Marley Toloi
email: marley.toloi@gmail.com

Rodrigo Carlo Toloi

Helton Silva

João Reis

Silvia Helena Bonilla

Marley Nunes

Vituri Toloi

Helton Raimundo

Oliveira Silva

João Gilberto Mendes Dos Reis

CO2 Gas Emissions of Soybean Production and Transportation in the Different Macro-regions of Mato Grosso State -Brazil

Keywords: Soybean, Transport, Emissions of greenhouse gases

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Currently, the international market requires sustainable practices to minimize environmental impacts and reduce emissions of greenhouse gases (GHGs) [START_REF] Raucci | Greenhouse gas assessment of Brazilian soybean production: a case study of Mato Grosso State[END_REF][START_REF] Phélinas | Is GM Soybean Cultivation in Argentina Sustainable?[END_REF]. The Brazilian government launched in 2010 the program "Low Carbon Plan in Agriculture" coordinated by the Ministry of Agriculture, Livestock and Food Supply [START_REF] Mapa | Pecuária e Abastecimento: Plano ABC -Agricultura de Baixa Emissão de Carbono[END_REF], regarding the reduction of carbon emissions.

Anthropogenic activities with intensive use of fossil fuels to support globalization, production and consumption lead to the emission of greenhouse gases. The sectors that most contribute to this increase in emissions are agriculture and transport, as highlighted by the Intergovernmental Panel on Climate Change [START_REF]Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF].

Brazil is one of the largest soybean producers in the world and the largest exporter. Soybean supply chain is not only devoted to human and animal food products but also it is involved in biodiesel production by supplying the main source of raw material [START_REF] Da Silva César | Competitiveness analysis of "social soybeans" in biodiesel production in Brazil[END_REF].

Mato Grosso which is located in the Midwest, stands out in the brazilian scenario for high grain production, and it is expected to reach 32 million tons (30.8% of the national soybean production), [6,[START_REF] Conab | Séries Historicas de Safras[END_REF]. According to the Institute of Agricultural Economy of the State of Mato Grosso [START_REF]IMEA: Agronegócio no Brasil e em Mato Grosso[END_REF], 59% of the production is destined to the foreign market.

With an average distance of 2,000 kilometers for the main Brazilian ports, the State faces logistical problems (transportation costs, environmental impacts) [START_REF] Fernando | Logistics and storage of soybean in Brazil[END_REF][START_REF] Lima | A logística de transportes do agronegócio em Mato Grosso (Brasil)[END_REF][START_REF] Kussano | Custos logísticos agroindustriais: avaliação do escoamento da soja em grão do Mato Grosso para o mercado externo[END_REF]. The Port of Santos/SP represents 45.34% of exports and Porto de Barcarena/PA, 20.02%. Therefore, the ports of Arco Norte are a new alternative for the flow of production from the north of the country, according to the National Agency of Waterway Transportation [START_REF] Antaq | Aumenta participação do Arco Norte no escoamento da soja e do milho para exportação[END_REF].

The objective of this study is to calculate the emissions of the main sources of GHG in soybean cultivation and transport in the macro-regions of Mato Grosso, pointing out the main routes and modal integration for the Brazilian ports.

2

Materials and Method

Adopted Method

Life Cycle Assessment (LCA) identifies the environmental aspects of a particular product or service throughout its life cycle [13,[START_REF] Alvarenga | Comparison of the ecological footprint and a life cycle impact assessment method for a case study on Brazilian broiler feed production[END_REF]. There are four steps to achieve the results: goal definition and scope, inventory analysis (IA), impact assessment and interpretation. The present study is limited to global warming category assessment through calculation of the mass of CO2eq emissions for soybean production and CO2 emissions for transport. Two scopes will be considered. The first scope includes the emissions of agricultural activities for the soybean production; and the second, considers the CO2 emission from diesel combustion, according to the type of modal adopted for transportation.

GHG emissions, expressed in CO2eq, are equivalent to the emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), corrected by the corresponding global warming potential (time horizon of 100 years) [START_REF]Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF].

The functional unit of the present study is considered as 1 kg of soybean, in order to allow comparisons with other related studies.

System boundaries and delimitations

Scope 1: It considers emissions of CO2eq due to agricultural activities (those activities performed inside the gate). The activities are related to the production of soybeans and the combustion of diesel in all agricultural activities (soil preparation, planting, cultivation, harvesting, and limestone application) [START_REF]Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF].

This study does not consider nitrogen fertilizer application since the presence of Bradyrhizobium enables the biological fixation of atmospheric nitrogen [START_REF] Vendrametto | Contabilidade Ambiental dos Sistemas de Produção Agrícola e dos Serviços do Ecossitema do Cerrado de Lucas do Rio Verde -MT[END_REF][START_REF] Sun | Importing food damages domestic environment: Evidence from global soybean trade[END_REF], so direct nitrogen emissions due to the application are not accounted for. Calculation of GHG emissions due to herbicide and insecticide applications are not considered since they are negligible inside the gate.

Scope 2: It includes the identification of the main routes from its origin located at the main soybean producers' municipalities to the destination ports, as well as the transportation modalities used. From the identification of transport modals and distances traveled, the direct emission of CO2 is calculated using the CO2 emission factors for each type of mode used.

Presentation of the Macro-regions and Raw Data obtention

Studies carried out by the IMEA [START_REF]IMEA: Agronegócio no Brasil e em Mato Grosso[END_REF] divided Mato Grosso State into seven macro-regions from the point of view of agroeconomic. Data collection provided by IMEA [START_REF]IMEA: Custo Variável I -Despesas de Custerio da Lavoura[END_REF] regarding agricultural production costs for the year 2018/2019 compose the IA. Data selection for each macro-region depends on the scope adopted. When the scope 1 is considered, diesel and limestone inputs must be calculated. In order to convert raw data expressed as costs into units adherent with emission factors, the input prices for the different macro-regions were considered. Thus, to carry out the conversion to mass units, the cost of diesel oil was divided by the diesel price from the National Petroleum Agency [START_REF] Anp | Gás Natural e Biocombustíveis: Sistema de levantamento de preços -Óleo Diesel em Mato Grosso[END_REF]. Analogously, the cost of the limestone was divided by the price practiced in the State [START_REF] Ribeiro | [END_REF].

To carry out scope 2 analysis, the main routes to the main Brazilian ports were obtained from data from the Ministry of Transport, Ports and Civil Aviation, and authors, including highways (transshipment stations) from to waterways and railroads, as shown in Fig 1. [START_REF] Kussano | Custos logísticos agroindustriais: avaliação do escoamento da soja em grão do Mato Grosso para o mercado externo[END_REF][START_REF] João | Emissão de CO2 na logística de exportação de soja do Mato Grosso: o caso das exportações pelo arco norte[END_REF][START_REF] Toloi | How to Improve the Logistics Issues During Crop Soybean in Mato Grosso State Brazil?[END_REF][START_REF] Mtpa | Portos e Aviação Civil: Corredores Logísticos Estratégicos: Complexo de Soja e Milho[END_REF]. The most productive municipality in terms of the largest soybean production [START_REF] Ibge | Pesquisa Agrícola Municipal[END_REF], was considered as the representative one for each macro-region and thus it was set as the origin for distance to destination calculation. Thus, the emission factors for each transportation model are used to calculate the emission of kg CO2/kg of soybean transported via the different intermodal routes. The ports considered (j) are: MA = Manaus; SL = São Luiz; ST = Santarem; SN = Santos; PR = Paranaguá;

Calculation of Emissions

The calculation of GHG emissions from diesel combustion and the application of the limestone used in the agricultural phase are carried out by multiplying mass and volume, respectively by the emission factors presented in Table 1.

Table 1. Emission factors for all inputs considering at scope 1 and scope 2. Source: the authors based on data from [START_REF]Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF][START_REF] Junior | Análise da matriz de transporte brasileira: consumo de energia e emissão de CO2[END_REF].

Emission of Diesel

𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑜𝑓 𝐶𝑂 2 𝑒𝑞 = 𝑄 𝑑𝑖𝑒𝑠𝑒𝑙 𝑥 𝐹𝐸

Emission of CO2eq is expressed in kg/kg soybean, Qdiesel corresponds to the quantity of liters of diesel/kg soybean, and FE to the emission factor, in kg of CO2eq per liter diesel.

Application of limestone

𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑜𝑓 𝐶𝑂 2 𝑒𝑞 = (𝑀 𝑘𝑔 𝑙𝑖𝑚𝑒𝑠𝑡𝑜𝑛𝑒 𝑥 𝐹𝐸) (2)
Emission of CO2eq is expressed in kg/kg soybean, M corresponds to the annual mass of calcium limestone in kg, and FE to the emission factor of calcium limestone Emission of CO2 from diesel fuel for each Transportation Mode.

The calculations of CO2 emissions from each Mato Grosso soybean route is composed by the combination of the distance (D), and CO2 emission factor of each modal of transportation (FEi).

𝐸𝑗𝑘 = ∑(𝐹𝐸𝑖 * 𝐷𝑗𝑘)

(3)

Ejk corresponds to total CO2 emission per municipality of origin j and port of destination k; FEi corresponds to the CO2 emission factor of each transportation modal i, in kg/t*km; D is the distance traveled via each modal, in km; The limestone input represents the largest emission for agricultural activity in all cases. Since annually large amounts of limestone are used to correct soil acidity, and considering its contribution to global warming, it deserves a judicious evaluation of the required amounts to attain the proper pH. In this sense, the adoption of precision agriculture techniques could be useful.

The Northern and Northeastern macro regions show the largest emissions of CO2eq, 0.228 and 0.226, respectively. The Middle North and West result in the lowest emissions, varying from 0.160 to 0.132 kg of CO2eq/kg of soybean, fact that can be justified by the applied technology and improved agricultural practices [START_REF] Phélinas | Is GM Soybean Cultivation in Argentina Sustainable?[END_REF][START_REF] Fuglie | Is agricultural productivity slowing?[END_REF].

