Obsolescence Mitigation in Automotive Industry using

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Procedia Manufacturing 00 (2017) 000–000 ProcediaManufacturing Manufacturing1600(2018) (2017)39–46 000–000 Procedia Procedia Manufacturing 00 (2017) 000–000

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7th International Conference on Through-life Engineering Services 7th International Conference on Through-life Engineering Services

Obsolescence Mitigation in Automotive Industry using Long Term Obsolescence Mitigation in Automotive Industry using Long Term Storage Feasibility Model Feasibility Model Manufacturing Engineering Storage Society International Conference 2017, MESIC 2017, 28-30 June a,∗ b c a 2017, Vigo (Pontevedra), Spain Kevin BOISSIE , Sid-Ali ADDOUCHE , Marc ZOLGHADRI , Daniel RICHARD a,∗ b c Kevin BOISSIE , Sid-Ali ADDOUCHE , Marc ZOLGHADRI , Daniel RICHARDa a Valeo Confort and Driving Assistance, 76 rue Auguste Perret, 94000 Creteil, France, [email protected]

Institut Universitaire Technologie de Montreuil, 140 Rue de la Nouvelle France, Montreuil, [email protected] Valeo Confortdeand Drivingcapacity Assistance, 76 rue Auguste Perret, 94000 Creteil, France, [email protected] Costing models for optimization in93100 Industry 4.0: Trade-off Supmeca - Institut Superieur de Mecanique de Paris, 140 3 rueRue Fernand Hainaut,France, 93400 Saint France, [email protected] Institut Universitaire de Technologie de Montreuil, de la Nouvelle 93100Ouen, Montreuil, [email protected] Supmeca - Institut Superieur de Mecanique de Paris, 3 rue Fernand 93400 Saint Ouen, France, [email protected] between used capacity andHainaut, operational efficiency b

a

c b c

Abstract A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb Abstract The diminishing of material sources leadsa University companies, industrial, to implement of especially Minho, 4800-058 Guimarães, Portugalremedial actions to continue to serve their b customers throughout the committed contractual period. Original Equipment Manufacturers face hard relations with car manufacThe diminishing of material sources leads companies, especially industrial, implement actions to continue to serve their Unochapecó, 89809-000 Chapecó,toSC, Brazil remedial turers and thousands suppliers. Thecontractual obsolescence and Original diminishing number Manufacturers of sources, specifically electronic and devices, customers throughoutofthe committed period. Equipment face hardinrelations with carITmanufaccause tough problemsoftosuppliers. OEM because of their dependency to the evernumber increasing numberspecifically of these devices in modern This is turers and thousands The obsolescence and diminishing of sources, in electronic andcars. IT devices, the reason in compliance the IATF TS16949 standard, is necessary to identify costs,devices feasibility of implementation, cause toughwhy, problems to OEM with because of their dependency to theit ever increasing number the of these in modern cars. This is as as the success and risk chances anyTS16949 obsolescence management plan. Intopractice, twocosts, solutions are regularly used: bridge Abstract thewell reason why, in compliance with the of IATF standard, it is necessary identify the feasibility of implementation, stock time buy. the long term storage causes real management problemstwo andsolutions cost difficulties when used: the supplier as welland as last the success andHowever, risk chances of any obsolescence management plan. In practice, are regularly bridge minimum order level isof much greater the product short termreal forecasts. aproblems technical, economical and riskwhen assessment point stock and time buy. However, thethan long term storage causes management difficulties the supplier Under thelast concept "Industry 4.0", production processes willFrom be pushed toand becost increasingly interconnected, of views, this paper looks at the feasibility study of long storage based onathe field in one of theassessment main OEMs in minimum order levelon is much greater than product shortterm termmuch forecasts. From technical, economical and risk point information based a real time basisthe and, necessarily, more efficient. In situations this context, capacity optimization the car industry. Based on a review of the literature, we identify the constraints related to the types of materials, their handling of views, this paper looks at the feasibility study of long term storage based on the field situations in one of the main OEMs in goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. and car storage conditions. By amodeling the decision-makings for management of obsolescence and diminishing sources, we study the industry. Based on review of the literature, we identify the constraints related to the types of materials, their handling Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of the and consequences. The preventingfor actions are studied through a keyand matrix as a decision tool. we A formal andpossible storage solutions conditions. Bytheir modeling the decision-makings management of obsolescence diminishing sources, study maximization. The study of capacity case optimization and costing models is an important research topic that deserves model is presented and a real is studied. the possible solutions and theirautomotive consequences. The preventing actions are studied through a key matrix as a decision tool. A formal contributions fromand both theautomotive practical case and theoretical model is presented a real is studied. perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been c 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) c 2018 The Authors. by Elsevier B.V. This is The an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) value. trade-off capacity maximization vs operational efficiency is highlighted and on it Through-life is shown that capacity Peer-review under responsibility ofthe theCC scientific committee of(https://creativecommons.org/licenses/by-nc-nd/4.0/) the 7th International Conference Engineering This is an open access article under BY-NC-ND license Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services. Services. optimization mightresponsibility hide operational Peer-review under of theinefficiency. scientific committee of the 7th International Conference on Through-life Engineering Services. © 2017 The Obsolescence Authors. Published by Elsevier B.V. Keywords: management, DMSMS, Automotive, Storability, Decision support Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference Keywords: Obsolescence management, DMSMS, Automotive, Storability, Decision support 2017. Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency

