows a potential of the proposed method for practical use.

Conclusions

This paper described an extension of a highly-distributed manufacturing management system to flexible job shops. A method for handling multiple dispatching rules and performing optimization has been applied to a set of processing flows of the management system for flexible job shops. Numerical experiments implied that the proposed

Fig. 1 .

 1 Fig. 1. Example used for case study.

)

  PT: Processing time (If the MC is unavailable, a large value is set to this variable.) 𝑃𝑇 : Minimum processing time for one operation in all machine 𝑃𝑇 : Maximum processing time for one operation in all machine

	𝑉	(2)
	NumberofJobs:Number of jobs in the buffer
	BufferSize:Buffer capacity of each MC
	The evaluation value 𝑉 is calculated for each rule and multiplied by the rule weight
	𝑊 . The sum of them is regarded as the priority 𝑃 of each job as follows. 𝑃 𝑊 * 𝑉 𝑊 * 𝑉

Table 1 .

 1 Number of message exchange which occurred in the simulation.

	Machine type	Number of message exchange
	AS/RS	547837
	MC1	1086559
	MC2	1071049
	MC3	865399
	Zone1	639457
	Zone2	589978
	Zone3	944737
	Zone4	829597
	Zone5	592567
	method can generate an optimal operation plan with a practical number of message
	exchanges.

Acknowledgement

This work was supported by JSPS KAKENHI Grant Number JP18K03872.


Torbjørn H Netland
email: tnetland@ethz.ch

Hajime Mizuyama

Rafael Lorenz

Assessing the Value of Process Improvement Suggestions

Keywords: Continuous improvement, Manufacturing cost deployment, Kaizen

Firms struggle to estimate the expected benefits of improvement suggestions. As a result, pointless or even damaging suggestions are sometimes implemented at the expense of potentially valuable improvement suggestions. This paper reviews, discusses, and advises on the use of available value assessment methods. Thereby, the paper contributes to the production improvement literature and practices with an overview and classification of common value assessment methods.

Introduction

The ability to continuously identify and implement improvement suggestions is a hallmark of successful organizations. In the lean literature and practice, the term kaizen-meaning change for the better in Japanese-is established as the reference method [START_REF] Bicheno | Manufacturing operations and supply chain management: The LEAN approach[END_REF][START_REF] Imai | Kaizen: The key to Japan's competitive success[END_REF]. It involves regular improvement workshops (known as quality circles [START_REF] Deming | Out of the crisis[END_REF], kaizen blitz, or kaizen bursts), the continual encouragement of improvement suggestions from all employees (sometimes referred to as a continuous improvement culture [START_REF] Netland | A lean world[END_REF]), and large-scale improvement projects (known as kaikaku [START_REF] Bicheno | Manufacturing operations and supply chain management: The LEAN approach[END_REF]). While kaizen, by definition, should improve performance, it is often unclear if and how much it does. How the potential gain from a kaizen activity should be measured has been an enduring challenge.

Evaluating production improvement suggestions is a frequent task in manufacturing, but it is usually less rigorous than it should be. Often, managers select improvement projects by taking a leap of faith. In other cases, middle managers battle for the attention and investments of senior management by writing up speculative business cases for their proposed improvement projects. As a result, firms spend a great deal of money on useless improvements, fixing issues that are non-critical or have low or no effect on factory performance. The literature has suggested some structured methods to overcome this challenge, but few are well-known and generally accepted.

It is arguable that a priori value assessment is of less importance because any kaizen is good by definition, and companies should take all the opportunities they can. However, this viewpoint is misguided for four reasons. First, companies operate under financial and resource constraints, which creates alternative costs for each investment. Hence, companies should not choose any project but the improvements that matter the most for the firm's strategic objectives (e.g., revenue growth; cost reduction; or meeting strategic goals related to sustainability, social responsibility, or other issues). Second, not all intended improvements are effective; sometimes, instead of making things better, changes make things worse (known as kaiaku) [5, p. 142]. Besides wasting resources before and after such changes, such results are detrimental to the further motivation of a continuous improvement culture. Third, companies can use quantified improvement data to prescribe the value of new improvement suggestions [START_REF] Schuh | Using Prescriptive Analytics to Support the Continuous Improvement Process[END_REF]. Finally, convincing skeptical senior managers to invest money and resources in an improvement project often requires some kind of estimate of its cash flow-even if it is purely based on speculation. Even if one agrees or not with the ubiquitous tendency to measure everything, metrics are part of modern management.

