Rafael Lorenz

Mathias Kraus

Torbjørn H Netland

Bringing Advanced Analytics to Manufacturing: A Systematic Mapping

Keywords: Smart Manufacturing, AI Applications, Advanced Analytics, Systematic Mapping Review

Advanced analytics has the potential to redefine manufacturing. However, practical implementation is in its infancy. One reason is a lack of management tools that enable decision-makers to choose suitable techniques from advanced analytics for domain-specific problems in manufacturing. This paper uses a systematic mapping review in order to identify seven application areas to which analytics can add substantial value. Each area is then matched with suitable techniques from the field of advanced analytics. The resulting systematic map provides a novel management tool for the purpose of identifying promising analytics projects in manufacturing and thus facilitates decision-making.

Introduction

Advanced analytics offers new ways to improve and innovate manufacturing processes [START_REF] Chui | Notes from the AI frontier: Insights from hundreds of use cases[END_REF][START_REF] Henke | The age of analytics: Competing in a data-driven world[END_REF]. Steady progress in terms of computing power and storage capacity, as well as the rapid development of advanced algorithms, has equipped companies with unprecedented possibilities to generate knowledge and business value from large datasets [START_REF] Kraus | Deep learning in business analytics and operations research: Models, applications and managerial implications[END_REF][START_REF] Wang | Deep learning for smart manufacturing: Methods and applications[END_REF]. In this way, analytics changes the basis of competition [START_REF] Henke | The age of analytics: Competing in a data-driven world[END_REF].

In order to thrive in today's competitive market environment, manufacturers can use advanced analytics to streamline complex operations while improving quality and reducing costs [START_REF] Thoben | Industrie 4.0" and smart manufacturing: A review of research issues and application examples[END_REF][START_REF] Wuest | Machine learning in manufacturing: Advantages, challenges, and applications[END_REF]. The manufacturing sector can benefit greatly from the use of advanced analytics, since data is abundantly available [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF], providing a still largely untapped potential for process improvement and innovation. Manufacturing strategies, such as Industry 4.0 in Germany, Industrial Internet in the United States, and the Made in China 2025 initiative, recognize the strategic importance of utilizing data in order to enhance manufacturing competitiveness [START_REF] Tao | Data-driven smart manufacturing[END_REF][START_REF] Thoben | Industrie 4.0" and smart manufacturing: A review of research issues and application examples[END_REF]. However, manufacturers are not exploiting the full potential of data and analytics. Recently, McKinsey speculated that less than 30 % of the potential has been captured so far [START_REF] Henke | The age of analytics: Competing in a data-driven world[END_REF].

A main barrier that prevents manufacturers from harnessing the full potential of analytics is a lack of management tools that enable decision-makers to choose suitable techniques from advanced analytics for domain-specific problems in manufacturing. While research has brought forward various techniques capable of solving complex problems in manufacturing, little has been done to equip practitioners and decision-makers with the necessary management tools to implement these techniques on an operational level [START_REF] Dreyfus | A framework based on predictive maintenance, zerodefect manufacturing and scheduling under uncertainty tools, to optimize production capacities of high-end quality products[END_REF][START_REF] Villanueva Zacarias | A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing[END_REF][START_REF] Wuest | Machine learning in manufacturing: Advantages, challenges, and applications[END_REF].

This paper uses a systematic mapping review in order to identify seven application areas to which analytics can add substantial value. Each area is then matched with suitable techniques from the field of advanced analytics. The resulting systematic map provides a novel management tool for the purpose of identifying promising analytics projects in manufacturing and thus facilitates decision-making. To the best of our knowledge, such a management tool, one that matches application areas in manufacturing with analytics, does not yet exist.

Background

Several authors have attempted to define the vague term "analytics" and yet there is no universally accepted definition. In this paper, we adopt the definition put forward by Davenport et al. [6, p. 7], characterizing analytics as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." With the term "advanced analytics", we subsume methods originating from statistical learning, e.g., data mining techniques, as well as methods stemming from artificial intelligence research, in particular machine learning algorithms.

Previous work in the field of analytics applications for manufacturing has predominantly been limited to systematic literature reviews [START_REF] Cheng | Data and knowledge mining with big data towards smart production[END_REF][START_REF] Choudhary | Data mining in manufacturing: a review based on the kind of knowledge[END_REF][START_REF] Harding | Data mining in manufacturing: A review[END_REF][START_REF] Köksal | A review of data mining applications for quality improvement in manufacturing industry[END_REF][START_REF] Pham | Machine-learning techniques and their applications in manufacturing[END_REF][START_REF] Sharp | A survey of the advancing use and development of machine learning in smart manufacturing[END_REF]. The available systematic literature reviews generally neglect to consistently systematize and link application areas with suitable analytics techniques in a structured and visually appealing way. A notable exception is Köksal et al. [START_REF] Köksal | A review of data mining applications for quality improvement in manufacturing industry[END_REF]. However, a drawback is the limited focus on quality improvement tasks, rather than considering additional important application areas such as production planning, product inspection, or machinery maintenance. Flath and Stein [8] have developed an adapted data mining process model for manufacturing prediction tasks, proposing guidelines for modelling, feature engineering, and interpretation. However, similar to already existing generic process models, such as the cross-industry standard process for data mining (CRISP-DM), their work does not provide decision support for the selection of suitable analytics techniques.

Villanueva Zacarias et al. [START_REF] Villanueva Zacarias | A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing[END_REF] propose a framework that automatically recommends suitable analytics techniques with respect to a domain-specific problem at hand. Similarly, Lechevalier et al. [START_REF] Lechevalier | A methodology for the semi-automatic generation of analytical models in manufacturing[END_REF] present a framework for the semiautomatic generation of analytical models in manufacturing and a proof-ofconcept prototype that allows practitioners to generate artificial neural networks for prediction tasks through a user interface. Both frameworks represent promising approaches tackling the problem of automated analytics technique configuration in the manufacturing domain. However, these concepts do not pursue the goal of identifying promising combinations of analytics and application areas in the first place. Therefore, both approaches are unsuitable for decision-making at the managerial level.

Table 1 stresses the research gap by reporting on related key literature. We explicitly searched for articles focusing on the fusion of both manufacturing application areas and analytics techniques. Evidently, existing solutions are either unsatisfactory or inadequate to facilitate the selection of suitable techniques from analytics for domain-specific problems in manufacturing. Although scholars have addressed the lack of decision support [START_REF] Dreyfus | A framework based on predictive maintenance, zerodefect manufacturing and scheduling under uncertainty tools, to optimize production capacities of high-end quality products[END_REF][START_REF] Wuest | Machine learning in manufacturing: Advantages, challenges, and applications[END_REF], no research has yet been devoted to comprehensively combining analytics with application areas in manufacturing. [START_REF] Lechevalier | A methodology for the semi-automatic generation of analytical models in manufacturing[END_REF] Framework

This paper Systematic map

Methodology

We used a systematic mapping review, following the guidelines of Petersen et al. [START_REF] Petersen | Systematic mapping studies in software engineering[END_REF], in order to design a management tool that facilitates analytics selection in manufacturing. The goal of a systematic mapping review is to build a twodimensional classification scheme, a map, to visually structure a research area. The analysis of results focuses on frequencies of publications for categories within the scheme, thereby creating a map which can easily help transfer knowledge to practitioners [START_REF] Petersen | Systematic mapping studies in software engineering[END_REF].

We chose Scopus to search for relevant literature, since it covers a broad, interdisciplinary field of research. The search was limited to journal articles written in English and published from 2008 until the end of 2018. This time frame of 11 years was regarded as sufficiently exhaustive, since research on analytics applications for manufacturing has only recently been addressed with growing interest [START_REF] Sharp | A survey of the advancing use and development of machine learning in smart manufacturing[END_REF][START_REF] Tao | Data-driven smart manufacturing[END_REF][START_REF] Wuest | Machine learning in manufacturing: Advantages, challenges, and applications[END_REF]. Furthermore, discrete-part and assembly manufacturing that are classified into division 26-30 by the International Standard Industrial Classification (ISIC) were designated as the target domains of this work. Manufacturing companies operating in these high-tech sectors (e.g., automotive, aerospace, machinery, and electronics) are profoundly affected by digital transformation processes [START_REF] Leurent | The next economic growth engine: Scaling fourth industrial revolution technologies in production[END_REF] and are, therefore, ideally suited to the adoption of analytics. However, some of the mentioned application areas also exist in continuous manufacturing processes, such as oil refining and chemicals.

