A NEW APPROACH TO MAXIMIZING THE VALUE OF YOUR

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Digital Supply Chain InstituteDATA TRADING:A NEW APPROACH TO MAXIMIZINGTHE VALUE OF YOUR DATAWhat It Is, Why It Is DifferentAnd How To Do ItData Trading: A New Approach to Maximizing the Value of Your DataA

“ If you think about it, following a big data approach is what powered our knowledgeof the sun, moon, stars, and Earth for years. Still, it was only when Galileo peered througha telescope that we could start to understand more deeply how these celestial bodiesmoved in relation to one another.”An Interview With Clayton M. Christensen, MIT Sloan Management ReviewAbout Digital Supply Chain InstituteThe Center for Global Enterprise’s (CGE) Digital Supply ChainInstitute (DSCI) is a leading-edge research institute focused on theevolution of enterprise supply chains in the digital economy, and thecreation and application of supply chain management best practices.How DSCI Can HelpDSCI is a membership-based, not-for-profit institute whose membersare focused on executing the supply chain of the future. We performresearch, conduct pilots, communicate the Digital Supply Chainstory, and link members with companies that are going throughsimilar journeys. Visit our website dscinstitute.org to learn more orreach out to Vivek Ghelani, Project Manager, DSCI atvghelani@thecge.net.About the Center for Global EnterpriseCGE is a New York-based nonprofit, nonpartisan research institutiondevoted to the study of global management best practices, thecontemporary corporation, economic integration, and their impacton society.COPYRIGHT DSCI 2020Data Trading: A New Approach to Maximizing the Value of Your DataB

PREFACEThis Guide and Simulator are important tools to help you recognize that new data is neededto better understand and shape demand and manage your supply chain in a way that makescustomers happy. We call the tool “Data-Trading.” It is a way to obtain specific, needed datafrom suppliers, distributors, customers, and even other departments within your company!As we see from the struggle to react to the novel coronavirus (COVID-19), managing supplychain efficiency and continuity is hugely complex in an environment with global supplyproblems, restrictions, and wildly fluctuating demand. Now, more than ever, is the timeto upgrade your data model and find the specific data that will make your supply chainmore visible and flexible. In the past, many companies talked about knowledge-sharingand collaboration but too often it ended with talk and no action. Data-sharing was focusedon the transaction, not on overcoming shared challenges or improving collective businessperformance. Now, you have it with Data-Trading. Companies and internal departments willcollaborate with a purpose: trading specific data to achieve clear objectives that are a win forall. We have seen it work to create excellent business results.We look forward to your stories and comments as we charge ahead!Data Trading: A New Approach to Maximizing the Value of Your DataC

TABLE OF CONTENTSChapter 1: Introduction to Data Trading1Chapter 2: Data Dilemma: Why this New Approach is Needed4Chapter 3: Data Trading: Making It Happen8Internal 1:1 Data Trading9External 1:1 Data Trading10External Value-added Data / Service Bundle (Many-to-Many Through One)12Chapter 4: Getting Started with Data Trading: DSCI Data Trading Framework16Chapter 5: Call to Action24Chapter 6: DSCI Data Trading Framework Simulator25Acknowledgments30Data Trading: A New Approach to Maximizing the Value of Your DataD

CHAPTER 1:Introduction to Data TradingYou have data that’s high value to your customers and suppliers.Your customers and suppliers have data that can power youralgorithms to accelerate your business growth.Make a trade.Companies are drowning in data. Yet, one of the major hurdles companies face in transforming to aDigital Supply Chain is their ability to get precise data from customers and suppliers — and to fullyrealize the value of the data they have. Many supply chain executives also cite difficulty in obtainingtimely, accurate data from other departments in their companies. To succeed in your transformation,you must find a way to overcome these hurdles.As a supply chain leader, you have an inventory of data just as you have an inventory of productsor parts. Optimizing the value of your data is part of your job in the new Digital Supply Chain — andone of the keys to your success and the growth of your company.Sophistication in the acquisition and utilization of new data is a competitive advantage. The gapbetween those that are good and those that are not will quickly widen as the strategic use ofalgorithms, Artificial Intelligence (AI) and Machine Learning (ML) grows and as companies evolve tomore data-driven decision-making.Today’s supply chains need to transform to meet the needs of the “New Customer” and having theright data is the only way to do it. (DSCI recently published a paper defining the New Customer’sexpectations). This goes beyond technology-driven digital transformation. Technology has createdData Trading: A New Approach to Maximizing the Value of Your Data1