There are plausible reasons why companies do not use rigorous value assessment methods. On the one hand, it is very difficult to correctly assess the cost reduction or revenue growth potential of improvement suggestions. It is also often difficult to assess the cost of implementing the suggestion. On the other hand, for small improvements, a rigorous process may slow down the improvement pace or discourage suggestions from employees. For these reasons, it is not clear when and what form of value assessment methods should be used to support the selection of suggestions for implementation. The challenge is to professionalize the selection method while simultaneously sustaining or growing the improvement culture.

If value assessment is crucial to improvement activities, why does the literature not offer a standard method for it? We assume this is because it is very complicated to take into account all the different requirements and contextual variables needed. Nevertheless, engineers, management researchers, and economists have come up with a range of methods that are used to assess the value of improvements at different stages of preimplementation. In this paper, we review common value assessment methods from the literature and practice. Additionally, we advise managers on when and how to assess the value of process improvement suggestions.

Existing value assessment methods

Not surprisingly, companies that use objective prioritization methods report a higher success rate for improvement projects compared to those companies that exclusively use subjective methods [START_REF] Kirkham | Prioritisation of operations improvement projects in the European manufacturing industry[END_REF]. The lack of structured project selection methods leads to lost opportunities, sub-optimization, and inefficient resource allocation. In our review of the literature-and drawing on our insights from working with many manufacturing companies-we found only a few established objective value assessment methods for improvement projects. We discuss these methods in terms of their reliance on different types of qualitative and quantitative data: arbitrary methods (Level 0), qualitative methods (Level 1), operational methods (Level 2), and financial methods (Level 3). Figure 1 illustrates these levels and the typical forms of decision support measurements they involve. These methods are cumulative; financial metrics (in dollars) are derived from operational metrics (changes in time use, quality, etc.) based on some qualitative judgments (e.g., what metrics are recorded), which are influenced by individual judgments. We briefly discuss a few common qualitative methods that are used at Levels 0 and 1 before reviewing in more detail objective assessment methods at Levels 2 and 3. At Level 2, companies use operational metrics. For example, suggestion X is expected to reduce material losses by 5% or suggestion Y is expected to decrease energy usage by 3%. While quantitative metrics like these can be convincing, they are incomplete. The financial metrics of Level 3 ultimately provide answers to the question: What will be the return on this investment?

Arbitrary methods (Level 0)

At Level 0, projects are started purely based on a gut feeling or leap of faith. This complete lack of an objective assessment methods may be justified if the cost of applying the methods would be higher than the expected benefits. Sometimes, the simple rule of thumb is applied to inform the decision to implement or not. For example, 'if an improvement suggestion X makes the workplace more compliant with a certification Y, then it should be implemented.' While this type of value assessment is quick and simple, it risks being incorrect or ineffective.

Qualitative methods (Level 1)

At Level 1, an elaborate qualitative assessment is conducted that informs the expected effectiveness of suggested improvement ideas. An example is to ask experts if the suggestion has been successful elsewhere in the past. Another example is to ask a larger group of stakeholders their opinion and discuss the advantages and disadvantages or to organize some sort of voting system. Often, suggestions are sorted qualitatively into a prioritization matrix comparing difficulty versus benefit (i.e., a pain/gain matrix).

2.3

Operational methods (Level 2)

We identified four prominent operational methods: 1. Manufacturing cost deployment (MCD): focus on process losses, 2. Material flow cost accounting (MFCA): focus on material flow losses, 3. Kaizen costing: focus on product cost drivers, and 4. Value stream mapping (VSM): focus on system flow improvement.