Our literature search was split into two parts in order to obtain relevant research articles. The first search 1 identified seven major application areas in manufacturing to which analytics can add substantial value, namely demand forecasting, job scheduling, product inspection, process diagnostics and prediction, machine parameter optimization, diagnostic maintenance, and predictive maintenance. The second search queried each identified application area independently using appropriate keywords. 2 From the entire corpus of 1,853 articles, 81 articles that reported a successful application of at least one machine learning method were considered in order to build the final systematic map. These 81 articles were selected according to their average number of citations per year in order to include widely appreciated research articles. A stop criterion limited the number of articles per application area to 15 (or fewer if articles were cited less than three times on average per year). The reading and selection process was solely conducted by the lead author of this paper.

We only considered analytics techniques applied for the final regression, classification, or optimization task, in order to facilitate an easy interpretation of the systematic map. This is a reasonable approach, since the way data is preprocessed primarily depends on the final technique applied [START_REF] Bishop | Pattern recognition and machine learning[END_REF]. We agree with [START_REF] Wuest | Machine learning in manufacturing: Advantages, challenges, and applications[END_REF] that a promising approach to select a suitable machine learning algorithm is to look for problems of similar nature. Therefore, we are convinced that the frequency of published articles can be used as a valid proxy for the purpose of identifying suitable machine learning techniques for a specific application area.

Results

In the following, the main results of our research are presented. First, the identified application areas to which analytics can add substantial value are described 1

TITLE(manufacturing OR ''smart production'') AND TITLE(data OR ai OR analytics OR learning OR artificial) AND TITLE-ABS-KEY(application).

2 For instance, articles regarding machine parameter optimization were queried with: TITLE-ABS-KEY(''data mining'' OR analytics OR learning OR ''artificial intelligence'' OR ''neural network'') AND TITLE-ABS-KEY(manufacturing) AND TITLE-ABS-KEY(process OR product) AND TITLE-ABS-KEY(''parameter optimisation'' OR ''parameter optimization'' OR ''parameter design'' OR ''process parameter'').

and pooled into three main groups, namely plan, make, and maintain. Afterwards, the map resulting from the systematic mapping review is presented.

Application Areas of Analytics in Manufacturing

Plan Demand forecasting and job scheduling are elements of the production planning processes of a manufacturing facility. Demand forecasting mostly uses historical time-series to predict future demand. Especially in large supply chains, demand forecasting can lead to a mitigated bullwhip effect [START_REF] Carbonneau | Application of machine learning techniques for supply chain demand forecasting[END_REF]. Job scheduling applies optimization techniques in order to derive the actual production plan while making a trade-off between the impact of early or late completion of a task.

Make Product inspection, process diagnostics and prediction, and machine parameter optimization aim at optimizing manufacturing processes with regard to quality, time, and costs. Product inspection can use image data in order to analyze product states, for instance the surface quality of steel, and classifies items into scrap or good parts. Process diagnostics and prediction are two major quality improvement and control activities. Whereas process diagnostics primarily determines variables that significantly affect quality, process prediction models the relationships between input variables and output quality characteristics. Furthermore, process prediction is usually a preliminary step undertaken in order to optimize manufacturing machinery input parameters. Thus, it is closely linked to machine parameter optimization, which yields optimal machinery parameters with respect to a target quality measure, such as surface roughness, for instance. Adjusting technological processes in relation to these parameters can result in improved productivity and product quality, as well as reduced costs [START_REF] Tao | Data-driven smart manufacturing[END_REF].

Maintain Diagnostic and predictive maintenance are two application areas that strongly build on each other [START_REF] Sikorska | Prognostic modelling options for remaining useful life estimation by industry[END_REF], aiming to reduce downtime of manufacturing machinery and tool equipment. A new maintenance paradigm, condition-based maintenance, has gained considerable attention in recent years [START_REF] Sharp | A survey of the advancing use and development of machine learning in smart manufacturing[END_REF]. Conditionbased maintenance utilizes online condition variables to detect, identify, and forecast potentially detrimental fault conditions. Current condition-based plans, designated as diagnostic maintenance, assess the current state of the system in real-time and perform maintenance when a triggering event occurs, such as the growth of a fault frequency beyond a particular threshold. However, a major drawback of diagnostic maintenance is that it cannot anticipate a breakdown well ahead of time. To overcome this, predictive maintenance looks at the current and past states of the system, as well as its expected future operational load, to predict how much remaining useful life a system has [START_REF] Sharp | A survey of the advancing use and development of machine learning in smart manufacturing[END_REF]. This temporal information advantage helps maintenance engineers to proactively order important spare parts that might not be immediately available.

Systematic Map

The resulting systematic map is shown in Fig. 1. The vertical axis presents all seven application areas, while the horizontal axis reports on techniques from advanced analytics that are classified in a two-level taxonomy. Since most analytics techniques applied in manufacturing are of a supervised nature [START_REF] Wuest | Machine learning in manufacturing: Advantages, challenges, and applications[END_REF], we chose to differentiates between discriminative learning, generative learning, and prescriptive techniques on a first level [START_REF] Bishop | Pattern recognition and machine learning[END_REF]11]. On the second level, discriminative learning techniques are further subdivided into structured, image, and sequence data-based models to allow for a more granular interpretation of the map. Examples of the most commonly encountered techniques are given below. For each application area, color intensities represent the relative frequency of proposed analytics techniques.

Empirical evidence from 81 reviewed journal articles shows that optimization techniques have thus far been applied in the areas of job scheduling and machine parameter optimization. Discriminative analytics techniques based on image data, e.g., convolutional neural networks, have become the primary tool for automated product inspection. Discriminative analytics techniques that rely on structured data are mainly used for demand forecasting, quality diagnostics and prediction, as well as diagnostic and predictive maintenance.

Conclusion

Advanced analytics is expected to have a significant impact on manufacturing. However, practical implementation is still in its early stages. One reason is that decision-makers have lacked management tools to choose suitable techniques from advanced analytics for domain-specific problems in manufacturing.

This paper applied a systematic mapping review to identify seven application areas to which analytics can add substantial value. Each area was then matched with suitable techniques from the field of advanced analytics. The resulting systematic map provides a novel management tool to identify promising analytics projects in manufacturing and thus facilitates decision-making.

Furthermore, the presented systematic map organizes interdisciplinary knowledge in a structured manner to serve as a means for common understanding in analytics projects. This is particularly helpful in order to bridge the gap between domain-specific manufacturing contexts and general-purpose analytics practices. In this way, collaboration between different stakeholders, e.g., domain experts and data analysts, can become a less complex and time-consuming task.

Fig. 1 .

 1 Fig. 1. Systematic mapping of techniques from advanced analytics (horizontal axis) to seven application areas in manufacturing (vertical axis). Color intensities represent the relative frequency of proposed analytics techniques with respect to an application area. The sum of the matrix is greater than 81 (number of reviewed articles) because some articles compared multiple analytics techniques.

Table 1 .

 1 Selected key literature on analytics applications for manufacturing. Full circle indicates compliance with the respective criterion.

	R e f e r e n c e	O u t p u t	M f g .	A n a l y t i c s F u s i o n
	Pham and Afify (2005) [18]	Literature review
	Harding et al. (2006) [9]	Literature review
	Choudhary et al. (2009) [4]	Literature review
	Köksal et al. (2011) [12]	Literature review
	Cheng et al. (2018) [3]	Literature review
	Sharp et al. (2018) [19]	Literature review
	Flath and Stein (2018) [8]	Process model
	Villanueva Zacarias et al. (2018) [23]	Framework
	Lechevalier et al. (2018)


Flemming M M Christensen

Soujanya Mantravadi

Iskra Dukovska-Popovska

Hans-Henrik Hvolby

Kenn Steger-Jensen

Charles Møller

Horizontal Integration in Fresh Food Supply Chain

Keywords: Retail supply chain, Integration, Real-time information sharing, Fresh food products, MES

Demand information sharing during planning and control of fresh meat production and replenishment in franchise supply chain is studied. Main findings are that some demand information is received much later upstream than when created downstream. Horizontal integration of systems in the supply chain allows all parties access to same critical information about store demand and availability in real-time. A conceptual model for horizontal integration in the triadic supply chain, allowing differentiated and timely sharing of information is suggested, to increase service level and reduce waste from over-/under-production.

