The terms "big data" and "analytics" are widely used but often are not clearly d efined. The explanations presented here will help managers understand what these concepts really mean and how they can use them to improve their supply chains.
Analitički koncepti i “big data” su u posle posledenje vreme stavljeni u centar pažnje od strane medija, konsultanata i trogovaca softverom. Ponešto od te pažnje je opravdano, dokle god određene kompanije koriste big da ta analtiku da transformišu svoj posao. Bez potpunog razumevanja stvarnog značenja big data analitike, menadžeri ne mogu adekvatno da koriste njene mogućnosti. Menadžeri koji postanu svesni njenih mogućnosti ostvariće dvostrku korist: za kompaniju i za sebe. Za početak, pre nego što shvate kako big data analitika može da se implementira u njihov svet, neophodno je da dobro shvate određene koncepte, njihovo značenje i napredak u poslovanju koji mogu da donesu. 1.
Big data nije namenjena samo IT odeljenju. Iako već u širokoj upotrebi, termin “big data” je nedovoljno i loše definisan. Manjkavost jasne definicije je
možda razlog zašto menadžeri greše mislivši da big data nije od značaja za njihov posao i da je mogu uzimati u opzir samo na odeljenju za informacione tehnologije (IT). Takvim vidjenjem big data analitike, analitike , menadžeri ispuštaju njen ekonomski potencijal. Trenutno su u upterbi ti definicije big data analitike. Svaka od njih je podjejdnako značajna, na raličite načine za menadžere. Prva i najITcentričnija definicija se odnosi na količinu podataka, gotovo isuviše veliku i kompleksnu za sadašnji, mainsteam system skladištenja podataka. podataka. Drugim rečima, ne možemo pohraniti pohraniti podatke u standardnu bazu i pokrenuti određeni upit. Zbog toga je ova definicija fokusirana na IT, tehnologiju za skladištenje ogromne kiličine kiličine podataka i razvoju novog tipa server i softvera, poput Hadoop-a. Hadoop- a. Hadoop je system skladištenja podataka namenjen skladištenju nestrukturisanih podataka. Facebook, na primer se bazira na Hadoop bazi ogromne količine nestrukturisanih nestrukturisanih podataka podataka kojima koji ma se kasnije kroz napredne algoritme daje odredjeno značenje. Kakogod, menadžerima nije potrebno tehničko znanje o Hadoop-u Hadoop-u i sličnim tehnologijama. tehnologijama. Ali bi trebalo da shvate da postoji mogućnost da se podaci sakupljaju i sakupljaju i analiziraju ukoliko ukoliko postoje poslovni razlozi za to. Na primer, ako imate hiljade senzora tj. očitanih podataka na vašim proizvodnim mašinama, magacinskim prostorima , ili prevoznim sredstvima, postoje načini na koje bi se ti sakupljeni podaci mogu analizirati. Menadžeri mogu da koriste te t e podatke da predvide kvarove na mašinama, optimizuju rasporede robe ili sirovina na lageru u odnosu na potrošnju ili da poboljšaju iskorišćenost prevoznih sredstava pri prevozu robe i uštede gorivo, što direktno čuva planetu jer je manja emisija štetnih gasova. Ili ukoliko imate tim koji prati komunikaciju sa kupcima proizvoda ili korisnika usluga, možete analizirati te podatke da bi kompanija postal bolja postal bolja prema kupcima. IT zajednica teži da misli da se ovde definicija big data analitike završava. Ustvari, sa aspekta menadžera, naredne dve definicije su izuzetno interesantne.