The first operational method, MCD, assigns costs to the root causes of losses in a manufacturing process and sorts the losses according to the potential effects of improvement [START_REF] Yamashina | Manufacturing cost deployment[END_REF]. Systematically selecting projects that eliminate the root problem-rather than the symptoms-can contribute to a sustained reduction of production costs. This method is based on a series of spreadsheet matrixes labeled A to E: The A Matrix identifies and quantifies losses in a manufacturing system, the B Matrix clarifies cause-and-effect relationships, the C Matrix connects losses and manufacturing costs, the D Matrix connects causal losses and improvement techniques, and, the E Matrix identifies benefit values and establishes the cost-reduction program. MCD follows a seven-step roadmap [START_REF] Høeg | Roadmap for Manufacturing Cost Deployment[END_REF] from identifying and categorizing losses to selecting improvement projects based on a total cost-benefit analysis. Although the seven steps are straightforward, MCD is an advanced improvement technique, which requires input from both the accounting and production departments. Since it is based on loss calculations, this method is most useful in a technology-intensive environment. MCD is an integrated part of the world class manufacturing program developed by Prof. Hajime Yamashina at Kyoto University and championed by leading automobile companies such as Fiat Chrysler and IVECO.

The second method, MFCA, is an environmental management accounting tool originally proposed in Germany and further refined in Japan, the application procedure for which was standardized as ISO 14051 in 2011 [START_REF] Christ | Material flow cost accounting: a review and agenda for future research[END_REF]. This six-step method systematically reveals losses and material usage wastes in a manufacturing process and evaluates them in terms of material, energy, system, and waste management costs, thereby supporting improvement projects targeting these losses and wastes. Hence, it focuses only on the physical material flow (and losses) throughout the manufacturing processes. As in the case of MCD, this method needs both operational and accounting data. MFCA has been used to support process improvement in many firms [e.g. , 11].

The third method, kaizen costing [START_REF] Monden | Target costing and kaizen costing in Japanese automobile companies[END_REF][START_REF] Kaur | Kaizen costing technique -a litertaure reivew[END_REF][START_REF] Yoshikawa | Continuous improvement and Kaizen costing[END_REF], applies to products in the manufacturing phase (as an extension of target costing in the product development phase). When using kaizen costing, firms define a competitive cost for a product and then break the costs of producing it into seven categories: supply chain (including materials); manufacturing; waste; disposal; legal; recruitment; and marketing, sales, and distribution costs. In kaizen costing, cost reduction targets are set regularly (e.g., every month), and teams work to reduce any of the products' seven cost categories. Kaizen costing is a method for involving all employees in the continuous pursuit of cost reduction.

The fourth method is the well-established VSM. The goal of VSM is to visualize the flow of a product from the supplier to the customer through the different processing steps of the plant [START_REF] Rother | Learning to see: value stream mapping to create value and eliminate muda[END_REF]. VSM draws a map of all the processes and transport for a specific product family. Thereby, it creates transparency about the value adding and non-value adding activities, and the user can derive improvement suggestions, which are visualized as kaizen bursts in the visual map. These improvement suggestions mainly intend to eliminate the non-value adding time and thereby decrease the lead time. However, there is no intention in VSM to combine these suggestions with a cost dimension.

For all these operational methods, the analysis is mainly performed in the operational layer and reveals operators' non-value adding actions, losses of material usage and machine time, and their physical amounts. Improvement suggestions are derived from the potential elimination or reduction of those non-value adding elements. Only by estimating the potential economic effects of eliminating or reducing wastes can these elements be translated into costs with the help of accounting data. Although MCD, MFCA, and kaizen costing calculate cost reduction estimates in monetary terms, these methods are only able to estimate improvements to the current production system (kaizen) and not to innovations that are radically new to the system (kaikaku).

Financial methods (Level 3)

Ultimately, to support an improvement idea, managers want to know how much cash must be invested and how much cash will be received and when. In the field of project finance [START_REF] Yescombe | Principles of project finance[END_REF], several approaches are commonly used to evaluate the economic potential of a project. A set of these methods is static, which means that they do not account for the change in values over time. These methods are simple and do not have complex formulas. However, they lack precision in calculating the return. For instance, the break-even point (or payback period) looks at how long it will take for the investment in the improvement to amortize (as a rule of thumb, a payback period longer than 3 years is a hard sell). The return on investment (ROI) can be used to define the invested resources in relation to the expected outcome. It is calculated by dividing the net profit (expected revenue minus expected costs) by the expected costs in order to get a return rate. An alternative to ROI is the internal rate of return (IRR), which is more complicated to calculate but take the time value of money into account.