Introduction

In retail supply chains, information sharing [START_REF] Simatupang | An Integrative Framework for Supply Chain Collaboration[END_REF][START_REF] Simatupang | The Collaboration Index: a Measure for Supply Chain Collaboration[END_REF] is a large part of collaborative materials management (CMM) [START_REF] Jonsson | Future of Supply Chain Planning: Closing the Gaps Between Practice and Promise[END_REF]. Sharing e.g. demand information allows transparency and visibility of product sales and availability in the supply chain. However, demand information is usually adjusted and aggregated downstream into orders before being shared, and is sent in batches at specified times or when manually initiating a transfer (e.g. sending purchase orders). It is often shared in dyadic structures via business level systems alike enterprise resource planning systems (ERP). Hence, supplier, wholesaler and retail stores plan (and schedule) and control their productions independentlybased on (adjusted) historical rather than real-time information and internal plans cf. internal setup [START_REF] Fleischmann | Advanced Planning & Structure of Advanced Planning Systems[END_REF][START_REF] Hübner | Demand and Supply Chain Planning in Grocery Retail: an Operations Planning Framework[END_REF]. Once scheduled, the production is frozen certain time into the future to reduce nervousness [START_REF] Fleischmann | Advanced Planning & Structure of Advanced Planning Systems[END_REF]. Albeit incremental planning is possible, options for changes continuously decrease, until finally impossible when production ends [START_REF] Fleischmann | Advanced Planning & Structure of Advanced Planning Systems[END_REF]. This is challenging when dealing with fresh food products. They need intensified and increased information sharing [START_REF] Lusiantoro | A Refined Framework of Information Sharing in Perishable Product Supply Chains[END_REF] during planning and control of production and replenishments, due to high demand variability and short shelf life [START_REF] Ferguson | Information Sharing to Improve Retail Product Freshness of Perishables[END_REF]. Fresh meat products (FMPs) such as e.g. ground beef/pork/chicken/fish and ready-made meals are processed down to few hours before shipment to wholesaler; even while stores still send orders to wholesaler. Thus, sharing demand information longer time in advance of production start both increases demand uncertainty (cf.forecast) and reduces the supply chain responsiveness to unforeseen changes in demand. Even if possible, responses are costly and labor-intensive non-systemized exceptions management (through phone calls and emails). Instead, sharing centrally managed demand information [START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF] in realtime allows common understanding of demand, effective instant decision-making, reduced risks and greater forecast accuracy [START_REF] Swaminathan | Models for Supply Chains in E-Business[END_REF]. To not put more pressure on business level systems, interest is in horizontal integration of planning and control systems online/in the sky to share real-time. In addition, for franchise, all decision-making is decentralized to stores, as opposed to corporate retail chains. It is thus relevant to also investigate how a triadic supply chain (supplier, wholesaler and retail stores) seamlessly can share real-time demand information horizontally during planning and control.

We analyze and identify issues in information sharing during fresh meat production and replenishment planning and control in franchise retail supply chain. A conceptual model is proposed to integrate systems in the supply chain with real-time demand information sharing along suggestions for which information to share. Following presents theoretical framework, methodology, case analysis, framework and conclusion.

2

Theoretical Background

Collaborative materials management (CMM) is "operational planning and control of inventory replenishments in supply chains" [START_REF] Jonsson | Future of Supply Chain Planning: Closing the Gaps Between Practice and Promise[END_REF]. Information sharing is a major part of CMM and governs the "capturing and dissemination of timely and relevant information for decision makers to plan and control supply chain operations" [START_REF] Simatupang | The Collaboration Index: a Measure for Supply Chain Collaboration[END_REF]. Especially demand information sharing influences CMM with direct impact on the planning and control effectiveness and waste from out-of-stock/oversupply situations [START_REF] Ettouzani | Examining Retail On Shelf Availability: Promotional Impact and a Call for Research[END_REF][START_REF] Alfalla-Luque | Supply Chain Integration Framework Using Literature Review[END_REF][START_REF] Adebanjo | Understanding Demand Management Challenges in Intermediary Food Trading: A Case Study[END_REF].

In CMM there is differently increasing demand information sharing depending on the level of collaboration [START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF][START_REF] Ståhl Elvander | Framework for Characterizing the Design of VMI Systems[END_REF][START_REF] Pramatari | The Impact of Collaborative Store Ordering on Shelf Availability[END_REF]. As example, in "vendor-managed inventory" (VMI) supplier may obtain full view of both historical demand, point-of-sales and inventory levels at wholesaler or retail stores (depending on whose inventory is managed). However, it only covers a dyadic supply chain limiting the efficient information sharing across entire (i.e. triadic) supply chain. Further, demand information sharing is through batch transactions from ERP or online access to ERP [START_REF] Ståhl Elvander | Framework for Characterizing the Design of VMI Systems[END_REF], causing redundant use of and pressure on ERP systems compared to if sharing directly between the systems where the information is created. The "collaborative planning, forecasting and replenishment" (CPFR) (VICS, 2014) includes demand exceptions and extends VMI with collaborative validation and synchronization of planning (incl. forecasting) by increasing demand information sharing. Yet, although information sharing is through special data transfer interfaces (e.g. EDI) [START_REF] Danese | Collaboration Forms, Information and Communication Technologies, and Coordination Mechanisms in CPFR[END_REF] or even online applications for real-time/near real-time [START_REF] Whipple | Building Supply Chain Collaboration: a Typology of Collaborative Approaches[END_REF], it is still through ERP in a dyadic supplier-wholesaler relationship. Similarly for "process of collaborative store ordering" -although information sharing is through an online platform to enhance real-time demand sharing [START_REF] Pramatari | The Impact of Collaborative Store Ordering on Shelf Availability[END_REF] it is dyadic between supplier and retail stores, i.e. without wholesaler' knowledge to and about demand information [START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF]. No CMM program entails real-time based triadic demand information sharing via planning and control systems to ensure complete (supply chain) demand visibility and efficient use of systems. Namely for supplier' manufacturing execution systems, wholesaler' warehouse management system and retail stores' cash register. For fresh food products with short shelf life there is stronger correlation between supply chain performance and level of information sharing, than long shelf life products [START_REF] Lusiantoro | A Refined Framework of Information Sharing in Perishable Product Supply Chains[END_REF]. Yet, although information sharing generally improves supply chain performance [START_REF] Cui | Information Sharing in Supply Chains: An Empirical and Theoretical Valuation[END_REF], responsiveness [START_REF] Thatte | Impact of SCM Practices of a Firm on Supply Chain Responsiveness and Competitive Advantage of a Firm[END_REF] and freshness of products [START_REF] Ferguson | Information Sharing to Improve Retail Product Freshness of Perishables[END_REF], research also suggest that the level of improvement is not per se always positive as sharing too much/irrelevant information may decrease performance and "result in an expected loss" [START_REF] Choi | Will a supplier benefit from sharing good information with a retailer?[END_REF]. In turn, information sharing (and thus also collaboration) depends on factors such as e.g. specific demand situation [START_REF] Adebanjo | Understanding Demand Management Challenges in Intermediary Food Trading: A Case Study[END_REF][START_REF] Jonsson | The Value of Sharing Planning Information in Supply Chains[END_REF], type of product [START_REF] Lusiantoro | A Refined Framework of Information Sharing in Perishable Product Supply Chains[END_REF] as well as type of information shared, with whom it is shared and how it is shared [START_REF] Kumar | Information Sharing in Supply Chains: An Overview[END_REF].

From the production perspective, the concept of inter-enterprise integration and the supplier's involvement in the supply chain using information systems is not new. Level 3 systems as per ISA 95 standard set by 'International society of automation' also address the need for systems to interconnect, to provide value to the manufacturing enterprises and beyond. Enterprise systems are known to enhance the collaboration between the supplier and the end user by reducing transaction costs [START_REF]Exploring the Rationales for ERP and SCM Integration[END_REF]. Since MES/MOM systems are real-time compliant, it becomes advantageous for fresh food supply chains to access the product centric data via factory control systems [START_REF] Mantravadi | Perspectives on Real-Time Information Sharing through Smart Factories: Visibility via Enterprise Integration[END_REF]. Supplier can thus play an important role in improving the supply network design as problems related to bullwhip tend to impact all chain parties.

There are various approaches to interenterprise integration based on the need for information sharing. Owing to that, information exchange via web based MES/MOM systems can follow several classes of information interfaces such as: SCOR, CPFR, RosettaNet for process data; EDIFACT for structured data; and TCP/IP reference model & basic internet services for unstructured data [START_REF] Scheer | Information Interface Classification of Actors in Supply Chains[END_REF]. Supplier and buyer integration in a supply chain for collaborative materials planning is a known method in the operations management. But the collaborative approach by integrating shop floor level systems is not well understood in theory. Over the last two decades, ERP systems have evolved from being monolithic to modular ERP II systems by extending into the supply chains [START_REF] Møller | ERP II: A Conceptual Framework for Next-Generation Enterprise Systems?[END_REF]. Similarly, the scope of MES/MOM systems could also be extended into supply chains, for which we present web-based service-oriented architecture (SOA) as a suitable approach for horizontal integration. Modularity and remote access via internet technology are key reasons for considering service-oriented MES/MOM systems.