The average of emissions is 0.188 kg CO2eq kg of soybean produced. [START_REF] Raucci | Greenhouse gas assessment of Brazilian soybean production: a case study of Mato Grosso State[END_REF] shows comparable values, although it also includes the emissions corresponding to inputs production.

From scope 2

Although an interval of CO2 emission values is obtained depending on the alternative routes covering each origin with destination ports, Figure 3 evidences the best routes (and combination of modals) for each macro-region. Santos is the destination port which shows the best route in terms of emission for four of the seven macro-regions. Each representative municipality is considered in the figure. The best-routes values range from 0.065 to 0,140 kg CO2. The North-western macro-region (represented by Brasnorte) presents the largest emission. The road modal, which corresponds to a high emissions modal when combined with other transportation modals minimize emissions of the total route.

Figure 4 shows the comparison of production emissions and emissions in the route (CO2eq and CO2, respectively) for 1 kg of soybean. The results show that in most cases, the production of one kg of soybean emits more CO2 than transportation of the same mass. This trend is more evident when the best combinations of transportation modals are considered for each macro-region (as in Fig. 4). The substitution of some highway stretch by water and/or railway has a positive effect, and it is possible to affirm that the use of multimodality, allows a significant reduction in CO2 emission. It is important to mention that for transportation only direct CO2 emission was considered by the emission factors, since it is the quantitively most important gas emitted during combustion. Anyway, quantities of other GHG should be considered depending on type of engine, vehicle, maintenance, etc. This is not considered in the present work.

Findings and Conclusions

This study calculated the GHG emissions of agricultural activities and transportation intermodal routes towards the main ports for the different macro-regions of the Mato Grosso State. Results at production shows emission values varying between 0.132-0.228, with an average of 0.188 KgCO2eq. It is evidenced that use of limestone is the largest emitter in the field. The adoption of more careful practices concerning limestone application according actual necessity will improve emission impacts. Public incentives for innovative technologies, such precision agriculture, should be promoted.

It has been observed that routes using road modal over 500 to 700 km increase the emission and must be combined with other transportation modals.

Public policies directed towards investments in logistics solutions that could assist in transportation infrastructure improvement could be a way to minimize environmental impacts.

Fig. 1 .

 1 Fig. 1. Main routes used to export soybean produced in Mato Grosso State. Source: Authors.

Fig. 2 .

 2 Fig. 2. Emission of CO2eq in the soybean farming activities in the different macro-regions ofMato Grosso Brazil. Source: Authors.

Fig. 3 .

 3 Fig.3. Emission of Kg CO2/Kg of Soybeans/Transport of the Port Final. Source: Authors

Fig. 4 .

 4 Fig. 4. Comparison of CO2 Emissions/kg of soybean in the production and transportation stages. Source: Authors

  Figure2compares the CO2eq emission during soybean production stage (inside the gate) for each Macro-region. Notice that for all cases, limestone application is largely the most impacting issue during production, while the diesel involved in culture practices ranges from 7% to 18% of the total emissions.

	3	Results and discussion
	3.1	From scope 1
		Emission Source	Type of Emission	Unit	Emission Factor	Reference
		Fuel Diesel	Direct kg CO2eq/ L diesel	2,68	[4]
		Limestone
		Application in soil	Direct kg CO2eq/ kg limestone 0,48	[4]
		Transportation modal
		Highway			0,11917
		Railway	kgCO2 /t*km	0,0346	[24]
		Waterway			0,018

Acknowledgments

To the Coordination for the Improvement of Higher Education Personnel -CAPES, by the finance grant Code 001; to the Federal Institute of Education, Science and Technology of Mato Grosso (IFMT) to support the research.


Flemming M M Christensen

Iskra Dukovska-Popovska

Casper S Bojer

Kenn Steger-Jensen

Asymmetrical Evaluation of Forecasting Models through Fresh Food Product Characteristics

Keywords: forecast, error, shelf life, fresh food, differentiation

Forecasting accuracy in context of fresh meat products with short shelf life is studied. Main findings are that forecasting accuracy measures (i.e. errors) should penalize deviations differently according to product characteristics, mainly dependent on whether the deviation is large or small, negative or positive. This study proposes a decision-based mean hybrid evaluation which penalize deviations according to type of animal, demand type, product life cycle and product criticality, i.e. shelf life, inventory level and future demand.

Introduction

Today's competitive fresh food grocery market is growing with high requirements to price, availability and quality (i.e. freshness) [START_REF] Jacobsen | Effekt & Effektivitet, Dansk Dagligvarehandel 2003-2013[END_REF][START_REF] Hübner | Demand and Supply Chain Planning in Grocery Retail: an Operations Planning Framework[END_REF]. Particularly fresh meat products (FMPs) have down to only few days total shelf-life (production to expiration) [START_REF] Evans | Effects of Food and Beverage Storage, Distribution, Display, and Consumer Handling on Shelf Life[END_REF]. This puts high requirements on planning for the wholesaler who merely balances converging and diverging flows between industry and stores [START_REF] Hübner | Demand and Supply Chain Planning in Grocery Retail: an Operations Planning Framework[END_REF]. As planning relies on forecasting (i.e. predicting) future demand, quality and cost-effectiveness of planning depends on the forecasting accuracy [START_REF] Petropoulos | Judgmental Selection of Forecasting Models[END_REF][START_REF] Adebanjo | Understanding Demand Management Challenges in Intermediary Food Trading: A Case Study[END_REF]. Inaccurate forecasting of FMPs has double waste-impact. Over-forecasting creates risk of reduced sales price (at best) or obsolescence [START_REF] Mena | Causes of Waste across Multi-Tier Supply Networks: Cases in the UK Food Sector[END_REF] and under-forecasting lost sales; both reducing the profit base. It is thus paramount to ensure that any chosen forecasting model performs best out of its alternatives. While a model' relevance relates to e.g. data characteristics, time periods to forecast and data availability, its performance relates to the forecasting accuracy i.e. error [START_REF] Hanke | Business Forecasting[END_REF].

Forecasting errors for products with constant demand include e.g. "mean forecast error", "mean percentage error" and "weighted mean absolute percentage error" [START_REF] Hanke | Business Forecasting[END_REF][START_REF] Kolassa | Evaluating Predictive Count Data Distributions in Retail Sales Forecasting[END_REF][START_REF] Kolassa | Advantages of the MAD/Mean Ratio over the MAPE[END_REF][START_REF] Mehdiyev | Evaluating Forecasting Methods by Considering Different Accuracy Measures[END_REF][START_REF] Hyndman | Another Look at Measures of Forecast Accuracy[END_REF][START_REF] Hyndman | Another Look at Forecast-Accuracy Metrics for Intermittent Demand[END_REF]. The errors mainly differ in the valuation of deviations (numerical/absolute/ squared) and the inclusion of relative impact to total demand. However, they penalize positive/negative deviations symmetrically and uniform across all time-points, regardless of product characteristics such as e.g. shelf-life, product life cycle stage and demand type. Further, planning software tends to use the same (set of) error(s) for all products when evaluating forecasting models. This is especially challenging for FMPs at wholesaler, as a deviation' impact is different from product to product, from time to time. As example, while ground beef' shelf-life (8 days) permit excessive amounts from over-forecast to be stored at wholesaler and sold to stores the following day(s), ground fish' shelf-life (4 days) does not due to consumer perceptions thus creating waste if not sold at same day. Similarly, in terms of e.g. product life cycle stage; while under-forecast of a product in decline may be accepted due to soon being out of assortment, underforecasting of a product in introduction may not. It is thus relevant to investigate how to penalize deviations and reflect the individual FMPs' characteristics when over-/under-forecasting, so the evaluation is hybrid and fits the individual FMP. By investigating traditional errors' evaluation and impact on inventory, it is possible to see how short shelf life impacts level of waste and service level to stores. The following presents theoretical framework, then methodology, case study, analysis, discussion and conclusion.

2

Theoretical Framework

Model Evaluation & Selection

Forecasting is per se always wrong [START_REF] Hanke | Business Forecasting[END_REF], but to ensure as small deviations as possible when forecasting, statistical accuracy measures (i.e. errors) help practitioners and systems towards more consistently and efficiently choose one model above another. Yet, there seems to be a lack of trust in automatic model selection [START_REF] Alvarado-Valencia | Expertise, Credibility of System Forecasts and Integration Methods in Judgmental Demand Forecasting[END_REF] -and research suggests that human evaluation of forecast models can outperform algorithmic selection [START_REF] Petropoulos | Judgmental Selection of Forecasting Models[END_REF]. Despite this, it is still utopia to think of human evaluation for all products in a typical wholesaler product portfolio with up to hundreds of thousands of different products, merely stressing the statistical errors' essential importance. Multiple statistical errors exist for evaluating forecasting models [START_REF] Hanke | Business Forecasting[END_REF][START_REF] Kolassa | Evaluating Predictive Count Data Distributions in Retail Sales Forecasting[END_REF][START_REF] Kolassa | Advantages of the MAD/Mean Ratio over the MAPE[END_REF][START_REF] Mehdiyev | Evaluating Forecasting Methods by Considering Different Accuracy Measures[END_REF][START_REF] Hyndman | Another Look at Measures of Forecast Accuracy[END_REF][START_REF] Hyndman | Another Look at Forecast-Accuracy Metrics for Intermittent Demand[END_REF], and Table 1 lists nine common measures for constant demand. For intermittent demand, other errors are applicable [START_REF] Kolassa | Advantages of the MAD/Mean Ratio over the MAPE[END_REF]. denotes the real value at time t, the forecasted value at time t for n data points, and -the forecast deviation. The MFE, MSE, RMSE and MPE use numerical valuation and, MAE, MAPE, MASE, SMAPE and WMAPE absolute valuation. Numerical valuation allows positive and negative deviations to balance out through time. This is challenging as errors may report "0" in deviation -despite presence of (several) large errors. Absolute values comprehend this by accumulating the deviations. Further, MFE and MAE calculates deviations directly, MSE and RMSE penalize (particularly large) fluctuations through squaring of the deviations and, MPE and MAPE calculates deviations relatively to true demand, i.e. scale independent. For widely fluctuating demand WMAPE extends MAPE so that while "the classical MAPE sets absolute errors in relation to the actual values, the WMAPE considers percentage errors and again weighs them by actual values" [START_REF] Kolassa | Advantages of the MAD/Mean Ratio over the MAPE[END_REF] resulting in high weight on highdemand deviations and low weight on low-demand deviations. However, WMAPE assume stationary demand making it inappropriate when demand has trend, seasonality or other patterns [START_REF] Hyndman | Another Look at Forecast-Accuracy Metrics for Intermittent Demand[END_REF]. To allow for non-stationary demand, MASE divide MAD for forecasting period with MAD from historical period derived from naïve method [START_REF] Kolassa | Advantages of the MAD/Mean Ratio over the MAPE[END_REF]. For demand with low or zero values the percentage-based errors (but WMAPE) become undefinable/infinite, hence SMAPE comprehends this by considering deviations in 200%-range thereby reducing the impact from zero and close to zero demands [START_REF] Hyndman | Another Look at Measures of Forecast Accuracy[END_REF][START_REF] Hyndman | Another Look at Forecast-Accuracy Metrics for Intermittent Demand[END_REF].

