Lise Lillebrygfjeld Halse

Bjørn Jaeger
email: bjorn.jaeger@himolde.no

Operationalizing Industry 4.0: Understanding Barriers of Industry 4.0 and Circular Economy

Keywords: Industry 4.0, manufacturing technology, sustainability, circular economy

The manufacturing industry has to withstand an increasing competition requiring customization of products, shorter time to market and a transition towards more sustainable operations and products. There is a need for a transition to business models that incorporate sustainability while keeping business activities profitable. Leveraging the advantages of new technologies within the concept of Industry 4.0 is seen as an important factor to maintain competitiveness while responding to the sustainability challenge. Changing the way businesses operate is not easy as is evident from studies that have identified many barriers, including costs, lack of competence, loss of jobs, and process, product or production facilities not suitable for Industry 4.0. Due to these barriers, firms are slow to make a transition towards customized products, shorter lead times and more sustainable operations and products. The commitment for sustainability includes a shift towards Circular Economy (CE) that poses additional barriers like geographic dispersion, product complexity, and lock-in to the contemporary linear 'take-makeconsume-dispose' model of operation. This paper addresses how manufacturers perceive Industry 4.0, what motivates their investments in Industry 4.0, and what barriers they see in adapting Industry 4.0 followed by a literature review identifying barriers for adhering to CE in the manufacturing industry sector. The study offers empirical insights identifying a need for a roadmap for implementation of Industry 4.0 to support CE as well as providing directions for future research.

Introduction

Recently there has been an upsurge of interest on the potential of new technology in manufacturing through digitalization. Concepts used interchangeably for digitalization are "Industry 4.0" [START_REF] Lasi | Industry 4.0[END_REF], "Industrial internet" [START_REF] Bruner | Industrial Internet -The Machines are Talking[END_REF], and "Industrial Internet of Things" [START_REF] Jeschke | Industrial Internet of Things: Cybermanufacturing Systems[END_REF]. These are based on innovations in technologies, smart materials, Big Data and manufacturing operations, brought together by the accelerated use of Internet of Things (IoT)-technologies [START_REF] Porter | How smart, connected products are transforming companies[END_REF][START_REF] Russo | Exploring regulations and scope of the Internet of Things in contemporary companies: a first literature analysis[END_REF], or "Cyber-physical systems" (CPS) [START_REF] Wortmann | Internet of things. Technology and value added[END_REF]. These initiatives enable servitization and new business models in which one extend product-based offerings to a focus on customer value and performance [START_REF] Ericson | Manufacturing knowledge: Going from production of things to designing value in use[END_REF]. The future is becoming increas-ingly digital and networked, changing businesses and private life radically [START_REF] Bauer | Transforming to a Hyper-connected Society and Economy -Towards an "Industry 4.0[END_REF]. Simultaneously, businesses need to respond to environmental concern from customers by providing more eco-friendly products and services. The circular economy (CE) model is based on the concept of changing the take-make-use-dispose pattern into closedloops of material flows through processes such as maintenance, repair, reusing, refurbishing, remanufacturing and recycling [START_REF] Masi | Supply Chain Configurations in the Circular Economy: A Systematic Literature Review[END_REF]. Supply Chain Management (SCM) is considered vital for the implementation of the CE model, because of the need for a joint effort of suppliers, manufacturers and customers. Co-operation and co-ordination between supply chain upstream and downstream partners is essential as upstream partners obtain eco-friendly inputs cooperating with downstream partners for environmental management practices such as product return, reuse and recycling [START_REF] Zhu | Persistent performance and interaction effects in sequential cross-border mergers and acquisitions[END_REF]. The transition to CE is not easy, which is evident from several studies that have identified many barriers. Due to these barriers, firms are slow to make a transition towards CE [START_REF] Masi | Supply Chain Configurations in the Circular Economy: A Systematic Literature Review[END_REF][START_REF] Preston | A Global Redesign? Shaping the Circular Economy[END_REF]. This paper aims at exploring the barriers that manufacturing companies experience when implementing new technologies related to Industry 4.0, and the transition towards sustainability and CE.

Definitions

Industry 4.0

The term "Industrie 4.0" was coined at the Hannover Fair in 2011, describing how digital technologies will revolutionize the organization of global value chains [START_REF] Schwab | The fourth industrial revolution[END_REF]. In the context of manufacturing, Industry 4.0 focuses on intelligent products and production processes [START_REF] Brettel | How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4[END_REF]. Brettel et al. [START_REF] Brettel | How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4[END_REF] point out that the concept of Industry 4.0 is being used in different contexts, but is lacking an explicit definition. Based on a literature review, Hermann et al [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios: A Literature Review in Working Paper[END_REF] provide the following definition of Industry 4.0: "Industry 4.0 is a collective term for technologies and concepts of value chain organization. Within the modular structured Smart Factories of Industrie 4.0, CPS monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, CPS communicate and cooperate with each other and humans in real time. Via the IoS, both internal and cross-organizational services are offered and utilized by participants of the value chain." [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios: A Literature Review in Working Paper[END_REF] p. 242. This definition itself contains several terms and concepts, as Internet of Things (IoT), Cyber Physical Systems (CPS) and Internet of Services (IoS). The core of this definition is that items, systems and humans communicate with each other over the Internet in real time implying that everything is connected. The second main aspect is the development of services based on this connectedness. The extreme connectivity enables global and instant communication, allowing for the development of new business models. The development of new business models is also made possible by the advances in data processing capacity enabling analysis of Big Data and machine learning.

Circular Economy (CE)

We adopt the understanding of CE given by [START_REF] Emf | Towards the Circular Economy[END_REF]:

"A circular economy is an industrial system that is restorative or regenerative by intention and design. It replaces the 'end-of-life' concept with restoration, shifts towards the use of renewable energy, eliminates the use of toxic chemicals, which impair reuse, and aims for the elimination of waste through the superior design of materials, products, systems, and, within this, business models." (p. 7) In this model of circularity, reuse at the product level is the most important in terms of positive environmental impact, followed by reuse at the component level and reuse at the material level respectively. Shifting towards a circular economy model requires a deep focus on raw materials and energy. When producing a product, the focus should be on minimizing the entire product life cycles negative effects on the environment from the very early stage of material extraction towards the product disposal. In [START_REF] Bocken | Product design and business model strategies for a circular economy[END_REF] the authors describe how to adapt product design and business model strategies to a CE. CE activities require new business models in which one pursue the opportunities for a shift from an 'end-of-life' focus a cradle-to-cradle cycle, from using un-renewable energy towards using renewable, from using toxic chemicals to their elimination, from waste to eliminating waste by design of materials, products, systems and business models.

Implementing Industry 4.0 and a Circular Economy focus

In order to prepare a strategy for implementation of Industry 4.0 related technology and concepts, knowledge is needed regarding the present level of technology implementation, as well as barriers to implementing these. In this regard, previous studies have mainly focused on technological readiness and maturity levels [START_REF] Jaeger | IoT Technological Maturity Model and assessment of Norwegian manufacturing companies[END_REF][START_REF] Atzori | The Internet of Things: A survey[END_REF]. However, Industry 4.0 applies not only to the purely technical aspects, but involves the socio-technological systems, as intelligently networked objects in manufacturing is expected to constitute tasks distributed in time, space and content, and thereby change the content of work [START_REF] Bauer | Transforming to a Hyper-connected Society and Economy -Towards an "Industry 4.0[END_REF]. Hence, implementing Industry 4.0 concepts require not only implementing new technology and infrastructure, but to adopt a completely different organizational setup, and a set of processes that are different from those found in traditional manufacturing [START_REF] Slama | Enterprise IoT. 2016[END_REF]. How companies perceive these changes will affect how they approach and operationalize elements associated with Industry 4.0. These are aspects that have gained less attention in the literature so far [START_REF] Bharadwaj | Digital business strategy: Toward a next generation of insights[END_REF][START_REF] Davis | Smart manufacturing, manufacturing intelligence and demanddynamic performance[END_REF]. Consequently, in this study we focus on the organizational knowledge related to the implementation and barriers for implementation of Industry 4.0. Furthermore, the commitment for sustainable growth includes a switch towards CE, which puts forward a need for innovative business models that keep business activities profitable while implementing new practices adhering to the CE.