new expectations for the “New Customer.” It doesn’t matter ifyou are selling to a consumer or a business; to truly understandthe needs of the New Customer, you will need data you do notcurrently have. Later in this paper, we will present the idea of a newdata model. Others hold much of this data for the new model, butdon’t make the mistake of thinking it’s all about collecting the mostdata. Big data is here to stay, but sometimes thinking small is thebetter approach. It is about finding the right data to address specificproblems.“ Technology has enabledcompanies to rush towardsBig Data, and I understand theexcitement. But sometimes, smartleaders need to get away fromthe herd. They need to encourageBased on our research, collectively agreeing on what data yourcompany wants is not easy. It’s also not easy to value the data youalready have and identify who would want it. For these reasons,we developed the DSCI Data Trading Framework and companionSimulator, which provides a step-by-step approach to getting whatyou need and getting value for what you have.their people to think about dataIn the digital economy, a lot of people say data is a new currency.That’s nothing new. However, unlike currencies, the value of datais relative. It is not a transparent market. What is new is the idea ofdeveloping and implementing a data trading strategy to accelerateyour Digital Supply Chain transformation.to others?”Leading other departments and value chain partners in understandinghow to value and trade data will help accelerate your transformationand sustain your competitive advantage. But because the value ofdata is relative, what is low value to you could be the missing pieceof the puzzle to another company.The raw data itself may have limited value, but cleaned andadded to other elements or an existing algorithm, it may becomeextraordinarily valuable. This works for both sides of a trade. For datatrading partnerships to be sustainable, they will need to be mutuallybeneficial.We introduced the DSCI Data Trading Framework in the springof 2019 in our paper, Data Trading as a Catalyst for Digital SupplyChain Transformation. Here, we take it to the next step by providingspecific tools and techniques for getting started, including aSimulator you can use to test scenarios or use as a checklist fortrading.at a granular level. Exactly whatdata do we need to improveperformance? Exactly what datado we have that would be valuableSam Palmisano,Chairman, Center for Global Enterprise“ Most companies still don’t realizethe value of their data. It’s keptin silos. Companies need tobreakdown the silos and realizetheir data is an asset they canmonetize and barter.”Deana Denton,Director of IT, GSM, CorningThe root cause of the lack of data trading is the vagueness of what data sharing means to eachcompany. Let’s be realistic: no company is going to give you all of their data. And you probably don’tData Trading: A New Approach to Maximizing the Value of Your Data2

28%Only 28% of therespondents out of 344supply chain executivesthink about data sharingbeyond the transactionin a recent DSCI survey.need it all. Of course, you need to identify the trulyproprietary data — the crown jewels — that give youa competitive advantage and protect them. But youprobably have a lot of data that could be valuable toother departments or companies.What is needed is a laser-focus on what you need andwhat you have that would be valuable to someoneelse. Your company can gain a competitive advantageby uniquely using their data.STO R IES FR OM T H E FI EL DA chemical company was seeking the sensor-based data a customer used to track inventory levelsof the liquified chemicals it sold and delivered to the customer’s manufacturing site; they knewthey could provide a more seamless inventory replenishment for the customer with that data. Thecustomer balked at the idea of sharing what they considered to be confidential inventory data.So, the chemical company went back to the customer and proposed a data trade — the chemicalcompany’s proprietary price forecasts in exchange for the customer’s inventory levels. Both partiesagreed to trade the data under a restricted-use agreement.Data Trading: A New Approach to Maximizing the Value of Your Data3

