A case study of the DMI4.0: the agro-food industry of the food valley

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The indexes 𝐼𝐼𝑃 and 𝑆𝐼𝐼 calculated by the HoD are then inserted and compared in the boxes of the SWOT4i analysis, which constitutes the second step of the DMI4.0 framework for assessing maturity.

Results are provided in Table 4.10.

As it emerges from quantitative results, with respect to possible integration of systems, Pro.PR SpA have pushed on integration of all the phases related to operations, for instance (i) maintenance of machineries (𝑆𝐼𝐼 = 23%, 𝐼𝐼𝑃 = 39%) and systems (𝑆𝐼𝐼 = 22%, 𝐼𝐼𝑃 = 33%), (ii) material handling (𝑆𝐼𝐼 = 24%, 𝐼𝐼𝑃 = 17%), and is developing interesting systems for controlling the process phases, namely (i) aging control (𝑆𝐼𝐼 = 43%, 𝐼𝐼𝑃 = 47%) and quality control (𝑆𝐼𝐼 = 31%, 𝐼𝐼𝑃 = 53%), as well as the (ii) weighting phase which is critical for monitoring the moisture loss of hams (𝑆𝐼𝐼 = 34%, 𝐼𝐼𝑃 = 56%).

On the contrary, it seems to be currently neglected and emphasis on digital optimization of the salting phase (𝑆𝐼𝐼 = 35%, 𝐼𝐼𝑃 = 73%) which is a limitation to all the quality control phase. Finally, the increasing stress on traceability of raw material for high-quality product manufacturing, is highlighted by an increasing digitalization and integration of the system (𝑆𝐼𝐼 = 43%, 𝐼𝐼𝑃 = 67%).

This consideration is resumed by the radar-chart expressing the DMI4.0, represented in Figure 4.20.

Values are calculated by equation (12). Analyzing the radar-chart is possible to define the first step for a development roadmap: this entails balancing the radar chart, pushing on Smartification. This is because lots of activities involving decision-making processes are still demanded to centralized departments, hence decreasing efficiency and flexibility. Consequently, the system is still not ‘Smart’. As it can be seen in room #5 of the HoD, except for weight controls of goods and environmental controls of cold rooms by remote, little intervention can be realized for further digitalize all the technologies allowing a better integration level. For instance, digitally managing the salting phases of the ham has a positive impact on monitoring the aging phase, since operators have data about the ‘processing history’ and can make decisions on position of hams in the cold room. This is also supposed to need further implementation of IoT infrastructures, positively affecting the ‘Webification’ and ‘Technology stack’. This demonstrates the cyclic nature of the development roadmap fostered by the DMI4.0.

It must be noted that the majority of activities are difficult to be further digitalized, for instance towards automation, although mechatronic technologies support these phases. Moreover, several important phases are difficult to further automate, although related phases can be done. For instance, the quality control is still of olfactory nature and humans sniff at invasive probes for sensing the tasting and deciding upon quality. Although some research is being carried out for switching to visive controls that forecast quality exploiting augmented reality as well as machine learning technologies, systems are still not reliable and marketable by manufacturing technologies. Nonetheless, an owned disruptive system that receive the vocal input from the operator and then handle the ham to the planned cellar position, integrating the information on the company information system, is effective and it actually supports the phase. On the contrary, all the phases related to the quality assurance (e.g. monitoring of environmental conditions of the cold room as well as the ham cellar, e.g. temperature, indoor and outdoor hygrometry and so on) are currently vertical and end-to-end integrated and represent and effective IoT system, however an outlook on horizontal integration is still missed.

A further step towards a development roadmap is realized by the roadmap for advancements, that allows to visualize management practices that have a gap to fill. This gap comes from the comparison between indexes computed for each hierarchical level as in equations (13) and (14). The digitalization level of management organization is well-balanced since the radar-chart is an equilateral triangle. Nonetheless, a gap with potential integration of activities belonging to all management levels exist. It seems to be

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necessary to primarily speed up the digitalization of strategy activities, since the higher target to achieve again AS-IS level, i.e. Roadmap for advancements (RFA) value 4, and Digital Maturity Index (DMI) value 2. However, since the high similarity of triangles other plans can be devised. For instance, a primary focus can be putted on firm priority, as well as it would be better planning digitalization and integration project of the most childish process phase. For instance, again the salting phase can be significatively improved (𝑆𝐼𝐼 = 35%, 𝐼𝐼𝑃 = 73%). Another approach can be working on the most significative process for having a high-level quality end product. Digital improving quality control is possible since a gap between 𝑆𝐼𝐼 and 𝐼𝐼𝑃. However, some phases have ‘external limitations’, namely further integration of phases (i) identification of goods (ID) and (ii) traceability are limited from cultural and technology gap of the upstream SC tiers and some ‘grey zone’ of regulation of the Consortium.

For validating the DMI4.0 results of the survey has been benchmarked with the narrative description of limits, gaps, and future direction of the cold meat industry. As highlighted by Table 4.6, focal firms of this SC are searching for efficiency of the whole processing line, SC visibility and transparency, as well as business sustainability and respect for the environment. Dealing with this transformation, Pro-PR seems to have a gap in integrating its systems towards decentralization of decision-making processes for intelligent units. Furthermore, Pro-PR is striving to push on SC transparency and traceability, although the whole SC seem to be still not ready for the incoming transformation. Thus, considerations emerged from the unstructured interviews with technology manufacturers are consistent with the result of the survey, and hence the model has been validated.