Companies need to develop knowledge on how to operationalize activities in a manner adhering to CE and to develop organizational knowledge of barriers for moving in this direction. We address this by the research question: What barriers do manufacturing companies see in moving towards Industry 4.0 with a circular economy focus? Relatively little literature on barriers towards CE adoption has focused on the manufacturing industries. This study aims at contributing to fill this gap in the literature.

Methodology

Two approaches were used to answer the research questions. First, structured interviews provided an indication of the general level of understanding of a set of manufacturers, while a case study of one company gave in-depth knowledge of their approach towards Industry 4.0. Structured interviews (N=23) were carried out in a cluster of small and medium sized companies representing different industries, but with short geographical, social and cognitive proximity to each other. At the time of the survey, the cluster consisted of 31 members, covering manufacturing companies, service and engineering companies. For CE barriers, we followed a qualitative approach method using data from semi-structured interviews. We interviewed two manufacturing companies, and a consortium of all manufacturing companies (and other companies) supporting product identification, labelling and information flow. The consortium work closely with the manufacturers in their move towards CE with appropriate standards, giving them a first-hand knowledge of barriers for CE adoption for the manufacturing industry as a whole.

Findings

Conceptualization of the term "Industry 4.0"

The structured interviews revealed that all except of one the companies had heard about the term, and that the companies emphasize the Industry 4.0 concept differently. The answers could be grouped into three main categories; 1) new business models, 2) more efficient and automated manufacturing internally to the company, and 3) the value of localization. A large share of the respondents [START_REF] Zhu | Persistent performance and interaction effects in sequential cross-border mergers and acquisitions[END_REF] emphasized the possibility to improve the efficiency of manufacturing processes. Fewer (5) mentioned the possibility to develop new services, but of the ones that did, they mainly related to monitoring and maintenance. These kinds of services involve sharing of data, which involves integration with external companies. We noted that none of the companies mentioned integration with their own suppliers and the use of track and trace technology as important for their main understanding of the concept of "Industry 4.0". However, this does not rule out that the companies also are concerned about this aspect. Several respondents had no clear definition of the concept "Industry 4.0, which was reflected in more general terms as "we need to follow the development in the industrial world", and "this opens up a new universe". One of these respondents expressed concern about how this could alter the competition, and was uncertain how to approach this. Finally, a respondent was skeptical to the whole concept and claimed "4.0 is only a buzzword used by many nowadays".

Barriers for implementation of new technology related to Industry 4.0

In the study, the companies were given a set of possible barriers that they could rank. These were costs, lack of competence, loss of jobs, process not suitable for Industry 4.0, product not suitable, and production facilities not suitable for Industry 4.0. Ranging from great, medium low, and not relevant. Compared to the motives, the barriers were considered less important than the motives. The factor that was identified as the most important barrier for implementing Industry 4.0, was the cost associated with the investment in new technology. This factor was also reflected in the open comments, where it was emphasized that for small and medium sized companies, the cost is the main challenge for implementing new technology. Another argument here was that the technology needed to be developed and commercially available before the company could invest, as it is too expensive to be a first adopter. One of the companies expressed that they were not able to invest in new technology, as they were a subsidiary of a large multinational company (MNC), who made the investment decisions.

Competence was the second barrier that was identified as being of above medium importance. Less important, but above medium, was that the product was not appropriate for Industry 4.0. This was also reflected in the open part of the questions, were the respondent could express barriers that were not part of the predefined motives. Here standardization was mentioned as a barrier. Related to this, one of the respondents responded that the low volume of the manufactured product made it less suitable for industry 4.0. We note that this might be based on the presumption that automation is associated with repetitive actions in mass production. However, an important advantage of Industry 4.0 is exactly that the use of technologies support customization and oneof-a kind production (mass-customization). Supporting customer-oriented flexible markets is an advantage of industry 4.0 that promise to help in reducing the gap between the manufacturer and the customer. Other barriers ranked below medium, were loss of jobs, and that the process and production facilities were not considered appropriate for Industry 4.0.

Barriers for a move towards Circular Economy

Based on our findings, the manufacturing companies have special attention towards sustainability, including actions on regeneration of resources, charging customers a recycling fee that is transferred to recycling funds to handle recycling and adopting certifications with ISO-standards ensuring they comply with the rules and regulations of the environmental management system. The following six barriers to CE adoption were identified by the companies: 1) Disassembly of products. The disassembly of products is considered to be an important element as most products have some amount of disassembly that is not just the reverse process of assembly due to elements like irreversible joints, maintenance, and up gradation and degradation during use. All the respondents said that the disassembly of products is not easy and it expensive and time-consuming because of complex nature of products. 2) Supply chain complexities. The complexity barrier results from an increase in the number of components of different nature with complex assembling procedures.

3) Coordination problem. The coordination among companies is a barrier because it needs multiple companies to adjust their daily operations. 4) Lack of CE in design and production. The products that are produced lack a circular design which is the reason the production and the reusing, disassembly, remanufacturing etc. is hard. 5) Quality issues. Companies' reluctant attitude towards CE is their concern regarding the quality of materials. They fear materials would be chosen based on the environmental aspects instead of quality of performance. 6) High start-up costs. In the long run the CE model is assumed to bring sustainable benefits and increase growth but in the short run, the costs are considered high.

Discussion and conclusion

The findings in this study reflect that the companies put different meaning into the concept "Industry 4.0". A central aspect of Industry 4.0 and the associated concepts is connectivity across traditional domains of humans, machines and products. From the structured interview as well as the in-depth case studies, it is clear that this issue is not very prominent among the interviewed companies.

When exploring how the companies in the study perceive the concept Industry 4.0, the connectivity aspect was only mentioned in association with developing new business models, based on data collected from sensors on sold products within monitoring and maintenance. Hence, down-stream integration is mainly considered as an opportunity to create new value propositions to customers, while integration of machines, products and humans across the value chain this has not been emphasized.

When it comes to enhancing efficiency, the companies are primarily concerned about the potential for internal improvements in the company and for the case company within certain departments, and to a lesser extent improvements in external supply chains through connectivity with other companies.

The study reflects the inherent duality or ambiguity of the Industry 4.0 concept, in that it represents both innovation (exploration) and efficiency (exploitation) [START_REF] O'reilly | Organizational ambidexterity in action: How managers explore and exploit[END_REF]. New business models involve radical and sometimes disruptive innovation, which is associated with exploration, while increasing efficiency of manufacturing reflects exploitation of existing assets. This may create a tension that needs to be handled by the management of each company.

Regarding what barriers manufacturing companies see in implementing Industry 4.0 technologies, the highest ranked factors were the cost associated with the investment in new technology, as well as the concern that the technology was widely available and not on the development stage. Competence was identified as a barrier, as the workforce need to adopt to new ways of executing processes involving a more intensified digital involvement. Also, with above medium score was the perception that their product and/or the production environment was not appropriate for Industry 4.0. The other barriers with below medium score were fear of loss of jobs, and a lack of standardization. Thus, Industry 4.0 is well known to most companies at a general conceptual level. Especially, the ambition to exploit Industry 4.0 technologies is high among top management. However, implementations of Industry 4.0 technologies at the tactical and operational level are harder to achieve. The insight provided by this study, shows that the Industry 4.0 represents a complex and unclear concept to many companies, and that there is a need for a roadmap for implementation of these concepts, starting with a strategic process of what to deliver to whom, leading towards a culture change at the tactical and operational level. As such, this study offers empirical insights providing directions for future research on what factors to address in a roadmap.

