Physical products have increased in availability and decreased in real prices for decades. This has been achieved through a two-step process: the application of technology, and the offshoring of manufacturing to counties with low labor costs, in particular China. As the world deals with the impacts of the COVID-19 pandemic, it has become apparent that offshoring manufacturing brings with it a consequence of making our supply chains more fragile. This post will explore how and why manufacturing was offshored in the first place, and how new technology can change this trend through local smart automation.
We rely on products made in factories for almost every aspect of our daily lives. With a growing global population, and rising quality of life for most people on the planet, the total amount of value generated by manufacturing has exploded in the past 50 years, from a base of $2.8 trillion in 1970 to almost $15 trillion today. The growth is only accelerating, with an increase of 2.5x since the turn of the millennium.
As the total volume of products we make and use has grown, so has the way we make them. The United States of America was once the world’s largest manufacturer, with many of the innovations we rely on for high quality products developed here. The first moving assembly line began operation on October 7, 1913 at the Highland Park Ford Plant, and allowed the Model T to be produced in just 93 minutes - faster than the paint of the day could dry.
While manufacturing in America has continued to grow, it is the growth of manufacturing in China that has led the world since the turn of the millennium. From a start of less than half the US’s output in 2000, China’s manufacturing growth allowed it to overtake the US in 2010, and today stands at more than 70% higher.
Seen another way, from an equal footing in 2010, the total manufacturing output of China compared to the US looks like this:
So why has this happened? There are many individual reasons, supported by various governmental policies. But the underlying trend was simply the movement of manufacturing to China from high income countries starting around the year 2000, with a resulting increase in the percentage of China’s population employed in making products for the rest of the world.
Automation has been the counter-balancing technology trend to the movement of manufacturing to lower cost labor. Both the capabilities of machines and robots, and the amount of capital deployed into them, have increased over the past decade. The total number of industrial robots in service has more than doubled in the past decade.
While the total investment into automation has increased, it is far from evenly distributed across countries. Both the total number of robots, and the capabilities of these machines, varies widely.
The capabilities of automation installed today are also not evenly distributed. For example, the total number of typically more advanced cobots - collaborative robots, designed to perform tasks alongside humans - is still a fraction of the total number of traditional industrial robots.
Looking at the details of applying automation to manufacturing from a finalised product design sheds some light here. Far from being a streamlined and simple process, it typically involves multiple parties, contractors, technologies and methodologies.
A typical automation project involves multiple iterations of each of these steps, especially if starting from a design that is not completely mature. The final step - line setup - requires specialised programmer knowledge, with weeks or months spent at the (overseas) production line itself. The programming required is very deterministic, with the goal of performing the same physical actions repeatedly and with high accuracy. Even with the advancement of software tools to do this, it remains a laborious task and prone to error.
Successful projects can take four months to implement, and more typically take six months to a year. Unsurprisingly therefore, automation projects tend to be reserved for high volume production, where the time and costs involved can be amortized over a very large number of units.
The advances in automation hardware has not yet been matched by software. Since a line typically takes months to set up, a small change in product design or operating parameters of the equipment requires time consuming debugging and reprogramming.
New innovations in artificial intelligence have allowed dramatic improvements in areas such as self driving cars, digital assistants, and online shopping. For example, Google’s word accuracy for speech recognition recently crossed the threshold for human accuracy with advancements in machine learning.
Similar improvements are now achievable in the physical world. For example, OpenAI recently demonstrated a robot hand solving a Rubik’s cube, a problem that was simply not feasible to solve without AI.
Key to these increasing capabilities are advancements in simulation environments, reinforcement learning, and domain randomization. The final point is a particularly exciting development, and means that vast training data sets can be generated by altering the input parameters into simulated environments. We will explore this in future posts.
The next few years will see these technologies applied to manufacturing, bringing benefits to product designers and consumers alike. Instead of a long and disconnected supply chain, designers will be able to get instant feedback, transparent pricing, and faster turnaround times than are possible today. Most significantly, more and more products will be able to be manufactured locally, bringing economic, societal and environmental benefits.