The second set of methods is dynamic, which means they take into account the potential change in value over time. An example is the net present value (NPV), which compares the monetary inflow with the monetary outflow over time. There are alternatives to this model (e.g., the internal rate of return), all of which are based on the same underlying logic. If there are many projects being proposed, a profitability index calculating the NPV per dollar investment can be used to prioritize and select among them.

In addition, and often based on the method above, numerous sophisticated mathematical optimization models for project prioritization have been suggested in the literature. However, these advanced models find limited use in practice because they are context specific, managers do not understand them, or the model assumptions do not hold.

Decision support model

Improvement suggestions can be very different in scope and impact. They can range from taping the floor to mark the place of a fire extinguisher to changing the layout of a value stream. We posit that the optimal method to evaluate improvement suggestions depends on the type of suggestion. To cluster the improvement suggestions, we differentiate them into two dimensions: impact and scope (see Figure 2). Fig. 2. A decision support model for when to prioritize different value assessment methods.

The impact dimension (along the horizontal axis) captures the magnitude of the change. A suggested change can range from incremental (kaizen) to radical (kaikaku). An incremental change would introduce small improvements to an existing system, for instance, changing the location of boxes of screws at an assembly station. A radical suggestion would fundamentally change the process, for example, by automating the screwing process or eliminating the need for screws through a change in the product design.

The scope dimension (along the vertical axis) captures the focus of the improvement. This can range from a point improvement to a system improvement. Point improvements have limited influence beyond the single process or activity improved. System improvements refer to holistic change at the system level, including several processes. For example, consider the redesign of a product where its previously separate parts can be produced in one piece with additive manufacturing. This suggestion would involve many processes in the value stream.

For incremental point improvements on the shop floor, qualitative methods are sufficient. Using a qualitative method helps to motivate and encourage employees to come up with many small improvements. However, if the suggestion becomes more radical or system oriented, operational methods are needed to justify and assure worthwhile investments. Finally, if the suggestions are both radical and system oriented, financial methods need to be used to calculate the hard financial returns of the suggestion. When using this decision support model, practitioners should consider a range of contextual variables that may affect the choice of using a particular value assessment method. In particular, they should pay attention to existing value assessment practices, managers' affinity for quantitative metrics, data availability, the organization's experience in continuous improvement, resource accessibility (cash, time, and people), and number and quality of improvement suggestions raised. For example, if an organization has no culture for improvement suggestions, using qualitative assessment methods (Level 1) can encourage the kaizen culture to develop. More advanced firms would typically move toward quantitative methods (Levels 2 and 3). Some mature, lean firms try to assist project selection by moving from traditional accounting to lean accounting. Traditional accounting practices have been criticized for not being able to capture the value of improvement projects besides simple cost-cutting point improvements (e.g., automation of a manual process). As a response, the lean accounting literature has suggested ways to allocate costs to product streams rather than functional cost centers [START_REF] Maskell | Why do we need lean accounting and how does it work[END_REF]. In the general accounting literature, similar concepts such as activity-based costing (ABC) are well-known but still not widely adopted (a recent survey found that only 18.7% of Irish firms have moved from traditional accounting to ABC [START_REF] Quinn | Reasons for not changing to activity-based costing: a survey of Irish firms[END_REF]). The accurate allocation of overhead to product streams remains one of the main difficulties with ABC. If properly implemented, lean accounting (or ABC) can be used to directly identify improvement potential. ABC can demonstrate how profitable different product streams are to the firm, and the company can use this information to prioritize the improvement of the streams with the highest return. It has been shown that companies that use ABC have a higher improvement rate than companies with traditional accounting systems [START_REF] Ittner | The association between activity-based costing and manufacturing performance[END_REF].

Conclusion

This paper presented a review and discussion of common value assessment methods used in manufacturing improvement activities. We derived four levels of assessment methods (see fig. 1): no method, qualitative methods, operational methods, and financial methods. Managers can use these assessment methods to assist and increase the effectiveness of decisions related to improvement suggestions. We suggested a simple framework to help practitioners select among the different methods based on two characteristics of the improvement suggestion: its impact and its scope (see Fig 2).