By carrying out case studies of three Norwegian companies, we found that the major barriers for implementation of CE were a quality issue in recycled materials, supply chain complexities, coordination problem between companies, design and production of the product, disassembly of products, and high start-up cost. Our findings show that the companies interviewed are well aware of the challenges of moving towards the CE, but that the link between digitalization and CE needs further exploration. A shift to the CE model or any other business models for sustainability of the economy requires a dramatic change for the whole company including all the stakeholders. This shift is somewhat disruptive in nature because the current mode of working would also be changed due to the new solution of the CE model.

As companies need to address both Industry 4.0 and CE barriers, it is important to investigate how these barriers relate to each other. Even if our data were collected separately using different methodological approaches for the two topics, we find that cost is a common barrier, as was expected from the literature [START_REF] Ritzén | Barriers to the Circular Economy -Integration of Perspectives and Domains[END_REF][START_REF] Kamble | Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry[END_REF]. For the remaining barriers, a different methodical approach would be needed to find possible connections. However, we contend that from the perspective of a viable business model of a company, Industry 4.0 and CE are convergent, since both strongly depend on digitalization to succeed. Digitization is at the very core of Industry 4.0, and for CE, a profitable model depends on detailed planning and control for each individual product. Since each individual product has its own life cycle, each unique product must be managed across its entire unique life cycle, which in practice is only achievable through digitalization. In conclusion, our findings show that the companies interviewed are well aware of the challenges of moving towards Industry 4.0 and CE, and through our discussion we find companies should put their attention towards digitalization and individualization as these are fundamental properties of emerging business models.


Martina Schiffer
email: martina.schiffer@ipa.fraunhofer.de

Hans-Hermann Wiendahl

Benedikt Saretz

Self-Assessment of Industry 4.0 Technologies in Intralogistics for SME's

Keywords: Intralogistics, Industry 4.0, Cyber physical systems, Smart factory

The 4 th industrial revolution generates a high potential for smart production systems. Many manufacturing companies considering therefore the application of cyber-physical systems in the sector of intralogistics. The aim is to achieve better logistics performance or lower costs. However, small and medium sized enterprises (SME) are hesitant about introducing Industry 4.0 technologies. They fear high implementation costs, low benefits and the lack of know-how increases the reluctance of the companies. This paper presents a procedure which enables SME's to assess the benefits of Industry 4.0 technologies by themselves. The model follows the recognized principle: First improve your processes, then automate them: • Methodical basis is a process model intralogistics, which also considers selfcontrolling cyber-physical systems. In addition, the benefit aspects are assigned to the individual process steps. • In the specific application, the company first determines the digitization potential of the individual activities and then the associated benefits of Industry 4.0 technologies. The procedure reduces on the one hand the uncertainty regarding of wrong decisions and creates on the other hand the possibility for companies to select Industry 4.0 technologies goal-oriented. The described procedure was validated with SMEs.

Introduction

Production companies are in a constant state of change. Comparable service offers with regard to functionality, quality and price of the products bring logistics services, such as short delivery times, to the fore as a competitive factor. The introduction of Industry 4.0 Technologies, in particular cyber-physical systems (CPS), is seen as a possible solution to the requirements of the market. Most of the companies examine the planning and evaluation of Industry 4.0 Technologies in value-added processes [START_REF] Dombrowski | Prozessorientierte Potentialanalyse von Industrie 4.0-Technologien[END_REF][START_REF] Schönmann | Planung und Bewertung von Produktionstechnologien[END_REF]. The ad-joining areas, such as Intralogistics, have received little attention. But the implementation of Industry 4.0 Technologies in this area hides a high potential. On the one hand, Intralogistics secures the flow of materials and information within the company and, on the other hand, enables a successful supply chain [START_REF] Fördern+heben | Gut gewartet, hält langer -Funktionssicherheit mit Life-Cycle-Plänen langfristig erhalten[END_REF]. Despite this high potential, many companies don't invest in Industry 4.0 Technologies. In particular, small and mediumsized enterprises (SME's) have deficits in planning and implementation. [START_REF] Wang | A holistic approach for introducing the strategic initiative Industrie 4.0[END_REF] The reasons are the high introduction costs, the missing know-how of the companies as well as the non-evaluable benefits of Industry 4.0 Technologies. The contribution of this paper is a two-step process which enables SME's to assess the benefits of Industry 4.0 Technologies by themselves. The procedure was validated with SME's in workshops. The procedure was profitably developed within the framework of the research project Industry 4.0.

Related Works

Intralogistics is responsible for the material and information flow between the value creation steps within a company. The use of cyber physical systems can realize great improvement potentials along the internal operational processes. Cyber physical systems can communicate with each other via the Internet and record their environment with their sensors. The generated data are evaluated, linked and used for the control of corresponding actuators. The result is a decentralized network that can optimize itself and counteract problems along the entire value creation process. CPS technologies offer great advantages especially for SMEs, which often produce small series or individual products. Through intelligent linkage of the material flow a flexible and fast reaction factory is realized [START_REF] Bauernhansl | Industrie 4.0 in Produktion, Automatisierung und Logistik[END_REF][START_REF] Hausladen | IT-gestützte Logistik. Systeme-Prozesse-Anwendungen[END_REF][START_REF] Ludwig | Arbeiten im Mittelstand 4.0 -KMU im Spannungsfeld des digitalen Wandels[END_REF].

For the introduction of CPS Technologies an economical evaluation is necessary. A benefit or potential analysis is especially important for SMEs with tighter budgets as large enterprises. A quantification of the potential of a certain CPS technology is however very difficult due to the cross-sectional function of logistics. In the literature different evaluation methods are called dependent on the problem definition and the area of application. Consequently, a uniform procedure for the potential analysis of CPS technologies for use in intralogistics has not yet been defined [START_REF] Hausladen | IT-gestützte Logistik. Systeme-Prozesse-Anwendungen[END_REF][START_REF] Saam | Digitalisierung im Mittelstand: Status Quo, aktuelle Entwicklungen und Herausforderungen[END_REF].

Methodic Procedure

The process model intralogistics build the methodical basis of the procedure. A (process)-model is a reflection of the reality which, through abstraction and simplification, provide conclusions about states, changes and functional relationships [START_REF] Karer | Optimale Prozessorganisation im IT-Management. Ein Prozessmodell für die Praxis[END_REF][START_REF]VDI3633: Simulation von Logistik-, Materiafluss-und Produktionssystemen-Grundlagen[END_REF]. The requirement criteria for the process model intralogistics are simplicity, completeness and intuitive presentation. Thus, the SME-suitability is on the one hand granted as the adaptability on larger enterprises is possible.

The model subdivides six process modules:

• Incoming goods • Internal transport • Storage • Order picking • Packaging • Outgoing goods
The object of consideration is the characteristics of the material and information flow, the used resources and the relevant data for planning and scheduling. Fig. 1 shows the generic process model. The model follows the process sequences preparation, implementation and completion of general process and action models [START_REF] Ballmer | Deutsche Verben. Eine sprachanalytische Untersuchung des Deutschen Verbwortschatzes[END_REF]. The process sequences apply to information and material flow level and give the model a clear structure.

Fig. 1. Generic Process Model

Process activities describe the respective process module in a sequential order. This applies both, information and material flow, levels. The preparation phase includes the steps 'Create order', 'Accept order' and 'Release order' on the information flow level.

