Request for Proposal

Transcription

1 Request fr Prpsal DMDII Advanced Analytics fr Supply Chain Operatins Technlgy Thrust Area: Agile, Resilient Supply Chain Revisin 1.0 Release Date: 4 August 2017 POC: Sctt Kruse Prject Innvatin Engineer Digital Manufacturing and Design Innvatin Institute DMDII@uilabs.rg Issued By: Digital Manufacturing and Design Innvatin Institute 1415 N Cherry Ave Chicag, IL DMDII@uilabs.rg

2 Table f Cntents 1 Recrd f Change Prject Call Purpse Key Dates Submissin Infrmatin Prject Evaluatin Criteria Request fr Prpsal Summary Prject Requirements Travel Requirements Perid f Perfrmance Requirements Funding Requirements Request fr Prpsal Questins & Answers

3 1 Recrd f Change Versin Date Sectins Descriptin August-2017 Original 3

4 2 Prject Call Purpse Digital Manufacturing and Design Innvatin Institute (DMDII) Request fr Prpsals are issued t address research and develpment needs in digital design and manufacturing technlgy that are aligned with the technical bjectives f the DMDII (als referred t as the Institute). This Request fr Prpsal (RFP) is a descriptin f a specific technlgy bjective. A separate dcument, the Prpsal Preparatin Kit (PPK), ffers detailed instructins fr the Prpsal develpment, frmat and submissin instructins. The Prpsal Preparatin Kit (DMDII_17-01_and_17-02_Prpsal_Preparatin_Kit_(PPK)_1.0_ ) can be fund here. 2.1 Key Dates Phase 1 Key Event Dates Request fr Prpsals released August 4, 2017 All Prject Participants are DMDII Members Octber 2, 2017 Technical and Cst Prpsals due Octber 2, 2017 Selectin ntices prvided Octber 31, 2017 Phase 2 Key Event Dates Initial review f Enterprise Award Agreement Nvember 20, 2017 All clarificatins/negtiatins n SOW and Cst Prpsal cmplete Nvember 20, 2017 Prject Awarded December 19, 2017 Prject Kickff Meeting January 19, 2018 *Phase 2 Dates are Estimates 2.2 Submissin Infrmatin Each prject team which is planning n submitting a Prpsal t this Request fr Prpsal must submit their Technical Prpsal and Cst Prpsal n later than 12:00PM Central Time, Octber 2, All Submissins shuld be made electrnically t DMDII@uilabs.rg. Please include the RFP designatin (e.g., DMDII-17-<xx> <RFP Title> - <Offerr Name> - <Prpsal Title> ) in the subject line f the . 3 Prject Evaluatin Criteria DMDII s primary gal is t apply digital manufacturing technlgies t slve business prblems. T this end, successful prpsers must demnstrate an understanding f bth the business needs as well as the technlgy slutins. Prpsals shuld prvide a crystal clear explanatin f the prblems that are t be slved, and hw the prject success will benefit the manufacturing rganizatins. Each Prpsal is evaluated by a specific set f criteria. The PPK defines a general list f Technical Prpsal evaluatin criteria, all f which are applicable t this RFP. 4

5 Evaluatin Criteria Requirements Cmpliance Prblem Statement and DMDII Relevance Methdlgy Technlgy Transitin Plan and Impact t Industrial Base Team Qualificatins Prgram Management Plan Cst Factrs Ttal Pints Pssible Pints Available 0-5 Pints 0-20 Pints 0-30 Pints 0-15 Pints 0-10 Pints 0-15 Pints 0-5 Pints 100 Pints 4 Request fr Prpsal Summary Supply chain management effrts in large enterprises quickly becme mre cmplex as the number f variables increase, including grwing numbers f rders, suppliers, gegraphic regins, data frmats, interface standards, systems, and persnnel invlved in management. These cmplexities hinder the rganizatin s ability t practively discver and reslve anmalies in the supply chain that impact a prduct s cst, schedule and quality. Cntributing prblems in supply chain management include: Piecemealed individual risk management by each supply chain partner Incnsistent appraches t quantifying risks Lacking cnsideratin f interdependent impact acrss multiple tiers and prduct segments Limited capability t quantify the relative influence f supplier perfrmance metrics n desired utcme Lack f risk analysis dne ahead f time t supprt trade-ff analysis f supply risks These challenges hinder a hlistic apprach t supply chain ptimizatin and risk management. Limited visibility in the supply chain als creates a prblem. The limited supply chain visibility invlves difficulties identifying and acquiring data, dealing with nn-hmgenus and dirty data, and latency issues in data cllectin and prcessing all prevent respnsive decisin making and render insights bth expensive and late-t-need. Certain data requests may be perceived as threatening t suppliers, while data surces are simple and standards-based yet underutilized. DMDII is interested in prjects that will develp a decisin supprt slutin which enables real-time r near-real-time identificatin f ptential risk/issues in the supply chain. Successful prjects will 1. Identify a risk scenari f interest based n impact t critical business measures; 2. Define risk measures f interest, hw thse are t be calculated, and any ptential interrelatinships with ther risk factrs; 3. Identify ptential surces f data, including whether the data required is simple signal data, raw data sets, r summary analysis data, and identify applicable standards fr delivery, if available; 4. Outline architectural plans fr acquiring and string data; and 5. Identify ptential business cnstraints fr acquiring needed data, and plans t address thse; 6. Develp a decisin supprt slutin which incrprates the items abve. 5