Launchpad is developing the software, hardware and AI tools to dramatically speed up the manufacturing of low and medium volume products, made here in America. We’re still early in our development, and will be launching a private beta early next year. Please get in touch with us at email@example.com if we can help you, or sign up to our newsletter to stay up-to-date with our developments.
Bidhaa halisi zimeongezeka katika upatikanaji na kupungua katika bei halisi kwa miongo. Hii imefikiwa kupitia mchakato wa hatua mbili: matumizi ya teknolojia, na offshoring ya utengenezaji kwa kaunti na gharama za chini za kazi, hasa China. """Kama ulimwengu unashughulika na athari za janga la COVID-19, imekuwa wazi kwamba kuhamisha uzalishaji huleta matokeo ya kufanya minyororo yetu ya usambazaji kuwa dhaifu zaidi." Katika chapisho hili tutajifunza jinsi na kwa nini utengenezaji ulihamishwa nje ya nchi, na jinsi teknolojia mpya inaweza kubadilisha mwenendo huu kupitia automatisering ya akili ya ndani. Tunategemea bidhaa zinazotengenezwa katika viwanda kwa karibu kila sehemu ya maisha yetu ya kila siku. Kwa sababu ya ongezeko la idadi ya watu duniani na kuongezeka kwa ubora wa maisha kwa watu wengi duniani, jumla ya thamani inayotokana na viwanda imeongezeka sana katika miaka 50 iliyopita, kutoka msingi wa $ 2.8 trilioni mnamo 1970 hadi karibu $ 15 trilioni leo. Utoaji wa bidhaa umekuwa ukiongezeka kwa kasi, na kwa kuongezeka kwa 2.5x tangu mwanzo wa milenia. Kadiri kiasi cha bidhaa tunazotengeneza na kutumia kimekua, ndivyo na njia tunazotengeneza bidhaa. Marekani wakati mmoja ilikuwa mtengenezaji mkubwa zaidi duniani, na uvumbuzi mwingi tunaotegemea kwa bidhaa za ubora wa juu ulitengenezwa hapa. Mstari wa kwanza wa kusanyiko ulianza kufanya kazi Oktoba 7, 1913 katika Kiwanda cha Highland Park Ford, na kuruhusu Model T kutengenezwa katika dakika 93 tu - haraka kuliko rangi ya siku hiyo inaweza kukauka. Wakati viwanda vya Marekani vimeendelea kukua, ni ukuaji wa viwanda vya China ambao umeongoza ulimwengu tangu mwanzo wa milenia. Kutoka mwanzo wa chini ya nusu ya uzalishaji wa Marekani katika 2000, ukuaji wa utengenezaji wa China uliruhusu kuizidi Marekani katika 2010, na leo inasimama zaidi ya 70% zaidi. Kwa upande mwingine, kwa mujibu wa takwimu za mwaka 2010, jumla ya uzalishaji wa China ikilinganishwa na Marekani inaonekana kama hii: Kwa nini hii imetokea? Kuna sababu nyingi za kibinafsi, zinazoungwa mkono na sera mbalimbali za serikali. Lakini mwelekeo wa msingi ulikuwa tu harakati ya utengenezaji kwenda China kutoka nchi zenye mapato ya juu kuanzia karibu mwaka 2000, na matokeo ya ongezeko la asilimia ya idadi ya watu wa China wanaofanya kazi katika kutengeneza bidhaa kwa ulimwengu wote. Automation imekuwa mwelekeo wa teknolojia ya kusawazisha mwendo wa utengenezaji wa gharama ya chini ya kazi. Uwezo wa mashine na roboti, na kiasi cha mtaji uliotumiwa, umekuwa ukiongezeka katika muongo uliopita. Idadi ya roboti za viwandani zilizotumiwa imeongezeka zaidi ya mara mbili katika miaka kumi iliyopita. Wakati uwekezaji wa jumla katika automatisering imeongezeka, ni mbali na kusambazwa sawasawa kati ya nchi. Idadi ya roboti na uwezo wa mashine hizo hutofautiana sana. Uwezo