After each activity a result must take place, which represents the possibility of a systemtechnical illustration. The trigger arrow forms the interface between information and material flow. The phase implementation starts only with the order release and the physical availability of the material on the input buffer area. In the implementation phase, the 'Start order' and 'Monitor order' activities are at the information flow level. Both activities reflect the progress of the order on the one hand and provide on the other the data basis for calculating the lead time. At the material flow level, implementation begins with the transfer of the performance object (PO) from the buffer area. Generically, the two activities 'take on PO' and 'transfer PO' take place within the model. Specific process-module activities are possible, e. g. 'Check PO' in incoming goods-process.

In order to consider the above-mentioned requirement criteria, assumptions and application limits of the process model must be drawn. The intralogistics activities of SME's and the associated interfaces to extra logistics are the object of consideration. The model is be subject to the following assumptions:

Information flow level

• Rework or cancellation orders are not taken into account • Generation of demand is prerequisite

Material flow level

• Within the six process modules transport is neglected • Complete and error-free order processing • The input and output buffer area is a defined transfer point for the upstream and downstream processes

Based on the generic process model, a detailed process module refinement was developed, see Fig. 2:

Fig. 2. Detailed Process Model (cut-out)
The detailed process module reflects the structure of the generic model. Only variants of the activities, sub-activities and variants of the sub-activities were created. The characteristics show the possible attributes of the respective activity. The color differentiation indicates whether the values are additive or alternative.

The potential assessment aims to make the benefits of Industry 4.0 Technologies in intralogistics transparent. A multi-stage procedure is recommended for estimating the potential, see Fig. 3. In the first step, the digitization potential of the individual intralogistics activities is determined. The potential is identified with the help of the four target dimensions variability, quality, velocity and effort [START_REF] Erlach | Wertstromdesign. 2nd edn[END_REF]. In order to take full account of the potential of Industry 4.0 Technologies, transparency is added as a fifth dimension. To ensure that corporate strategy and goals can be taken into account when evaluating the digitization potential, it's possible and recommended to prioritize the dimensions.

[12] Furthermore, there are conflicting objectives between the dimensions which must be taken into account when determining the digitization potential [START_REF] Ward | Configurations of Manufacturing Strategy, Business Strategy, Enviroment and Structure[END_REF]. The step of determining the digitization potential enables the company to make a strategically correct selection of the processes to be digitized.

The degree of detail of the target dimensions is not sufficient for estimating the potential of Industry 4.0 Technologies. Therefore, in the next step KPI's were defined on the basis of the target dimensions, see Fig. 3. The calculation of these KPI's does not deviate from the commonly used one in the literature, so that no further definition is given here. The potential estimation is carried out with the values low, medium and high.

As with the determination of the digitization potential, the conflicts of objectives of the KPIs must also be taken into account when estimating the potential of Industry 4.0 Technologies. The process model forms the basis of the potential estimation. Possible Industry 4.0 Technologies were assigned to the activities of the process model. The assignment of the technologies as well as the estimation of potential took place with the help of experts from research and industry.

Results / Validation

The validation of the methodical approach took place at two medium-sized factory equipment suppliers during a workshop. Within the scope of the workshop the requirements for the applicability of the procedure in SME's were to be examined. Criteria for this included comprehensibility, extensibility, survey effort and consistency. The procedure for the self-assessment of Industry 4.0 Technologies was essentially confirmed.

In particular, the approach of optimizing the processes first and then digitizing them met with approval. For example, the process model revealed gaps in process responsibility and thus provided initial fields of action for process improvement. The procedure for determining the digitization potential and the benefit potential of Industry 4.0 Technologies was confirmed under the aspects of SME suitability. Three extension requests were mentioned by the participating companies:

Possible combination of the process modules

It was noted that in SME's, several process modules are often processed together, e.g. combined picking and transport orders. Fig. 4 shows the possible combinations of the individual process modules. For a better understanding the production / assembly has been included in this overview. The internal transport is shown in this diagram between the process modules.

Extension of the KPI's by soft factors

In the course of the potential assessment, participants would note that Industry 4.0 Technologies are not only being introduced to improve process capability. Especially for SME's the external impact is very important. On the one hand towards the customer but also towards potential employees. SME's often have the problem to find suitable personnel. To be seen as an innovative and sustainable company, companies should also invest in new technologies because of these aspects. To this end, the potential assessment could be supplemented by KPI's such as degree of innovation and employee motivation.

Extension of the model by life cycle costing

Not only the estimation of potential is relevant for the introduction of Industry 4.0 Technologies. As already mentioned, SME's fear the introduction of such technologies. These results are on the one hand from the not recognizable benefit and on the other hand from the high costs. In order to have a comprehensive picture for the decision, the procedure should be extended by the life cycle costing. The extension of the life cycle costing in relation to Industry 4.0 Technologies is also currently part of the research project Industry 4.0 profitable.

Conclusion

Industry 4.0 Technologies will gain in strategic importance in the future. SME's in particular should not lose their connectivity with regard to digitalization. Nevertheless, the approach is: First improve your processes and then digitize them. The step-by-step approach in this article supports SME's in improving intralogistics processes and in making decisions about these Industry 4.0 Technologies. After the process improvement the procedure gives a decision assistance which process step the company should digitize first and afterwards with the help of which Industry 4.0 Technology. The results of the validation show that the procedure appears conclusive and plausible. The extensions to the procedure requested by the companies are currently being examined and subsequently taken into account in the model.

Fig. 3 :

 3 Fig. 3: Procedure for Potential Assessment

Fig. 4 :

 4 Fig. 4: Possible combination of the process modules

Step 1: Process analysis and -improvement of Intralogistic processes

	Incoming goods	Internal transport	Storage	Order picking	Packaging	Outgoing goods
	Step 2: Prioritisation target dimensions for digitization potential
	Variability	Quality		Velocity	Effort	Transparency
	Step 3:

Prioritization of KPI's for potential assessment Industry 4.0 technologies

	1	2		3			4				6
	Quantity flexibility Variants flexibility 1 1	Throughput time deviation 2	2	delivery Adherence to	2	Reject	Throughput time 3
	3	4		4			5	Time expenditure	5
	Turnover rate	Time expenditure	Cost expenditure		for information	Statement reliability
									acquisition
	Step 4: Potential-oriented allocation of Industry 4.0 Technologies
	CPS 1	CPS 2	CPS 3	CPS 4	CPS 5		CPS 6	CPS 7	CPS 8	CPS 9	CPS 10

Acknowledgements

The IGF project 19183 N of the Forschungsvereinigung Bundesvereinigung Logistik (BVL) e.V., Schlachte 31, 28195 Bremen was funded by the AiF within the framework of the program for the promotion of joint industrial research (IGF) of Federal Ministry of Economics and Energy by resolution of the German Bundestag.


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

Malte Brettel²
email: brettel@time.rwth-aachen.de

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

Andreas Gützlaff
email: a.guetzlaff@wzl.rwth-aachen.de

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

Katharina Thomas
email: k.thomas@wzl.rwth-aachen.de

Mario Piel²
email: piel@time.rwth-aachen.de

From a theory of production to data-based business models

Keywords: Theory of production, business model, global production network

Producing companies are challenged by competition in global markets, in which customers have in general a strong negotiation position. In order to improve their competitive situation, companies constantly attempt to decrease production costs. However on the one hand, it can be observed that companies often do not only have an issue in decreasing their production costs but also in the determination of current production costs as basis for improvements. On the other hand, producing companies face an increasing volume of production data in the course of Industrie 4.0. This data is expected to be potentially usable as additional sales asset. Yet, especially traditional companies do not know how to translate generated production data into incoming cash flows. In order to tackle both named issues, this paper presents both an overview of data-based business models for producing companies and a tool for increasing transparency of production costs in global production networks.