CHAPTER 2Data Dilemma: Why thisNew Approach is NeededOne thing is for sure, most companies do not have an inventorylist of their data, and no one is managing their data as a strategicasset. They are unaware of their data’s value to others. Based onour survey and ongoing interviews with supply chain executives,most companies are frustrated by the lack of data sharing withtheir customers and suppliers — and with other departments intheir own company. Of course, companies share some data withtheir suppliers and customers, but it is usually focused on thetransaction — not on improving business performance.Companies are grappling with the onslaught of new data and howto best utilize fields like decision-science. They are seduced by thebeautiful stories vendors tell about shimmering data lakes. They aretransfixed on bright shiny new technologies that promise to makeeverything fast, flexible, and secure. But the reality on the groundis different.As we interviewed companies from many industries and countries,it became clear that there is a big gap between the promise andthe reality. One of the major reasons for the gap is that there isa New Customer with new expectations. It doesn’t matter if youare B2C or B2B. To make the New Customer truly happy, youneed specific data that lets you know and be present with them.Data Trading: A New Approach to Maximizing the Value of Your Data“ Finding an edge in privatemarkets is often about exploitinginformation asymmetries. Thelack of data underlying the modelsof decision-making is a keychallenge. At the same time, anopportunity lies in the enormousamount of information availablein alternative data sources suchas news, customer reviews, andsocial media posts on the web,as well as from sensor dataanalytics.”EFront, Sept. 26, 20194

Yet, too often, companies do not have all the data they need to understand and serve the NewCustomer. Sure, a company can buy data from data aggregators and research firms – but so can itscompetitors.THE SITUATION TODAY90%of companies are lookingfor ways to acceleratetheir Digital Supply Chaintransformation.OnlyOnly31%of the respondents haveprojects underway toidentify and evaluatethe potential use of newdata to directly improvebusiness performance.of companies have aprogram in place thatroutinely assigns a rankingor value to the impactspecific new data wouldhave on their businessperformance.28%55%strongly agree/agreethat having a frameworkfor sharing data withcustomers and suppliersthat identifies and valuesexactly what data isneeded and what could betraded would be useful.Source: DSCI survey results 2019 of 280 supply chain executivesAnother thing we discovered in our interviews was that companies are trying to dowonderful new things with data using AI/ML, but with an old supply chain data model. So,DSCI developed a new, customer-centric Digital Supply Chain data model in our 2018 paper, DrivingDemand in the Digital Supply Chain: Algorithms and the Untapped Power of Applying Real-Time BigData and AI/LM. Look at the new data model, and the need for effective data trading will jump outat you — you will see the critical importance of obtaining and integrating the right data from newsources.Data Trading: A New Approach to Maximizing the Value of Your Data5

Data Trading: A New Approach to Maximizing the Value of Your Data6

THE PROMISE VERSUS THE REALITYThe availability of huge amounts of data is forcing companies to change how they planand operate. However, for many companies the change is slow and difficult. There is a gapbetween the promised future and today’s reality. This is the data dilemma.Data DilemmaTHE PROMISETHE REALITY Data-driven decision-making Too much data Improved demand forecasting Too much time spent cleaning andsorting data Predictive analytics to drive demand andreduce risk Unused data Strategic use of algorithms Data in internal silos AI/ML leading to continual improvement Slow process to get data from otherdepartments Seamless data sharing Being present with your New Customer Legacy systems that don’t communicate Data governance and protectionchallenges Missing key pieces of data Data exchanged with customers andsuppliers is usually limited to the directtransactionData Trading: A New Approach to Maximizing the Value of Your Data7

CHAPTER 3:Data Trading: Making It HappenOne of the significant challenges companies face in transforming to a Digital Supply Chain is theability to share data internally and gain specific, critical data from customers and suppliers. Everybodytalks about “Big Data,” but sometimes the secret to success is to get the right “Small Data.”Companies need to break down internal departmental silos and collaborate with customers andsuppliers. The DSCI Data Trading Framework can be a catalyst to help you prepare, negotiate, andgovern data trades.Based on interviews with companies around the world in multiple industries, we discovered thereare three distinct approaches to data trading. The first two approaches apply to any company in anyindustry. The third is appropriate for companies that have a specific role in the supply chain.Three Approachesto Data Trading1. Internal 1:1 Data TradingIn all cases, the key to the DSCI Data Trading Framework is to have a laserfocus on specific pieces of data. Don’t think in broad terms about large dataexchanges. It will take too long to negotiate, require too many approvalsand be too difficult to govern. Think laser-focus. Be realistic and start smallwhile you gain experience – at least in the beginning.2. External 1:1 Data TradingThe lack of effective collaboration between departments and withcustomers and suppliers is a fundamental hurdle. But too often, even whencompanies do “collaborate,” it means more committees and workinggroups, more talk, and more planning. But what companies need is moreaction and what we call “collaboration with a purpose.” We do not thinkmeaningful collaboration will happen unless it is in the economic self-interest of the people involved.Fellow employees, suppliers, distribution partners, customers are all too busy to give you what youwant unless it helps them. Establishing short-term data trading projects with measurable goals canhelp you go over, under, and around the hurdles. It will create incentives for both parties.3. External Value-added Data/ Service Bundle(Many-to-Many Through One)Data Trading: A New Approach to Maximizing the Value of Your Data8