Use cases of the DMI4.0: the dairy industry and canned vegetable cases

Once the effectiveness and reliability of the model has been proven, the model has been used for two different use cases. The former assesses the maturity of a ‘Parmigiano Reggiano’ cheese producers for the dairy industry, named her Pa.Re.Cheese Srl for confidentiality reason. In this case, the survey has been carried out involving the producer and two equipment suppliers, namely a machinery manufacturer and systems engineering corporate company. The latter assesses the maturity of the tomato sauce industry chain. Four players have been involved, two goods producers and two systems engineering manufacturer.

In both use case the process description filling in room #2 with the process phases has been again described ‘neutrally’. In the latter use case, the digitalization weight of technology adopted, i.e. 𝑑_𝑤𝑖, is inserted as representing the state-of-the art of producers as emerged from the surveys, rather than as representing the configuration of a single firm. Quantitative results of the use cases are provided in Figure 4.21. Then discussion follows.

Concerning Pa.Re.Cheese Srl, a company operating in the foothills of the Parma district, the most digitalized phases relate to the preparation of ingredient, the management of the recipe, and the controls systems during the preparation of block cheeses preceding the aging phases, and they manly refer to quality assurance and tracking of wheel of cheese arrangement (e,g. cooking 𝑆𝐼𝐼 = 43%, 𝐼𝐼𝑃 = 100%;

ingredient management 𝑆𝐼𝐼 = 43%, 𝐼𝐼𝑃 = 100%; dosing whey and rennet 𝑆𝐼𝐼 = 43%, 𝐼𝐼𝑃 = 100%).

Traceability is a core element also for aging and warehousing and needs integration for achieving efficiency and visibility. However, phases devoted to the control of goods during the aging process are very craft, (e.g. warehousing 𝑆𝐼𝐼 = 31%, 𝐼𝐼𝑃 = 100%). The pushed digitalization of the ‘cooking’

process is a corrective measure to overcome the lack of control systems that continuously and in near/real-time give information on the aging progress for discriminating quality of end products.

Moreover, it needs to be noted that the sample showed that he, and more in general producers as well, strives to ‘accept’ automation and integration technologies as useful for improving the process towards

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the value-network creation, and producers seem to be more focused only on their own activities.

Although technologies that allows to improve the processes towards automation and operations intelligence (i.e. Smartification mean values 𝐼𝐼𝑃 = 75%), and total integration (i.e. Integration and Webification, mean values of 𝐼𝐼𝑃 = 83% and 𝐼𝐼𝑃 = 100%) do exist, a gap still exists for the producer in implementing these solutions: for instance the adoption of technologies that can foster digitalization and integration (i.e. Technology stack) verifies a mean value of 𝑆𝐼𝐼 = 25% whereas the mean possible value of 𝐼𝐼𝑃 = 78%. All these considerations are simply translated in the radar chart in Figure 4.21, where it is immediate to see that Pa.Re.Cheese is still childish in adopting disruptive technologies of I4.0 and the only web technologies digitalize the process (mainly the phases relating to the cheese wheel preparation). This result is furthermore highlighted by the roadmap for advancements radar-chart. In this chart, the only activities digitalized almost significantly are the operations (i.e. 𝑅𝐹𝐴 = 2), to which belongs the cheese wheels preparation. It seems to lack a focus on value-network creation and market orientation (i.e. 𝐷𝑀𝐼 = 1 for both strategies and tactics), although I4.0 technologies allow to push towards operations (i.e. 𝑅𝐹𝐴 = 5) and tactics (i.e. 𝑅𝐹𝐴 = 4) as well as strategies (i.e. 𝑅𝐹𝐴 = 3).

Since a very different environment, the canned vegetable industry is continuously asking for efficiency and robustness. Two nodes of the SC have been surveyed, i.e. two producers and two manufacturers:

• A big corporate company (still SMEs, however) producing tomato sauce and fruit juice.

• A small company producing tomato sauce.

• Two manufacturers providing systems engineering solutions and equipment for controlling the system. Two aspects arise and concerns are listed below:

Two aspects arise and related concerns are listed below:

1. The product is ‘poor’ and the plant availability⁸⁹ low, thus producers have pushed on automation and mechatronics adoption for (i) maximizing the production volumes, and (ii) continuously monitoring and controlling the system towards (a) process efficiency⁹⁰ and (b) decreasing the production unit costs.

2. Continuously monitoring and controlling the system makes way to systems engineering manufacturers to become service providers.

Cross-selling activities for technology manufacturers have been already discussed, and it needs digitalization and integration technology provisioning, that actually they are able to supply. As a result, technology solutions provided by the systems engineering manufactures are installed and used by the goods producers: the digitalization level to achieve (orange blank-chart line in Figure 4.21) is the same of the digitalization level implemented (blue full-colored-chart in Figure 4.21). The only difference relates to the smartification level. Actually, although producers are interesting in disruptive technologies that allows to making best decisions for scheduling activities and maintaining equipment, a gap still exist between (i) what is proposed by technology manufacturers and asked by the firms, and (ii) what is actually bought by the same firms. Of course, developments for tomato sauce producers first entails pushing on smartification and leveraging what engineering manufacturers are able to supply. Next step is about the development of technologies, by systems engineering manufacturer) that can integrate and network SC systems (i.e. 𝐷𝑀𝐼_𝑊= 3, 𝐷𝑀𝐼_𝑂 = 3, 𝐷𝑀𝐼_𝑇 = 2): technologies are available, but they are not

89 Total time in which the plant operates for producing goods in a year: for tomato sauce industry it is limited by the seasonality of raw material

90 Quantities produced with respect to the plant capability during steady speed operations

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implemented toward the value-creation network, and this especially justify the value 𝐷𝑀𝐼_𝑇 = 2, albeit the technology level of solution provided is high-tech. Same consideration can be done concerning the roadmap for advancements: also in this case the operations are very automated and exploit mechatronic technologies, but the total integration point of view towards the value-creation network is missed.