Managers must be aware of the limitations of the available methods. For example, all quantitative models have problems cost setting soft issues related to human factors and the work environment. Issues related to risk and safety, for example, would usually not be picked up by cost-benefit assessments alone. It is also important to remember that even the most advanced quantitative methods represent simplifications of realworld systems. Moreover, all methods can be manipulated by users who have particular agendas or incentives. It is perhaps pertinent to close with the following quote from W. Edward Deming: "No one knows the cost of a defective product -don't tell me you do. You know the cost of replacing it, but not the cost of a dissatisfied customer" [quoted in 20].

Fig. 1 .

 1 Fig. 1. Levels of value assessment.


Margherita Pero
email: margherita.pero@polimi.itvcannas@liuc.it

Violetta G Cannas

Cross-functional coordination before and after the CODP: an empirical study in the machinery industry

Keywords: ETO, cross-functional coordination, machinery industry, CODP

Cross-functional coordination among engineering, sales and production departments is known to be beneficial for improving order fulfillment processes. In Engineer-to-Order (ETO) companies, sales, design and production activities are strongly interrelated and sometimes they overlap, thus requiring crossfunctional coordination. In these companies, design and production activities can be both partially performed before the customer order arrival. ETO companies pursue different objectives and implement different managerial approaches before and after the customer order decoupling point (CODP). However, despite its relevance for company performance, how ETO companies manage cross-functional coordination and how departments are coordinated before and after the CODP is still understudied. This paper sheds light on this topic by investigating 12 case studies in the Italian machinery industry. Results suggest that the coordination mechanisms used before and after CODP are different, and vary depending on the CODP configuration chosen.

Introduction

The Customer Order Decoupling Point (CODP) is defined as the point in the value chain where a product becomes linked to a specific customer order [START_REF] Hoekstra | Integral Logistic Structures: Developing Customeroriented Goods Flow[END_REF]: before the CODP, activities are forecast-driven, while, after the CODP, activities are planned and controlled based on actual customer orders. CODP positioning is a strategic choice for the company [START_REF] Amaro | Competitive advantage, customisation and a new taxonomy for non make-to-stock companies[END_REF], and it has implications on the ability of the company either to provide customers with a high degree of choice (i.e. flexibility) or to maintain high internal efficiency [START_REF] Barlow | Choice and delivery in housebuilding: Lessons from Japan for UK housebuilders[END_REF].

In Engineer-to-Order (ETO) companies, engineering and production activities have been traditionally considered by the literature as performed after the customer order arrival [START_REF] Wortmann | Production management systems for one-of-a-kind products[END_REF]. However, recently, ETO companies are simultaneously facing the challenges of maintaining high flexibility while reducing costs and lead-times [START_REF] Birkie | Understanding dynamism and complexity factors in engineer-to-order and their influence on lean implementation strategy[END_REF]. To face this challenge, changes to company's and supply chain processes are advisable [START_REF] Patrucco | Industry 4.0 and supply chain process reengineering[END_REF]. To addres these challenges, ETO companies started to perform some engineering and production activities to forecast, moving their CODP closer to the customer [START_REF] Willner | Exploring the archetypes of engineer-to-order: an empirical analysis[END_REF][START_REF] Cannas | Determinants for orderfulfilment strategies in engineer-to-order companies: Insights from the machinery industry[END_REF]. This change poses new challenges for ETO companies: among the others, how to guarantee the coordination among engineering, production and sales [START_REF] Mello | The role of coordination in avoiding project delays in an engineer-to-order supply chain[END_REF][START_REF] Mello | Improving coordination in an engineer-to-order supply chain using a soft systems approach[END_REF][START_REF] Mello | Analyzing the factors affecting coordination in engineer-to-order supply chain[END_REF]. In particular, Cannas et al. [START_REF] Cannas | Engineering and production decoupling configurations: An empirical study in the machinery industry[END_REF], highlighted that, in ETO companies, before and after the CODP the objectives and the managerial practices to be applied to pursue these objectives change, with a consequent need for different coordination mechanisms.