Methodology

After investigating demand information sharing in the supply chain and where different demand information is created, the purpose is to propose a conceptual model for realtime sharing through horizontal integration of planning and control systems. This, to ensure all parties access to same critical information about store demand and availability in real-time. The focus is on FMPs and ensuring decentralized order decision-making cf. franchise retailing. The goal is to ensure complete transparency of demand (i.e. store sales) and inventories across the supply chain in real-time, allowing live production scheduling at supplier. The case is studied in natural context to ensure enriched understanding and insight [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF] as both context and product type is critical. To provide a generalizable view, focus is on beef, pork, chicken and fish with total shelf-life of 8 days or less which are produced short time before delivery to wholesaler. The supply chain is triadic, i.e. supplier, wholesaler and retail stores (franchisors) with retail chain (franchisee). Wholesaler is one of the largest grocery wholesalers in Denmark and supplies 328 franchise stores with FMPs from five suppliers each day through a central warehouse, via two replenishment cycles: supplier-wholesaler and wholesaler-store.

The past year stores have ordered 10 to 47 SKUs each day; 4-25 beef incl. veal and cattle, 1-16 pork, 1-9 chicken and 1-4 fish; depending on season/campaigns. All FMPs are shipped from supplier, consolidated at wholesaler and delivered to stores. Waste levels in stores from theft and expiration are considered very low (<1%, estimated) but included in the calculation of inventory levels. Information and data are obtained via semi-structured interviews with IT-manager, purchaser and purchasing manager (wholesaler), purchasing assistant (retail chain), personnel (retail stores) and, sales manager, production planner and vice president (supplier), from standardized questions about planning/scheduling/control processes.

Information Sharing in Fresh Meat Supply Chain

First, wholesaler creates an order in the purchase planning system (PPS) and sends it to supplier via ERP by latest 16:00 (day 1). After confirmation (via ERP), supplier schedules the order for production during the night/following morning and deliver to wholesaler between 06:00 and 13:00 (day 2). While the production still runs (supplier) and FMPs are received (wholesaler), stores create orders in their ERP via hand-terminals and send to retail chains' ERP at latest by 11:00 (day 2). The store orders are then transferred to wholesaler' ERP and further to the warehouse management system (WMS), releasing orders for picking from 14:00 in two batches (dependent on delivery times to stores). The FMPs are physically delivered to the stores between 18:00 (day 2) and 05:00 (day 3). This results in a lead-time of down to 14 hours for wholesaler, and down to 7 hours for stores, from sending store order until received. Figure 1 depicts where the demand information is created and available, grouped vertically by supply chain stage and type of demand information (see 1/2/3 in figure). White boxes, except "R Studio" (forecasting), are real-time information systems and the grey are ERP. Based on this, inventory levels (from WMS) and yesterday' store demand (from ERP), wholesaler estimates the following day' demand. Although (aggregated) POS from yesterday' sale in stores can be accessed, is not used since logging on retail chain' ERP and looking up each product' sale in stores is rather time-consuming. When order quantity is set (i.e. expected demand minus incoming pre-orders and inventory), the order is loaded into wholesaler' ERP. The purchaser manually sends the order to the supplier through EDI-FACT, awaiting an order confirmation. In Figure 1, from right to left.

Creation of Store Orders (2) and Transfer to Wholesaler (Cycle 2). When stores order products, personnel walk around in the store and scan shelf labels with a handheld terminal as seem needed, i.e. less available than desired. The personnel can see usual sales the given weekday (manually uploaded to the terminal from ERP before starting the ordering process). Quantity is determined from what pre-determined amount to be available minus actual available amount in boxes -and adjusted for sales the following day if it is expected to increase. When the order is final, it is transferred from to the ERP. For each product, the personnel see what is usually sold at surrounding days, general historical sale, campaign information (last/current) and how much is in preorder. When adjusted the order is sent to retail chain' ERP, and then to wholesaler' ERP and from here further to the WMS for picking. In Figure 1, from right to left.

Creation of POS (3) and its Transfer across Supply Chain. POS is created and registered at an individual local database at each CR in each store (app. 1,100 for entire chain). Every three minutes, the POS is sent the store' back-office database, where it is consolidated with POS from the other 2-3 CRs in the store. This receipt-level POS is saved for three months and constantly deleted as time pass by, due to storage limitations. From here, the receipt POS is transferred to the ERP' database in each store which constantly aggregates with latest POS (aggPOS) cf. 3 minutes transfer interval. Here all sales data is saved on a native database, and this is the first time the store, retail chain and wholesaler can manually log on and access aggPOS data older than 3 months. Around midnight all aggPOS (amount per SKU per price per day) from all stores is transferred to the retail chain' database. From around 6-7 in the morning, procurement at retail chain can readily access the aggPOS in their ERP. Logging on to retail chain' ERP, wholesaler may also access the information. In Figure 1, from right to left.

To sum up, one of the main-findings is that the true demand (POS) is aggregated and only available in certain systems in batches and from certain time-points. This cause unstructured manual exceptions management (by phone) and reduces the ability to immediately react to changes in demand in stores. Further, wholesaler decides order quantity on two days' old demand-data influenced by 328 different stores' decision-making rather than real sales. This, albeit different information is available at different supply chain stages in real-time, hereunder demand at cash register (CR) in stores (i.e. point of sale (POS)), inventory levels at wholesaler and production status at supplier. Another main-finding is the time it takes to transfer and save aggPOS from stores to wholesaler through ERP -and the consequent delay before being available for decision-making. Since supplier produces FMPs down to hours before shipment, decreasing transfer time and sharing information directly between relevant systems (CR, WMS and MES) will increase the ability to react to demand changes. Further to this, retail stores manually control amounts of products available when determining order size, albeit having a predetermined max-amount of each product. Given the low level of theft and waste (cf. close-to-expiration products sold at reduced price), inventory levels may be derived from ordered quantities subtracted POS with only weekly/periodic check. And chosen max-amounts may even further be evaluated cf. amount of products sold at reduced price is registered in POS.

5

Proposed Framework for Real-Time Information Sharing through Horizontal Integration Not only does current systems integration make actual demand rather opaque, it also increases the risk of bullwhip effect. In literature, sharing of POS is argued as having positive impact on the supply chain performance, and a necessity for collaborative materials management [START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF][START_REF] Ståhl Elvander | Framework for Characterizing the Design of VMI Systems[END_REF][START_REF] Pramatari | The Impact of Collaborative Store Ordering on Shelf Availability[END_REF]. To allow timely information sharing Figure 2 illustrates a conceptual model for integration of systems via horizontal integration, allowing decision-making based on real-time information sharing. Given wholesaler' role in the supply chain [START_REF] Hübner | Demand and Supply Chain Planning in Grocery Retail: an Operations Planning Framework[END_REF], the model assumes wholesaler to be coordinator of information and product flows. Information about demand (i.e. POS) and derived inventories from stores, production execution and status (i.e. MES) from supplier as well as inventories at wholesaler is shared in real-time. Based on this information, and (by store) pre-determined max-amounts in each store, wholesaler applies an ongoing fuzzification-process which is based on a knowledge repository (retrieving information from the web and business intelligence) and inference engine. E.g. if weather is expected to increase, then the derived expected consequence on demand is included in the suggestive actions. From this, the system will constantly evaluate ongoing demand and changes in demand (POS) against chances for changing already sent demand information to suppliers -filtered according to how far supplier is in production schedule.

If demand in stores sudden deviates, an alarm will occur informing the system about potential need for additional products (and vice versa, if no sale happens). Then, based on supplier' production status, the system will inform MES about additional/fewer quantities to be produced. Products which can be changed constantly follow open production orders in real-time. Thus if production of product A just finished, product A cannot be subject for any alteration. The further into the future the production is, the greater allowance for quantity changes. In this way systems across supplier (i.e. MES), wholesaler (i.e. WMS) and store (i.e. POS) will integrate horizontally, eliminating transfer through ERP. By allowing for "blanket" orders, the MES, WMS and CR will inform ERP about workload, opposite today, where ERP informs about workload. For reasons of speed, web-based information transfer is suggested.