wa automatisering imewekwa leo pia si usawa kusambazwa. Kwa mfano, idadi ya cobots ya kawaida ya juu zaidi - roboti za ushirikiano, iliyoundwa kutekeleza kazi pamoja na wanadamu - bado ni sehemu ndogo ya idadi ya jumla ya roboti za viwanda za jadi. Kuangalia maelezo ya kutumia automatisering kwa utengenezaji kutoka kubuni bidhaa ya mwisho hutoa mwanga hapa. Mbali na kuwa mchakato rahisi na rahisi, kwa kawaida inahusisha vyama vingi, wakandarasi, teknolojia na mbinu. Mradi wa kawaida wa automatisering unahusisha marekebisho mengi ya kila hatua hii, haswa ikiwa unaanza kutoka kwa muundo ambao haujakomaa kabisa. Hatua ya mwisho - line kuanzisha - inahitaji maalumu programu maarifa, na wiki au miezi alitumia katika (nje) mstari wa uzalishaji yenyewe. Programu inayohitajika ni ya kuamua sana, na lengo la kufanya vitendo sawa vya kimwili mara kwa mara na kwa usahihi wa juu. Hata kwa maendeleo ya zana za programu kufanya hivyo, bado ni kazi ngumu na rahisi kwa makosa. Miradi iliyofanikiwa inaweza kuchukua miezi minne kutekeleza, na zaidi kawaida kuchukua miezi sita hadi mwaka. Kwa hiyo, haishangazi kwamba miradi ya automatisering huwa na kuhifadhiwa kwa uzalishaji wa kiasi kikubwa, ambapo wakati na gharama zinazohusika zinaweza kuondolewa juu ya idadi kubwa sana ya vitengo. Maendeleo katika vifaa vya automatisering bado hayajafanana na programu. Kwa kuwa mstari kwa kawaida inachukua miezi kuanzisha, mabadiliko madogo katika kubuni bidhaa au vipimo vya uendeshaji wa vifaa inahitaji muda mwingi debugging na reprogramming. Ubunifu mpya katika akili bandia umeruhusu maboresho makubwa katika maeneo kama vile magari ya kuendesha gari, wasaidizi wa dijiti, na ununuzi wa mtandaoni. Kwa mfano, usahihi wa neno la Google kwa utambuzi wa hotuba hivi karibuni ulivuka kizingiti cha usahihi wa binadamu na maendeleo katika kujifunza mashine. Maendeleo kama hayo sasa yanaweza kufanywa katika ulimwengu wa kimwili. Kwa mfano, OpenAI hivi karibuni ilionyesha mkono wa roboti ukisuluhisha kiini cha Rubik, tatizo ambalo halingeweza kutatuliwa bila AI. Ufunguo wa uwezo huu unaoongezeka ni maendeleo katika mazingira ya uigizaji, kujifunza kuimarisha, na randomization ya uwanja. Hatua ya mwisho ni maendeleo hasa ya kusisimua, na ina maana kwamba seti kubwa ya mafunzo ya data inaweza kuzalishwa kwa kubadilisha vigezo vya pembejeo katika mazingira simulated. Tutachunguza jambo hilo katika makala zijazo. Katika miaka michache ijayo tutaona teknolojia hizi zinatumika kwa utengenezaji, zikileta faida kwa wabunifu wa bidhaa na watumiaji sawa. Badala ya mnyororo mrefu wa usambazaji, wabunifu watapata maoni ya haraka, bei ya uwazi, na nyakati za haraka za kuzunguka kuliko ilivyo leo. Zaidi ya yote, bidhaa zaidi na zaidi zitaweza kutengenezwa ndani ya nchi, ikileta faida za kiuchumi, kijamii na mazingira. Launchpad inaendeleza programu, vifaa na zana za AI ili kuharakisha sana utengenezaji wa bidhaa za kiasi cha chini na cha kati, zilizotengenezwa hapa Amerika. Bado tuko mwanzoni mwa maendeleo yetu, na tutakuwa tukizindua beta binafsi mapema mwaka ujao. Tafadhali wasiliana nasi kwa barua pepe@example.com ikiwa tunaweza kukusaidia, au jiandikishe kwa jarida letu ili uendelee na maendeleo yetu.