6 7. Assess business metrics achieved by implementing the decisin supprt slutin within an rganizatin. Prject teams will need t identify specific use cases that the technlgy develpment effrt will fcus n. Example risk scenaris f interest include: Advance warning f delivery delays r related events that wuld impact schedule; Insight int incming quality f materials and parts t avid dwnstream nn-cnfrmances; Prpsed slutins that include data acquisitin need t accunt fr the different data types available. Fr example, signal data data that is simple, bjective, and easier t deliver rapidly might include start/stp times n relevant peratins at suppliers, r ntificatin when a threshld is exceeded. Signal data is the least cntrversial t share and is ften supprted by a standard, such as MTCnnect fr machine data and EDI fr supply chain events. Raw Data cmprehensive data sets that take mre time t prepare and deliver must be assembled and time-aligned, pssibly frm acrss multiple surce systems. Raw data may be used t investigate unanticipated analytics use cases; it is ften the mst cntrversial t share. Summary data is a refined, interpreted, and reprt-quality data set. It includes analysis, interpretatin, and expert judgement abut events r ther metrics f interest. This takes the mst time t prepare and deliver, as it ften invlves additinal manual data preparatin, analysis, and investigatin that ges well beynd basic data harmnizatin. Prpsals need t clearly identify data types and surces necessary t supprt the identified use case. Prject teams shuld be cmprised f multiple industry rganizatins with different prduct types t prvide varying perspectives within the supply chain prblem space. Ideally, teams will include OEMs and at least ne direct supplier. The prject team must assess existing technical appraches and ensure that the prpsed research is unique r cmplimentary t the existing technlgies. The prpsed slutin shall detail the testing and validatin effrts f the decisin supprt capability. Ideal prject slutins will be tested within each participating industry rganizatin. Prject teams must als clearly define the use case fr the prpsed decisin supprt slutin in respnse t the prject call per the DMDII prvided template. Deliverables must include: Mapping f participating enterprise s supply chain prcesses and architecture alng with an identificatin f key risk factrs Definitin f inputs and utputs f the system that are abstracted t allw fr prtability and scalability Pseudcde r suitable mathematical representatin f risk analysis apprach. Reprt n the business rules f engagement between rganizatins and their suppliers Decisin supprt sftware tl A plan fr transitining the decisin supprt tl t market Prvide a user guide and recrded webinar mdules fr the slutin. Mdules shuld include, but are nt limited t: 6

7 Detailed user training including identificatin f the resurces needed and skills required t utilize the slutin Explanatin f the brad value f the prject alng with immediate business impact Identificatin and detailed descriptin f successes and failures Steps needed t further develp and/r integrate the technlgy int existing systems 5 Prject Requirements 5.1 Travel Requirements Prpsals shuld include funding fr three trips per year fr tw peple fr presenting t the DMDII membership. These trips may be fr travel t UI LABS r t anther lcatin at the request f DMDII (e.g., a cnference, wrkshp, shwcase, etc.). Fr estimatin purpses, use Chicag, IL as the destinatin. 5.2 Perid f Perfrmance Requirements Prpsed prjects shuld be n mre than twelve (12) mnths in duratin. Please nte that prjects are initiated nce an Enterprise Award Agreement is signed, therefre, the prject duratin must include the subcntracting f all prject participants between the Lead Organizatin and the Prject Participants. 5.3 Funding Requirements The DMDII anticipates awarding 1-2 prjects fr up t $500,000 per prject, nt inclusive f expected cst share, under the DMDII RFP. Final award amunts will be adjusted accrdingly based n Prpsals received and subsequent evaluatins. This prject requires a minimum 1-t-1 Cst Share in aggregate by each Offerr team. 6 Request fr Prpsal Questins & Answers Interested parties may submit questins t DMDII@uilabs.rg. All new questins and answers received may be psted here. 7