Conclusion & Future Research Directions

The FMP production at suppliers is scheduled certain time after wholesaler sends order based on adjusted demand (i.e. wholesale order), despite actual sales is still recorded in stores -and it still runs while actual store orders are sent. No integration of planning and control systems challenges the timely information sharing [START_REF] Simatupang | An Integrative Framework for Supply Chain Collaboration[END_REF][START_REF] Simatupang | The Collaboration Index: a Measure for Supply Chain Collaboration[END_REF] with centrally adjusted demand [START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF]. By integrating the planning and control systems where demand information is created (MES, WMS and POS) across the supply chain then e.g. sudden changes to demand may be shared (near) live. Whether increase or decrease in demand it allows the entire triadic supply chain to react instantly and respond accordingly. This is particularly crucial for products processed each day e.g. ground beef/chicken/fish. This research has focused on major common FMP-types in grocery industry in a conceptual model. More research is needed for other FMP-types to establish the validity in sharing POS in real-time and define what supply chain characteristics must be in place -when comparing against FMP with long shelf life. Also, the framework should be tested out empirically to investigate and quantify impact on service level across the supply chain, as well as ability to meet sudden changes in demand. The focus is regular demand, hence to test the generalizability, it would be favourable to test the model in different types of demand such as campaign, product introduction or seasonal demand. Also, this study has focused on franchise retail stores with decentralized decision-making. Additional research is needed for other store-types where centralized decisionmaking may be used such as corporate retail chain.

Fig. 1 . 1 2

 11 Fig. 1. Order-information creation and storing in systems

Fig. 2 .

 2 Fig. 2. Real-Time Information Sharing through Horizontal Integration


Günther Schuh
email: g.schuh@wzl.rwth-aachen.de

Jan-Philipp Prote
email: j.prote@wzl.rwth-aachen.de

Philipp Hünnekes
email: p.huennekes@wzl.rwth-aachen.de

Frederick Sauermann
email: f.sauermann@wzl.rwth-aachen.de

Lukas Stratmann
email: l.stratmann@wzl.rwth-aachen.de

Impact of modeling production knowledge for a data based prediction of transition times

Keywords: Production planning, machine learning, transition times

An increasing demand for customer-specific products is a major challenge for manufacturing companies. In many cases, companies attempt to satisfy this demand by increasing the number of product variants. In those companies, cost-oriented production processes have to be transformed into flexible workshop or island production structures in order to be able to produce this variety. This leads to an increasing complexity of production and subsequently planning. In order to reliably meet due dates, it is necessary to improve the quality of planning. This paper presents an approach for predicting transition times, the times between two production steps, by employing machine learning methods. In particular, the influence of the modelling of production knowledge of experienced employees on the prediction quality compared to a pure optimization of the methods' parameters is investigated.

Introduction

A trend of an increasing demand for customer-specific products can be observed. [START_REF] Elmaraghy | Product variety management[END_REF][START_REF] Gyulai | Lead time prediction in a flow-shop environment with analytical and machine learning approaches[END_REF] The increasing number of variants forces many companies to transform their former cost-oriented flow production structures into flexible workshop or island production structures. [START_REF] Duffie | An analytical approach to improving due-date and lead-time dynamics[END_REF] Typically, in the latter ones lead times (LT), logistics efforts and costs tend to increase, while the delivery reliability tends to worsen. [START_REF] Berlec | Forecasting of Production Order Lead Time in SME's[END_REF] Today, most companies' highest logistical target is a high logistical performance and especially a high delivery reliability. [START_REF] Duffie | An analytical approach to improving due-date and lead-time dynamics[END_REF] In times of an ever increasing globalization, a high logistical performance is crucial for a strong competitiveness, as markets all over the world have changed mostly from sellers' to buyers' markets. [START_REF] Berlec | Forecasting of Production Order Lead Time in SME's[END_REF] In those, a high planning reliability plays a central role in achieving a high logistical performance. [START_REF] Schuh | Towards a Technology-Oriented Theory of Production[END_REF] In principle, companies can positively influence the delivery reliability by measures such as additional shifts, employee overtimes, short-term outsourcing or targeted overcapacities. However, those measures come at high costs. Those costs are a challenge for companies, especially for those in high-wage countries. An approach for acting more proactively and achieving a high delivery reliability is a more accurate prediction of order LT. Those LT consist mainly of transition times (TT), the times between two processing steps, which consist of waiting times before and after processing steps and transport times. TT scatter in general widely, which is why companies struggle to predict TT properly. [START_REF] Öztürk | Manufacturing lead time estimation using data mining[END_REF] This paper aims at analyzing the impact of modeling and using production knowledge on the accuracy of TT prediction compared to a pure optimization of a prediction model's parameters using only raw feedback data. The research question is how much the prediction accuracy is influenced by these two tasks.

In the following section the challenges and consequences of a more accurate prediction are discussed briefly. In section three the state of the art of TT prediction is presented. Section four presents the conceptual design on the developed approach of an accurate TT prediction. In section five, the impact of modelling production knowledge of experienced employees is investigated and compared to a pure optimization of parameters being used in a machine learning (ML) method. Section six summarizes the findings and gives an outlook on future research.

Challenges and consequences of an accurate transition time prediction

An accurate prediction of TT is a challenging and uncertain task. [START_REF] Pfeiffer | Improving the Accuracy of Cycle Time Estimation for Simulation in Volatile Manufacturing Execution Environments[END_REF] Production planers often attempt to cope with this uncertainty by allocating time buffers in the manufacturing process. This leads to a phenomenon called vicious cycle of production planning that was already described in the 1970s. Allocating time buffers leads to an earlier release of orders. Assuming orders are not finished substantially earlier, an increasing work load at work stations leads to longer waiting queues. Caused by this, orders need even longer than planned and the LT scatter more widely. [START_REF] Mather | Priority Fixation versus Throughput Planning[END_REF] Instead of allocating time buffers, the right approach would be to predict LT order-specifically. By this, LT scattering decreases while process and planning reliability increases. However, current planning systems do not support a more accurate prediction of TT. [START_REF] Niehues | Adaptive Produktionssteuerung für Werkstattfertigungssysteme durch fertigungsbegleitende Reihenfolgebildung[END_REF] Thus, companies often use static estimations of TT. On the other hand, research shows that a more accurate prediction of TT is possible. [START_REF] Berlec | Forecasting of Production Order Lead Time in SME's[END_REF] Yet, there are difficult obstacles to overcome when predicting TT. In general, a profound and error-free database is a prerequisite for a robust and accurate prediction. [START_REF] Gyulai | Lead time prediction in a flow-shop environment with analytical and machine learning approaches[END_REF] However, companies often do not possess such a database. Often they have a high degree of missing or erroneous data [START_REF] Schuh | Increasing data integrity for improving decision making in PPC[END_REF] and/or they do not have a large amount of nonredundant feedback data. As TT are related to orders in general and processing steps in particular, there is a limitation in the number of data entries per attribute by the number of orders or processing steps. Each attribute represents a data source, such as sensors, feedback data or master data. Generating more data points per order or processing steps would only lead to a more redundant data set that does not bring substantial benefits. An additional challenge is the data representation. Production data is characterized by a high heterogeneity in type, structure, semantics, organization and granularity, which is difficult to handle for most ML methods. [START_REF] Chen | Big Data: A Survey[END_REF] Besides the mentioned challenges, research states that it is important to model human expertise and especially production knowledge for a transformation of raw data into useful data. [START_REF] Guyon | An Introduction to Feature Extraction[END_REF][START_REF] Niggemann | Datenanalyse in der intelligenten Fabrik[END_REF] Thus, in section four, an approach for modeling this production knowledge into a data based prediction of order-specific TT is presented. In section five, the approach is applied both with and without production knowledge to a data set containing real feedback data of a producing company from the machine equipment industry. By doing so, the impact of modeling production knowledge is investigated and compared to a pure parameter optimization of a ML method.

State of the art of the prediction of transition times

In the following, eight papers are presented that have been identified as the most relevant ones in a semi-structured literature review with a total of 75 analyzed papers. In [START_REF] Öztürk | Manufacturing lead time estimation using data mining[END_REF] LT estimations based on two different decision tree algorithms are compared for different basic job shop production configurations. It is found that feature selection has a significant impact on the prediction accuracy. At best, a deviation as high as 18 % between predicted and actual LT is achieved. In [START_REF] Berlec | Forecasting of Production Order Lead Time in SME's[END_REF] an approach of LT prediction in a tool shop based on real industry data is presented. In [START_REF] Meidan | Cycle-Time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining[END_REF] both a feature selection for and prediction of cycle times in a simulation model of a use case from the semiconductor industry is described. In [START_REF] Tirkel | Forecasting flow time in semiconductor manufacturing using knowledge discovery in databases[END_REF] a short term prediction model for LT in a semiconductor factory is proposed. In [START_REF] Wang | Deep neural networks based order completion time prediction by using real-time job shop RFID data[END_REF] three deep neural networks are compared for predicting order completion times. In [START_REF] Gyulai | Lead time prediction in a flow-shop environment with analytical and machine learning approaches[END_REF] the use of a multivariate linear model, a non-linear random forest and a support vector machine algorithm is analyzed, all using real feedback data from a flow-shop production in the optics industry. In [START_REF] Lingitz | Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer[END_REF] LT in a semiconductor factory are predicted for three process steps by applying eleven different prediction algorithms. In [START_REF] Wang | A Data Driven Cycle Time Prediction With Feature Selection[END_REF] cycle times of wafer lots in a company from the semiconductor industry are predicted when releasing orders into production.