Internal 1:1 Data Trading:Breaking Down the SilosInternally, data trading between departments can be the “purpose” behind “collaboration with apurpose.” It is a one-to-one trade between two departments — each that can benefit from data theother has.We were somewhat surprised by how quickly companies saw the application of data trading tosolve internal problems. We heard many examples of the difficulty one department has in gettingdata from another department. Yet the cross-departmental use of algorithms is a key component toeffectively implementing an effective Digital Supply Chain strategy.STO R IES FR OM T H E FI EL DOne supply chain executive for a major retail chain told us they were stuck in a data gap betweenthe physical stores and the e-com department. Both departments had data that would help eachother track customers and improve the supply chain’s demand management. Yet, no one wasincentivized to share the data. Any discussions on the topic ended up talking about broad dataexchanges that required system integration, significant IT investment, and a lot of time. They werealways bogged down on time and cost. Finally, the supply chain executive decided to start small.Retail and e-com agreed to trade customer data on one SKU to see how it went. They started theold-fashioned way and exchanged the data on spreadsheets.To initiate internal data trading, it is critical to find a common problem — something that will pullthe departments together around meeting a common goal. It’s great if it is a goal that will help eachdepartment improve its performance against an existing performance indicator. Ideally, it can lead tothe two departments having a new joint performance indicator.Data Trading: A New Approach to Maximizing the Value of Your Data9

Data Trading Examples: InternalRetail Supply Chain Executive Problem to Solve:Where to hold the inventory for a popular SKU to serve their retail locationsNeeded data from stores:Actual consumption by SKU by the store each weekData to trade:Total inventory in the pipeline by SKUSupply Chain Executives of Consumer-Product Manufacturers Problem to Solve:Improve raw material sourcing efficiency for one productNeeded data from sales:Multi-channel sales data for the productData to trade:Total inventory in DC and in transit for promotional planningSourcing Executive - Problem to Solve:One product group is trying to respond to productiondisruption from an unexpected eventData to trade:Available production capacity over next 60 daysNeeded data:Order-volume of similar product from a related product groupData Trading: A New Approach to Maximizing the Value of Your Data10

External 1:1 Data Trading:Accelerating the Digital Supply Chain TransformatioCompanies are seeking more data from customers and suppliers,yet they are struggling to do so. One of the reasons is that they arenot specific about what data they are seeking - and what data theyare willing to give in exchange. The DSCI Data Trading Frameworkis a unique way to address this. It allows you to assess the value ofwhat you have and to provide a value to the specific data you want.As we said earlier, the Digital Supply Chain is a customer-centricmodel that captures and maximizes the utilization of real-time datacoming from a variety of sources. One of the critical elementsof doing The Frontside Flip is access to and usage of new data.However, there is tension between having a lot of data and havingthe right data. There is a reluctance to spend the time to cleanand sort data to make it usable to others, as there is the reflexivereaction that data trading is complicated and that it will requiresophisticated technology and application programming interfaces(API).“ Information is inherently valuableand data is the currency in whichinformation is traded. The DSCIframework of data trading bringsneeded focus to a process thatalready happens to some extentwithin and across organizations.The biggest opportunity arounddata trading is to formalize theprocess and take advantage ofvaluable data assets.”On the one hand, companies say they can’t process and effectivelyBrian Simons,use all the data they have now. But on the other hand, theyCEO, Janus Logistics Technologiesare eagerly clamoring for more data to make critical businessdecisions. More data from customers. More data from thecustomer’s customers. More data from suppliers. More data from the supplier’s suppliers. Whiledata is ubiquitous, the necessary data for improved decision making and bottom-line results is toooften out of reach.We hear how critical it is to get the “right” new data in interview after interview, yet it’s nothappening. One of the major hurdles to accelerating Digital Supply Chain transformation iscollaboration and information sharing with companies in the end-to-end value chain.QThe following best describes our approach to data sharingwith key companies in our supply chain.We usually only share data related to the transaction(i.e. price, specifications, delivery).31%We usually only share data that will directly help us,and the company, improve our future transactions.25%We establish data sharing programs that broadly helpboth companies to improve their business performance.We don’t share data.28%16%Source: DSCI 2019 Survey of 344 supply chain executivesData Trading: A New Approach to Maximizing the Value of Your Data11

