Iprocess pdf

KU Leuven has demonstrated that X-ray CT Computed Horticultural products transport the oxygen they re- Tomography is an effective method for the accurate quire and waste materials, such as carbon dioxide and and non-destructive mapping of whole fruit porosity water, through their pores. Porosity thus also exerts based on a simple model using the correlation be- an influence on a number of changes that result from tween CT images grey shades and porosity.

The cor- oxygen lack or the inadequate disposal of waste mate- relation is proven to be valid for a wide variety of rials. Examples of such changes include the internal products, demonstrating its broad application poten- brown discolouration observed in apples or celeriac.

Gas transport is more difficult in those parts The new porosity measurement technique is convenient and easily applicable for a variety of other products. A non-destructive method of porosity mapping is a first step towards the development of sensors for in- line quality assessments of porosity on sorting lines with a view to storability. Porosity maps of Jonagold apples, Purple-globe eggplants, Purple-top turnips and Conference pears Figure 1 demonstrate that fruits and vegetables exhibit very different internal struc- Figure 1.

Correlation between the grayscale intensity of CT images tures. On average, egg- plants are the most porous Transaxial X-Y, top and coronal X-Z, bottom slices of eggplant a , turnip b , apple c , and pear c porosity maps translated from grayscale CT images. Based on the simple linear followed by turnips The metabolic gases and water during post-harvest han- porosity of a pear is very low and consistently less dling and storage.

The most dense tissue is found in the core. Good contact control time. Some open robot controllers allow real-time under demanding conditions requires 1 ms, or better, trajectory feeding at the micro level, and these repre- in the sensor to servo control loop.

However, for toler- sent obvious candidates for such real-time sensor- ant control under compliant conditions and moderate based motion applications. We may define the meso level of real-time control from 10 ms to 1 s.

Macro If a given application only requires real-time trajectory level real-time control is at the level of 1 s and above, generation at the meso level, interpolation may be and may adequately be called real-time task genera- used to alleviate the application from the micro real- tion. Performing tough robot motion generation stuff with an adequate level of coding effort. Illustration of mesoscopic interpolation points in red, defining a smooth trajectory. In green are shown the microscopic The interpolator is an independent, network- interpolation points that are sent to the robot controller, obtai- connected and long-lived process that keeps the robot ned by Cubic Hermite Splining of the mesoscopic points.

Sensor inte- gration for motion generation may thus adequately be performed at the meso level. Howev- er, the robot controller may still need to be fed 1 ms interpolated trajectory points.

In experimental and development settings, the inter- polator plays a key role in maintaining operation of the robot controller modus, because restarting the robot controller system generally requires a certain degree of manual interaction and waiting time.

Illustration of the deployment of components surroun- ding the interpolator called RobotFacade, top of diagram. This is important for tions may distort communication with the robot con- fast development cycles during research investi- troller, sometimes leading to divergence between the gations. Current activity A software program has been developed that uses is focusing on the prevention and handling of such Cubic Hermite Splines to generate smooth trajectories malfunctions in network communication.

At the other end, the program lis- The prevention and mitigation of network malfunc- tens for a network connection from a sensor-based tions are achieved using appropriate computing and application motion generator, which is required to network hardware, and by optimizing the real-time feed a trajectory at the meso level of resolution 10 performance of the software.

The software per- Malfunction management involves monitoring the iProcess Report forms well for sufficiently smooth application trajecto- robot in its divergent, post-trajectory, state, and then ries. However, experiments using it in a Python- based motion generation framework indicate that the Morten Lind principle is sound.

All actors can benefit from sharing relevant infor- mation in a timely manner. Information on data that supply frozen fish. This lack of vertical integration management and planning practices was acquired by appears to limit information sharing between the ves- means of semi-structured interviews. Limited amounts of data The management of food supply chains is particularly place constraints on decision support at the pro- complex due to an intrinsic focus on product quality.

Fishing for whitefish. Proposed schematic for information exchange in the whitefish supply chain. In recent years, several In terms of production planning, improved infor- studies have investigated the value of information mation sharing may also contribute to the process of sharing and its impact on supply chain performance. Historical and season-specific information Moreover, detailed information on catches such as about catch areas and factors affecting catch quality temperature conditions and product status, which is can be used to improve fishing strategies.

If such information were available at an Further work is needed to investigate factors such as early stage, processors could use it to improve pro- the willingness among industry actors to share supply duction planning decisions.

Such work will also serve to identify new opportunities for both fishermen and the processors. Infor- willingly share information for their mutual benefit. Maitri Thakur Product quality information is held by the processors Maitri. Thakur sintef. Many NTNU research sci- entists and students have been working on this issue as part of the iProcess project. Supply chain planning SCP and job losses, the industry has succeeded in pro- aims to mitigate uncertainty by coordinating and inte- moting product and process innovation, and expand- grating key business processes throughout the supply ing its market reach.

