You can’t separate successful mobile application development from either data or analytics. Consumers demand immediate insight into their “mobile moments” — exact points in time where context and real-time data fuels decision-making, prompts a buyer to purchase, and allows companies to present brand consistency across devices. Meanwhile, enterprises need immediate data from a wide array of sources in order to fuel processes by means of the combination of big data and analytics. We will focus on global supply chain management (“SCM”) in this piece in order to demonstrate how relationship between big data and analytics results in more efficient business processes. (For helpful background, see these pieces to look at how big data is changing industries such as healthcare and manufacturing, albeit not in the context of mobile app development.)
I have written at length about the scope of big data, as well as the challenges it presents. For the purpose of this piece, I will limit that discussion to just a few points.
First, the size of big data alone is overwhelming. While we speak of petabytes and zettabytes, we are approaching the era of the yottabyte (10 raised to the power of 24 bytes).
Second, this data avalanche consists overwhelmingly of unstructured data that cannot be stored in a traditional database. Examples include videos, Word documents, PowerPoint presentations, and telecommunications. Unstructured data has surpassed in only the past few years all data ever created before it.
Third, the so-called Internet of Things (“IoT”) contributes more big data than another source. With sensors around the world embedded in our wristbands, cars, street corners, and geolocation systems, the IoT is growing exponentially. This has benefits. As MIT Professors Andrew McAfee and Erik Brynjolfsson point out in their superb book, The Second Machine Age (Norton 2014), the genius of apps such as Waze is the ability to recognize third-party or external conditions and to inform users of meaningful data points, as well as collect data from them. From there, Waze uses this information to turn all the smartphones running its program into sensors that upload data continuously — speed and location, for example — to the company’s servers. Social participation and input is also critical so that users know where policemen are or that an accident took place just before an upcoming bridge, for example. This is then processed and returned to users in the form of intelligence that matters — what is going on around them and how can they respond to it — so that they make informed driving decisions. That was impossible before the IoT and analytics, and the give and take (use) of data contributed by actors is equally critical to predictive analytics and SCM.
Business Intelligence And Global Supply Chain Management
As an example of the premise set forth in the title of this piece, we will examine big data and mobile app development in the context of SCM. The challenges that it faces; its myriad, globally dispersed actors; and the need for data worldwide and immediately make it a strong use case.
We hear stories about pre-CEO Tim Cook mastering Apple’s supply chain such that Cupertino could build and deliver any product within four days, not to mention Cook’s inspired and exclusive bilateral relationships that control the supply of key parts to competitors. Cook got it right, and Apple profited immensely.
Yet his story is rare. Most business line managers of SCM are not even remotely similarly empowered as was Cook. That’s an understatement. Yet what they do now have is actionable business intelligence derived from big data. To find the nuggets of wisdom held therein, they require tools such as predictive analytics to mine them and answer the question: “What is going to happen given everything we know?” This question is distinct from the question: “What should we do?” The latter goes to the heart of what Gartner calls prescriptive analytics, which is beyond the scope of this post.
Intelligent predictions of the future can occur within predictive analytics platforms — a highly competitive market that includes Oracle, IBM, Tibco, and Esri, as well as a host of companies that specialize in geospatial analysis and data visualization. Business analysts apply algorithms to the company’s data sets in order to yield granular, real-time predictions. This is a recursive process. For example, predictive analytics — using Open Data (government) sources, no less — can consistently predict Federal Aviation Administration airport ground stoppages before they occur.
With the power of predictive analytics in mind, consider the follow SCM scenario.
Company X, based in Sacramento, manufacturers airplane parts. For the sake of this example, let’s assume that (X) realizes tremendous savings by transporting its parts only by train, trucks, or cargo ships. In order to shorten its delivery cycle and inventory carry, it sends Europe-bound shipments via train and its own trucks across the United States, and then by cargo ship to Hamburg, Germany, one of the world’s largest ports. From there a third-party German trucking company delivers parts to (X)’s clients.
Every shipment presents challenges. Railways can break down or be delayed during inclement weather. The American port from which (X) sends its cargo may have a strike on its docks that have nothing to do with (X), but impact it tremendously. This results in idle trucks, excessive inventory, and increased storage costs. Clients in Europe may second guess their choice of supplier before the cargo touches dry land.
Yet assume the parts clear the dock on the East Coast of the United States and proceed to the high seas. One hundred miles from Hamburg, the ship contends with gale force winds and waves. The Captain of the freighter needs to know precisely how strong the storm (i) is now and (ii) will be 8 hours from now. Without analytics, he does not have that visibility. Should he proceed through the storm or unload instead at Amsterdam with all the downstream effects that may entail for his customer, (X)? If so, alternative arrangements need to be made for the third-party European trucking company, which is looking at enough demand for trucking from other companies on the docks that it considers breaching its contract with (X).
You can see all the dependent variables at play. And you may ask: Is such a series of events realistic? The answer is yes, and pound per pound, its not overly serious compared to fleets that merge cargo in transit on the high seas or face piracy. Even so, our friends at (X) face challenging circumstances.
Mobile applications fueled by big data and analytics provide the solution.
Company (X) knows that it will have to rely on analytics. But then what? In our scenario, the foibles go around the world quickly and raise serious questions along the way. How does one convey that information throughout the supply chain? What of the company’s business intelligence? How does (X) create transparency so that its workers around the world know what’s happening? How do we provide the company’s men on the Hamburg docks the information to convey to the third-party trucking fleet? With proper information and alerts — e.g., the cargo will be here in 36 hours — the trucking fleet may wait. There are costs incurred, but they pale in comparison to the alternative. And (X) can be proactive about its customer relations management with its European clients.
Analytics-powered mobile applications can transmit this data worldwide and in real time. Yet it’s insufficient to say that app dev is a panacea. Enterprise apps need to be able to share information on smartphones and tablets, especially as the latter replace the old-fashioned clipboard among field workers such as truckers or men on the docks. Mobile app triggers can be built into business intelligence based on present and future conditions (e.g., the storm above). They can also relay information about delays, dock conditions, proximity of the cargo freighter to military naval exercises in the North Sea — all in real time. The accumulation and distribution of this data is not a one-way street. The fidelity of the data and analytics also depends on inputs from workers. The presence of political unrest in Hamburg is an example. The third-party trucking fleet being stuck on the Autobahn is another. These are subjective points of data that need to make their way to (X). All of this information can be uploaded through a mobile application on a tablet or smartphone in the field so that Company’s X’s analytics can immediately recalibrate its predictions, and thereby its outgoing messages, trigger alerts, halts to production in Sacramento, and information to assuage antsy partners, etc.
This is a really big deal.
Rasalkhaimah, ras, al, khaimah, dubai, university, salford, manchester, @hishamsafadi, hisham, safadi, European, medical, center, business, entrepreneur, startup, economy, money, motivation, education, Leadership, Transactional, analysis, emotional, intelligence, organisations, development, innovative, technology, care, health, investor, investment, production, shark, tank, sharktank, USA, UK, London, group, european, canada, india, china, japan, KSA, projectmanagement, datascience, bigdata, IOT, internetofthings, cloud