1. Introduction 1. Introduction first known motor vehicle dates back to 1769. It is the brainchild of Nicolas-Joseph Cugnot, a French military 1.The Introduction The first known motor backvehicle to 1769.toItcarry is theheavy brainchild French military engineer who develops a vehicle conceptdates of steam loadsofofNicolas-Joseph artillery. Later Cugnot, in 1886,aGermany’s Karl engineer who develops concept of steam vehicle to carry heavy loadsand of their artillery. Later 1886, Friedrich Benz made thea world’s first gasoline-powered vehicle. Nearly 250 yearsmanagement later, andinwith the Germany’s evolution ofKarl the The cost of idle capacity is a fundamental information for companies of extreme importance Friedrich made systems. the world’s first gasoline-powered Nearly 250 years later,potential and withand thecan evolution of the in modern Benz production In general, it is defined as vehicle. unused capacity or production be measured in∗ several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity

KevinAfonso. BOISSIE, Tel.: +33-668-178-484. * Paulo Tel.: +351 253 510 761; fax: +351 253 604 741 ∗ Kevin E-mail address: [email protected] BOISSIE, Tel.: +33-668-178-484. E-mail address: [email protected] E-mail address: [email protected] c 2018 The Authors. Published by Elsevier B.V. 2351-9789 2351-9789 © 2017 Thearticle Authors. Published Elsevier B.V. This is an open access under the CC by BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) c 2018 2351-9789 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific the Manufacturing International Conference 2017. 2351-9789 © 2018 The Authors. Published bycommittee Elsevier of B.V. Peer-review under responsibility of the committee of the 7th InternationalEngineering ConferenceSociety on Through-life Engineering Services. This is an open access article under the scientific CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under of the scientific committee of theof7th International Conference on Through-life Engineering Services. Services. Peer-review underresponsibility responsibility of the scientific committee the 7th International Conference on Through-life Engineering 10.1016/j.promfg.2018.10.156

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Kevin Boissie et al. / Procedia Manufacturing 16 (2018) 39–46 Author name / Procedia Manufacturing 00 (2017) 000–000