Characteristics Impacting Forecasting

Multiple different characteristics influence planning across the supply chain [START_REF] Ivert | Sales and Operations Planning: Responding to the Needs of Industrial Food Producers[END_REF][START_REF] Dreyer | Proposals for Enhancing Tactical Planning in Grocery Retailing with S&OP[END_REF][START_REF] Jonsson | The Implications of Fit between Planning Environments and Manufacturing Planning and Control Methods[END_REF][START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF][START_REF] Romsdal | Can Differentiated Production Planning and Control enable both Responsiveness and Efficiency in Food Production ?[END_REF]. Given planning' dependence on forecasting [START_REF] Petropoulos | Judgmental Selection of Forecasting Models[END_REF][START_REF] Adebanjo | Understanding Demand Management Challenges in Intermediary Food Trading: A Case Study[END_REF] we select four characteristics of particular importance to the forecasting at wholesaler: product life cycle, demand type, shelf-life of products and animal type. When under-forecasting, consumer requirements [START_REF] Jacobsen | Effekt & Effektivitet, Dansk Dagligvarehandel 2003-2013[END_REF][START_REF] Kuhn | Integrative retail logistics: An exploratory study[END_REF] related to the product life cycle (i.e. demand stage) and, subsequently the desired accuracy in forecasting, should be appropriately penalized in the forecast error. Introduction and growth are crucial for a product' success in market (thus future demand) requiring high penalization, while maturity and particularly decline are less crucial requiring less penalization [START_REF] Alftan | Centralised Grocery Supply Chain Planning: Improved Exception Management[END_REF][START_REF] Nagashima | Impacts of Adaptive Collaboration on Demand Forecasting Accuracy of Different Product Categories Throughout the Product Life Cycle[END_REF][START_REF] Jüttner | Demand Chain Alignment Competence -Delivering Value through Product Life Cycle Management[END_REF]. Similarly, for demand type (campaign and normal), where campaigns are considered more important [START_REF] Ettouzani | Examining Retail On Shelf Availability: Promotional Impact and a Call for Research[END_REF] due to the (often strategic) underlying stimulation to demand, making under-forecasting relatively more critical than in case of normal demand. When over-forecasting and creating temporarily inventory, the risk of waste from obsolescence due to short shelf-life [START_REF] Mena | The Causes of Food Waste in the Supplier-Retailer Interface: Evidences from the UK and Spain[END_REF] is larger when seemingly not being able to sell excessive amounts before expiration. Hence, the error relative to upcoming days demand should be penalized accordingly. Similarly, for animal type; over-forecasting has different impact since different consumer perception of remaining shelf-life depending on if FMP is fish, chicken, beef or pork. Products with very sensitive perception should be penalized higher than those with less sensitivity.

Methodology

After investigating forecasting errors for constant demand, the purpose is to suggest an error that includes FMPs' characteristics when penalizing deviation. The goal is to evaluate forecast models so e.g. positive deviations causing excessive amounts (but no waste) are penalized differently than deviations where the FMPs cannot be sold and do cause waste. Since both context and product type is critical to this study, phenomena is studied in-depth in natural context, enriching both understanding and insight [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF]. The case study focuses on Denmark' largest (independent) wholesaler, called ABC throughout this study. Information and data are obtained through semi-structured interviews with purchasers and purchasing manager, evolving from standardized questions about forecasting and ordering processes.

Case Description

ABC is one of Scandinavia' largest independent grocery wholesalers and uses a central warehouse to supply +330 stores with FMPs. ABC' overall goal is to be known as the most "value driven company", measuring performance primarily through service level to stores. Recently, ABC changed their logistics setup for FMPs from transit-flows (with a single replenishment cycle across industry-wholesaler-stores) to two replenishment cycles: industry-wholesaler and wholesaler-store. Now, ABC forecasts FMPs' demand (i.e. store orders) for the following day, at which the stores send actual orders. Wholesaler forecasts total store demand at daily level with consideration of weekdaypatterns. Forecasting models are evaluated through one same error, WMAPE, as it allows an easily understandable and relative evaluation with independent scaling of the data (allowing cross-comparison across products). The applied forecasting model differ depending on if the demand in normal or campaign. The forecasts are generated and evaluated through R Studio, allowing efficient and consistent evaluation of the different forecasting models for the hundreds of FMPs.

Demand Forecasting & Errors

True and forecasted demand (on the left) and deviations between the two (on the right) for a limited amount of time of a given product is shown in Figure 2. The solid line is true demand and dashed line forecasted demand. Black filled columns is for over-forecasted demand, and light grey columns for under-forecasted demand. to demand. In general, neither of the errors seem to reflect the deviations which are large enough to cause stored products to waste and seemingly stock-out situation.

Inventory Transactions & Shelf Life

Looking closer at the resulting inventory records, stock outs and waste are evident. Figure 2 depicts demand and forecasting related information, transactions from the inventory, and the products' distribution across shelf life days. Status of delivery is also shown. Each colour indicates different deliveries and their transactions across inventory and shelf life days. It is clear, that FMPs with one day less shelf life are consistently present due to over-forecasting, with some even wasted due to too short shelf life (circle in figure). Further, one day there is a reduction in service level of 6%. From the forecast deviation it also evident that in fact a deviation was up to 175%. To penalize the individual time points' deviation more relative to the FMP' characteristics it is necessary to use a hybrid error allowing different penalizations. Based on numerical valuation, the suggested Mean Hybrid Error (see Equation 1) multiplies deviations with an alpha-factor. In Equation 1, n = number of time points included in calculation, t = time point in period, = actual demand, = forecasted demand.

= 1 , -, (1)
Please note that true and forecasted demand are reversed compared to those in Table 1, due to reflecting inventory levels. The α represents different characteristics' penalties for respectively positive and negative deviations. Different characteristics may be relevant to other products, however since focus is on FMPs, we use the four previously identified characteristics. The decision-making diagram when determining the alphavalue is depicted in Figure 3. Depending on if the deviation is negative, zero or positive, the penalty will differ according to risk of stock out and inventory building. In terms of size of deviation, the alpha-value also differ between percentage-limits of deviation, as well as it takes product life cycle assessment and demand type into consideration when there is chance for stock out (i.e. under-forecasting). Similarly, when chance of inventory (i.e. over-forecasting), the alpha-value considers the animal type. From interview, consumers generally accept that pork, chicken and particularly beef FMPs are few days old from production date. For beef, there is even evidence of buying products at up to full price, when they expire same day. However, for fish products, consumers generally don't buy products more than one -maximum two -days old since production -despite threefour days remaining shelf life. To reflect this against consequent inventory levels in relation to chance of waste we develop a criticality value, C (Eq. 2).

, = - , -, ∑ , + , , < 0 , , = 0 , , > 0 , (2)
C is the difference between the amount of inventory due to over-forecasting and the amount of days it is possible to store the given products, before selling at reduced price (best case) or wasting. In Equation 2, = days product p can be stored cf. shelflife/guarantee days, , -, = deviation between forecasted and true demand of product p at day t, ∑ , = sum of demand for product p the following number days t that can be covered by the excessive amount in relation to shelf-life/guarantee days and , = available inventory level of product p at the beginning of the day, t, after subtracting the products that will be waste the following days due to expiration.

To exemplify, an evaluation through classical errors and MHE is performed. A random FMP (a mature fish product) is chosen and its demand is characterised by two sudden disruptions in the beginning of the period and again towards the end. Elsewise no significant impact from trend, seasonality or campaigns is observed. Nine models of naïve (3), mean (4) and exponential smoothing (2) model families are used, considered most widely used in practice [START_REF] Petropoulos | Judgmental Selection of Forecasting Models[END_REF]. From Table 2 the errors are shown for the different forecasting models. Light grey indicates which model has lowest error and dark grey second most favourable error. If choosing model according to the classical errors, weighted moving average is most favourable, followed by exponential smoothing and naïve with trend. However, MHE suggests basic naïve model. As second best performing models, mainly exponential and naïve models are suggested. If looking closer at the individual deviations for exponential smoothed, weighted MA and naïve model, it is evident that the pattern of deviations differs. When facing sudden disruptions in demand, exponential smoothing and weighted MA tend to have their deviations characterised by clusters of similar deviations (positive or negative). Hence, if a sudden disruption in demand, the models over/under-forecast for the following periods, entailing an extended impact on either potential lost sale or potential inventory building. Looking at naïve model and comparing, this indicates that albeit merely projecting last period' demand as current period' demand, the impact from periodically lost sale/inventory building is relatively smaller, even when not being able to have inventory (as in this example with fish cf. Figure 3). By considering FMPs' characteristics, it is possible to evaluate the impact in relation to the individual products and thereby ensure effective evaluation of forecasting models.

