Balance your Supply Chain with Big DataDocument Transcript
Balance your Supply Chainwith Big DataAuthor: Manju DevadasVP Solutions and Technology, Bodhtreemdevadas@bodhtree.comwww.linkedin.com/in/manjudevadas
Let’s start by going back…way back from a tech perspective. In the 1840s Samuel FinleyBreese Morse, the American co-inventor of Morse code, envisioned laying cable across theAtlantic to enable telephonic communication from US to Europe. The business benefit metricof the solution was a reduction in message transmission time from 10 days to only a fewminutes. With this massive return, the initiative would seem like a “no brainer” from today’sperspective where communication is at milliseconds speed from your cell phone; believe it ornot, the question commonly asked then was ‘Do we really need communication so fast?’ Theproject ultimately took over 18 years to complete when US president James Buchanan finallyconversed with Queen Victoria over the transatlantic cable, hence demonstrating the firstbusiness benefit. Let us call this the ‘Paradigm Shift Period’ for communication. Modernbusinesses now rely on instant communication across the world with voice and data transfersoccurring at lightning speed. People, processes and technologies within business have allevolved to conform to this new paradigm of global data interconnection.In fact, the original challenge has now come full circle. Business and government havebecome so efficient at capturing and transmitting data that getting the data is no longer thecore of the issue. The challenge and opportunity now lay in processing and interpreting theterabytes, even petabytes, of available structured and unstructured data to influence effectivebusiness strategy.The chances are that you’ve been bombarded with Big Data buzz over the last year. But inspite of all the noise, you’ve probably noticed that few of these descriptions contain focusedbusiness use cases for applying Big Data technologies. I am the first to acknowledge andagree with Gartner research that Big Data is riding a hype cycle that will likely peak sometimein 2013. Between now and then a lot of mind share will go into figuring out if there is valuefor your domain, your industry and your job. If you work in supply chain, irrespective of theindustry, continue reading to understand how Big Data is expected to bring both direct andindirect impact. Some of these reverberations may fundamentally change the nature andduties performed in supply chain jobs. In 2010 we have witnessed a ‘Paradigm Shift Period’for Big Data Analytics with major players like SAP announcing the next generation of real-time analytics as many ask a similar question to 170 years earlier, ‘Do we really needanalytics so fast?’ SAP is now seeing their Hana analytics customers grow rapidly, similar toother big players like Oracle. We are witnessing an epic shift in supply chain data analyticsthat will make the approaches of the last decade seem antiquated.
The Supply Chain DomainThe core of any supply chain strategy is maintaining an appropriate balance between thesupply and respective demand. Every other related model, including the well-known JIT (Justin Time), really targets the same goal with different degrees of precision and timeliness. Everytime you enter the car repair shop and the mechanic mentions a part will take X days toorder, you get a prime, though frustrating, example of a supply-demand imbalance. It isevery organization’s goal to maintain a supply-demand balance by optimizing cost andquality with operational efficiencies.On a much larger scale, I have observed operations at a $40B Hi Tech manufacturer wheremaintaining the supply-demand balance is a far more complex proposition. Everydayemployees and partners in this supply chain ecosystem are trying to find answers to key supplychain questions, but their view is constrained to only a piece of the picture since reports relyprimarily on structured data. How fast the person can get accurate and relevant informationhas a significant impact on the growth, profitability and productivity of the supply chainfunction.The following are some ballpark metrics for the annual activities involved in keeping supplyaligned with constant variation in market demand:
Does this look ugly? It is. But think about what these numbers will be after data volumesgrow 16X by 2016.It’s a category 5 hurricane of data.All of the above communication is related to one or more of the following four areas: Assessthe demand, Assess the supply, Fulfillment of demand, Delivery of the product/service. Theefficiency and success of these activities can be tracked through metrics such as lead-timevariance, forecast inaccuracies, on-time shipments and quality metrics to name a few.Big Data for Supply ChainNOW, let us bring Big Data into the picture and see how this outlook changes. A Big Dataproblem exists if data Volume, Velocity and Variety become difficult or impossible to store,process, and analyze using traditional technology and methods. With Big Data technologies,the capability to find answers faster and cheaper has grown exponentially.While we predict 16X growth in data volumes in just a few years, human ability tocomprehend does not keep the same pace. From the perspective of people, processes andtechnology within supply chain management, improvements will need to catch up as youimplement Big Data technologies. The probability is high that Big Data technologies willplay a key role in handling your rapid data expansion, so gear up your people and processesto match the potential of these technological innovations. Within the broad range of supplychain roles, let us consider the role of planner to see how his/her activities change fromtoday’s traditional technologies vs. Big Data technologies of tomorrow.