concept of connected cars, we estimate at 30,000 the average number of components in a modern car [3]. In addition, this rapidly growing market over the past two decades has seen vehicle sales, by the 50 largest manufacturers, rise from 54.5 million units in 1996 to 95 million units in 2016 [6]. It represents 2.85 1012 components manufactured and put on the market each year. These components are manufactured internally by the manufacturer or purchased from its suppliers called original equipment manufacturer (OEM). The OEM is a specialist supplier. It provides to manufacturers customized solutions to be integrated into one or more vehicle platforms. The supply contracts generally cover a period of 3-5 years of serial life followed by 10-15 years of after-sales. The OEM is also committed to a warranty period, during which it will be held responsible for the repair and immobilization costs of the final customer vehicle [9]. To meet these requirements, the International Automotive Task Force (IATF) requires the OEM Quality Management System (QMS) to be IATF TS16949 certified, a standard written and followed by IATF [5]. The minimum manufacturer’s warranty is 2 years, but some companies use an increased period as a commercial argument. In 2013, Honda guarantees its ninth generation of CIVIC, 10 years or 1 million kilometers. In 2018, it repeats the operation but reducing the warranty period to 5 years. These constraints are partly transferred through specifications to the OEMs. This allows manufacturers to offer customers more reliability. OEMs integrate these customer requirements, from a design, supplier management and production point of view. Therefore, the OEM has to secure the quality and deadlines of the modules, throughout the car’s life, despite all possible evolution of its own sources and therefore the required material availabilities. Moreover, the OEM has to respect the very tough cost schemes. Dealing with (i) demanding car manufacturers requirements and contracts, and (ii) evolving sources of components and materials, is quite challenging for all OEMs because it that could reduce drastically their profitability. Our research looks to find out practical solutions to this dilemma. This study is focused on an OEM who has to have a relevant long-term inventory management. Among all types of suppliers, the study concerns only the so-called ”production suppliers”, i.e. those suppliers from which the OEM buys components or material, directly or indirectly, that fall in the bill of materials (BOM) of the sold modules by the OEM. The paper is organized as follows. Next section browses briefly the state-of-the-art. Section 3 suggests a formal model of the long term stockability. In section 4, we illustrate the approach through an industrial case. Section 5 concludes the paper and discusses the research niches for future works. 2. Literature and focused issue Taking into account the context presented in introduction, and facing Diminishing number of materials sources and material shortage problem (DMSMS) [12], the OEMs deals daily with the problems of breakage and end of life components while minimizing their impact and making sure that their customer or the user does not suffer from any disturbance. These problems appear most often in the decline phase, however we observe such cases in the growth and maturity and even in the introduction phase because of DMSMS. Many authors agree that the root cause of obsolescence issues is the mismatch of the system and the components or parts lifecycles, see [12, 2, 10]. The resolution by a redesign or an alternative is more favorable when the volume growth but largely unfavorable in terms of OEM image as the car manufacturer validation is needed. The OEM’s suppliers are not the only source of disruption. Changes in standards and regulations may affect product and process characteristics. Additionally, variations in needs, both upwards and downwards, generate tensions in the supply chain. According to Sandborn et al. [8], Romero Rojo et al. [7], the possible curative solutions to the diminishing sources are: Authorized Aftermarket, Emulation, Minor Redesign, Major Redesign, Alternative, Equivalent, Existing Stock, Last Time Buy and Cannibalization. However, in any case, it is crucial for the reliability of the process and products, to check the consistency of the new purchasing conditions and storage times with the recommendations and manufacturers warranty [1]. One other issue, should be to avoid ”planned obsolescence presumption”. If the intended period of storage exceeds these recommendations and the company neglects these elements by putting on the market of degraded components may be linked to limit voluntarily the life of the product. From an operational point of view, for a long term storage (LTS), the logistic managers have to determine the storage conditions to put in place, to compare them with the disposal conditions. They have, if needed, to quantify the required investment, the cost of use and the success/risk rate associated to this LTS. This analysis is decisive in the analysis of remediation solutions allowing decision-makers to have all the encrypted elements in their possession.