This study has demonstrated MHE through a single product and further research should aim at increasing validity by investigating multiple different products. This, to investigate how differences within e.g. shelf life may impact inventory levels, differentiate the penalties and ultimately choice of forecasting model. Additional research should also focus on how MHE may impact penalties for demand characterised by high campaign and/or seasonal demand, due to its focus on inventory coverage before expiration cf. positive deviations.

Fig. 1 .

 1 Fig. 1. Gaps between forecasted and true demand, an example

Fig. 2 .

 2 Fig. 2. Inventory records of a selective product

Fig. 3 .

 3 Fig. 3. Decision-making during forecast-evaluation, with exemplifying punishment-values

Table 1 .

 1 Common forecast error measures

	Error	Equation	Error	Equation		Error	Equation
	Mean forecast error, MFE	1	-	Mean percentage error, MPE	1	-	Weighted MAPE, WMAPE	1	∑ 1	∑	-
	Mean absolute error, MAE	1	-	Mean absolute percentage MAPE* error,	1	-	Symmetric MAPE, SMAPE* zero demand) (close-to-	2		-+
	Mean squared error, MSE	1	-	Root mean squared error, RMSE*	1	-	Mean absolute scaled error MASE*/**	1 -	∑ ∑		-| -	|

*can also use median rather than mean, respectively MdAPE, RMdSE, SMdAPE and MdASE

[START_REF] Hyndman | Another Look at Measures of Forecast Accuracy[END_REF]

, **shown equation is for seasonal demand, in situation of non-seasonal pattern then m=1.

Table 2 .

 2 Forecast errors for selective forecasting models

	Forecasting model	MAE	MFE MSE	RMSE MAPE	MPE	MHE
	Naïve	16.29	-0.20	473.37	21.76	26.55%	-6.83%	0.18
	Naïve w/ T, type1	24.56	-0.95	1,055.39	32.49	37.73%	-3.62%	0.59
	Naïve w/ T, type2	16.47	-1.41	484.21	22.00	27.10%	-8.68%	4.21
	Average	15.75	-4.72	450.94	21.24	32.68%	-21.35%	27.31
	Moving Average (MA)	16.28	-0.22	489.16	22.12	27.62%	-9.11%	-2.90
	Double MA	20.06	-0.61	674.26	25.97	35.90%	-13.93%	-8.85
	Weighted MA	14.74	-0.49	376.15	19.39	25.43%	-9.42%	-13.32
	Exponential smoothed	15.42	0.08	431.98	20.78	27.71%	-10.98%	-1.91
	Exponential w/ T	15.56	0.39	436.65	20.90	28.65%	-11.76%	-3.55


Johannes Cornelis De Man

Felix Mannhardt
email: felix.mannhardt@sintef.no

Keywords: Data-driven production management, Event Logs, Problem Diagnosis, Performance Measurement

This paper demonstrates a detailed performance diagnosis of a production process. With limited investment power for new technologies, managers want to diagnose the reason for system underperformance, i.e. diagnosing performance gaps. This paper found detailed performance measures for specific production orders by using event log data, i.e. a set of timestamps that denote the occurrence of an atomic event in production. Sequential time registrations for each production order give detailed insights in how the production process is behaving. The reported case study gave managers a web application that lets them zoom in and out of different characteristics to get an understanding how their production process results in a certain performance. Based on the background and case, a framework and way forward are proposed on how to perform detailed diagnosis to explain performance gaps in production.

Introduction

While computing power and data science are developing at high speeds, production companies often rely on self-constructed spreadsheet solutions for data-driven decision taking for planning and control [START_REF] De Man | Spreadsheet Application still dominates Enterprise Resource Planning and Advanced Planning Systems[END_REF]. This is not wrong and has been proven useful over the last 20 years to measure along the performance of resources, output and flexibility [START_REF] Beamon | Measuring supply chain performance[END_REF].

Companies rely on less advanced data sources as they are not matured into digital factories. Using sensors and buying expensive systems to support them is neither feasible nor the core business of most companies. This does not mean that there is nothing to be won from the data stored in existing systems; with little capital investment data analysis tools are available to give performance insights along the production process, at a more detailed level than established single number key-performance indicators (KPI) used in traditional production control methods. This enables the possibility to diagnose problems occurring in production at a more detailed level.

In this paper we present a web application for detailed performance diagnosis in production based on event logs, i.e. a set of timestamps that denote the occurrence of atomic events. The background describes the current challenges with translating per-formance measurements into relevant diagnoses first. Second, a background on the use of event logs is sketched, coming from the domain of process mining. Then we describe a method and case where event logs were used for diagnosis, discuss the results and a framework for data-driven performance diagnosis, before concluding the paper.

Background

From performance measure to diagnosis

Performance measurement systems (PMS) in production measure along the lines of resources (cost containment and efficiency), output (customer service) and flexibility (ability to respond to a changing environment) [START_REF] Beamon | Measuring supply chain performance[END_REF]. Having a performance measure, however, does not ensure that corrective action will result in the desired outcome. It is therefore unclear whether users of PMS outperform those that do not [START_REF] Koufteros | The effect of performance measurement systems on firm performance: A cross-sectional and a longitudinal study[END_REF]. So while it seems imperative that "if you don't measure progress toward an objective, you cannot manage and improve it" [START_REF] Kaplan | Mastering the management system[END_REF], organizations can perform with or without measuring it, as long as they learn how to perform [START_REF] Bititci | Performance Measurement: Challenges for Tomorrow[END_REF]. Learning how to perform or how to solve problems, however, requires diagnosis. Wagner [START_REF] Wagner | Problem solving and diagnosis[END_REF] frames diagnosis as "the analysis of a present condition or present state of the system" [START_REF] Wagner | Problem solving and diagnosis[END_REF], differentiating between causal diagnosis in which one tries to determine a cause to an issue, and situation understanding, in which no deviations from a desired state are researched. A single aggregated performance measure does not give a causal analysis or a situational understanding and more information would be required to explain why performance is as it is.

There is a large theoretical and practical domain for diagnosis or problem analysis, e.g. root-cause analysis, fishbone diagrams, and 5-whys. Wagner [START_REF] Wagner | Problem solving and diagnosis[END_REF] summarizes how diagnosis should be undertaken:

1. Have an indication that there is a performance gap, i.e. the performance is different from the desired situation. 2. Description of the system under investigation. 3. Listing potential causes. 4. Planning the search, i.e. how to identify problem sources. 5. Analysis and model building; identifying what is wrong by eliminating all the causes and explanations that are not supported by evidence.

This leads to today's challenges in translating a performance measure to a diagnosis. Many manufacturers want to achieve a combination of on-time performance, low work-in-progress (WIP) and related low throughput times, low inventory, and high output [START_REF] Beamon | Measuring supply chain performance[END_REF]. The basis for improving these performance measures could start with the theory-of-constraints approach [START_REF] Goldratt | Theory of constraints[END_REF] or a lean approach. To see the development of these performance measures over time, one could use the throughput diagrams from load-oriented manufacturing control [START_REF] Bechte | Theory and practice of load-oriented manufacturing control[END_REF]. To be able to diagnose a performance gap, however, the buildup of a KPI must be understood. Unlike other theories, this paper considers the possibilities of modern data-analytics to analyze multiple factors that influence the KPI, arguing against aggregated KPIs.

The use of event logs and data-driven diagnosis

When production data is collected, it often comes with a timestamp, i.e. a time and date associated with an atomic event that indicates an activity or action has occurred. Moreover, it is often possible to attribute events to an instance of a certain process, e.g., the handling of a specific Production Order (PO). One data-driven method to apply on such timestamped data that can be grouped according to process instances, which are also denoted as event logs, is process mining. Based on event logs, process mining method can discover a model of how processes are actually executed [START_REF] Van Der Aalst | Process Mining -Data Science in Action[END_REF]. Such discovered models are then used to analyze deviations from the planned process both to investigate compliance (quality, regulations) and performance (bottlenecks, rework) related questions [START_REF] Van Der Aalst | Replaying History on Process Models for Conformance Checking and Performance Analysis[END_REF].

In a production context, some applications of process mining have been reported. For example, in [START_REF] Park | Workload and Delay Analysis in Manufacturing Process Using Process Mining[END_REF] workload in a production process is analyzed using process mining methods, in [START_REF] Bettacchi | Understanding Production Chain Business Process Using Process Mining: A Case Study in the Manufacturing Scenario[END_REF] production shutdowns are analyzed, and in [START_REF] Tu | Analysis and Prediction Cost of Manufacturing Process Based on Process Mining[END_REF] process mining is used to predict the cost of a manufacturing process. In addition to process mining, there is a large domain of other data analysis methods reaching from data-collection technologies, data processing and various methods for data visualization.

Methodology and case description

This paper builds a new method for diagnosing the performance of production. The method is built from a pragmatic perspective using a real-life dataset and existing theory as described in the background, advancing the use of data analytics for performance diagnosis, but at the same time considering the limitations of a real case. The case company is a metal parts producer in the Netherlands. Production orders (POs) are prepared to customer specification. A PO leads to the production of a batch of the same part. The factory floor is organized in machining centers for each operation. Each machining center has multiple machines with different specifications, e.g. the bending center has multiple machines with different capabilities.