Introduction

In recent years, customers of producing companies demand increasingly individualized products at similar or even lower costs. In competitive markets companies have responded to this trend by increasing their product variants. Formerly cost-efficient oriented production configurations have at least partially been transformed into flexible job shop structures. [START_REF] Elmaraghy | Product variety management[END_REF] While decreasing the transparency of cost allocation to jobs and machines, the challenge of high quality but efficient cost calculations for new orders has increased. This challenge does not only affect local production sites but even more complete global production networks. [START_REF] Schuh | Global Footprint Design based on genetic algorithms[END_REF] However, due to global markets, that have developed from supplier to customer ones, cost pressure is another important influencing factor for producing companies. Thus, for decreasing costs an important first step is creating transparency of production costs in the production network.

In the last decade, the use of new production technologies, e.g. 3D printing, could widely be observed. [START_REF] Brecher | Integrative Production Technology[END_REF] In parallel, newly designed machines were equipped with numerous sensors, too. Not only digitization but also the connection of machines is a major trend that is commonly known as Industrie 4.0. [START_REF] Wang | A Data Driven Cycle Time Prediction With Feature Selection[END_REF] As a result, companies face a significant increase of production data in growing granularity, that have the potential not only to be used for increasing transparency but also to be sold as a good itself.

This paper aims to give guidance for producing companies in the identification of the right data-based business model for their need and to increase transparency of global production network costs as particular example of a data-based business model. The remainder of the paper is structured as follows. In section 2 a brief introduction to the research background of data-based business models is given. In section 3 different data-based business models are presented that have been researched in the past. As a particular example of one of the derived business models, the concept and implementation of a tool for increasing transparency of production costs in global production networks is presented in section 4. In section 5 the main findings are summarized.

Towards a theory of production

In the cluster of excellence "Integrative Production Technology for high-wage Countries" at RWTH Aachen University, research was conducted on developing a generic theory of production. This theory aimed to increase the assessability of individual, technological and managerial advances with respect to a company's profitability. The developed theory of production relies on a number of both cost and profit drivers.

Quantifying relevant parameters of those cost drivers supports the determination of different costs for newly designed products that can potentially be produced with different technologies. Comparing the advantageousness of the considered technologies, a profound and objective decision on the best alternative can be made. By doing so, the trade-off between efficiency and flexibility is objectified. [START_REF] Brecher | Integrative Production Technology[END_REF] Production research often focuses on quantifying cost drivers. Yet, researching profitability drivers of companies is an important objective, too. Therefore, an investigation of data-based business models that are built on or are supported by Industrie 4.0 has been conducted in the transition phase from the above mentioned cluster of excellence to the new cluster of excellence of RWTH Aachen University called "Internet of Production". Against this backdrop, the intention of this paper's underlying research is to provide answers to the question, on how digitized manufacturing firms can use production data to increase profits through business model innovation.

The results of this research are presented and validated in the following sections.

3 Data-based business models

Foundations of business models

Digitization revolutionizes the production industry with respect to new possibilities of collaboration among individuals, machines, and firms. In order to leverage these opportunities, the German federal government has adopted Industrie 4.0 as a key pillar of its Hightech Strategy 2020. [START_REF] Roth | Einführung und Umsetzung von Industrie 4.0[END_REF] In this context, Industrie 4.0 involves ICT-caused radical technological and organizational changes within the production industry. [START_REF]Industrie 4.0: Aachener Perspektiven[END_REF] A central element of Industrie 4.0 is the so-called "smart factory", which enables ICT-based, flexible, autonomous and self-optimizing production systems and networks. [START_REF] Lucke | Smart Factory[END_REF] Within smart factories processes, machines, products, and resources are represented via cyberphysical systems (CPS) and Industrial Internet of Things (IIoT) as a "digital twin". [START_REF] Schuh | Integration als Grundlage der digitalen Fabrikplanung[END_REF][START_REF] Shariatzadeh | Integration of Digital Factory with Smart Factory Based on Internet of Things[END_REF] Besides the advantages of smart factories (e.g. improved flexibility, productivity, and transparency), it also offers new opportunities with respect to data availability and use in in new business models. [START_REF] Yin | Big Data for Modern Industry: Challenges and Trends [Point of View[END_REF] Based on the value chain concept by Porter, the data value chain serves as a base for exploring monetization opportunities of production data. [START_REF] Porter | Competitive advantage: Creating and sustaining superior performance[END_REF] Referring to the information systems-based conceptualization that includes a hierarchical link of data, information, knowledge, and wisdom (DIKW), corresponding management activities for the four phases, and analytical methods that enable the transition between the phases, it is proposed that firms can directly generate value from production data in all four phases (Fig. 1). [START_REF] Friedli | Industrie 4.0 -ein Beitrag zur Entwicklung von "Smart Networks[END_REF][START_REF] Rowley | The wisdom hierarchy: Representations of the DIKW hierarchy[END_REF][START_REF] Tempich | Turning data into profit success factors in data-centric business models[END_REF] Actual monetization of production data can be achieved through (innovation of) data-based business models. Fig. 1. Data value chain [START_REF] Friedli | Industrie 4.0 -ein Beitrag zur Entwicklung von "Smart Networks[END_REF][START_REF] Rowley | The wisdom hierarchy: Representations of the DIKW hierarchy[END_REF][START_REF] Tempich | Turning data into profit success factors in data-centric business models[END_REF] As shown in Table 1, Osterwalder & Pigneur [START_REF] Osterwalder | Business model generation: A handbook for visionaries, game changers, and challengers[END_REF] and Holm et al. [START_REF] Holm | Openness in innovation and business models: Lessons from the newspaper industry[END_REF] conceptualize business models via four dimensions, i.e. value proposition, value delivery, value creation, value capture. These, in turn, involve nine components, i.e. value proposition, customer segments, channels, customer relationships, key resources, key activities, key partnerships, revenue streams, and cost structure.

Casadesus-Masanell & Zhu [START_REF] Casadesus-Masanell | Business model innovation and competitive imitation[END_REF] describe business model innovation (BMI) as a change of key elements of organizations and their business logic to generate additional value for stakeholders. As for production data, existing or new data can be used to generate new business models or improve existing ones to make them fully data-based.

Decision-making ability

'What can be done?'

Past & Real-Time Future [START_REF] Dinter | Big Data und Geschäftsmodell-Innovationen in der Praxis[END_REF] Data-based business models can be categorized according to the use of data, i.e. solely firm-internal use of data, sharing data with partners, trading data as a product, or making data available for free. [START_REF] Walker | From big data to big profits: Success with data and analytics[END_REF] Against this backdrop, a holistic and practice-oriented framework was developed that involves production data-based, innovative business models for the production industry in high-wage countries.

Value creation

Key resources

Key resources are the assets required to offer and deliver previously described elements.

Key activities

Number of key activities performed by key resources.

Key partnerships

Some activities are outsourced and some resources are acquired outside the enterprise.

Value capture

Revenue streams

Revenue streams result from value propositions successfully offered to customers.

Cost structure

The BM elements result in the cost structure.

Framework for production data-based business models and innovations

As starting point for the development of a framework for production data-based business model types and innovation, a systematic literature analysis on keywords, such as "data-based business models", "data monetization" and "data strategy" has been conducted. Referring to Nickerson et al. the business model categories that evolved from the literature review have been structured and selected so that the remaining business model categories form a mutually exclusive and collectively exhaustive framework.