41 3

In any case, the most operational solution for OEM is the long-term storage of parts of the sold modules. However, this could be very expensive, especially when specific inventory conditions (temperature, dust-free, etc.) [4]. The next section will present a Long-Term Storage Feasibility (LTSF) modeling that allows analyzing the feasibility of one of numerous DMSMS mitigation practices. It is adapted to automotive context concerning specific materials and storage duration (2, 5 and 10 years). The calculation of the LTS inventory is done, considering the overall volume and standard cost (including the parts cost, the discount rate, the standard holding cost and the storage loss) or consecutive to a minimum order quantity of raw material much higher than yearly consumption. The LTSF will allow to confirm the feasibility and to optimize the location based on the storage cost for the company in a global view. Obtained model is optimization-oriented formulation in a warehouse list.It was develop in order to identify warehouses able to cover storability of all considered parts or optimize the investment in a designed warehouse. 3. Optimization-based LTSF Model The formal LTSF model, presented in section.3.2 has two goals. To identify the possible storage durations for each part of a product, depending on the constituent materials and to calculate the cost of action to do, for each considered warehouse in order to make feasible a LTS. 3.1. Storage duration calculation The LTSF model looks to confirm the requested storage durations for each part depending on its constituent materials. These materials are numerous and have different storage conditions. However, the specifications of the storage conditions are often similar considering the intrinsic similarities of these materials (plastics, ferrous, chemical, ...). In the following, we will present an internal classification of automotive materials based on authors expertise of the sector. Basically, the storage conditions depend on a number of ”risk factors” such as the climatic environment that would cause deterioration of stored parts prematurely. Based on some material and industrial expert interviews and the internal reports (not revealed here due to condifentiality)related to a storage failure, we have defined a list of factors to control for a good storage. These risk factors have been studied to allow users to find out storage durations for each material under relevant storage conditions. 3.1.1. Material classification The storage requirements and constraints depend on the considered automotive subsystem. Analyzes and interviews of R&D, reliability and quality experts led us to conclude that these requirements are mainly linked not to the subsystems but to their materials. Thus, the evaluation method of the LTSF is based on the material typology. This allows formulating constraints by subgroups on different levels. The model should be used by the operational staff and not product experts and therefore they have to be able to easily navigate through the classification and the terminology of materials. We particularly took into account the reliability experts advices and lessons learned from materials mechanics engineers. Standards such UTE C96-029 [11] have been taken into account. The materials suppliers were also consulted. After analysis of the whole elements, we have adopted the classification indicated partially in table 1. This classification has primary level named ”Commodity” like steel, non ferrous metal, plastic, electronics, lighting, electro-mechanics and others. There are exactly 7 commodities. Each one is divided into several ”segments”. The total is 48 segments. The third level is named ”Category” and gives more description of material/component. There are 226 categories. The last level gives technological information of item useful to define precisely the storage conditions. The whole classification table contains then 572 technological descriptions. Table 1 only shows a very small part of the classification. We chose it to be sufficient for the example discussed in this paper. 3.1.2. Risk Factor Survey Using the commodity, segment, category and technological descriptions, the Nominal in-Stock Lifetime for each material can be estimated. Generally, suppliers recommend a duration of 2 years with more or less precise storage conditions. During these 2 years (which is the usual maximum duration), the materials and components are not subject to DMSMS. After these 2 years, the storage is qualified as a long term one. Storage from 2 to 5 years is considered as specific. From 5 to 10 years, it is risky. Beyond this horizon, the longtime storage is critical.

electronics

e active power and discrete components

plastics aspect parts

cd

eb

plastics technical plastic parts

cc

non ferrous casting

Segment Description

Deletion of the migrant element or expiry applied Reduce root-cause (Transport / vehicle traffic / Machine proximity)

Temporal

Element separation by gravity (liquid) Element migration Vibration

Specific

Too static

Too acide

Too alkaline

Environment pH

Salinity

Hygrometry

Climate control Climate control Transfer chamber Humidificateur / Ventillation Dry machine / Ventillation Transfer chamber Specific packaging, put away from the source Specific packaging, put away from the source Specific packaging, put away from the source Frequential solicitation enough amplitude and frequency

Too low Too high Sudden change Too low Too high Sudden change Too high

Temperature

Source proximity

Sensor

Visual

Visual / electric

100

2000

2000

Active temperature sensor Active temperature sensor Oven Active hygrometric sensor Active hygrometric sensor Oven Source proximity

Source proximity

Countermeasure Investment cost 50 50 5000 7500 7500 7500 2000

mosfet and bipolar

/m3

/m3

/m3

/m3

/m3 /m3

/m3 /m3

Unit

gearing parts mosfet and bipolar

ce2 eb1

cd2

cd1

cc2

cc1

complex stamping (with potential 2nd operation) technical parts below 250t (incl.) technical parts over 250t below 450t (incl.) aspect parts injected below 250t (incl.) aspect parts injected over 250t (incl.)

aluminum hpdc raw casting

Category Description

bd2

Cat. Code bc1

500

200

200

/m3

/m3

/m3

/m3 /m3 /m3 /m3 /m3 /m3 /m3

1

1

500 500 500 500 500 500

/year

/year

/year /year /year /year /year /year

Monitoring Unit cost

mosfet above 5 amp.

eb11

Unit

overmolded technical parts (former cf) gearing parts mosfet above 5 amp

ce10 ce20 eb10

Countermeasure Yearly using cost 2 2 2 2 2 2 200

aspect parts injected over 250t

aspect parts injected below 250t (incl.)

Total Cost

technical parts over 250t below 450t (incl.)

al hpdc raw - alt & starters brackets & gearhousings al hpdc raw - heatsinks & frames, supports complex stamping (with potential 2nd operation) technical parts below 250t (incl.)