Druga definicija big data analitike dolazi nam od dvjice naučnika, Viktora Majer Šenbergera i Keneta Cukera, iz njihove knjige Big Data: A Revolution That Will Transform How We Live, Work, and Think. U ovoj knjizi oni definišu podatke kao “univerzum podataka relevantnih za datu temu”. Oni nw tvrde da je količina podatakaa prevelika za sadašnju mainstream tehnologiju, nego još tvrde da je sada napokon moguće prikupiti sve podatke. Autori raspravljau da postoje dve važne impikacije koje slede iz činjenice posedavanj a (ili mogućnosti prikupljanja) ovog univerzuma podataka. Prvo, jednom kada imate sve podatke, moguće je na osnovu zadatih algoritama utvrđivati njihove međusobne relacije I dobiti uvide koje niste imali pre. Ukoliko, na primer, imate podatke o svemu što se dešavalo u kamionu pre nego što se dogodio kvar ili nesreća, možete bolje da otkrijete šta je dovelo do nesreće i iskoristite ove podatke da sprečite takve neželjene događaje u budućnosti. U zavisnosti da li su nezgode ili kvarovi česti, prikupljanje “običnog” malog uzorkapodataka ne može da vam da informaciju neophodnu da utvrdite adekvatnu međusobnu povezanost podataka. Druga važna implikacija tiče se važnosti posedovanja univerzuma podataka. Na primer, poljoprivreda. Velike fabrike semena mogu da zatraže od poljoprivrednih proizvođača podatke o detaljnom kvalitetu zemlje, kvadrat po kvadrat, (što nije teško izvodljivo sa modernim ratarskim mašinama) u cilju pravljenja i odabira što kvalitetnijeg i optimalnijeg semena i samim tim povećanja prinosa. Treća definicija je izvučena iz toga kako big data analitiku predstavljaju u popularnoj štampi. Ključna reč u štampi je kreativnost. Na koji način kreativno iskoristiti potencijale big data analitike? Kako koristiti veliku količinu Vama dostupnih podataka na adekvatan, a kreativan i jedinstven način interpretirati, i proizvesti novi set podataka koji može pomoći u donošenju bol jih poslovnih odluka. Ukratko, čini nam se da menadžerima mogu da koriste sve tri definicije big data analitike. Prvo, menadžeri moraju biti svesni da kolege iz IT odeljenja imaju mogućnost prikupljanja velike količine podataka. Takođe je važno razumeti podatke u kontekstu “univerzuma podataka” da bi bili u mogućnosti da pronađete uzroke retkih događaja koje je bilo nemoguće otkroiti ranije, a koji imaju neke ekonomske implikacije. Konačno, kad već imate to bogatstvo u vidu podataka 1. Big data is not just for the IT department.
Finally, now that you have this wealth of data—purchases from suppliers, shipments to customers, and the performance of your assets, to name just a few possibilities— you need to start thinking creatively about how to use it. 2. There is more than one type of analytics.
Sometimes the term "analytics" is used interchangeably with the term "big data." But they actually are distinct concepts. At the highest level, analytics is "the ability to collect, analyze, and act on data."1 As we saw, big data says something interesting about the size of the data, the universe of data, or the creative use of data. Based on our high-
level definition, it is clear that analytics can be used with old, ordinary, and small data sets as well as with new, creative, and big data sets. Our high-level definition of analytics, however, does not give us enough details to see what is unique or new about the field of analytics. Haven't managers always collected, analyzed, and acted on data? To add to the confusion, too often an organization or a vendor will use the term analytics to refer to just one type of analysis—typically the use of a business-intelligence or reporting system. Or, if you read about e-commerce companies, analytics will refer to tracking and analyzing user clicks on a website. But the field of analytics is much bigger than just a reporting system or analyzing Web clicks. (Otherwise, it would not have captured the attention of the business community, and companies across a wide range of industries would not be reporting on its benefits.) Serious thinkers and the academic community have identified three different types of analytics. First, there is descriptive analytics, which presents your data in a way that helps you make sense of what is happening in your supply chain. This is where a business intelligence system will sit. It gathers data from your entire supply chain and organization and presents it to you as dashboards, scorecards, and ad hoc queries. Descriptive analytics also includes visualization of data and geographic mapping, which helps you tell a story with the data in a way you could not do with a tabular report. The second type is predictive analytics, which are all the techniques that allow you to take the data you have available (internally and externally) and make better predictions. This can be anything from creating better forecasts to predicting when a machine will break down, and from estimating your chances of having to go to the spot market for transportation capacity to predicting which products your customers will likely buy. Finally, prescriptive analytics refers to using your data and your predictions to make recommendations on what action to take. Prescriptive analytics is most often associated with optimization technology. In supply chains, optimization technology is commonly used to help managers decide such matters as how many facilities they should have and where they should be located, how to best route trucks, and how to schedule warehouse or factory operations. When these three definitions are presented, they often are ranked in terms of their degree of complexity and strategic importance. Descriptive analytics usually sits at the bottom because it is considered to be the easiest to implement and to provide the least amount of strategic value. Predictive analytics is next, as it is a little more difficult to
implement but brings more benefits. And prescriptive analytics usually sits on top, because it is the most complicated to implement and provides the greatest value. Not everyone agrees with this ranking. A descriptive analytics project can be very complex to implement—it can be difficult to clearly describe a large, complex, global business. Moreover, giving an entire management team a clear picture of the organization may lead to significant, strategic change. In contrast, it may be very easy to implement prescriptive analytics for truck routing, say, and while the savings may be nice, it won't lead to a significant strategy shift. Instead of ranking the types of analytics, you should think of each as having its own place and realize that each company or organization will value different types of analytics at different times and places. As a supply chain manager, it is important for you to understand the three areas of analytics so you can make sure you are properly addressing each one. Rather than have a strategy for just one type of analytics, it's necessary to have a strategy for each. For example, do you have good descriptive analytics in place to understand your supply chain? Are you using predictive analytics to forecast demand and machine failures? Are you using prescriptive analytics to determine where to make products, where to locate facilities, and how to schedule resources? Knowing these three definitions will also help you assess proposals for analytics projects. The people presenting these projects may not fully define what type of analytics they are promoting. The definitions explained here will give you a framework for determining exactly what the project will do and how it fits into your overall analytics strategy. 3. Machine learning has many potential supply chain applications.