[20] Business models that are not applicable to production firms have been excluded. Besides, business models that are not based on proprietary data, but only use external data, have been excluded, too. The remaining business model types have been categorized by the authors across the data value chain in three core categories, i.e. "measure", "infuse", "apply" (first level in Fig. 2) and visualized in a framework that is presented in Fig. 2.

Fig. 2. Derived framework for data-based business models

Infuse entails data-based business model types that include DIKW in specific products, services or networks of products and services. By making products and services more engaging for customers, these business models enable firms to expand their revenue streams. [START_REF] Tempich | Turning data into profit success factors in data-centric business models[END_REF] Apply involves those business model types that apply knowledge and wisdom without combining it with existing products and services. Predictive and prescriptive data analytics enable firms to identify patterns, relations, and future outcomes. [START_REF] Auschitzky | How big data can improve manufacturing[END_REF][START_REF] Delen | Data, information and analytics as services[END_REF] Fig. 2 depicts the corresponding eight innovation paths (second level in Fig. 2) for digitized production firms that, in turn, are distributed in thirteen production data-based BMIs (third level in Fig. 2).

Validation

The business models have been evaluated using different criteria, i.e. implementation complexity, upside potential and downside risks. Within the proposed framework of data-based business models in section 3, the business model production network location consulting, as example of the innovation path "Consultancy" (business model type "apply" in Fig. 2), has been validated by developing and implementing a data-based online tool for designing production networks. Production network location consulting derives its competitive advantage over other consultancies by leveraging actual production data from smart factories. For this reason, the software demonstrator OptiWo has been developed at the Laboratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University with the goal of supporting product allocations and distributing value added in global networks. Nowadays, production network designers face the challenge of a globalized world with linked markets, calling for shorter planning cycles and with production networks demanding to be adapted more rapidly. Therefore, all production network decisions need to be based on viable data at a com-prehensive level of aggregation in order to make faster as well as more profound decisions in uncertain business environments. [START_REF] Schuh | Integration als Grundlage der digitalen Fabrikplanung[END_REF] Relevant data is spread over different IT systems, such as ERP, MES or PDM systems. The OptiWo tool creates value for the end user by streamlining planning and decision-making processes through focused visualization and precis and transparent dissection of production network interdependencies. Considering that designing production networks involves many uncertain influencing factors, the assessment of dependencies is crucial. Therefore, OptiWo takes into account different target values such as visualizing costs, delivery times and network risks to allow a profound network analysis.

The necessary prerequisite for using the tool is the availability of data on production sites, sales regions, demand quantities, production resources, processes and transport routes. The data can be entered directly in the tool at a freely selectable aggregation level. Therefore, the user can decide for each project which products, processes and cost types should be considered for network analysis. The OptiWo tool works based on a total landed costs approach, where all production costs of a product, including all transport costs and customs duties, are included. [START_REF] Reuter | An IT Driven Approach for Global Production Network Design[END_REF] Hence, fixed costs such as basis costs for production sites and depreciation costs as well as variable costs such as direct and indirect personnel, variable machining costs and purchases parts are taken into account. A major advantage of the tool is the freely selectable level of detail of the data acquisition and following the level of detail for network visualizations. At the same time, the tool supports the user through the process of data acquisition and displays the data requirements of the relevant areas in successive menu tabs.

After data collection, the tool summarizes the results of the network analyses in a "viewer" as shown in Fig. 3. The viewer offers several interactive visualizations where the user gets an overview of the existing production network as well as a deeper insight, if desired by several mouse over effects and access to additional information. In detail the named network target values costs, delivery times and network risks are visualized for each production site. Thus, the connection between these target values is visualized in a user-friendly way, which supports the user in understanding the impact of his decisions about product allocations and value added distribution. As a result, the user gets a holistic transparency about the performance of the production network and can derive specific needs for action to improve the network such as distributing value added or decreasing transport efforts.

Summary and outlook

The approach presented in this article has been developed to illustrate the potential of data-based business models in the field production management. In particular, the developed framework includes three core categories of business model types with eight corresponding innovation paths and thirteen specific production data-based BMIs. Thereby, the framework depicts the spectrum of strategic opportunities that producing firms can leverage to generate value by using proprietary production data. While the presented demonstrator tool OptiWo mainly focuses on increasing transparency over the existing distribution of value added in production networks, the next step will be to support the decision maker in proactive designing the production network by illustrating different network scenarios and the assessment of their impact on strategic targets. By supporting the network designer in creating different network scenarios, sensitivity analyses can be performed which is becoming increasingly important in uncertain business environments. Furthermore, the tool-based decision support should be developed further with regards of identifying an optimal interaction of data-based decision preparation and integrated expert knowledge. Therefore, different user groups should be surveyed to analyze the interaction with the tool and to evaluate the different tool features. Thus, a new way of strategic decision-making can be developed by autonomous decision preparation for enabling decision makers to focus on the value-adding part of decisions in designing future production structures.

Fig. 3 .

 3 Fig. 3. Visualization of transport relations, delivery times and costs in a production network

Table 1 .

 1 Business model components

	Meta-	Components	Description
	components
	Value	Value proposition	Gives an overall view of a company's bundle
	proposition		of products and services.
	Value	Customer segments An organization serves one or several cus-
	delivery		tomer segments.
		Channels	Value propositions are delivered to customers
			through communication, distribution, and
			sales channels.
		Customer relation-	Customer relationships are established and
		ships	maintained with each customer segment.

Acknowledgement

The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence "Integrative Production Technology for High-Wage Countries".


Casper Solheim Bojer

Iskra Dukovska-Popovska

Flemming Max Møller

Kenn Steger-Jensen

Retail Promotion Forecasting: A Comparison of Modern Approaches

Keywords: grocery retail, promotion forecasting, machine learning

Promotions at retailers are an effective marketing instrument, driving customers to stores, but their demand is particularly challenging to forecast due to limited historical data. Previous studies have proposed and evaluated different promotion forecasting methods at product level, such as linear regression methods and random trees. However, there is a lack of unified overview of the performance of the different methods due to differences in modeling choices and evaluation conditions across the literature. This paper adds to the methods the class of emerging techniques, based on ensembles of decision trees, and provides a comprehensive comparison of different methods on data from a Danish discount grocery chain for forecasting chain-level daily product demand during promotions with a four-week horizon. The evaluation shows that ensembles of decision trees are more accurate than methods such as penalized linear regression and regression trees, and that the ensembles of decision trees benefit from pooling and feature engineering.

Introduction

Grocery retail supply chains are facing pressure on margins because of the tougher competition, more demanding customers, and increasing focus on reduction of food waste. Managing the supply chain efficiently is important, particularly for perishables, as a mismatch of supply and demand leads to lost profit due to lost sales, markdowns or waste.

Forecasts at the product level are crucial for the alignment of supply and demand in the retail supply chain to ensure a smooth and timely flow of products. They are needed by manufacturers for planning of capacity and materials, and by retailers as input to the replenishment process at both stores and distribution centers. However, forecasting at the product level is challenging due to several characteristics of the retail environment, such as stockouts, intermittency and promotions [START_REF] Fildes | Retail forecasting: Research and practice[END_REF]. Promotions' demand is particularly challenging to forecast due to the often-limited history of similar promotions. Research has shown that stockouts are more frequent during periods of promotion [START_REF] Taylor | Retail on-shelf performance of advertised items: An assessment of supply chain effectiveness at the point of purchase[END_REF][START_REF] Corsten | Desperately seeking shelf availability: An examination of the extent, the causes, and the efforts to address retail out-of-stocks[END_REF], cementing that they pose a challenge for the supply chain.