Key Supply Chain functions Today – Traditional Technologies Tomorrow – Big Data TechnologiesF orecasting Running reports and analysis on a daily Forecasting using real time dashboards, basis (reports alone can take hours to eliminating the concept of running reports. produce). Data is ready at lightning fast speeds with the capability to capture snapshots of analysis.D em and Planning Mostly using human-fed structured data Demand Planning using structured and unstructured data (e.g. web clickstreams, Facebook likes, Twitter Feeds , Customer reviews, news article mentions)Su pply Planning Traditional reports and email Supply Planning using real time data with communications deep insights to the news of vendors and partners.F u lfillment & D elivery Tracked through workflows and report Proactive delivery tracking to predict status possible delays and correlated interdependent events.There is a fundamental shift from planners reading the data and recommending changes tothe machine recommending changes and planners managing the exceptions. This has beenthe goal of many organizations for the last decade, but recent Big Data technologyinnovations represent quantum-leap advances toward true strategy automation.The traditional model makes local copies of data which the planner edits and writes back.The read/write process might take anywhere from seconds to many hours depending on thetasks. With Big Data, the turnaround becomes milliseconds. The natural reaction is, “Do Ireally need information flow that fast?” The important question is not how fast the informationflows, but how quickly you can change your decision from A to B, capturing a time-sensitiveopportunity or averting a major cost. Cancelling a wrong work order or not considering allavailable information for analysis could mean a poor decision in current model. Visualize theplanners viewing all the information they want to see in real time while the competition is stillupdating data and processing reports.Bringing the Supply Chain Contacts, Content and Context Together for decisionsThe most critical factor for effective corporate decisions is to bring the contacts, content andcontext closer to each other. For example, a supply chain company that knows a part defectwould potentially affect the assembly, which could in turn delay customer delivery and
eventually affect services. Predicting the occurrence of defects well in advance throughanalysis of historical Big Data has huge ROI potential by enabling appropriate adjustments toevery event in this chain. Additionally, with Big Data recommending related content andrelaying all of this to the right contacts, the result is direct ROI in the form of improved qualitymetrics, increased customer satisfaction and reduced maintenance costs for part replacement.Today’s Big Data technologies have the capability to demonstrate how in the automobileindustry an alternator part data sheet (Content) can be analyzed against all cars sold(Contacts) and reveal the root cause for battery replacements (Context), an issue which hascost the company millions of dollars in repair services. Similar examples can be found inmany Big Data technology use cases across industry verticals.All of these scenarios are primarily connecting the 3Cs, the Contacts (e.g. Customerinformation or internal employee) and Content (Use case specific information e.g. Batteryfailure) with Context ( How a battery replacement is due to alternator failure ) .Much of a Planner’s time is spent searching for information across multiple tools, reports andmanual communication with traditional technologies. One gauge of an effective Big Datatechnologies implementation is to reduce the number of reports to 1/10 the current volume.Let the machines do the job of relating and correlating the huge flow of information, and putthe planner in the command seat to review recommendations and approve/disapprove. Thiswill directly increase the productivity of the planner as he/she has to focus on reviewing therecommendations rather than searching for information.Where to StartAll of this means that you need to first conduct an assessment of your supply chain ecosystemwith a specific use case in mind to which Big Data technologies will be applied. The specificarea targeted for improvement may be forecast inaccuracies, which in today’s model reliesmostly on structured data combined with massive exchanges of manual communication,ignoring much of the available market feedback (unstructured data). Measure the baselineand set realistic targets. Traditional Forecast/Demand planning fundamentally relies on a setof numbers entered by internal and external users. It does not factor in some of the Big Dataelements such as sentimental analysis of the market, internal/external unstructuredcommunication (e.g. blogs, chats, Tweets, customer reviews). When the unstructuredinformation is correlated with structured data, new insights arise prompting better decisions.1% improvement in your forecasting drives multi-fold improvements to your entire supply
chain based on empirical research. Upon realizing these early Big Data benefits, we canthen expand it to broader supply chain use cases.ROINow, where do you initiate the change and get the quick ROI? Our recommendation is topick the top five supply chain reports you run on your traditional BI platform, analyze themand assess whether Big Data technologies would bring in improved results. Considerdimensions of accuracy, precision, and timeliness. For example, forecasting traditionallydepends on sales, BU or operations entering their forecasts and coming up with some form ofconsensus. Inherent forecast inaccuracy exists, which are mitigated by a continuousimprovement process. Now, with Big Data you start feeding unstructured market informationinto the analysis, casting more light on external reactions to your product. This insightprovides early indications of demand variations, allowing for corrections to forecasts.ConclusionThe fundamental disruption in our supply chain eco system has begun through Big Datatechnology capabilities impacting People, P rocess and Technology. Faster, better andcheaper processing of Big Data will drive improvements in people’s behavior and actions,bringing improved supply/demand balance. Similarly, process improvements learned fromvarious supply chain driven companies (e.g. automobile) will flow into other industries like HiTech and Healthcare. The traditional daily job of a supply chain employee who reads andwrites Content relating it to a Context working with his set of Contacts will dramaticallychange. Human-driven searching will fundamentally shift to machine-driven searching,mapping relevant information for faster decision making with recommendations. Get startedwith a use case which can be easily measured for ROI realization, then use this success as alaunch pad to expand Big Data insights across the organization.Contributors: Ryan Madsen (Bodhtree)References: Real Customer Case Study Gartner’s Hype Cycle for Emerging Technologies in 2012 Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011–2016 Wikipedia – “Transatlantic Telegraph Cable”