Technology Description

cd20

cd10

cc20

cc10

bc11 bd20

Tech. Code bc10

4

Table 2. Extraction of Verified in-Stock Material Lifetimes (VSML) given risk factors (years) Parameters Condition Countermeasure Monitoring

plastics and transformation

c

Table 1. Extraction of materials 4-levels classification. Com. Commodity Description Seg. Code Code b non ferrous and transforbc mation

42

Author name / Procedia Manufacturing 00 (2017) 000–000

Kevin Boissie et al. / Procedia Manufacturing 16 (2018) 39–46


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The possible DMSMS issues require to consider the long term storage policy for the durations over 2 years. It is therefore important to estimate the maximum verified storage duration of the whole constituents of a given automotive subsystem depending on the storage conditions. To do this, the risk factors must be listed. By interviewing the automotive experts, a list of risk factors and their respective undesirable states has been established. To illustrate, Table 2 shows a part of this list, applied just on considered commodities of a Jack knife key (JKK). Among these risk factors, three are issued from the OEM”Lessons Learned”. 3.2. Problem formulation As a response to the second goal, the model should indicate what action or investment have to be done for each considered warehouse to make feasible the long term storage of each part. This section describes the LTSF Model which consists on calculating the maximum expected in-stock part lifetime of each part given each suitable and unsuitable storage condition. In fact, looking for storing a part a certain time, one should identify the conditions that would compromise this target. The next step could be identifying the warehouse that has control over these storage conditions. An OEM has to guarantee the production of a whole system or module, and therefore it should guarantee the availability of almost all of its parts. This is a real challenge for an OEM, to survey such availability for all products at the same time. In fact, for instance for a single product, there are more than 8 materials, more than 27 storage conditions, and more than 140 possible warehouses worldwide. The next mathematical model gives for each warehouse, long term storage feasibility of each part. Considering all the warehouses, it gives the possibility to build the ”Long Term Storage Feasibility Model” (LTSF). Let product or subsystem p suspected or doomed to DMSMS. We need to plan all constituents long-term storage according to the whole used materials m and related storage conditions noted λ. The expert must begin to provide the BOM of considered product and the Verified in-Stock Material Lifetimes (VSML) given each unsuitable storage condition. This procures the maximum Expected in-Stock Part Lifetimes (VSPL) given each unsuitable storage condition also. Given the Needed in-Stock Part Lifetime (NSPL years) for each part, it is possible to determine which part can be stored and which other have to be redesign for example. Related notations are given below: m is integer parameter indicating material reference in M. p is integer parameter indicating considered product or subsystem in Π. λ references one of the Λ unsuitable storage conditions relating to risk factors parameter indicating that the part p is composed, partially or totally, of the material m. VALEO consider 27 unsuitable conditions related to 21 long term risk factors. D p,m is binary parameter indicating that the part p is composed, partially or totally, of the material m. is integer parameter indicating the verified in-stock material lifetime (VSML) of material m considVλ,m ering unsuitable storage condition λ. Eλ,p gives the maximum Expected in-Stock Part Lifetime (ESML) of part p in view of its materials composition and the considered unsuitable storage condition λ. The mathematical formulation is: Eλ,p = min Vλ,m ; m|D p,m =1

∀λ ∈ Λ, p ∈ Π

(1)

”Min” operator is used because when a part is composed of several materials, the storage period chosen is the shortest of them all. Now, it is possible to map all warehouses and their ability to store each part for the Needed in-Stock Part Lifetime (NSPL). We use for this, the following two formulas: Aωλ,p = Fλω .Eλ,p + 1 − Fλω . min (Bm ) ; ∀λ ∈ Λ, p ∈ Π, ω ∈ Ω (2) m|D p,m =1

  1 min Aωλ,p ≤ N p    λ∈Λ  M ωp =  ; ∀p ∈ Π, ω ∈ Ω     0 otherwise

(3)


44 6

where ω Fλω Bm Aωλ,p Np

designates the warehouse in Ω OEM warehouse list. designates the suitable storage condition λ in the warehouse ω. is an integer parameter indicating the maximal in-stock material lifetime (BSML) of material m for Best favorable storage conditions. gives the maximum Expected in-Stock Part Lifetime for part p without any compliance of warehouse ω (ASPL) according to unsuitable storage condition λ. Gives the Needed in-Stock Part Lifetime (2, 5, 10 years or beyond it) for part p.

ω parameter Fλ combined with Eλ,p and Bm give the ”Long Term Storage Feasibility Model” (LTSF – noted The M ωp ) like shown in figure 2.