Analyzing the presented papers in detail, certain improvement measures can be derived. First of all, all papers focus on the prediction of LT that do not only contain highly scattering TT but also processing times. As processing times are often dependent on technological influencing factors whereas TT often depend on organizational factors, a separate analysis of both is recommended. In some of the papers significant reductions of the inspection areas have been made. For instance, in [START_REF] Wang | Deep neural networks based order completion time prediction by using real-time job shop RFID data[END_REF] only 12 workstations are considered, while in [START_REF] Öztürk | Manufacturing lead time estimation using data mining[END_REF] only 6 workstations are considered. With the inspection area being to small, it can be explained, why in some cases selected features do not change their importance for the accuracy of LT prediction with respect to different production configurations. In general, a high usage of simulation data can be observed (in five of the twelve papers). Simulation data has in principle a high quality in terms of fewer instances of missing data or outliers. This characteristic is favorable for data analysis methods but real world applications are different. In four papers, use cases from the semiconductor industry are selected. As per [START_REF] Wang | A Data Driven Cycle Time Prediction With Feature Selection[END_REF], semiconductor factories are among the most digitized ones. With prediction accuracy is highly dependent on the number of training data, it is obvious, that an application in different industries is challenging. Lastly, it can be observed that e.g. in [START_REF] Meidan | Cycle-Time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining[END_REF] feedback data from production is used for the prediction of LT, which is not known a priori, i.e. at the time when predicting LT. Hence, the accuracy of such a model decreases, when removing such features that were identified as relevant for prediction. In the following section, an approach is presented that addresses the mentioned challenges and weaknesses.

4

Conceptual design of an approach for an accurate prediction of transition times Essential for any data analysis is the quality of the underlying data set [START_REF] Rahman | A Decision Tree-based Missing Value Imputation Technique[END_REF]. Especially the feedback data of highly differentiated and complex processes of mediumsized machine equipment companies pose a challenge for predicting TT. As a result, this approach focuses on generating a high quality and informative data set.

In research, two approaches have shown to be effective methods for increasing data quality (see also section three): Feature selection and feature engineering [START_REF] Guyon | Feature extraction: Foundations and applications[END_REF][START_REF] Kanter | Deep feature synthesis: Towards automating data science endeavors[END_REF][START_REF] Zhang | Materialization Optimizations for Feature Selection Workloads[END_REF]. Features are the attributes which characterize a specific data set [START_REF] Witten | Data mining: Practical machine learning tools and techniques[END_REF]. In the process of feature selection, an analyst selects the features to decrease total number of features which improves modeling accuracy [START_REF] Meidan | Cycle-Time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining[END_REF][START_REF] Zhang | Materialization Optimizations for Feature Selection Workloads[END_REF]. In feature engineering, a data scientist combines preexisting features or creates new features from scratch. Thus, feature engineering enables the opportunity to enrich a data set with knowledge from production experts, increasing the accuracy of TT predictions beyond the level of raw data sets. This task is especially challenging regarding the necessity of understanding the underlying production system as well as the available data set. Neither a data scientist nor a production expert can easily provide both.

Due to a potential extensive effort of generating a significant benefit from feature engineering, a systematic approach is fundamental [START_REF] Anderson | Brainwash: A Data System for Feature Engineering[END_REF]. This challenge has been addressed by several researchers, but mostly with high level and general concepts. In principle, a data scientist should begin by selecting obvious features such as dates and durations, and then continue to generate individual features such as LT or percentiles of a feature [START_REF] Kanter | Deep feature synthesis: Towards automating data science endeavors[END_REF]. [START_REF] Anderson | Brainwash: A Data System for Feature Engineering[END_REF] suggest a three-phase method consisting of exploring the data, extracting relevant features and evaluating the engineered features. However, both approaches offer very few concrete actions and instead focus on the ability of a data scientist to understand the underlying system. The presented approach is based on previous work with the extension to include production experts into the process [START_REF] Schuh | Determination of order specific transition times for improving the adherence to delivery dates by using data mining algorithms[END_REF].

For feature selection, the most common method uses and ranks the set of features according to their relevance. The wrapper method accomplishes that by employing the learning algorithm and returning the features' impact on the prediction accuracy [START_REF] Kacprzyk | Feature extraction: Foundations and applications[END_REF].

Having generated the high-quality data set, the learning model's performance can significantly be improved by optimizing its hyperparameters. Those are parameters of modeling algorithms that can be tuned to optimize the fit of a model to a data set [START_REF] Witten | Data mining: Practical machine learning tools and techniques[END_REF].

Considering the steps above, we applied and evaluated the impacts of feature engineering, feature selection and hyperparameter optimization on a set of production data of a German medium-sized manufacturing company as follows.

5

Investigating the impact of modeling production knowledge for usage in ML methods

Validating the thesis of creating a significant benefit by enriching production data with additional production knowledge, the used raw set of production data consisted of real production information of approx. one year taken from a company's MES. Features included for analysis were e.g. the number of orders per day, number of operations per order, planned dates for starting and finishing the order, number of parts per order and estimated processing times by the processing production planner. Summing up, the data set contained 25 features and 100.000 data entries. In this, total LT accounts for 458,000 hours, of which TT has a share of 78 %. The used features are all known a priori.

In a first step, we designed a case-based approach to infuse additional knowledge into the data. Then, basic analysis on data set was conducted, promptly identifying two findings. First, the TT per processing step varied with the time left until the order was completed. Second, the workload of the factory had a significant effect on the TT. By extracting daily data of all processed orders, new features were created accumulating the processing times of orders outstanding (i.e. calculating the workload of that day). Beyond that, the production expert stated that the work schedule (i.e. planned downtimes) causes a great portion of the TT. With this knowledge, the created feature eliminates planned downtimes such as weekends or nights for machines not operating at these times. This led to a decrease of TT to 44 % of LT. After implementing all engineered features, the data set included about 90 features.

In the process of generating an optimal data set, we applied some basic data preparation methods to clean the data of missing and incorrect entries. In accordance to [START_REF] Grzymala-Busse | A Comparison of Several Approaches to Missing Attribute Values in Data Mining[END_REF], instances with missing values were ignored, if their total appearances summed up to less than one percent of total instances and the median was inserted in residual cases. However, for some specific features missing values were interpreted as nulls, if missing values are allowed in the first place, e.g. additional processing times or priority.

Fig. 1. Results showing the prediction error per number of used features

Following, the wrapper method iteratively computed TT predictions adding a new feature each iteration to find the most influential ones. As seen in Fig. 1, the steps indicate a severe improvement when specific features were added to the model. Shown is the mean absolute error (MAE) of the TT predictions in working days that decreases over the number of features included into the model. Out of the best 25 features, we engineered 14 through expert knowledge. Among the most influential features are e.g. the time prediction by the production planner or the order-specific amount of time left to finish the complete order.

For modeling, we used python as programming language including its ML library. In particular, the scikit Regression Tree was used to perform the TT predictions [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF]. Optimizing its performance, the following hyperparameters were adjusted: Decision tree depth, minimum number of samples to be at a leaf node and minimum number of samples required to split a node. To evaluate the engineered features in comparison to the raw data set, four scenarios were analyzed. The scenarios I and II use only the raw data, while III and IV use the enriched data set. While I and III have default hyperparameters, in II and IV those hyperparameters were optimized.

As seen in Fig. 2, the TT prediction error is lowest for the data set that includes optimized hyperparameters as well as selected and engineered features. It reaches a 14.6 percent lower MAE than the data set without engineered features and thus shows a significant improvement. The effect of optimizing the hyperparameters is explicitly high for the decision tree due to its tendency of overfitting and therefore shows a great improvement of about 25 percent for the raw data set and 18 percent for the enriched data set. One can assume that engineering features helps the fitting of the model by providing more informative features, which leads to a lower need for optimization. This could prove important in cases of high amount of data, in which an evaluation, such as this, would need too much time to compute.

Summary and outlook

In this paper, the importance of a more accurate prediction of TT, that represent the time between production process steps, has been discussed. By using more accurate TT, logistical performance can significantly be approved by simultaneously reducing costs.