However, the industry operates chain from raw materials procurement to production, in a highly global and competitive market that con- distribution and sales, and by managing supply and de- stantly demands higher levels of performance, effi- mand.

This is particularly relevant in the whitefish sector. In terms of infor- The iProcess project has identified and ranked the fac- mation sharing, performance can be improved by opti- tors that impact on supply uncertainty and has proposed mal supply chain operations management that guar- a method by which whitefish processors can improve antees supply and demand alignment by applying nov- their ability to boost capacity utilisation, service levels, el integration and coordination mechanisms.

Such topics have included the impact of supply and demand uncertainty on supply chain operations, and the ways in which supply chain planning can im- SUPPLY AND DEMAND prove performance, with a focus on the role of infor- We have proposed integrated tactical planning design mation and technology. This work has resulted in a strategies based on an analysis of supply and demand proposal for a process design for integrated tactical uncertainty. Such strategies will contribute towards inte- supply chain planning.

You have to accept this market dynamic and sporadic trends. The use of information ness have made supply uncertainty a key factor, par- dashboards in support of market-related decision- ticularly in sectors such as the food industry, which derives its raw materials from natural resources but making may also improve performance.

With the exception Tactical planning at supply chain level is particularly of parameters such as supply quantity, quality, lead important in environments characterised by long-term time, price and product information raw materials uncertainty, in which decision-makers struggle to pre- size and type , less is known in terms of supply uncer- iProcess Report dict changeable internal and external factors such as tainty about inputs such as probabilities, severity and seasonality and the imposition of quota systems.

Heidi Carin Dreyer Heidi. Dreyer ntnu. Fo- The concept of operational production planning focus- cus during the initial problem structuring phase is di- es on the issue of establishing optimal production rected at identifying decision variables such as the plans. A production plan specifies the products in content of the various execution plans, the number of question, the production lines utilised, and the alloca- hours spent following each plan, the number of pro- tion of resources such as personnel and machinery.

In duction line workers assigned and whether to pay the fisheries sector, key factors such as the volumes, them overtime. The next phase involves the opera- quality and distribution of production raw materials, tionalisation of the various uncertainty factors, for the fish species, are unknown when planning is taking example by assigning probabilities to a variety of pos- place. One scenario involves a discretisation A production plan that is optimal for high catch vol- of supply with an added probability, such as a high umes may be very costly in low catch scenarios.

Sto- volume catch, a high proportion of high quality raw chastic programming enables the preparation of ro- materials, and a medium proportion within the catch bust plans that maximise profits in spite of catch size of the species haddock and pollock. It is known from the literature that treating and vessel type. It will enable by their expected values.

Our case study has considered only the ed gain is balanced against the complexity of the mod- processing of cod, and future models will be required el.

Moreover, the catch prediction model may be used for species such as haddock and pollock. Instead of to reduce the supply uncertainty. The use of a multi-plant production model will demonstrate how production can optimally be distrib- iProcess Report compared with cases in which uncertainty is ignored. Refining the model by the addition of discretisation uted across a number of different facilities.

This has required innovative analytical toolboxes and digital infrastructure that have opened up new opportunities for value creation from data. But where is it stored, have generated a wealth of opportunities. But they and is it accessible? One size does not fit all. Full-steam-ahead commitments to advanced data In paper files. Analysis of data in paper formats may science projects are vulnerable to failure if they are require costly labour-intensive and time-consuming not fully thought through and properly implemented.

This article discusses a few issues worthy of considera- In detached databases without keys. These data can- tion before starting out on data science projects in the not be linked, turning our Big Data dreams into a se- food production industry. We are all the elusive keys. Goals in data science are not always easy to into shape. And so to our project. Our planning must formulate, but we also know that vague goals yield be realistic, and this part is time-consuming. Analyses vague results.

What would bring most value to our company? Do we really In a database. Even when our data appears to be easi- need insights from complex algorithms? AI may not be ly within reach there is still a massaging process to be necessary if we can generate value from a simple data done in order to obtain full access.

Variables often display technology. The pro- ject that did not find such skeletons in its data closet is yet to happen. And finally, there is data protection legislation. Vague goals yield vague results. It is a challenge to sort and collate multi-source data. The cleaning and handling data are the most forgotten and time-consuming aspects of data science projects. Our data must be relevant to our project from all parties. Data science has been oversold as a goals.

But things are not that simple. The knowledge possessed by data owners is crucial. Rubbish in, rubbish out. No amount of erroneous data will yield the correct result, and there Data gathering.

However, different dures change over time and data engineers provide machine learning methods can to some extent be ap- key assistance to data scientists in addressing data plied to address missingness, outliers and other arti- infrastructure issues and general data dirtiness. Domain knowledge. This is essential if we are to obtain Amount. Our project will require an adequate amount a full overview of what are investigating and what we of data, but as quality increases, the need for data to can expect; the extent to which existing knowledge identify signals is reduced.