Companies are aggressively utilizing AI/ML to gain a competitive advantage. One of the mostimportant aspects of the Digital Supply Chain is collaboration beyond the boundaries of yourorganization. We hear it from the executives we talk to: the need for better cooperation withcustomers and suppliers; the need for shared performance metrics with customers and suppliers;the need for data sharing with customers and suppliers. But data sharing with customers andsuppliers is easier said than done.28%Only 28% of the respondentsthink about data sharingwith supply chain companiesbeyond the specific transaction.23%Only 23% of respondents havean established program thatidentified and evaluates thepotential use of new data todirectly improve businessperformanceBased on our survey, the vast majority of the data sharedbetween a company and its suppliers or customersis transaction data. Yet strategic data sharing withcustomers and suppliers is essential to unlocking thepower of AI/ML. The critical management question ishow to do it? Data trading is the answer. This is whereit gets interesting because, unlike money, the value ofdata is relative. The value depends on how it fits intoeach company’s strategic puzzle – how it enhances thealgorithms.Source: DSCI 2019 survey of 344 supply chain executivesData Trading: A New Approach to Maximizing the Value of Your Data12

Data Trading Examples: ExternalFootwear Manufacturer – Problem to Solve:Improve demand forecasting for a popular women’s shoeNeeded data from the retailchain:Age and gender of the buyerData to trade:Comparative, aggregated sales of popular women’s shoe in otherretail chainsConstruction Company – Problem to Solve:Shift project management schedule given material shortagecaused by an unexpected supply chain disruptionNeeded data from materialsupplier:Revised product allocation and delivery schedule on a weeklybasisData to trade:Overall shifts in construction plan on a weekly basis so suppliercan re-allocate product if possibleConsumer Electronics Component Manufacturer Problem to Solve:Improve demand forecasting for replacement partsNeeded data from OEM:Warranty return data for a specific productsimilar product from arelated product groupData to trade withConsumer Electronics OEM:Volume of the replacement part sold to out-of-warranty repaircenters for that specific productData Trading: A New Approach to Maximizing the Value of Your Data13

External Value-Added Data / Service Bundle(Many-to-Many Through One)Historically, trading companies have acted as a hub betweensuppliers and buyers.In today’s digital economy, platform business models play a similarrole in linking buyers and suppliers. In both cases, the company inthe middle is like the waist of an hourglass.This position in the value chain creates a unique opportunity for datatrading. But rather than going into the business of being a data brokerand selling data, we advocate they should integrate data tradinginto their core services. Today’s technology provides greater supplychain visibility that threatens traditional trading companies by thoseseeking to “cut out the middleman.” However, the same technologyand visibility provides the opportunity to aggregate data and bundleit into current products/services to create new value, which reducesthe risk of being cut-out.“ We have an endless thirst for data.We are constantly looking for thirdparty data to enhance our modelbecause our customers expect usto bring them the best innovativeservices at the right cost, withgreat quality and an assurance ofsupply. To do that, we need theright data. Data trading can be thetool to get the data we need andget more value from the data wehave.”Richard Porcaro,Head of Client Services & Solutions,ChainIQData Trading: A New Approach to Maximizing the Value of Your Data14