Capacity is calculated by the maximum revenue the factory can generate. POs are scheduled backwards from the order due date with five days buffer, one day per planned operation and seven days for preparing the order for production. This gives a two-week lead-time for a PO that needs to be processed in two machine centers.

The cutting operation is the first operation performed on each PO and is controlled for by optimizing the nesting of sheet metal. The other operations are organized based on earliest due date, machine availability and machine changeover time. This process is controlled by the supervisors of the different machining centers and is not formalized.

The case company has one enterprise resource planning system (ERP). For each PO, the system registers start and end times for all its operations which results in an event log. In addition, each logged event contains information on the end-customer, planned production dates, and the kind of operation performed. The registration of start and end times is a manual process by scanning the order slip barcode. For each PO production operation, a machine is assigned. This assigned machine satisfies the minimal capabilities to perform the required operation. During execution, POs can be operated on a machine with a better specification, but this is not registered in the ERP system. Wrong time registration and variations in machine use can skew the data.

Using the start and stop timestamps of all the operations of the POs, a detailed record of all POs flowing through the factory can be constructed from the moment an order is finished at first machining center until finishing its last operation, i.e. each PO is a process instance of possible processes in the factory.

The data analysis was performed with the R statistical software package. This resulted in a web-application that builds up performance measures and graphs from the available event log. Table 1 summarizes the different performance measures that can be calculated, and the possibility to select specific PO timestamps in the event log based on selection criteria by filtering the data. Figure 1 demonstrates how for all selected POs throughput time and waiting time between cutting and other operations is visualized in a graph, such that variation and outliers become immediately apparent.

Table 1. Performance measures per focus area, based on selection possibilities

For example, one can select the time range 1 January 2017 -21 February 2017, for a specific customer and for POs that had between 3 to 6 operations. For this selection of POs in the event log, all performance measures and graphs are calculated instantly.

Performance measures Selection possibilities for these performance measures by filtering data

Diagnosis results

Based on perceived performance gaps, diagnoses were performed by using the webapplication. Before addressing the perceived performance gaps, trends over the last two years are discussed. From the first half year in 2017 to the last year in 2018, the throughput time on average increased by 1.5 days and the delivery performance dropped by 20% points. At every moment in time between 50 and 225 POs were on the shop floor as there is no limit on the amount of orders that can be released into production.

For each perceived performance gap, the performed diagnosis is described:

1. A general perception of no flow in the factory, POs queuing at various points, especially after the first operation for each PO, metal cutting.

Using the web application, it was tested whether orders had a throughput-time equal to the planned throughput time. The planned throughput times were exceeded significantly mainly due to an average waiting time of over 3 days with a standard deviation of more than 6 days between cutting and the subsequent machining operation.

This indicates that there is no connection between the planning of the first operation and the subsequent operations, overloading some machining centers after cutting the metal.

The different machining centers focus on optimizing the utilization of their bottle-

neck machines and less on delivery dates, leading to bad delivery performance.

The analysis found a great spread in delivery performance and throughput times. This leads to potential explanations: Many POs run on bottleneck machines, while other POs flow through without delaying queues. A second explanation is that optimizing the usage of machining leads to unnecessary delays for certain POs.

Perceived low delivery performance.

This could be confirmed and visualized by plotting the earliness or lateness of each order in a two-year period, seeing the variation in delivery performance, also seeing that many orders were delivered early. In addition, some customers were favored over others, having a much higher delivery performance (difference of 20% points to the average), while this is not official policy. Orders that were delivered on time, on average took 2.9 days less to produce than orders that were not on time.

The diagnosis confirmed several challenges:

• The planned days to perform an operation are underestimated and throughput times have increased significantly in the last two years. The planning method based on revenue capacity does not account for bottleneck operations. As the number of orders keep on growing, this will become more visible. • The order release for the first operation is not linked to the availability of machines in subsequent operations, leading to long and variable waiting times after cutting. • There are most likely bottleneck machines that are not planned for.

• Delivery performance has drastically decreased in the time-period of the provided dataset.

Discussion

The diagnosis in the last section lead to potential explanations of several performance gaps under investigation. It also gives the possibility to formulate hypotheses and start testing improvements in practice, as it gave a detailed image of a production environment up to the machine center level. In this also lies a risk: The data does not contain all information and other methods might be necessary to diagnose the performance gap. For the case described in the last paragraph, this applies for bottleneck machines and a recommendation would be to start registering timestamps for the use of specific machines instead of machining centers. Figure 2 illustrates a framework that integrates decision-making from production control with data available from past performance. From production, timestamp data is recorded that is used to calculate performance measures. For each performance measure a threshold must be set, such that a performance gap can be detected. Once a performance gap is detected, the diagnosis process starts. The case in this paper used performance measure calculations based on timestamp data. The diagnosis should lead to potential causes to explain the performance gap. The case study did not go further than this activity.

Based on the potential causes, hypotheses can be formulated to describe the effect between the potential cause and the performance gap. Testing the hypothesis with the available data can be a difficult exercise. Instead we propose to introduce improvements that reduce the effect of the potential cause on the performance and monitor the effect of the outcome.

Timestamp data

Hypotheses

Production

Introduction of improvements

Leads to

Affects

Performance measures

Diagnosis

Conclusion and future work

In this paper, we discussed a realistic way forward for detailed performance diagnosis using an event log. For our case we demonstrated that traditional performance measures for production management can be specified for a specific case selection, depending on the quality and granularity of events recorded in an event log The use of timestamped data for each process instance lead to a process perspective where we could calculate performance measures for a specific dataset that complies with certain conditions. We related this method to problem diagnosis to come up with a method for detailed performance diagnosis in production.

Compared to traditional methods, the filtering of an available event log on characteristics such as time-period, number of operations performed, customer and delivery performance, gives managers a method to quickly identify causes for certain performance gaps.

This research can be extended in multiple ways:

1. An integration of process mining techniques and performance measurement to facilitate better performance gap diagnosis within production. This specifically holds for visualizing production processes in process models and enriching them with performance measures. 2. Using atomic event production data in existing methods for visualizing workload control (see [START_REF] Bechte | Theory and practice of load-oriented manufacturing control[END_REF]). 3. The use of different types of data in production and data-analysis techniques to facilitate the diagnosis of performance gaps. 4. The use of new technologies to facilitate better data and easier, faster or more thorough diagnosis. One way to overcome data quality issues is automatic data regis-tration, e.g., based on activity recognition using sensorised environments and wearables in a smart manufacturing environment.

Even though factories are not fully digitalized, valuable information can often be extracted from existing systems to diagnose the performance gaps that factories are experiencing. General available software can be used to gain new insights, avoiding a technology push to facilitate the hype around IoT, machine learning and artificial intelligence. Data-driven production management should not be a solution, but an increasing powerful set of tools to facilitate diagnosis and problem solving. Connecting these toolsets to performance measurement within supply chains and general problem-solving techniques is as equally important as further developing technologies under the digitalization or Industry 4.0 paradigms.

Fig. 1 .

 1 Fig. 1. Visualizing the individual POs to show variation and outliers for the filtered data

Fig. 2 .

 2 Fig. 2. Framework for detailed performance diagnosis using an event log

Detailed performance diagnosis based on production

timestamps: A case study


Andreas D Landmark

Emrah Arica

Pål Birgit Kløve

Furu Kamsvåg

Eva Amdahl Seim

Manuel Oliveira

Situation Awareness for Effective Production Control

Keywords: Situation Awareness, Production Control, Manufacturing Industry

Situation awareness is a growing need for manufacturing operators with the digital transformation of manufacturing environments where operators are expected to take larger responsibilities and tasks on the production flow. To make effective production control decisions, workers need to be aware of the situation that consists of multiple factors such as the production status, and internal and external demand requirements. Adapting existing models for situation awareness, this paper presents four case examples of manufacturing companies that implement digital technologies for situation awareness.

Introduction

Most of the modern manufacturing companies operate in complex and dynamic organizations composed of equipment, people, information and information systems, which are influenced by a multitude of internal and external factors. In such complex environments, the decision makers often lack the holistic view of the situational factors, which leads to suboptimal local decisions and actions [START_REF] Arica | A framework for production rescheduling in sociotechnical manufacturing environments[END_REF]. Earlier work [START_REF] Lall | Towards Industry 4.0: Increased Need for Situational Awareness on the Shop Floor[END_REF] has attempted to apply the situational awareness framework of Endsley and Garland [START_REF] Endsley | Situation awareness : analysis and measurement[END_REF] in order to describe and understand operators need for data and information in their decision-making on the shop floor. Situation awareness (SA) is "the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future" [START_REF] Endsley | Toward a Theory of Situation Awareness in Dynamic Systems[END_REF]. This relates back to classic decision-making theory to the more recent frameworks for describing workspace awareness [START_REF] Gutwin | A Descriptive Framework of Workspace Awareness for Real-Time Groupware[END_REF]. In workspace awareness, there is the recognition that awareness in personalized technology, such as the personal computer, and the need for "groupware" in order to collaborate virtually.

In the highly automated and digitalized factories of the future, operators will have fewer physical tasks, but more decision making and problem-solving tasks. Today's information systems mainly address the higher-level decision makers such as planners' needs, while there is a certain gap for decision support on the shop floor. In line with Industry 4.0 thinking, the idea was that technology can enhance and extend the human abilities in production control -and in the most advanced form the tightly integrated cyber-physical system of production machine and operator. However, the impetus in Industry 4.0 has been heavy on the technologies and has less focus on the softer side of enhancement. Nevertheless, true SA only exists in the mind of the human operator. Data does no good unless it is successfully transmitted, absorbed and assimilated in a timely manner by the human to form SA [START_REF] Endsley | Designing for Situation Awareness: An Approach to User-Centered Design[END_REF]. This shortcoming has previously been described at length within the computer-supported collaborative work field [START_REF] Schmidt | The Problem with `Awareness': Introductory Remarks on `Awareness in CSCW[END_REF]. While the idea was that media-rich shared spaces would foster comprehension and projection; it often fails to do either.