However, to the best of our knowledge, there is little research on how ETO companies are coordinating engineering, production and sales activities before and after the CODP. Thus, the purpose of this research is to explore cross-functional coordination mechanisms between engineering, sales and production functions adopted in ETO companies. In doing so, we aim to answer the following research question (RQ):

RQ: How do ETO companies manage engineering, production and sales processes, before and after the CODP?

The study answers to the RQ by empirically observing and analyzing a specific ETO sector, i.e., the machinery industry, by means of multiple case studies.

Background

Coordinating engineering, production and sales processes requires to manage three sets of coordination mechanisms related to, respectively, engineering and production, engineering and sales, and sales and production. As far as engineering and production is concerned, Adler [START_REF] Adler | Interdepartmental Interdependence and Coordination: The Case of the Design/Manufacturing Interface[END_REF] distinguishes among "Standard", "Schedule and Plan", "Mutual adjustment" or "Team". The same interface has been studied when engineering and production activities are performed by different companies Twigg [START_REF] Twigg | Managing the design/manufacturing interface across firms[END_REF]. As far as engineering and sales coordination is concerned, Griffin and Hauser [START_REF] Griffin | Integrating R&D and marketing: A review and analysis of the literature[END_REF] proposed six general approached to integrate the efforts of engineering and sales: relocation and physical facilities design, personnel movement (multi-functional training), informal social systems, matrix organizations and project teams, incentives and rewards, and formal integrative management processes such as phase review process, stage-gate process, product and cycle time excellence (PACE) and quality function deployment (QFD). Finally, Lamberti and Pero [START_REF] Pero | The supply chain management-marketing interface in product development: An exploratory study[END_REF] discusses marketing, supply chain and product development interfaces in an electromechanical company. Whereas, Tang [START_REF] Tang | A review of marketing-operations interface models: From coexistence to coordination and collaboration[END_REF] presented a framework for the sales-production interface models in which he proposed: integrated decision models (mathematical models), balanced scorecards to avoid sub-optimization, cross-functional teams for developing coordinated plans, iterative negotiation and joint planning to move towards a more collaborative relationship among marketing and operations. Some of the mechanisms proposed for these two interfaces, engineering-sales and production-sales, are in common with the one proposed for engineering-production by [START_REF] Adler | Interdepartmental Interdependence and Coordination: The Case of the Design/Manufacturing Interface[END_REF].

In the ETO cross-functional coordination literature, Mello et al. [START_REF] Mello | The role of coordination in avoiding project delays in an engineer-to-order supply chain[END_REF] promoted the application of the coordination mechanisms developed by [START_REF] Adler | Interdepartmental Interdependence and Coordination: The Case of the Design/Manufacturing Interface[END_REF] in ETO companies. Mello et al. [START_REF] Mello | Improving coordination in an engineer-to-order supply chain using a soft systems approach[END_REF] included the integration of engineering and production activities in the list of principles to improve coordination in ETO supply chains; more precisely, they proposed the following mechanisms to integrate engineering and production: "Co-located teams", "Integrated IT systems", "Establishment of common goals", "Direct communication among the two functions", "Similar organizational culture".

Cannas et al. [START_REF] Cannas | Engineering and production decoupling configurations: An empirical study in the machinery industry[END_REF] demonstrated that in ETO companies the coordination needs of engineering and production processes change upstream and downstream of the CODP. Upstream of the CODP engineering and production coordination reduces unexpected design updates, reworks and late defects as much as possible, as they cause delays and additional costs. While, downstream of the CODP, engineering and production coordination reduces the risk of exceeding the stock holding costs due to obsolescence, excessive space occupation, etc. or facing stock-outs. Despite this contribution, little studies addressed how ETO companies can manage cross-functional coordination and how engineering, sales and production departments are coordinated before and after the CODP. For this reason, this study empirically analyzes ETO companies operating in the machinery industry, aiming to extend and refine the existing frameworks in the literature and adapt them to the complex ETO context.