The maximum expected in-stock part lifetime of any part can be enhanced when each unsuitable storage condition of a given warehouse is controlled. For example, high level of humidity can be removed by installing an air conditioning. It is an Hygrometry-based ”compliance”. Thus, the expert must provide, in a second time, the list (noted Fλω ) of not controlled unsuitable storage conditions λ for each candidate warehouse ω. Let the following additional notations : is the decision variable taking value 1 when compliance done for warehouse ω according to unsuitXλω able storage condition λ, zero otherwise. takes Fλ,ω value when there is no compliance of warehouse ω according to unsuitable storage conYλω dition λ, zero otherwise. This variable indicate new configuration of the warehouse relatively to the risk factors. Xλω is a decision variable that let the expert decides to make compliances or not at a given warehouse to make it operational for the storage of all the parts. In the same way, Yλω indicates the further capability of warehouse ω to parts storage. The formulation is following: ∀λ ∈ Λ, ω ∈ Ω (4) Yλω = Fλω . 1 − Xλω ; So due to compliances, ESML is replaced by the After compliance one (noted ASPL) and formulated via variable ω Aλ,p . It gives: ω = Yλω .Eλ,p + 1 − Yλω . min (Bm ) ; Aλ,p m|D p,m =1

where: ω Aλ,p

∀λ ∈ Λ, p ∈ Π, ω ∈ Ω

(5)

gives the maximum Expected in-Stock Part Lifetime for part p After compliance of warehouse ω (ASPL) according to unsuitable storage condition λ.

The new formulation of LTSF is given by the next formula :  ω  ≤ Np 1 min Aλ,p    λ∈Λ  ω ; ∀p ∈ Π, ω ∈ Ω Mp =      0 otherwise

(6)

For each combination of compliances, there is a cost 1 . As shown in right side of table 2, the global compliance cost is function of (1) Countermeasure Investment cost. This cost is related to the equipment to invest, eg. a nitrogen cabinet or a moisture generator , (2) Countermeasure Yearly using cost. It is related to the equipment used to reach the LTS success rate in the given warehouse. It consider the maintenance, the training of operator, the energy consumption, etc. These calculations are done with a yearly rate per m3 of storage), and (3) Monitoring cost that consider the cost of equipment in place to control the specific storage equipment, eg. it could be a humidity sensor. So, it is not a question 1

All real costs are confidential. The cost communicated in this paper are for illustration only.


45 7

of making the warehouses able to store everything for the required duration but of analyzing the map and making decision on the possible improvements to realize the storage with less expenses of investments. When warehouse ω is chosen for compliance, it is a constrained optimization model whose objective function is simple: constrained by M pω = 1; ∀p ∈ Π (7) Cλω .Xλω Minimize λ∈Λ

Cλω is defined as the global cost to ensure compliance of warehouse ω according to unsuitable storage condition λ. The following section illustrates the use of the model.

4. Automotive Jack Knife Key case Let us take the jack knife key (JKK) as shown in figure 1. One consider from 17 parts in 2 levels, an assembly composed of p=10 parts and m=7 materials. The composition matrix D p,m is given in right side of figure 1. The calculated VSML Vλ,m and ESPL Eλ,p matrices are calculated below. Figure 2 shows calculated LTSF for a sample of 20 warehouses located in Europe due to JKK’s commercial space. Columns are sorted from lower necessary compliances number to the higher one. All related materials can be stored for 10 years under nominal conditions (Bm = 10; ∀m).

Fig. 1. Exploded view of Valeo Jack Knife Key                        Vλ,m =                      

10 10 10 10 10 10 10 2 2 10 10 2 5 10 10 10 10 2 10 10 10 10 2 2 2 2 2

10 10 10 10 10 10 10 0 0 10 10 10 5 10 10 10 10 10 10 10 10 10 5 5 2 2 2

10 5 0 2 10 0 10 2 10 10 10 10 5 10 10 2 10 10 10 10 0 10 10 5 5 2 2

10 5 0 2 10 0 10 2 2 10 0 10 2 10 10 2 10 10 10 10 0 10 2 2 2 2 2

10 5 0 2 10 0 5 5 5 10 2 10 5 10 10 2 5 10 10 10 0 10 10 5 5 2 2

10 2 2 10 2 2 0 0 0 2 10 0 0 2 2 0 0 0 0 10 0 0 0 0 0 2 0

10 5 2 10 2 2 2 0 0 2 10 2 2 2 10 2 2 2 0 10 0 2 0 0 0 2 0

 10     5     0     2   10     0     10     2   2     10     0     10     2   Eλ,p =  10    10     2   10     10      10     10   0     10     2     2   2     2 2