Data Trading Examples: ExternalCustomer Problem to Solve:Improve their ability to predict possible supply disruption for a specific productData to trade:Which suppliers of that product experienced significantproduction disruption in the last two yearsNeeded data fromCustomer:Sensitivity to shipment delays for that product (number of daysor weeks)Supplier Problem to Solve:How to allocate a specific product to customers during major,widespread supply chain disruptions Data needed from Supplier:Current inventory and available weekly production capacity forthe specific productData to trade:Inventory levels of Customers so product can be allocated tominimize disruption to any one customerCustomer and Supplier Problem to Solve:How to quickly re-design a specific product to avoid materials or components that havebecome scarce or high-tariffData needed fromCustomers:Acceptable alternative componentsData needed from Suppliers:Availability of alternativesData Trading: A New Approach to Maximizing the Value of Your Data15

CHAPTER 4Getting Started with Data Trading:DSCI Data Trading FrameworkDSCI Data Trading Framework takes the general concept of sharingdata between departments or with customers and suppliers downto the specifics of precisely what data you want and what you arewilling to give. It provides you with a framework for assigning avalue to data and understanding what you are trying to improvewith it.In this section, we outline the stages for completing your firstdata trade, then building and maintaining an effective data tradingprogram. We provide tips and tools to get you and your companystarted. This section applies to any form of data trading, thoughit focuses on 1:1 trading. This section can be used in conjunctionwith the attached DSCI Data Trading Framework Simulator.Developing a Data Trading Mindset Use a laser-focus to find datavalue gaps Find departments or companiesthat need your data Identify the missing data piece(s)to your puzzleThe DSCI Data Trading Framework has three interrelated stages:1. Preparation2. Negotiation3. GovernanceData Trading: A New Approach to Maximizing the Value of Your Data16

DATA TRADING: SOMETIMES THINKING SMALL IS BETTERAs we interviewed supply chain executives, we started to see a pattern in what came tomind when we said “data trading.” They tended to think big and complicated. Here’s a quickcomparison of what many thought it would be and what we mean by “data trading” at DSCI.TRADITIONAL WAYDSCI WAY“Big Data”Targeted “Small Data”Get as much data as we canFocus on getting the exact data that will solvea specific problemGet as much detail as we canFocus on getting the right level of detail – don’task for all of the details if a summary will workWe need it as quickly as possibleDetermine how often you would use itWe need large scale technology andcomplicated APIs to manage itUse a spreadsheet if you need toComplicated legal approval processQuick legal approval — start with a restricteduse licenseCompliance risks associated with tradingPersonal Identifiable Information (PII)Focus on business information — don’t tradePIIComplicated data governance protocolsGovern and protect the data as you do yourownCan we trust them?Trade with internal departments or trustedsuppliers/customers“ Big data also tends to gloss over or ignore anomalies unless it’s crafted carefully tosurface these to humans. That is, big data tends to be far more focused on correlationrather than causation and, as such, ignores examples where something doesn’tfollow what tends to happen on average. It’s only by exploring anomalies that we candevelop a deeper understanding of causation. If you think about it, following a bigdata approach is what powered our knowledge of the sun, moon, stars, and Earth foryears. Still, it was only when Galileo peered through a telescope that we could start tounderstand more deeply how these celestial bodies moved in relation to one another.”Clayton M. Christensen, Disruption 2020: An Interview With ClaytonM. Christensen, MIT Sloan Management ReviewData Trading: A New Approach to Maximizing the Value of Your Data17

Stage 1: PreparationThoughtful preparation is the foundation. Laser-focus is the essential element.Data trading is best done by forming a small team to lead the project. Ideally, itwould include people from different areas within the supply chain function — andpossibly people from other functional departments.TIP: To get started, focuson business information,not Personal IdentifiableInformation (PII)Here are the steps in the preparation stage:1. Identify the business problem you are seeking to solve or the performance improvement youwant to make. You can start by identifying the general area — like demand forecasting. Thinkabout algorithms you may have in use that would benefit from new or more data.2. Get specific — the more specific, the better — to establish a measurable short-t

proprietary data — the crown jewels — that give you . a competitive advantage and protect them. But you probably have a lot of data that could be valuable to . are B2C or B2B. To make the New Customer truly happy, you need s

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