Methodology

This research was conducted using multiple case studies, taking as the unit of analysis the company's core product families (the ones that have the major impact on the company's turnover). This research counts twelve case studies in nine companies operating in the Italian machinery industry. To ensure replication and variations within the sample, the criterion for selecting companies to be included in the sample was including companies operating in different sectors (e.g. machine tools, plastic and rubber, automotive and food) and producing different products (e.g. cutting machines, automated assembly lines, packaging lines and machine tools). In the data collection phase, the main sources of information were: semi-structured face-to-face interviews, official documents and internal documents. Semi-structured interviews (that lasted about 2 hours each), collected information on the core product family of the company interviewed, focusing on how the company coordinates engineering, production and sales activities, before and after CODP. The informants have been chosen on the basis of their involvement in the key processes subject of the analysis (e.g. Operations Manager, Engineering Manager, Sales Manager, Plant Manager, etc.). Each interview was recorded with the informants' consensus and then transcribed to deeply analyze all the information collected.

Findings

The results shown that there is a difference in the coordination mechanisms employed by the companies based on the CODP positioning. In particular, in the sectors analysed, this difference depends on the number of engineering and production activities, performed before and after the order. Accordingly, the units of analysis have been mapped into the two-dimensional (2D) CODP framework proposed by Cannas et al. [START_REF] Mello | Analyzing the factors affecting coordination in engineer-to-order supply chain[END_REF], which is the most recent taxonomy for classifying CODP positioning strategies in ETO companies (for details on this framework, please refer to this reference). What emerges from these results is that almost all the product families of the interviewed companies fall into the four main decoupling configurations identified by [START_REF] Mello | Analyzing the factors affecting coordination in engineer-to-order supply chain[END_REF], namely: Special Machines (C, H), Customized Machines (E, G), Standard-customized machines (A, D, F) and Modular machines (B, I). Table 1 summarizes the coordination mechanisms adopted by tbe companies in the sample before and after the CODP depending on the configuration.

Engineering-Production coordination

The outcomes of the case study analysis show that, in all the cases, the mechanisms adopted to coordinate engineering and production activities, before and after the order arrival, are different. Before the CODP, when all the production-related activities are performed internally (cases A, C, E, D), companies adopt coordination mechanisms belonging to the standard coordination mode category, such as: (i) compatible CAD/CAM software through which companies exchange information; (ii) stage-gate process, i.e., a determined set of tasks is performed by a function and reviewed by another function which decide to go/ not to go on; and (iii), visual work instructions software, which is a coordination mechanism not considered in the previous literature, representing a software tool providing work instructions to employees of the assembly department. After the CODP, companies providing customers with special machines (C, H) chose mechanisms falling into Team and Mutual adjustment (both meetings and coordination roles). Whereas, companies providing customers with customized machines (E, G), adopt only mutual adjustment mechanisms (both meetings and coordination roles). Finally, companies that provide customers with standard-customized machines (A, D, F) and modular machines (B, I), chose mutual adjustment mechanisms falling only into meetings. Among team mechanisms there are the transition teams, i.e., design engineers are temporarily transferred into the production department to be available in case design reviews are required. Among mutual adjustment mechanisms, the mostly applied are: (i) meetings, such as kick-off, i.e., a first meeting after the order is received in which interest parties gather to analyze order details and plan future activities, and weekly project review to monitor the state of progress of a current order; (ii) coordinating roles such as the line managers, which are responsible for inter-functional communication, and the project engineers, which are technical project managers in charge of coordinating the effort of engineering and production resources of a specific project.