With the introduction of more personalized technology on the shop floor, going from collective-by-design solutions such as whiteboards and various KANBAN solutions to individual-by-design solutions such as smart glasses or smart watches -this mirrors the development in the office. The need for industrial groupware -to support coordination and collaboration when hardware by design is non-inclusive and lacks the option for physical collaboration between users. While SA in manufacturing is an emerging topic receiving interest from academia in recent studies, there is a research gap in investigating the practical applications of technologies providing SA to operators. In this study, we have attempted to explore how SA of operators is supported in manufacturing companies, through four cases of technology for operative production control.

Situation Awareness (SA) in Manufacturing

SA has been coined into the literature by aviation researchers fails [START_REF] Endsley | The out-of-the-loop performance problem and level of control in automation[END_REF], studying the cognitive tasks of aviation practitioners, and then have been spread to numerous application areas and work environments, including manufacturing. A central SA concept in the manufacturing context is the level of automation (LOA) that aims to provide various levels of support to the operators [START_REF] Lorenz | Automated and interactive real-time systems[END_REF], depending on their task. Identifying and using various types of support is needed to avoid the potential out-of-the-loop performance problem (OOP) which refers to the slow intervention of operators when automation fails [START_REF] Endsley | The out-of-the-loop performance problem and level of control in automation[END_REF]. Following types of support are identified for SA of operators.

Perception support

Perception-support is at the core of Endsley's model, the basic ability to perceive and be aware of processes in your environment. At the basics, perception support can be measurement equipment (e.g. thermometers, calipers) that allows the operator to observe physical quantities more precisely -or observe quantities not visible to the human eye (e.g. radiation). With connected digital sensors, perception support also includes teletranslation, i.e. that one can observe quantities not directly in the local environ or inaccessible due to hazards (inside operating machinery). However, basic focus on the data accessibility from multiple sources lead to information intensive unmanageable complexity for the operator with the increasing complexity of production environments [START_REF] Holm | The future shop-floor operators, demands, requirements and interpretations[END_REF]. For an efficient perception support, cognitive standpoint should be taken to integrate the required information with operator's cognitive tasks [START_REF] Endsley | Situation awareness oriented design: from user's cognitive requirements to creating effective supporting technologies[END_REF].

Comprehension support

At the next level, putting physical observations or quantities into context requires the ability to comprehend the values. This requires knowledge of the manufacturing context on the operator. The context model of the operators in manufacturing consists of several elements; the context model of the operators built according to the operational (e.g. process, task), organizational (e.g. the team that operator belongs to) and usercentric (e.g. competency profile of the operator) dimensions of context [START_REF] Dhuieb | Context-awareness: A Key Enabler for Ubiquitous Access to Manufacturing Knowledge[END_REF]. Or a more extended context model, including the environmental (e.g. outdoor conditions) and system (e.g. devices) dimensions [START_REF] El Kadiri | Current trends on ICT technologies for enterprise information systems[END_REF]. For some processes, it is also necessary with comprehension support due to complex causality or similar. For example, handling unexpected events in production control in a timely and appropriate manner require that operators comprehend the cause, impact, and context of the event [START_REF] Arica | A framework for production rescheduling in sociotechnical manufacturing environments[END_REF]. Such support is traditionally available through control-room systems where a mathematical model or simulation is conducted on the sensor data before results are presented to the operator. One can see a similar use of statistical process control charts [START_REF] Shewhart | Statistical method from the viewpoint of quality control[END_REF], which is a manual tool for putting measurements into a monitoring and control context.

Projection support

At the highest level of Endsley's SA model, the ability to not only reason over the current state, but also over the project measurements and observations into a future state given the current comprehension [START_REF] Endsley | Situation awareness : analysis and measurement[END_REF]. This requires predictive power in the causality relations that underlie the comprehension support. Predictive Situation Awareness in manufacturing can benefit from artificial intelligence (AI) methods that can enable reasoning methods to estimate current situations as well as to predict future situations [START_REF] Park | Predictive situation awareness model for smart manufacturing[END_REF]. Nevertheless, most AI methods are tested for limited use case scenarios. In real world applications, where the situation is far more complex, the prediction accuracy is still a critical issue to be solved for the implementation success of such methods.

Coordination support

In all processes where there are interdependencies between tasks or products, where multiple workers must work together to achieve overall quality or flow -there is a challenge in coordinating the production. Cooperative work involves many interdependencies, effective management of these interdependencies -i.e. coordination -is pivotal [START_REF] Malone | The interdisciplinary study of coordination[END_REF]. The traditional way of managing coordination in manufacturing is standardization of work and procedures. Takt time is one way of also standardizing the flow of work in manufacturing. However, in production mixes where takt is infeasibleother forms of coordination are necessary. Many of which, requires perception, comprehension and projection of the situation of colleagues onto your own situation. Therefore, improving awareness by increasing the visibility of information's through technology is important means for support coordination.

Collaboration support

Collaboration may be defined as "departments' willingness to work together, whereby they share resources and understand and have consensus on common vision and goals." [START_REF] Kahn | Logistics and interdepartmental integration[END_REF]. It is now possible to realize collaborative work and SA from technological point of view, however there are still big gaps from human factors point of view [START_REF] Bernard | The importance of situation awareness in efficiency of distributed collaborative working[END_REF]. Collaboration requires more than cooperative work; it is necessary to do master the mutual adjustment and trade-offs of cooperative work -but for true collaboration it requires even more streamlined production and closer collaboration between members of the value chain in order to achieve efficiency and smooth flow through the dependencies between each process steps [START_REF] Gamme | Operational integration in a craftoriented small enterprise[END_REF].

Case Examples

In this paper, case study research methodology has been applied to study, exemplify, and validate the research constructs drawn in section 2. Case companies have been selected from ongoing or completed industry-driven research projects focusing on digitally enhancing the manufacturing operators' tasks. The three of the cases are collected from production companies in Norway, all of which at the corporate level have turnovers in excess of $100 MUSD and operate in a primary export-oriented global market. All three have processes involving metal-work and machining operations. The last case is collected from European companies involved in an EU project. The authors have worked with the companies over years -both in development, implementation and evaluation of technological as well as organizational solutions for operator productivity and efficient production. This collaboration has enabled accessibility to rich data from companies. The primary empirical data source has been workplace observations and semi-structured interviews with the operators. Enterprise data and documentation (e.g. production data from ERP system) have also been collected and analyzed.

Case I: Tablets connected to the control system on each machine

The case company has implemented a manufacturing analytical solution called Vimana on the tablets located at the CNC-control system for six of their machines at the department of cutting heads. In this department, each operator runs two machines simultaneously. The analytical software provides visibility and deep insight into the machinery performance, for all of the six machines. The operators obtain a good indication of machine status by taking a quick glance at the tablets. Information on key figures, such as machine stops, production time, and amount of production over the last few hours, with relevant, actionable data are provided. This increases situational awareness of the operators, enabling better planning, problem solving, and execution so that assets are optimized, work is simplified, and a data-driven culture is adopted. Using these tablets, the operators have built a stronger team feeling internally in the cutting head department. They work together towards common production goals and assist each other if downtime on the various machines occur. Furthermore, the operators move orders between machines in case of downtime or absenteeism of colleagues.

However, worth noting, changes in the production site require a good implementation process and great leadership. This is especially true in a change process where new digital tools can be used as a mean for surveillance, which is a well-known problem when implementing digital technology for collecting and sharing data. During the introduction of the tablets, some operators expressed their concerns, fearing that this digital solution would contribute to increased surveillance and not lighten their workday as intended. It could be argued that this happened as a result from weak leadership and a poorly managed implementation process. The management did not sufficiently inform the operators before implementation. Now, one year later, most operators are far more positive. They have realized that the tablets are implemented in order to support them in their workday by providing important and useful information, and as a result, the tablets have contributed to significant productivity gains for the department.

Case II: Computers on each working station with software-solution for production planning

The case company developed their own production planning tool and made it readily available to all operators in production. The idea behind the software was for it to act as a replacement for mainframe systems, where one could control production orders and sales orders. The company wanted to place computers in the production, so that the operators themselves could plan their own working day and be able to optimize the production orders and attain visual control over the queue on their machine. The system is used to obtain all kind of information regarding different orders. The operators can find information on each order, such as 3D models, drawings, specifications, production priority, delivery deadline, raw materials and inventory, picking list, production statistics, reasons for previous wrecks, the load on the various machines, production deviations, and the value chain for each order. The operators can keep track of where each order is located, and whether the order is in working progress in the relevant step in the value chain. They can find order history, such as deviation history and production details, related to specific orders. This kind of information is often very valuable and facilitates work. The system displays the order list by priority. When priority order changes, for example as a result of an urgent delivery, the information is updated.

The system also shows the planned workload on each machine in the coming weeks, so that the operator can facilitate optimal planning of his own working week. If a machine has a lot of queue, it is possible for the operators to coordinate between themselves and move orders to other machines with less workload. The planning phase is largely entrusted to the operator and the operator actively uses the information from the system into the planning phase to make everything ready for the production phase. The system is based on transparency. All information is available to everyone in the company, and this is highly valued among the employees. The operators appreciate the fact that that the software is made in-house considering that both operators, planners and designers can continuously propose new features and enhancements to make the system more customizable to display the information they need in their workday. However, as the system can continuously be developed, there is a danger of a user interface which becomes less user-friendly and messy as a result of information overload.