10 5 0 2 10 0 10 2 2 10 0 10 2 10 10 2 10 10 10 10 0 10 2 2 2 2 2

10 5 2 10 2 2 2 0 0 2 10 2 2 2 10 2 2 2 0 10 0 2 0 0 0 2 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 5 0 2 10 0 5 5 5 10 2 10 5 10 10 2 5 10 10 10 0 10 10 5 5 2 2

10 10 10 10 10 10 10 0 0 10 10 10 5 10 10 10 10 10 10 10 10 10 5 5 2 2 2

10 10 10 10 10 10 10 2 2 10 10 2 5 10 10 10 10 2 10 10 10 10 2 2 2 2 2

10 10 10 10 10 10 10 2 2 10 10 2 5 10 10 10 10 2 10 10 10 10 2 2 2 2 2

10 2 2 10 2 2 0 0 0 2 10 0 0 2 2 0 0 0 0 10 0 0 0 0 0 2 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

                                            

                                              

F 1 X 1 Y 1   λ λ λ   0 0 0     0 0 0     1 1 0    1 1 0     1 0 1     0 0 0     1 1 0    1 1 0     1 1 0     0 0 0     1 1 0    0 0 0    1 1 0  A 1 =     λ,p 0 0 0     0 0 0     1 1 0    0 0 0      1 1 0     1 1 0     0 0 0    0 0 0      1 1 0     0 0 0     1 1 0    1 1 0      0 0 0  1 1 0

10 5 0 2 10 0 10 2 2 10 0 10 2 10 10 2 10 10 10 10 0 10 2 2 2 2 2

10 5 0 2 10 0 10 2 2 10 0 10 2 10 10 2 10 10 10 10 0 10 2 2 2 2 2

10 5 2 10 2 2 2 0 0 2 10 2 2 2 10 2 2 2 0 10 0 2 0 0 0 2 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 5 0 2 10 0 5 5 5 10 2 10 5 10 10 2 5 10 10 10 0 10 10 5 5 2 2

10 10 10 10 10 10 10 0 0 10 10 10 5 10 10 10 10 10 10 10 10 10 5 5 2 2 2

10 10 10 10 10 10 10 2 2 10 10 2 5 10 10 10 10 2 10 10 10 10 2 2 2 2 2

10 10 10 10 10 10 10 2 2 10 10 2 5 10 10 10 10 2 10 10 10 10 2 2 2 2 2

10 2 2 10 2 2 0 0 0 2 10 0 0 2 2 0 0 0 0 10 0 0 0 0 0 2 0

0   0  0   0   0   0  0   0   0   0  0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0  0

(8)

The combination of compliances that allows the total storage of the 8 pieces (except the battery and the screw) at a lower cost is indicated by the column. The last row detached from matrix above indicate the number of compliances necessary to ensure the same storage for each warehouse. Warehouses 9, 14 and 10 allow the in conformity storage of all parts. Only the first one requires no any compliance. The list of warehouse compliance costs is not produced here for reasons of confidentiality.It will remain to consider other parameters such as storage capacity, transfer costs, etc.


46 8 Needed in-Stock Part Lifetime NSPL Parts Upper case S/A Lower case PCBA Battery Battery cover Logo Key ring holder Mecasnisme Transponder Screw

5 5 2 0 2 2 2 2 2 0

8 9 13 12 16 4 Optimized storage lifetimes OSPL 10 0 1 0 1 0 1 10 0 1 0 1 0 1 2 1 1 1 0 0 1 0 1 1 1 1 1 1 10 1 1 0 1 0 1 10 0 1 0 0 0 0 10 0 1 1 0 0 0 10 0 1 1 0 0 0 2 1 1 1 0 1 1 0 1 1 1 1 1 1

Compliances number to be done >

0

0

0

5

5

6

Sorted Warehouse references 14 18 7 10 6 11 17 3

2

19 15

1

5

20

1 1 1 1 1 1 1 1 1 1

0 0 1 1 1 0 0 0 1 1

1 1 1 1 1 0 0 0 1 1

1 1 1 1 1 1 1 1 1 1

0 0 1 1 0 0 0 0 1 1

0 0 1 1 0 0 0 0 1 1

0 0 0 1 0 0 1 1 0 1

0 0 1 1 0 0 1 1 1 1

0 0 0 1 0 0 1 1 0 1

1 1 0 1 1 0 0 0 0 1

0 0 1 1 0 0 0 0 1 1

6

6

8

8

9

10 10 11 12 12 13

1 1 1 1 1 0 0 0 1 1

1 1 1 1 1 0 0 0 1 1

0 0 1 1 0 0 0 0 1 1

nb.