Engineering-Sales coordination

For what concerns coordination among engineering and sales, coordination mechanisms adopted before and after the order are different and this is true for all the CODP configurations. Before the CODP, companies providing customers with special machines (C, H) favor coordination mechanisms based on team, i.e., cross functional teams, and mutual adjustment, i.e., the coordinating role of sales engineers, people with a strong technical background belonging to the sales department supporting sales managers during the negotiation phase, acting as a reference point of the engineering department. This underlines a high coordination effort needed. While, after the CODP these companies mainly rely on one-time meetings (i.e. Kick-off), thus indicating a lower coordination effort needed among engineering and sales once the order has been acquired. For companies that anticipate some activities before the order, the coordination effort seems more balanced before and after CODP, but for sure is high before the CODP, since they adopt coordination mechanisms based on team, i.e. cross-functional teams and coordinating groups (E and G). After the CODP, they rely on meetings because they do not include people with strong technical competences (e.g. sales engineers) into the sales department. Finally, companies that provide customers with standard-customized machines (A, D, F) and modular machines (B, I), before the CODP, rely on the use of standard coordination mechanisms such as: (i) design rules, which can be used by product engineers in the design phase to assure the producibility of their designs; (ii) design configurator, i.e., software used by sales managers allowing the automation of the tendering and design phase leveraging on CAD and PLM platforms to generate ready-made bill of materials and product descriptions; and (iii), standard procedures, i.e., commercial offers created using pre-defined standard forms. While, after the CODP, they mainly adopt meetings (i.e. kick off and project review meetings). Interestingly, since face-to-face coordination underlines a higher coordination effort, with respect to standard coordination modes, the coordination effort in this case seems to be higher once the order is received.

Sales -Production coordination

The mechanisms used to coordinate the effort of sales department and internal production vary upstream and downstream of the CODP. Company A, which is totally vertical integrated, employ cross-functional teams before the CODP and mutual adjustment, through line managers, after the CODP. While, it has emerged that, for all the other cases, which outsource part or most of the production, before the CODP there is no coordination among sales and production related activities performed internally. After the CODP, the great majority of these cases, adopt meetings (kick-off or project review). Therefore, it is reasonable to assume that the choice of coordination mechanisms, in this case does not depend on the CODP configuration the company adopts. Finally, concerning coordination among sales department and external suppliers, the companies analyzed do not adopt any coordination mechanisms, since the department in charge of managing the interface with external suppliers is the Purchasing department for all the cases.

Discussion and conclusions

This paper investigates through a case study research whether and how cross-functional coordination mechanisms vary before and after the CODP. Results show that, before the order (pre CODP), the companies interviewed recognize a high coordination effort needed for engineering-sales interfaces. Thus, they rely on mechanisms belonging to team and mutual adjustment, when customization levels are high; while, moving towards more standard product configurations, cross-functional coordination is achieved through standardization of procedures, sales' experience from previous project, the use of IT tools, which of course correspond to a lower coordination effort to be sustained. Differently, after the order (post CODP), our findings revealed that coordination regards mainly engineering and production activities (both internally and externally performed) and the choice of coordination mechanisms is influenced by the type of products. In particular, also in this case, the higher the customization level of the product, the higher the intensity of coordination. When products are highly customized, after the CODP it is necessary to employ different modes of mutual adjustment (e.g. project engineers, kick-off meetings) and teams. Finally, findings concerning sales-production interface show that coordination happens after the order and only with the internal production. Moreover, for this interface, the mechanisms adopted are the same for each type of product.

The novelty of this study consists of (i) introducing a new temporal dimension when studying cross-functional coordination mechanisms (i.e. pre and post CODP), (ii) studying cross-functional coordination in the machinery industry, and (iii) considering in the same research more interfaces (i.e. engineering-sales, engineering-production and sales-production). The results underline the importance for studies focusing on ETO cross-functional coordination of considering all the possible interfaces, including also the sales department and differentiating between the activities performed before and after the customer order. This can be considered an interesting research direction for future studies. Surely, this study presents also limitations. Although the research was designed considering all the relevant aspects for ensuring the reliability of the results, the focus on one specific industry, as well as the size of the sample, could limit the generalizability of the results. Thus, further research addressing different ETO sectors and/or adopting methodologies that study larger samples, such as survey-based research, are recommended to extend and validate the results of this research.

Table 1 . Summary of coordination mechanisms used before and after the CODP

 1

				Configuration	Before-CODP	After-CODP
	Engineering-	Production		Special Machines Customized Machines Standard-customized machines Modular machines	Standards	Teams and Mutual adjustment
	Engineering -	Sales		Special Machines Customized Machines Standard-customized machines Modular machines	Teams and Mutual adjustment Teams Standards	Mutual adjustment
	-			Special Machines
	Sales	Produc-	tion	Customized Machines Standard-customized machines Modular machines	No coordination