Several researchers [START_REF] Cooper | PromoCast™: A new forecasting method for promotion planning[END_REF][START_REF] Ali | SKU demand forecasting in the presence of promotions[END_REF][START_REF] Ma | Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information[END_REF] have dealt with the challenge of forecasting promotions at product level. One of the widely used methods in retail, especially as a benchmark in the literature to justify the use of more complex methods, is the base-time-lift approach. This approach uses an estimate of the regular demand and adjusts for promotions using a multiplicative factor, the "lift" [START_REF] Fildes | Retail forecasting: Research and practice[END_REF]. Different methods that achieve greater predictive performance than the base-time-lift have been proposed, ranging from linear to nonlinear methods. However, the evaluation conditions for the proposed methods differ in terms of aggregation level, forecast horizon and available data, which are context specific. The methods also differ in terms of: (1) model scope, i.e. whether the model deals with forecasting of both regular and promotion demand, or promotion demand only, (2) the level of pooling, i.e. whether multiple SKUs at a given aggregation level are included in the same model, and (3) the variables constructed given the available data, also known as feature engineering. Most of the previous research only compares the proposed methods to the simple baseline method and thus it is unclear how the methods compare to each other under different conditions and modelling choices. This paper adds to the previously evaluated methods in literature a class of emerging techniques, based on ensembles of decision trees, and aims to provide a comprehensive comparison of different promotion forecasting methods, under different levels of pooling and feature engineering.

Background

The previous work in the area of product level promotion forecasting differs widely in their approaches and evaluation conditions.

A number of papers have evaluated ordinary least squares (OLS) linear regression models. Foekens et al. [START_REF] Foekens | A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation[END_REF] examined forecasting accuracy for weekly product demand at different aggregation and pooling levels using the log-linear SCAN*PRO model. The best performing model at both chain and market level in terms of mean absolute percentage error (MAPE) and median relative absolute error was the chain-specific storelevel SCAN*PRO model without weekly seasonality indicators. However, they did not include any other models in the comparison. Cooper et al. [START_REF] Cooper | PromoCast™: A new forecasting method for promotion planning[END_REF] presented a linear regression based model formulation for forecasting promotional product demand at store level. Information on price, advertising, display conditions, major events and historical performance of promotions were included. Additionally, products were categorized into slow movers or fast movers, while promotion events were categorized based on their duration with one model created for each combination of product and duration category. Thus, pooling was conducted with regards to stores and items. The authors find that the model is superior to using historical averages of matching display and advertising conditions in terms of forecast error measured in cases. Van Donselaar et al. [START_REF] Van Donselaar | Analysis and forecasting of demand during promotions for perishable items[END_REF] presented a linear-regression model for forecasting the lift factor at chain and product level including information on price, competitive information, advertising, display, baseline sales, weight and shelf life. They compare pooling at category level and one model for all categories. They find that the best pooling level differs by category and that the regression model outperforms a moving average of historical lift factors in terms of MAPE and root mean square error (RMSE). Huang et al. [START_REF] Huang | The value of competitive information in forecasting FMCG retail product sales and the variable selection problem[END_REF] examined forecasting weekly product demand at chain level considering competitive information using an autoregressive distributed lag (ADL) model, and not using pooling. They found that the ADL model incorporating competitive information is more accurate than the base-time-lift method using a variety of error measures, including mean absolute error (MAE) and MAPE.

A number of recent papers have dealt with more modern statistical or machine learning approaches, including penalized linear regression, decision tree models, support vector machines and neural networks. Ma et al. [START_REF] Ma | Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information[END_REF] considered the use of competitive information using a multi-stage LASSO model with an ADL formulation for forecasting weekly product demand at store level without the use of pooling. They found that the model improves upon base-time-lift and that including competitive information outside the product category only helps marginally in terms of a variety of error measures.

Gür Ali et al. [START_REF] Ali | SKU demand forecasting in the presence of promotions[END_REF] compared a variety of methods for forecasting weekly product demand at store level, including linear regression, support vector machines and regression trees, using MAE. Different pooling schemes were considered: one model for all observations, pooling by store and pooling by subgroup. Compared to the previously mentioned papers, they use a more data-driven approach with extensive feature engineering as is often seen in the machine learning community. They found that regression trees outperform the other models considerably, particularly during promotions. The best pooling scheme for the regression tree was one model for all observations, and the feature engineering improved forecast accuracy. A later study by the same lead author compared the aforementioned models as well as penalized linear regression and neural networks for forecasting both weekly and daily product demand at store level using MAE and MASE [START_REF] Ali | Driver moderator method for retail sales prediction[END_REF]. Pooling was conducted at subcategory level, as well as more extensive feature engineering. They found that regression trees and penalized linear regression show similar performance for weekly forecasting, whereas penalized linear regression is best at daily forecasting.

In addition to the published research, a retail forecasting competition was held by Ecuadorian grocery retail chain Corporacion Favorita. The challenge given was to forecast weekly product sales at store level given transaction data including historical sales and promotion indicators, but not price. The top five performers of the competitions used gradient boosted decision trees, neural networks or a combination of both.

To sum up, most of the studies conducted only evaluate their proposed models against simple baselines. In addition, it is difficult to get a unified overview due to differences in modeling choices and evaluation conditions. It is therefore unclear how these proposed models stack up against each other. Two exceptions are the studies conducted by Gür Ali et al. [START_REF] Ali | SKU demand forecasting in the presence of promotions[END_REF] and Gür Ali [START_REF] Ali | Driver moderator method for retail sales prediction[END_REF], which compare several different techniques of varying complexity. However, it remains unclear how large the performance gap is between regression trees and penalized linear regression, and under which conditions one outperforms the other. In addition, the studies do not include recent advances in the field of machine learning such as random forest [START_REF] Breiman | Random forests[END_REF] and gradient boosted decision trees (e.g. [START_REF] Chen | XGBoost: A scalable tree boosting system[END_REF]), which have shown great promise in forecasting competitions and are widely used by machine learning practitioners. In this study, a comparison of the different methods presented in literature will be conducted, including recent developments within the area of machine learning. The aim is to provide further evidence as to which models are most accurate for product level retail forecasting, and thereby also contribute to the question of whether non-linear models, specifically decision tree-based models, are superior to linear models and warrant the added complexity.

Method

The purpose of the paper is to compare and evaluate the main methods used for forecasting demand during promotion events at a daily product level. More specifically, the regression-based methods -ordinary least squares linear regression, penalized linear regression using LASSO and regression trees, are compared to modern machine learning methods based on ensembles of decision trees -random forest [START_REF] Breiman | Random forests[END_REF] and XGBoost [START_REF] Chen | XGBoost: A scalable tree boosting system[END_REF], which are non-linear. In addition, the historical average is used as benchmark. The historical average method simply forecasts the historical average under matching conditions of price and advertising, with price as the dominant condition in case of no full match. The fallback forecast in case of no match is simply a naïve forecast of the last promotion. Base-time-lift is not included as a benchmark, as it is not possible to use it for items not sold outside promotion periods.

The method comparison is conducted on promotional sales of fresh meat and fish from a large Danish discount grocery chain, as these items present a major challenge due to their perishable nature. A chain level forecasts four-weeks prior to a promotion are sent to the suppliers for creation of a shared plan for meeting promotion demand. Promotions primarily have a duration of one week, and a forecast is required at the daily level for the promotional period. The case company uses a relatively simple promotion strategy based on price discounts advertised in a weekly flyer and occasionally on TV & radio. Most products are promoted at two or three price points. The data available consists of aggregated POS data, product master data and promotion master data, including price and advertising information, for the period of January 2015 to November 2018. Information on display conditions were not available in the case company databases. Only data from promotional periods are used for fitting the models. The data is split into training, validation and test sets, where five weeks are used as the validation set to tune hyperparameters, and twenty weeks are used as the test set. A total of 152 SKUs are present in the test dataset, with 48 of the SKUs having less than five promotions in the training and validation period. These items have therefore not been possible to forecast using item level models, although the use of pooling allows for forecasting new products with few or no observations. We evaluate the forecasting accuracy on these SKUs separately to illuminate which method performs best for products with short promotion history and what accuracy can be achieved.