Based on a CRISP-DM based approach for a order-specific prediction if TT, a validation with real production feedback data took place. Modeling production knowledge in combination with optimizing a ML method's hyperparameters decreased prediction errors by 44 %. In the future, a profound evaluation is planned on what type of production knowledge can improve the prediction accuracy even further and how that knowledge can be modeled for a use in ML methods. One concrete example will be to evaluate the impact of modeling order sequence corrections at machines. At last, the integration in a company's planning process needs to be conceptualized, including a control loop for a self-learning development of the prediction model in order to maintain a high prediction accuracy. A major challenge for this control loop is to deal with the fact that production managers will actively influence the TT based on the predicted length.

Fig. 2 .

 2 Fig. 2. Mean absolute error of transition time predictions in working days

Acknowledgement

The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence "Internet of Production" (ID: 39062161).


Jo Wessel Strandhagen
email: jo.w.strandhagen@ntnu.no

Sven-Vegard Buer

Marco Semini

Erlend Alfnes

Digitalized manufacturing logistics in engineer-to-order operations

Keywords: engineer-to-order, digitalization, manufacturing logistics

The high complexity in Engineer-To-Order (ETO) operations causes major challenges for manufacturing logistics, especially in complex ETO, i.e. one-of-a-kind production. Increased digitalization of manufacturing logistics processes and activities can facilitate more efficient coordination of the material and information flows for manufacturing operations in general. However, it is not clear how to do this in the ETO environment, where products are highly customized and production is non-repetitive. This paper aims to investigate the challenges related to manufacturing logistics in ETO and how digital technologies can be applied to address them. Through a case study of a Norwegian shipyard, four main challenges related to manufacturing logistics are identified. Further, by reviewing recent literature on ETO and digitalization, the paper identifies specific applications of digital technologies in ETO manufacturing. Finally, by linking manufacturing logistics challenges to digitalization, the paper suggests four main features of digitalized manufacturing logistics in ETO: i) seamless, digitalized information flow, ii) identification and interconnectivity, iii) digitalized operator support, and iv) automated and autonomous material flow. Thus, the paper provides valuable insights into how ETO companies can move towards digitalized manufacturing logistics.

Introduction

The need for coordination of material and information flows in ETO operations is significant [START_REF] Mello | Improving coordination in an engineer-to-order supply chain using a soft systems approach[END_REF] and tailored approaches are required for an effective and efficient management of manufacturing operations [START_REF] Adrodegari | Engineer-to-order (ETO) production planning and control: an empirical framework for machinery-building companies[END_REF]. Several studies have been aimed at addressing the challenges related to manufacturing logistics in different ETO cases, however, the aspect of digitalization has not yet been sufficiently addressed in this type of manufacturing environment [START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF]. The new, digital technologies within Industry 4.0 has the potential to change the manufacturing industry by enabling new and more efficient processes. Concepts and developments such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), augmented reality, artificial intelligence and big data analytics are expected to lead to a paradigm shift in industrial manufacturing [START_REF] Lasi | Industry 4.0[END_REF]. Digitalization emerges as a way of managing complexity, and is introduced as one of the main areas for future research in complex ETO manufacturing [START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF]. With the high complexity in these manufacturing environments, there is a need to investigate how digitalization can improve manufacturing logistics performance. Therefore, this paper aims to identify how digital technologies can be applied in order to address the challenges in ETO manufacturing logistics.

The remainder of this paper is structured as follows. Section 2 presents and describes the characteristics of ETO manufacturing logistics. Thereafter, a case study serves to identify specific manufacturing logistics challenges. Applications of digital technologies in ETO is then identified through reviewing literature, before digitalization and manufacturing logistics challenges are linked in Section 5. The paper ends with the conclusions, limitations and further research in Section 6.

The characteristics of complex ETO manufacturing

Manufacturing logistics concerns the coordination of the operations related to the flow of materials through the manufacturing departments up to the production of the end product [START_REF] Caron | Engineer to order'companies: how to integrate manufacturing and innovative processes[END_REF]. Achieving cost-efficient manufacturing logistics in ETO is challenging due to the characteristics of the manufacturing environment [START_REF] Seth | Application of value stream mapping (VSM) for lean and cycle time reduction in complex production environments: a case study[END_REF]. With the ETO approach the activities of design, engineering, as well as the actual production processes, are performed after an actual customer order has been received. The customer order decoupling point is located at the design stage, with actual customer orders driving the production [START_REF] Gosling | Engineer-to-order supply chain management: A literature review and research agenda[END_REF]. The large degree of customization, the product structure complexity, and the overlapping of manufacturing and design activities are reasons for a very high complexity of the internal ETO supply chain [START_REF] Mcgovern | Modelling supply chain management processes in engineer-to-order companies[END_REF].

The most complex type of ETO manufacturing, which is the main focus of this paper, is the production of one-of-a-kind products [START_REF] Willner | Exploring the archetypes of engineer-to-order: an empirical analysis[END_REF]. Producing unique products every time has major implications for the manufacturing logistics processes, such as production control, as it creates a dynamic, uncertain and complex manufacturing environment [START_REF] Bertrand | Production control in engineer-to-order firms[END_REF]. Table 1 presents the main characteristics of complex ETO manufacturing.

Manufacturing logistics challenges in complex ETO

To get empirical data on how the characteristics of ETO manufacturing are affecting manufacturing logistics, a case study of the Norwegian shipyard Ulstein Verft AS (UVE) was conducted. Case data was collected through semi-structured interviews, observations at the yard and background data on the company from several years of research collaboration. This section includes a brief description of the case company and its manufacturing logistics. UVE is part of Ulstein Group ASA, a Norwegian industrial group with activities in ship design and shipbuilding. The group's main business consists of designing and building highly customer-specific vessels, typically advanced offshore vessels such as supply vessels, anchor-handling vessels, offshore construction vessels and seismic vessels, in close collaboration with the customers. In recent years, they have also started building expedition cruise ships, yachts and passenger ships, in addition to ships for the offshore wind industry and developing designs for fishing vessels. UVE is the shipyard responsible for outfitting the ships delivered by the group. The hull production is carried out at a foreign yard, before the hull is towed to UVE in Ulsteinvik, Norway.

The production at UVE is a highly complex ETO production and the characteristics of the production environment at UVE bear a close resemblance to the ETO characteristics presented in Table 1. In general, there is a highly complex material and information flow related to outfitting activities at UVE, with non-repetitive and non-routine work processes. This is a result of the complex production of one-of-a-kind products, and a high uncertainty in process specifications. Moreover, processes are prone to disruptions due to changes occurring after the outfitting activities has started. This affects the planning, scheduling and sequencing of tasks, the supply of materials and the supporting documentations needed by operators to perform jobs. It is today challenging to achieve the tight integration of IT systems needed for efficient outfitting of the ships.

Paper-based documentation of product models and drawings are critical sources of information for operators in this type of manufacturing. Operators have a particularly important role in performing the outfitting activities at UVE, as standardization and automation of processes is difficult due to the non-repetitive type of work. Many operations are thus manual, including production processes, material handling and internal transportation of materials. Providing the required information to operators is further complicated when changes occur, as models and drawings then must be updated accordingly. Furthermore, it is difficult to have an overview of the yard from a manufacturing logistics perspective as operations are spread across a vast area. Materials, tools and equipment are thus geographically dispersed, and operators spend a considerable amount of time walking to collect or search for them.

From this, four main manufacturing logistics challenges at UVE are derived:

 IT system integration and sharing of up-to-date information  Localization of materials, equipment and tools  Complex and information demanding work for operators  Manual material handling and irregular and disrupted flows

Digital technologies in ETO manufacturing logistics

Digital technologies emerge as promising means for managing complexity. While the technical developments of these technologies are rapidly advancing, applications in ETO still lags behind and requires research focus [START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF]. To have a structured overview of the available technologies and their features, eight technology groups were identified by integrating the technology clusters of smart manufacturing [START_REF] Mittal | Smart manufacturing: Characteristics, technologies and enabling factors[END_REF] and the nine advances in technology forming the foundation for Industry 4.0 [START_REF] Rüßmann | Industry 4.0: The future of productivity and growth in manufacturing industries[END_REF]. This is shown in Table 2.

Table 2. Overview and description of digital technologies.

Tech. group Description Autonomous robots

Automatic Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), and Collaborative robots (COBOTS) for material handling and performing logistics operations.

Integration of IT systems

Horizontal and vertical integration of IT systems for production management (PLM, ERP, MES).