Ref. of warehouse that ensure all needed storage durations

1

Storage duration ensured relatively to Part/Warehouse

0

Storage duration no ensured relatively to Part/Warehouse

1

inconsidered part

14 17 18

5. Conclusions and further works The presented work adresses OEM facing to DMSMS. In a reactive and sometimes proactive approach, they are led to consider long-term storage. It can concern all parts needed to fulfill customer orders, the car manufacturers. The major difficulty to dealing with purchasing and supply logistics managers is that long-term storage requires a good knowledge of the compliance with the storage conditions of the parts for each warehouse. We saw that it depends on the materials of the parts, the expected duration, the control of the storage conditions and the associated costs, etc. As part of the long-term storage project, we have presented a classification of more than 573 variants of automotive materials, the risk factors relating to 27 adverse conditions and the means to defining them. The result is a storage feasibility mapping model and an appropriate formulation for optimizing warehouse compliance operations. They are two limits to work which have to be addressed in further works. The first is that we do not have to store all parts of a product in the same warehouse. That is, instead of providing the optimum combination of compliance of a warehouse to receive all the parts, it is rather necessary to produce the combination of warehouses to be used to store all the parts and this, with less investments. The costs related to the transport logistics will have to be taken into account. Precisely, the second limit comes from the economic function which considers, for the moment, only a fixed cost of storage over the whole duration whereas the volumes fall with the time and the associated cost too. References [1] Baker, A., 2013. Configurable obsolescence mitigation methodologies. Procedia CIRP 11, 352 – 356. URL: http://www. sciencedirect.com/science/article/pii/S2212827113004861, doi:https://doi.org/10.1016/j.procir.2013.07.013. 2nd International Through-life Engineering Services Conference. [2] Bartels, B., Ermel, U., Pecht, M., Sandborn, P., 2012. Strategies to the prediction, mitigation and management of product obsolescence , 234. [3] Beaudet, A., Nishiguchi, T., 1998. The toyota group and the aisin fire. [4] Gao, C., Liu, X., Wang, X., 2011. A model for predicting the obsolescence trend of fpga, in: The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety, pp. 1354–1358. doi:10.1109/ICRMS.2011.5979481. [5] Hoyle, D., 2005. Chapter 3 - role, origins and application of iso/ts 16949, in: Hoyle, D. (Ed.), Automotive Quality Systems Handbook (Second Edition). second edition ed.. Butterworth-Heinemann, Oxford, pp. 95 – 114. URL: https://www.sciencedirect.com/science/ article/pii/B978075066663350004X, doi:https://doi.org/10.1016/B978-075066663-3/50004-X. [6] OICA, 2018. International Organization of Motor Vehicle Manufacturers. URL: http://www.oica.net/. [7] Romero Rojo, F.J., Roy, R., Shehab, E., 2010. Obsolescence management for long-life contracts: State of the art and future trends. International Journal of Advanced Manufacturing Technology 49, 1235–1250. doi:10.1007/s00170-009-2471-3. [8] Sandborn, P., Mauro, F., Knox, R., 2007. A data mining based approach to electronic part obsolescence forecasting. Components and Packaging . . . URL: http://ieeexplore.ieee.org/xpls/abs{_}all.jsp?arnumber=4295166. [9] Shankar, P., Morkos, B., Summers, J.D., 2012. Reasons for change propagation: a case study in an automotive oem. Research in Engineering Design 23, 291–303. URL: https://doi.org/10.1007/s00163-012-0132-2, doi:10.1007/s00163-012-0132-2. [10] Tomczykowski, W., 2003. A study on component obsolescence mitigation strategies and their impact on R&M. Annual Reliability and Maintainability Symposium, 2003. , 332–338doi:10.1109/RAMS.2003.1182011. [11] UTE C96-029, 2011. Electronic components - Long duration storage of electronic components - Guide for implementation. [12] Zolghadri, M., Addouche, S.A., Boissie, K., Richard, D., 2018. Obsolescence prediction: a bayesian model. Procedia CIRP 70, 392 – 397. URL: http://www.sciencedirect.com/science/article/pii/S2212827118303354, doi:https://doi.org/10.1016/j. procir.2018.02.037. 28th CIRP Design Conference 2018, 23-25 May 2018, Nantes, France.