3.3

Case III: Production Control events delivered to smart watch

The case company has a factory wide machine monitoring solution, that monitors OEE and similar metrics for all CNC machines and machine control. This is a partly custom solution, tightly integrated with the maintenance scheduling system, allowing operators to view the operational status of all machines live. This is also used for calculating and showing key performance indicators in various dashboards. Parts of the production is based on multiple machines per operator, hence certain parts are machined with the machines running un-attended. However, operators are on hand (covering a pool of machines) to address any unforeseen events or stops in the programs. When covering multiple machines, the ability to perceive and project future states becomes a challenge requiring good short-term memory skills and mental capacity to be able to balance and coordinate their own workload. Unforeseen events cause loss of productivity with machines remaining idle until the fault is corrected. To support the workers in addition to more traditional "traffic lights" indicating machine status, visible from a distance, the company extended their production control system with a smart-watch interface that allows certain messages to be pushed to individual operators. This includes projection cues such as "15 minutes remaining in program" to the more perception aiding messages such as "Machine [X] stopped due to [Y]". This reduces time-to-discovery of unforeseen stops, and ideally also aids the operator's ability to plan and self-coordinate.

Case IV: Knowledge in Time, an industrial Augmented Reality solution for the operator on the shopfloor

The Knowledge in Time (KIT) service aims to provide cognitive support tailored to the needs of each individual operator. The KIT service has been deployed at three endusers covering a range of different industries, namely aeronautics, robot manufacturing and furniture manufacturing. Each of the end-users has differing production rates and product/process complexities, which have their own challenges: Furniture Manufacturing: The primary use of the system is for training purpose of the operators. However, the system is also used by the experienced operator by bringing to their attention intricacies of the assembly operation based on their past performance and evidence of retention concerning process. The secondary use of the system is to verify the quality of the training and decide on potential workplace optimization.

Robot Manufacturing: The system provides step-by-step instruction support, whilst monitoring the operator's activities to assess the quality of their work and capturing evidence of the work. The secondary use of the captured data is to support quality auditing of the production process.

Aeronautics: The system supports the operators by indicating the most common process mistakes incurred in the recent past. The system in this case monitors the operator's actions and indicates the error probability in the task, thereby bringing to the attention of the operator. The secondary use of the system is to improve the process optimization.

The operators have appreciated the use of augmented reality with machine learning to increase their cognitive capabilities tailored to the particular context of work being carried out. However, the secondary use of the system to support long-term cycle provides the means to augment and improve existing company knowledge.

Discussion and Conclusion

This paper has investigated the topic of SA in the manufacturing context. The case examples for SA support from real manufacturing companies are summarized in Table 1. Referring to the research gap stated in Section 1, we see that the emphasis is on immediate support for situational awareness-building aspects in real life production environments. The coordination and collaboration needs are less supported in the technology used in our case companies. The more interesting discussion is on the introduction of individualized technology in the workplace. Several of the technology cases, has taken up smart watches and individual tablets. The extreme of this trend is the introduction of head-mounted computers (e.g. HoloLens). All of these convey information and foster an individual interaction. This is an interesting conflict with the principle of visual control ("mieruka" in Toyota parlance), wherein the concept of transparency and visibility strengthens the control aspect. This is very similar to experiences in software for office workers, where the technology is by design mostly individual. In contrast to collaborative-by-design technology such as physical status indicators on a shop floor; individualized technology needs software to aid coordination and collaboration. Future research will focus on wider adoption of the digital technologies to enhance the SA of manufacturing operators in consideration with the human factors, as well as will address the individual and collaborative SA concepts. Only self-coordination.

None.

Case IV (KIT)

Better understanding of the work context, through sensed environment.

The reasoning modules allow KIT to decide when the operator is in need of cognitive support.

Insight into upcoming steps in the process.

With contextual information, SA in a team of operators is improved even if just one is using the device.

Collaborative workplace optimization.

Table 1 .

 1 Summary of case examples

	Support	Perception	Comprehen-	Projection	Coordination	Collab-
			sion			oration
	Case I	Machine	Impact on the	Showing	Helping opera-	None.
	(Tablet)	status and performance	rest of the order, time available to	when to change tools	tors work to-gether towards
		such as sys-	perform the or-	to avoid	common goals
		tem failures.	der.	production	and assist each
				failure.	other.
	Case II	Status of or-	Possible to co-	Root-cause	Possible to co-	None.
	(Com-puter)	ders and re-sources, changes in	ordinate be-tween orders on the machine	analysis of variation in input/output	ordinate be-tween orders on different ma-
		orders and			chines.
		plans.
	Case III	Provides re-	None.	Provides
	(Smart watch)	mote per-ception for certain key		temporal cues for the operator.
		events.


Benedikt Moser
email: benedikt.moser@fir.rwth-aachen.de

Achim Kampker

Philipp Jussen

Jana Frank

Organization of Sales for Smart Product Service Systems

Keywords: Smart Product Service Systems, Sales Organization, Sales Management, Network Thinking, Case Study Research

Many industrial companies face their digital transformation. In addition to an existing portfolio of products and services, new digital services are being developed to offer a portfolio of smart product service systems (Smart PSS). While the development of new digital services is rarely a problem for the companies, the organization of sales and distribution of Smart PSS in particular is a key issue. The sales of Smart PSS differs considerably from the sales of only products or services and must therefore be designed differently in order to meet customer requirements and successfully commercialize the developed Smart PSS. This paper therefore describes how the sales organization of Smart PSS should be designed successfully in various forms. The network thinking methodology is used in combination with a case study research approach to describe the connection between the offered portfolio, the customer requirements and the different elements of a sales organization. Furthermore, four different types of a sales organization for Smart PSS are described. This paper gives a recommendation for companies on a design of their sales organizations on which practical implications may be developed.

Introduction

Many industrial companies today already use a service business to differentiate themselves from competitors in the product business. They use the combination of products and services to offer systems that provide the customer a solution. Research literature refers to this as product service systems [START_REF] Gebauer | Service-driven manufacturing[END_REF][2][3] [START_REF] Lightfoot | The Servitization of Manufacturing: A Systematic Literature Review of Interdependent Trends[END_REF]. In addition, companies start to develop digital services [START_REF]Smart Service World: Recommendations for the strategic initiative 'web-based services for business[END_REF] [START_REF]Industrie 4.0 Maturity Index: Managing the Digital Transformation of Companies[END_REF]. Digital services use data as a central resource, e. g. data from the sensors of machines [START_REF] Bullinger | Smart services in manufacturing companies[END_REF][8][9] [START_REF] Kampker | Industrial Smart Services: Types of Smart Service Business Models in the Digitalized Agriculture[END_REF]. Manufacturing companies are trying to avoid the strong price competition in traditional product markets by offering digital solutions in addition to services, such as condition monitoring, remote services or value-added services [START_REF] Herterich | Stepwise Evolution of Capabilities for Harnessing Digital Data Streams in Data-Driven Industrial Services[END_REF][11] [START_REF] Paluch | Contrasting Risk Perceptions of Technology-Based Service Innovations in Inter-Organizational Settings[END_REF]. The interplay between physical products, services and digital services is shown in the shell representation of Smart PSS (fig. 1) [START_REF] Belz | Erfolgreiche Leistungssyteme in der Industrie[END_REF]. For many manufacturing companies, the physical product is still at the center of the portfolio. This is accompanied by a complementary business of spare parts and an expanded product range.

Fig. 1. Shell illustration of product service systems [START_REF] Belz | Erfolgreiche Leistungssyteme in der Industrie[END_REF] In the fourth shell, classic services are added, which complement the product business to form a product service system. Although the following shells only describe a deeper integration of the system, many of the digital solutions make it possible to establish the deep integration to offer a Smart PSS [13][14][15] [START_REF] Schuh | Leistungssysteme[END_REF].

However, many companies struggle to sell Smart PSS. Studies about companies, which offer only product service systems, show that even they struggle to sell the solutions properly [START_REF] Dachs | Servitisation of European manufacturing: evidence from a largescale database[END_REF]. Referring to case studies we conducted for this paper, companies also struggle to sell Smart PSS properly. A first indicator of why these companies have problems is provided by a study showing that 39 percent of the surveyed top performing companies set up their own sales department to sell digital services [START_REF] Kampker | Lernen von den Besten: Fünf Erfolgsfaktoren bei der Entwicklung von Smart Services[END_REF]. Although this step seems logical at first in order to transfer the speed from the development process into sales, problems arise during integration with sales for existing products and services [START_REF] Ulaga | Hybrid Offerings: How Manufacturing Firms Combine Goods and Services Successfully[END_REF]. The design of Smart PSS follows the same principles as for PSS [START_REF] Valencia | The Design of Smart Product-Service Systems (PSSs): An Exploration of Design Characteristics[END_REF]. This includes the integration of the system in order to create synergies [START_REF]Produktpolitik. Kohlhammer[END_REF] [START_REF]Servicemanagement mit Systemerfolgreiche Methoden für die Investitionsgüterindustrie[END_REF], and the oneface-to-the-customer principle, which describes that there should be a single point of contact for every customer [START_REF] Schuh | Leistungssysteme[END_REF]. The current division of the sales departments into products, services and digital solutions leads to a one-sided focus in which each department optimizes itself and to individual customer contact persons for products, services and digital solutions [START_REF] Kampker | Lernen von den Besten: Fünf Erfolgsfaktoren bei der Entwicklung von Smart Services[END_REF].

This gap between the design principles and business practice is the motivation for this paper. Many companies invest large sums in the development of Smart PSS, but seem to have problems in the distribution of those systems. Due to the lack of research in this area, we aim to provide new insights in this paper to describe the relevant elements of a sales organization and to describe possible characteristics for different types of Smart PSS. In order to achieve this, the paper addresses the following research question: How can the sales organization of smart product service systems be designed successfully?

To answer this question we first briefly summarize the theoretical background of the relevant literature. Second, we describe the research methodology that was used to identify the elements of a sales organization for Smart PSS. Third, we present the results of the developed model with regard to the research question. Fourth, we conclude with the key contributions of this work to the field of sales organizations for Smart PSS.