The methods are compared using the basic data set, and with feature engineering. The features constructed include competitive intensity information, historical averages of sales by product, category, promotion conditions etc., similar to the work of Gür Ali [START_REF] Ali | SKU demand forecasting in the presence of promotions[END_REF][START_REF] Ali | Driver moderator method for retail sales prediction[END_REF]. The feature engineering is not included for simple OLS regression, as it does not have any built-in variable selection method and hence will overfit. Instead, a model formulation similar to that of the SCAN*PRO model is used. In addition, we consider the models at various levels of pooling:  Full pooling, i.e. a single model used for forecasting all products  Category pooling i.e. one model per category  Subcategory pooling, i.e. one model per subcategory  No pooling, i.e. one model per product.

The simple OLS model is considered with no pooling and subcategory pooling as any higher pooling level is likely to produce bias. The decision tree-based models are evaluated with full pooling and with category pooling, since these methods generally need a larger sample size to be effective. This is in line with Gür Ali et al. [START_REF] Ali | SKU demand forecasting in the presence of promotions[END_REF] that found that including all observations for the regression tree improved performance significantly. Table 1 summarizes the models considered in the evaluation. The methods are evaluated using time series cross-validation also known as rolling origin evaluation [START_REF] Tashman | Out-of-sample tests of forecasting accuracy: An analysis and review[END_REF]. At each time step of one week the model is fitted and a four-week ahead forecast is created for the promotion event. For the hyperparameter tuning, the model is not refitted due to the large computational demands. The forecast accuracy is evaluated in terms of forecast error magnitude and bias using both scale-dependent and scale-independent measures. The volume weighted MAPE (WMAPE) is chosen as it is scale-independent and stable with zero values, while the RMSE is chosen as it is widely used. The mean error (ME) and a mean-scaled version are used to evaluate forecast error bias. The evaluation is carried out in the statistical computing language R [START_REF] Core | R: A language and environment for statistical computing[END_REF].

Table 1. Models considered in the evaluation including dataset used and pooling strategy. Nevaluated using basic dataset, B -evaluated using both basic dataset and with feature engineering.

Results

The forecasting accuracy of the best combination of dataset preparation and pooling for each of the evaluated methods for SKUs with at least five historical promotions can be seen in Table 2, whereas the forecast accuracy for newly introduced SKUs are presented in Table 3. From Table 2, it is clear that the best method for SKUs with historical information in terms of all measures of error magnitude is XGBoost with category-level pooling and feature engineering. The results are in general dominated by XGBoost and random forest, but XGBoost is slightly biased, whereas the random forest has lower bias at a very small decrease in accuracy. The regression trees and OLS models perform significantly worse, not managing to beat out the historical average method. In the middle of the performance spectrum lies LASSO, which is best without feature engineering and at the subcategory level.

From Table 3, it is clear that the LASSO is the best performer for new SKUs, with no feature engineering and category level pooling coming out on top. The WMAPE and bias is much higher for the new items in general, with the WMAPE of 33.1% for the best model, compared to 16.2% for the SKUs with longer history of promotions.

Overall, the results show that the modern machine learning approaches outperform the historical averages, OLS, LASSO and regression trees given that there is more than five historical promotions available for the SKUs being forecasted. In addition, feature engineering and either category or full pooling improves the performance of these methods.

Discussion

The results are impacted by both the forecasting conditions and the modeling choices used in the evaluation. We hypothesize that the modern machine learning methods outperform linear models in situations with strong patterns, interaction effects and a large relevant sample. The daily aggregation level has the effect of increasing the sample size, but also presents more noise than at the weekly level, whereas the chain aggregation level has the opposite effect. Our findings indicate that at this particular aggregation level the sample size and pattern strength is large enough to make the machine learning models superior to linear models. It is difficult to compare the findings to the findings of Gür Ali [START_REF] Ali | Driver moderator method for retail sales prediction[END_REF], as they look at forecasting store level demand and do not include ensembles of decision trees. Contrary to previous research, our results show that for this case, linear regression and regression trees are surpassed by a simple benchmark method: historical averages under matching conditions. This benchmark method has to our knowledge only been used by Cooper et. al. [START_REF] Cooper | PromoCast™: A new forecasting method for promotion planning[END_REF], but its strong performance suggests that it should be considered when evaluating forecast accuracy for promotions.

Pooling in general proved useful, as it improves performance for all models except for OLS. This underlines that patterns exist across products, and that the models can effectively use these. The linear models seem to benefit less from pooling, and subcategory level seems to be the best trade-off between bias and variance in coefficient estimates, whereas the decision tree-based models favor more pooling, even performing well with one model for all fresh meat and fish products. This is likely due to the nature of decision trees, as they can effectively choose between pooling and no pooling where appropriate. The feature engineering conducted benefitted the decision tree-based models and led to greater forecast accuracy, particularly for the random forest model. This was not the case for the linear models, which could be due to non-linear relationships between the created variables or high correlations. It is therefore plausible that feature engineering aimed specifically at linear models would have improved their performance. However, this would also demand greater effort on feature engineering caused by the more restrictive nature of linear models.

For the forecasting of products with limited demand history, the results indicate that while the models can provide forecasts for these products, they are not very accurate and it is likely that a judgmental forecast by a category manager can provide better or at least similar accuracy.

Conclusion

The comparison of methods for forecasting product level promotion demand found that modern machine learning methods in the form of ensembles of decision trees outperform previously proposed methods such as linear regression, penalized linear regression and regression trees for SKUs with more than five historical promotions on the task of forecasting chain-level daily demand. For SKUs with less promotion history, penalized linear regression is the best performer, although all of the methods have relatively high forecast errors. In addition, the comparison found that the ensembles of decision trees benefit from both feature engineering and pooling, either in the form of category level or full pooling, whereas the linear models perform better without feature engineering at subcategory level. The implications of these findings are that ensembles of decision trees should be considered candidates for forecasting product level promotion demand at daily chain-level. An interesting area for further research is whether also holds at weekly chain level, where the sample size is reduced by a factor of seven, or at weekly store level, where the sample size is increased significantly at the expense of much greater noise. Limitations of the study include that the results are based on one case only with one particular forecast horizon. A shorter horizon could potentially change the results, as lagged sales thus becomes a valuable form of information not currently considered. In addition, we only use promotional data to fit the model, and it is possible that the linear models particularly can benefit from non-promotional data to obtain better estimates of seasonality, making this a topic worthy of further research.

Table 2 .

 2 Best forecast accuracy for each method on SKUs with historical information.

	Method	Dataset	Pooling	WMAPE RMSE ME Scaled ME
	XGB	With	Category	0.162	1488	363 0.077
	RF	With	Full	0.168	1578	174 0.037
	LASSO	Without	Subcategory 0.182	1702	160 0.034
	Hist. Avg. Without	None	0.189	1692	164 0.036
	RT	With	Category	0.229	2076	90	0.019
	LM	Without	None	0.249	2601	395 0.084

Table 3 .

 3 Best forecast accuracy for each method on SKUs with few historical promotions.

	Method	Dataset Pooling	WMAPE RMSE	ME	Scaled ME
	LASSO	Without Category	0.331	522	118	0.125
	RF	Without Full	0.399	588	-129 -0.137
	XGB	With	Full	0.420	631	-60	-0.063
	RT	Without Category	0.451	716	-112 -0.119