Fundamental changes in can be incorporated into our modelling, and the iden- conditions, such as new equipment, may mean that tification of important factors that have not been the data cannot be compared before and after the measured or registered. We must consider the expectations and analysis. By Variation. The implementation of a food recipe entails putting outcomes into context we can achieve project very little data variation. This may be good for produc- success in terms of ownership, adaptation of results, tion, but if the aim of our project is to learn how the and value creation.

Long-term and sustainable solutions based on data science require model outcomes to be anchored in a Dirty data. Data dirtiness represents one the biggest business context. Project participants must always be barriers to obtaining insights from our data. Models must be time, dates and timestamps, and suitable feature rep- checked and updated in order to remain relevant. An resentation. It is important to note that these tasks effective hand-over is crucial to the longevity of pro- usually consume the majority of our project hours, ject success.

Kira Svendsen kirasvendsen gmail. Mage nofima. However, cessing companies, we conducted a tailored, innova- food processing has so far stood out as an exception tion survey. A total of companies of different siz- to this trend. There are many reasons for this, alt- es, making up about 10 percent of all processing firms hough the nature of the product is probably para- in Norway, participated in the survey.

These compa- mount. New and innovative technologies that can nies process food raw materials from both agricultural better handle fragile raw materials are now readily and marine sources. Data gathering entailed tele- available, but levels of automation remain low. This phone interviews with CEOs. In this study, we aim to analyse why technology and collaboration with others.

Our aim is to examine the specific The overall aim of our survey was to investigate if barriers or bottlenecks that prevent the adoption of strategies or business models were being applied that technologies involving artificial intelligence.

This is im- portant because the introduction of new and more flexible technologies offers greater efficiency and profitability, which in turn yields greater added value. Thus, to better our understanding of the factors that boost the competitiveness of the Norwegian food processing industry, we considered it important to examine the barriers that prevent the adoption of new technology.

Our econometric analyses identified several barriers Figure 1. Level of education of most employees in the food to the introduction of new technology, the most im- processing industry. Left: Diagram illustrating levels of automation and the application of artificial intelligence in the Norwegian food processing industry.

They must also develop their and too difficult to understand. There is good reason to believe that business models hensibility. We found that low levels of education and will change in the years to come. Organisational struc- collaborative effort within the industry also have a tures that emphasise technological and collaborative major impact on the adoption of innovative technolo- skills must achieve increasing importance if we are to gies.

Future human activity must become more sustainable Design for product-life extension slow down loops by adopting the concept of the circular economy. Unlike a linearly- Analysis and Cradle-to-Cradle design represent alter- designed product, which is simply delivered into the native sustainable design options. This requires de- packaging design and associated issues. Vendor com- signers to build feedback loops into their products and panies have different concerns linked to their be fully aware of the life-cycles of the materials they knowledge of materials, such as design for disassem- use1.

The iProcess project has investigated several instances of how new and flexible technologies can improve Design for long-life products slow down loops current production methods. Others have studied robotics and the ways in which robots can be used to cut ham or package frag- ile, compliant, and shape- and size-variant objects such as fish. Circular design guide. Product design and business model strategies for a circular economy. Journal of Industrial and Production Engineering, 33 5 , Raw materials Production Use Waste Figure 1.

A linear versus a circular economy. It is not like linear design, where you design a product and send it off into the world. In circular design, the product will return for repair, upgrades, reuse or recycling. From a cir- create products more suited to a circular economy. It is linked to the whitefish sector. The greater the volume also important to design with the aim of extending of catch information available onboard fishing vessels, product life so that they remain easy to clean, main- the greater the volume of data that can be transferred tain and repair.

It must be possible to replace parts if to the processing plants for use in operations plan- necessary, and for software to be upgraded. And last, ning. The iProcess project has also investigated information flow. However, circular design is now identi- flow from the farm to the tannery, and the ways in fied as the means of safeguarding a sustainable future.

This strating how various methods can be applied within iProcess Report information can then be used to improve performance different companies. They can be the drivers of a tran- at each successive stage and thus optimize hide quali- sition towards a circular economy, characterised by ty. Optimal hide quality provides manufacturers with products and services that a sustainable future de- greater flexibility in their choice of which product to produce from each hide. This is turn enables the man- mands.

Salomonsen sintef. Dreyer, H. Lind, M. Proposals for enhancing interpolation for robot trajectory execution. International Journal of Physical Distri- Misimi, E. Robotic Handling 2. Eskildsen, C. IEEE: pp. Journal of Near Infrared Spectroscopy, 27 8. Nugraha, B. Non-destructive porosity mapping of fruit and vegetables 3. Isachsen, U. Postharvest biology and tech- Fast and accurate GPU accelerated nology, , pp. Computers and Electronics in 9. Farstad, P. The impact of botics and Automation, pp.

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