2

Research Background

Smart Product Service Systems

The term 'smart product service systems' is quite new to research literature and therefore has to be described in detail before dealing with it in the following. CHOWDHURY ET AL. deliver an in deep systematic literature review on the term of Smart PSS [START_REF] Chowdhury | Smart Product-Service Systems (Smart PSS) in Industrial Firms: A Literature Review[END_REF].

They present in detail, that most of the work done in the field covers product service systems, but little effort was put in conceptualizing the 'smart' aspect of the PSS. The 'smart' aspect is often implied in the definition of these systems, but there a no distinct answers given to how PSS can be enabled to get 'smart'. VALENCIA ET AL. describe three general types of PSS: result-oriented, use-oriented and product-oriented. Furthermore, they answer to how PSS are getting 'smart'. "Smart PSSs are 'smart' because they carry some of the characteristics of smart products, such as the capacity to transform data into knowledge to perform more effectively" [START_REF] Valencia | The Design of Smart Product-Service Systems (PSSs): An Exploration of Design Characteristics[END_REF]. There is not one sole definition covering all aspects necessary for a satisfying characterization. However, as CHOWDHURY ET AL. deliver the most recent definition, it is likely the most sophisticated. The basis for their definition are the interactions of smart technologies, physical products, services and business models. Common to all previous literature the main goal is to satisfy the customers' needs [START_REF] Porter | How smart, connected products are transforming competition[END_REF][20] [START_REF] Chowdhury | Smart Product-Service Systems (Smart PSS) in Industrial Firms: A Literature Review[END_REF]. This satisfaction is reached through functional integration of digital services [START_REF] Zheng | A systematic design approach for service innovation of smart product-service systems[END_REF][35].

Sales Organization

The sales organization is a functional department of a business that is responsible for the sale of the portfolio offerings and the design of the distribution channels. Therefore, it may be seen as a tool of the distribution policy. Further, a comprehensive and complete task description is harder to conceptualize, because of the different scope in each individual business [START_REF]Sales Excellence, Systematic Sales Management[END_REF]. Most literature is dealing with characteristic approaches to sales of products alone. On the one hand, there are approaches, which may be transferable and applicable for Smart PSS. On the other hand, the Smart PSS alter from the common approaches in a way that these concepts have to be revisited and thought through again [START_REF]Serviceinnovation. Potenziale industrieller Dienstleistungen erkennen und erfolgreich implementieren[END_REF]. The sale and distribution of Smart PSS have a strong focus on business-to-business markets. Thus, customers are mostly businesses as well, which leads to other characteristic behaviours compared to Business-to-customer customers [START_REF] Kampker | Market Launch Process of Data-Driven Services for Manufacturers: A Qualitative Guideline[END_REF].

Methodology

The design of the sales organizations for smart product service systems followed an iterative five-step approach, which is based on the methodology of network thinking as shown in Fig. 2 [27] [START_REF]Vernetztes Denkenganzheitliches Führen in der Praxis[END_REF]. Designing a successful sales organization for Smart PSS is a complex problem. A tool for mastering this complexity is the method of networked thinking. The goal of networked thinking is to describe cause-effect relationships in systems in such a way that the behavior of the system becomes predictable under changed conditions. In particular, the method makes it possible to map and analyze the type, intensity and time behavior of the interrelationships between the individual system descriptive characteristics [START_REF]Die Praxis des ganzheitlichen Problemlösens[END_REF][28][29] [START_REF] Vester | Die Kunst vernetzt zu denken[END_REF]. The research in this paper is limited to steps 1 and 3 of the process. For this purpose, the basic model elements were described by means of a case study research approach. This method is suitable whenever a theory for a so far unknown phenomenon is to be built [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF] [START_REF] Eisenhardt | Theory Building from Cases: Opportunities and Challenges[END_REF]. Case study research ensure that the achieved results are closely connected to the everyday reality of companies. To record the case studies a standardized interview was conducted with managers from the sales and product management departments. In table 1 all recorded use-cases are listed. The number of case studies examined for this paper is limited to five, as the elements of the developed model and the different types of sales organizations were the same for all companies. Even though the focus of this paper is on machine manufacturers, we also included cases from neighboring industries in the study to gain a holistic view of the sales organization.

Sales Organization for Smart Product Service Systems

In this section, the results of the case study research are presented. First, the typical elements of a sales organization for Smart PSS and their connection will be discussed. Afterwards, different types of sales organizations and their effects on the aspects of the organization will be presented.

Elements of a sales organization for smart product service systems

Based on the conducted interviews from the case study research with five companies the following basic model of a sales organization for Smart PSS was derived through network thinking (fig. 3). The model is based on the interviews and the connections between the different elements. The interrelationships were collected with the help of networked thinking. The model is divided in four main sections, which are interconnected with each other. The variables of the sales organization of a Smart PSS are at the center of the model, which includes 'organization and controlling', 'resources', 'motivation and incentives' and 'channels'. These elements are also described in the literature as typical elements of a sales organization [25][26]. Two sections are influencing these variables of the sales organization. This is on the one hand the influencing factors of the Smart PSS itself with the elements 'Smart PSS value proposition', 'market segmentation' and 'pricing' and on the other hand, the influencing factors from the customer side with the elements 'type of customer' and 'buying process'. To measure the success of the Smart PSS sales organization companies use the key success indicators 'customer life time value' and 'customer acquisition costs'. These two indicators already show that companies want to minimize the costs to convince a new customer of the Smart PSS and try to increase the perceived value of the Smart PSS during a longer time following the long-term principle of PSS [START_REF] Schuh | Leistungssysteme[END_REF].

This basic model already shows the complexity of the design of a sales organization for Smart PSS. Especially the two influencing sectors have in combination a huge impact on the sales organization. The Smart PSS portfolio is usually developed independently from the sales organization and different types of market segments and types of customers want differently be addressed by a sales organization [START_REF]Sales Excellence, Systematic Sales Management[END_REF], which is also acknowledged in the next chapter by the description of different types of a sales organization for Smart PSS. The complexity of the model arises from the interaction of the individual elements, since they are of different intensity and time behavior. The change in the organizational structure, for example, can be changed quickly, while the culture and cooperation of the employees are rather slow elements.

Different types of sales organization for smart product service systems

The case studies show that, depending on the current Smart PSS portfolio and the company's customers, there are different types of sales organization. A total of four of these types are shown in fig. 4. In the first type, the offering is divided into three categories (product, service, digital), of which all are sold separately. The second type integrates the respective service and digital departments, as both work from a customer-centric perspective [START_REF] Kampker | Lernen von den Besten: Fünf Erfolgsfaktoren bei der Entwicklung von Smart Services[END_REF]. The third type offers Smart PSS for the first time, but as a bundle with individual revenue models [13][16]. The last stage represents a subscription model for the entire Smart PSS, where the entire service is sold via a subscription model in the digital ecosystem [START_REF] Tzuo | Subscribed. Why the Subscription Model Will Be Your Company's Futureand What to Do About it[END_REF]. The fundamental aspects of a sales organization are changing. These include the dominant organizational form, the focus of the organization, the culture and the level of integration. All four aspects are changing from a rather separate and non-integrated to an organization whose aim is to jointly sell Smart PSS. In addition to the mere organization or processes, softening factors such as the culture of the employees are also a decisive changing factor, which show the complexity of the whole research and application field. The sales organization must be able to fulfill all four characteristics, as the case study research shows that there are customer groups for each type of business model. This finding, together with the already prevailing complexity described in the previous section, shows that the sales organization for Smart PSS needs to be completely rethought and -designed to ensure successful sales and to respond to the needs of customer groups of Smart PSS.

Contribution and Discussion

The literature examined show that companies have few problems with the technical development of Smart PSS, but the sale leads to major problems. This happens due to the high complexity, which Smart PSS as well as different customer groups put to the sales organization. The contribution of this paper is a model that illustrates the complexity of sales organizations by presenting the individual elements in a first draft model. In addition, the demand is shown that the future Smart PSS sales organization is designed by different types in order to successfully serve various customer groups.

The results of the paper provide an initial contribution to understanding the complexity of the sales organization, which lead to recommendations for companies for the optimal design of their sales organization. Nevertheless, this paper can only provide a basic model for the sales organization, but is the first of its kind to focus on the sales organizations while other authors usually focus on the portfolio or development of Smart PSS [START_REF] Raddats | Servitization: A contemporary thematic review of four major research streams[END_REF]. Next steps are to detail the individual elements and to gain a deeper understanding of the intensity and time relationships of the elements described in fig. 3. Based on the resulting dynamic model of the Smart PSS sales organization, influencing variables can be determined that could be changed better or worse. Based on these influencing variables, recommendations for action can be developed, which transfer the

Fig. 2 .

 2 Fig. 2. Approach of Network Thinking for the development of sales organizations

Fig. 3 .

 3 Fig. 3. Basic model elements of a sales organization for Smart PSS and their connection

Fig. 4 .

 4 Fig. 4. Different types of sales organizations for Smart PSS

Table 1 .

 1 Overview of selected cases

	Industry	Employees Turnover	Sales organization Type
	Agricultural machinery	11.500	€ 3.7 bn	Integrated	OEM
	Equipment manufacturer 6.000	€ 1.5 bn	Separated	OEM
	Machine manufacturer	13.500	€ 3.5 bn	Integrated	OEM
	Equipment dealer	3.000	€ 1.1 bn	Integrated	Supplier
	Equipment manufacturer 1.700	€ 280 mio Separated	OEM

findings of the theoretical model of a sales organization for Smart PSS into design guidelines applicable for companies. In order to carry this out, both extensive research on Smart PSS, as well as the inclusion of further case studies is necessary.