Supply chain managers need to possess a basic understanding not only of analytics but also of machine learning. Machine learning refers to the collection of algorithms that have been deve loped over the last severa l decades in a variety of fields, such as statistics, data mining, and artificial intelligence. These algorithms represent the "brains" behind a lot of what is new in predictive analytics. In the simplest sense, machine-learning algorithms take a set of input data and then cr eate a model based on the data that will either predict future outcomes or will uncover patterns in the data. It may seem less intimidating once you recognize that regression analysis (the statistical process for estimating relationships among variables) is classified as one type of a machine-learning algorithm. It may seem strange that supply chain managers should need to know about machine learning; it sounds like something that belongs in computer science or robotics. But just as supply chain managers today should know about regression analysis, they should also know about machine learning. Knowing how machine-learning algorithms work
and what they can accomplish will help you better understand what is now possible with predictive analytics. That is, it will give you new ideas you can apply in your organization to get more value from y our data. Supply chain managers should be familiar with several of the more popular machine-learning algorithms. For example, with some data sets, these algorithms (like "k-nearest neighbor," "decision trees," or "random forests") can out-predict traditional regression analysis by better teasing out patterns in the data or by allowing text-input values. Other algorithms are well suited for predicting whether an event will happen or not—for example, will the order be late, will the carrier accept the load, or will the machine break. This is typically done using logistic regression (a statistical technique used to predict the probability of possible outcomes). There are also algorithms for understanding text documents—like trying to determine whether an e-mail sent to customer service is negative or positive. This is done with naïve Bayes algorithms (a probabilistic algorithm used to classify inputs). Algorithms can help you determine which items are likely to be ordered or shipped together— very helpful for determining which items should be stored together. These are known as "association rules" or "market-basket analysis." And finally, there are algorithms for detecting clusters in y our data. Once you start to see the power of the various machine-learning algorithms, you will realize that you can combine them in interesting ways to solve complex supply chain problems. Some companies' transportation departments have built sophisticated models to first predict when a carrier will accept a load and then use price optimization to set the best price for that load. Some companies use the algorithms to better determine which products their sales teams should recommend to their customers (similar in some ways to the recommendation engines that Amazon and Netflix use). Others use the algorithms to predict when i nventory might go obsolete and to adjust prices accordingly to move the stock. Supply chain managers should be aware of the different ways these algorithms are being used to solve business problems. In the field of analytics, good ideas can start out in one organization and then be picked up and adapted by others. For example, association rules have long been used in the grocery industry to see what items consumers would buy together. One retailer's e-commerce managers realized that they could use the same algorithms to determine which items they should place in the same warehouses because they tend to ship together. As this example suggests, the more you know about the different applications, the more likely you will be able to apply them to your business. Time to enhance your skills
The field of analytics is very exciting right now. It is getting a lot of attention and is having a big impact on all types of businesses and organizations. Although it is more often associated with high-tech companies like Google or Amazon,
it is quickly moving into more traditional supply chain environments. Companies that don't embrace it may be left behind or at a big disadvantage. This article should provide you with both a framework for thinking more clearly about analytics and a starting point for conducting more research on your own. There is a wealth of books and online resources about analytics and big data as well as commercial tools and open-source software available to help you get started. As a supply chain manager, you will benefit from challenging yourself to come up with new ways to use the data in your possession. Be a leader in data and analytics; it will help propel your company —and your career—forward.