Internet of Things

Objects equipped with sensors and actuators, enabling storing and exchange of information through network technology. Cyber security The secure and reliable protection of industrial production systems from cyber threats. Cloud manufacturing Cloud-based solutions for sharing and exchange of data between systems, sites, and companies.

Visual technology

The visual representation of an object, in the form of augmented reality (AR) through superimposing a computer-generated 3D image in the real world, creating a virtual reality (VR) or projecting 3D images as holograms.

Data analytics

Transforming data into knowledge and actions within a manufacturing system. Big data for analysis of large sets of real-time data, artificial intelligence, machine learning and advanced simulations are all part of this group. Additive manufacturing 3D printing of objects layer by layer, based on 3D models or CAD files of the objects.

Reviewing recent literature on ETO manufacturing and digitalization has identified a range of possible applications of these technologies. These are described in the following paragraphs of this section.

Several different IT systems are used at the different levels of today's manufacturing systems, but these are often not fully integrated [START_REF] Rüßmann | Industry 4.0: The future of productivity and growth in manufacturing industries[END_REF]. However, the current technological developments in ICT increases the opportunity of achieving such an integration. Also in one-of-a-kind production, fully integrated, digitalized factories are possible through integrated sensor networks and supporting information systems [START_REF] Ball | Systems integration and verification in an advanced smart factory[END_REF]. Enhancing integration between modeling, scheduling and monitoring processes is particularly relevant for ETO [START_REF] Rauch | Complexity reduction in engineer-to-order industry through real-time capable production planning and control[END_REF]. Eventually, everything should be connected to a cloud-based solution and also taking the aspect of cyber security into account [START_REF] Uhlemann | The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0[END_REF].

Digital technologies can be applied to assist operators to become smarter [START_REF] Romero | Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies[END_REF], and this is particularly relevant considering the high operator density in complex ETO. Visual technology such as Augmented reality [START_REF] Blanco-Novoa | A Practical Evaluation of Commercial Industrial Augmented Reality Systems in an Industry 4.0 Shipyard[END_REF] is one example of operator support that can enable schedules, product models and work instructions to be displayed on tablets or AR-glasses. Integration of such mobile devices with higher-level enterprise systems enables rapid sharing of updated information to the production floor. Such digital interfaces will also enable updating status of tasks through mobile devices, thus digitalizing progress reporting. Building Information Modeling (BIM) for information sharing through "BIM kiosks" is another means to provide operators with fast access to digital product models available from the PLM system [START_REF] Montali | Knowledge-Based Engineering in the design for manufacture of prefabricated façades: current gaps and future trends[END_REF].

Several papers have investigated the use of RFID for identification, localization and tracking [START_REF] Ball | Systems integration and verification in an advanced smart factory[END_REF][START_REF] Blanco-Novoa | A Practical Evaluation of Commercial Industrial Augmented Reality Systems in an Industry 4.0 Shipyard[END_REF][START_REF] Pero | RFID technology for increasing visibility in ETO supply chains: a case study[END_REF]. Tracking and localization technology for automated data capturing of materials movement can enable real-time planning and control [START_REF] Ball | Systems integration and verification in an advanced smart factory[END_REF] such as realtime monitoring of assembly processes [START_REF] Dallasega | Industry 4.0 fostering construction supply chain management: Lessons learned from engineer-to-order suppliers[END_REF]. Furthermore, the integration of e.g. RFID, GPS and GIS technology with AR technology allows operators to get information on the location of materials, tools and equipment on mobile devices such as smart glasses or tablets. Drones is another possible application for localization purposes, as they can be utilized for inspection of the overall status of the shipyard [START_REF] Morais | Ship Design, Engineering and Construction in 2030 and Beyond[END_REF]. Combining drones with 3D photography can then be used to provide 3D footage of the yard.

Although 3D printing mainly concerns production technology, such applications are also relevant for manufacturing logistics as it provides an ability for suppliers to send part designs to the yard for printing at the yard [START_REF] Morais | Ship Design, Engineering and Construction in 2030 and Beyond[END_REF]. Moreover 3D printing can be used for printing of tools and equipment on the spot [START_REF] Morais | Ship Design, Engineering and Construction in 2030 and Beyond[END_REF], hence it can reduce the time operators spend walking to acquire the tools and equipment necessary to perform a job.

Automated solutions for material handling are however the most promising developments to reduce time spent walking, waiting and searching. Automation of production processes, material handling and transportation of materials and equipment on the production floor has traditionally been difficult in complex ETO. However, with the increased flexibility of automated solutions today, the possibilities to automate such activities are increasing, exemplified by the use of AGVs, mobile robots, collaborative robots and automated material handling and feeding [START_REF] Hansen | Generic challenges and automation solutions in manufacturing SMEs[END_REF].

Features of digitalized manufacturing logistics in ETO

Having identified various applications of digital technologies in ETO, it is now possible to link these to the manufacturing logistics challenges. Each of the identified challenges are here linked with a feature that can be provided by digital technologies. The close integration between engineering and production in ETO manufacturing requires integrated IT systems for the efficient control and execution of manufacturing logistics activities. Moreover, with product changes occurring after production has started, it is necessary to provide operators with updated product drawings and models. Efficient information sharing is also required in the opposite direction, from shop floor to higher level IT systems, e.g. status and progress reporting from the production floor. With these challenges, there is a need for a seamless, digitalized information flow, where all subsystems at the various levels of the manufacturing system are integrated. Information should flow continuously from the production floor via MES system up to higher-level IT systems, such as the ERP system. This gives access to real-time information for planning and controlling operations.

The complexity of ETO products, with their deep bill of materials, makes it challenging to maintain an overview of all materials, equipment and tools necessary to perform operations. They are geographically dispersed across the facility, and workers often spend time searching for these assets, as well as walking over significant distances to acquire them. These challenges related to localization of materials, equipment and tools requires that Identification and interconnectivity is provided through digital technologies. It is now to a large extent possible to identify and interconnect objects in a facility through the utilization of new technology, and this will enable a highly integrated way of managing operations. Identification technology, networking technology and equipping products with sensors are keys to create a connected factory.

Operators in ETO manufacturing facilities such as UVE's shipyard must perform a range of highly complex, and non-routine tasks, as products are one-of-a-kind. Information about products, assemblies, processes etc. are therefore critical for the operators for them to be able to perform the scheduled tasks and activities. Digitalized manufacturing logistics should therefore include digitalized operator support. Human workers will still be important in a digitalized shipyard, and digital technologies should therefore be utilized to provide enhanced support for them, giving rapid and easy access to required and up-to-date information about the processes and activities.

With the manual material handling and irregular and disrupted material flow, there is a need for a more Automated and autonomous material flow. Products, components, tools, equipment and other objects can then be transported more efficiently, and with less human intervention. In manufacturing logistics, digital technologies can bring autonomy and automation to the physical flow of materials.

Fig. 1 shows the manufacturing logistics challenges identified from the case study, and their corresponding required features of digitalized manufacturing logistics.

Conclusions, limitations and further research

This paper has identified a number of manufacturing logistics challenges in ETO manufacturing. It has further linked these to a set of required features of a digitalized manufacturing logistics system, outlining the needs that should be met by digital technologies. To be able to address the manufacturing logistics challenges in ETO there is a need for seamless, digitalized information flow, identification and interconnectivity, digitalized operator support, and automated and autonomous material flow. Digital technologies can enable these features, and there is a range of possible applications also in ETO. For digitalized manufacturing logistics in ETO several of the technologies should be applied and combined. Although there is still a lack of research on digitalization in ETO manufacturing [START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF], this paper identifies a range of digital technologies that has been applied or described conceptually for this type of manufacturing. Further work related to this research will focus on developing more concrete descriptions of how the digital technologies can be implemented in the case company, and estimate the benefits in terms of relevant and measurable performance indicators such as time, cost, flexibility and quality. Future research should also include case studies of ETO manufacturers with similar characteristics as the case company in this paper, in order to generalize the findings.

Fig. 1 .

 1 Fig. 1. ETO Manufacturing logistic challenges and corresponding required features of a digitalized manufacturing logistics system.

Table 1 .

 1 Main characteristics of complex ETO manufacturing.

	Product characteristics:
		Big-sized, complex products with deep product structures [3, 11]
		High level of customization [9]
		High product variety and low volume on product level (one-of-a-kind products) [2, 9]
	Process characteristics:
		Manufacturing carried out as large projects in fixed position layouts [9]
		Frequent changes [11]
		Highly integrated and overlapping activities [12]
		Focus on flexibility [11]
	Market characteristics:
		Customer order decupling point located at the design stage [7]
		Fluctuations and uncertainty in mix and sales volume [10]
		Uncertainty in product specifications [10]