The wave of data science for busi­ness

By lever­ag­ing the huge in­flux of data, busi­nesses may not only over­come chal­lenges but also op­er­ate sup­ply chains that can cuts costs and meet ex­pec­ta­tions of con­sumers, shares Prab­hakar Chaud­hary, MD, HAL Robotics.

Cargo Talk - - Guest Column -

Sup­ply chain man­age­ment is a cru­cial wing for any en­ter­prise, e-com­merce in­dus­try or even a start-up. With in­creas­ing glob­al­i­sa­tion, tech­nol­ogy com­plex­ity and given how volatile the sup­ply chain sys­tems tend to be, busi­nesses that are un­able to ad­e­quately reg­u­late sup­ply chains stand at a greater risk.

Lo­gis­tics, as a sec­tor, is wide­spread across the globe with com­plex net­works. It is a vi­tal in­dus­try, clock­ing in rev­enues worth $8 tril­lion. Fur­ther­more, the sec­tor is ex­pected to be val­ued $15.5 tril­lion by 2024, clearly demon­strat­ing the huge po­ten­tial of the in­dus­try. At the same time, ar­ti­fi­cial in­tel­li­gence has made sig­nif­i­cant in­roads amidst our daily lives. As per the fore­casts re­leased by IDC, the global spend­ing on AI will grow at a CAGR of 55 per cent to be worth USD 47 bil­lion by 2020. Amal­ga­mat­ing ar­ti­fi­cial in­tel­li­gence with the grow­ing adoption of In­ter­net of Things can rev­o­lu­tionise the lo­gis­tics sec­tor across the globe. UN­DER­STAND­ING STRUC­TURED TECH­NOL­OGY

While lever­ag­ing the true po­ten­tial of data sci­ences may ap­pear to be the most plau­si­ble so­lu­tion, get­ting started with the same would war­rant par­a­digm shifts and tech­nol­ogy re­vivals. As of to­day, sev­eral fac­tors have con­trib­uted to the up­surge of data. This in­cludes op­er­a­tional data, so­cial me­dia, data sourced from sprawl­ing IoT de­vices, nav­i­ga­tions ser­vices and more. While a por­tion of this data is struc­tured, a ma­jor chunk con­tin­ues to be un­struc­tured. Struc­tured data may in­clude in­for­ma­tion re­gard­ing transactions, such as sales, pur­chases and pro­duc­tion orders. The lo­gis­tics com­pa­nies have been deal­ing with the struc­tured data for a long while, how­ever, tak­ing ad­van­tage of the un­struc­tured data al­lows com­pa­nies to op­er­ate in real-time, en­hance vis­i­bil­ity, and op­ti­mise op­er­a­tions live.

Mak­ing sense of all the avail­able data and trans­lat­ing the same in busi­ness met­rics re­quire data science to be­come an in­te­gral part of the busi­nesses to­day. The IoT tech­nol­ogy is be­com­ing in­creas­ingly af­ford­able, and can be in­te­grated with a wide gamut of func­tions, such as low-cost con­sumer goods, at the pro­duc­tion floor etc to col­lect data in real-time. The sen­sors help reg­u­late the tran­sit en­vi­ron­ment, up­date routes in real-time in­case of any de­lays. Fur­ther­more, com­pa­nies also need to get a re­al­is­tic es­ti­mate of the cus­tomer sen­ti­ments. The same al­lows lo­gis­tics firms to get fore­see risks and op­por­tu­ni­ties.

In ad­di­tion, lo­gis­tics com­pa­nies op­er­ate in a globalised sce­nario, co­or­di­nat­ing with a di­verse and com­plex net­work com­pris­ing ven­dors, ve­hi­cles, part­ners and sub­con­trac­tors. The com­plex net­work ex­ists within the spheres of ever-chang­ing global eco­nomics, spikes in re­gional de­mand, cli­mate, geopol­i­tics and more. The learn­ing fac­ul­ties un­der data sci­ences can in­te­grate th­ese real-world chal­lenges to up­grade the ex­ist­ing mod­els and bet­ter adapt to the chang­ing en­vi­ron­ment. EN­ABLING SUIT­ABLE PROCESS

Adopt­ing data sci­ences has sig­nif­i­cant di­rect im­pact on cost sav­ings as well. Take for in­stance the road freight. Lo­gis­tics com­pa­nies must co­or­di­nate with a num­ber of fleet ve­hi­cles, spread across the ge­og­ra­phy. To en­sure proper health, con­stant main­te­nance is re­quired. While pre­ven­ta­tive main­te­nance may cost more, data sci­ences pro­vide sav­ings in the form of pre­dic­tive main­te­nance. By analysing trans­port data, lo­gis­tics firm can fore­see the like­li­hood of a com­po­nent fail­ing. Owing to the same, the com­po­nents can ei­ther be fixed or re­placed ahead of time; hence, sav­ing lo­gis­tics com­pa­nies enough time, money and loss of busi­ness due to un­fore­seen de­lays and re­sult­ing cus­tomer griev­ances.

In ad­di­tion to sav­ing costs, data sci­ences also al­low lo­gis­tics firms to usher in a wave of op­er­a­tional ef­fi­cien­cies. For in­stance, by con­sid­er­ing myr­iad fac­tors like eco­nomic in­di­ca­tors, past transactions and con­sumer pro­files, lo­cal mar­ket dy­nam­ics etc, data sci­ences can pro­vide lo­gis­tics firms with ac­cu­rate de­mand fore­casts. The same can be bro­ken down to com­pute daily vol­umes, prop­erly al­lo­cate re­sources, op­ti­mise the de­liv­ery routes etc. As a con­se­quence, not only the en­tire process is ef­fi­cient but also helps lo­gis­tics com­pa­nies de­liver a greater sat­is­fac­tion to their cus­tomers.

Speak­ing in re­gards to fourth in­dus­trial rev­o­lu­tion, the World Eco­nomic Fo­rum cited that the cur­rent world is stand­ing “on the brink of tech­no­log­i­cal rev­o­lu­tion that will fun­da­men­tally al­ter the way we live, work and re­late to one another.” With the emer­gence of start-ups that com­bines data sci­ences and lo­gis­tics, the space is go­ing to re­main abuzz with ac­tiv­ity in the com­ing years. Am­bi­tious lo­gis­tic firms need to be quick to take note of this up­com­ing rev­o­lu­tion and in­vest in es­tab­lish­ing learn­ing sys­tems that can make sense of all the dis­parate and com­plex data, while cre­at­ing greater op­por­tu­ni­ties for cost re­duc­tions, draw­ing op­er­a­tional ef­fi­cien­cies and stream­lin­ing sup­ply chains. (The views ex­pressed are solely of the au­thor. The pub­li­ca­tion may or may not sub­scribe to the same.)

Prab­hakar Chaud­hary MD HAL Robotics

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