Connecting systems and data sources to manage supply chain risks

In the wake of the pan­dem­ic, man­u­fac­tur­ing sec­tor mar­gins have nev­er been thin­ner. Rely­ing on lean inven­to­ry man­age­ment and at times just a skele­ton staff, with work­ers off sick suf­fer­ing Covid, many man­u­fac­tur­ers have found their oper­a­tional out­put has been reduced, caus­ing sup­ply chain bot­tle­necks. 

Lean inven­to­ry man­age­ment, also known as just-in-time (JIT) man­u­fac­tur­ing, is designed to reduce flow times and min­imise delays to work process­es. Com­po­nents, parts and mate­ri­als are ordered as close to when they are need­ed as pos­si­ble. The think­ing behind this is that stock­pil­ing inven­to­ry for long peri­ods can be unpre­dictable. Not only are there hold­ing costs, but the qual­i­ty of a com­po­nent, part or mate­r­i­al could dete­ri­o­rate over time.

Accord­ing to Sam Roscoe, senior lec­tur­er in oper­a­tions man­age­ment at the Uni­ver­si­ty of Sus­sex, the advan­tage of JIT is that com­po­nents, parts and raw mate­ri­als are “pulled through the sup­ply chain based on demand sig­nals and fin­ished goods are made only when cus­tomers need them”.

How­ev­er, the down­side to a lean approach to inven­to­ry man­age­ment is man­u­fac­tur­ers have no buffer if there’s an imbal­ance between sup­ply and demand. 

As Roscoe points out, JIT is suit­able for localised sup­ply chains or inter­na­tion­al sup­ply chains where there are open bor­ders. But in the case of the semi­con­duc­tor indus­try, the major­i­ty of the world’s chips are pro­duced in Tai­wan. 

When demand for elec­tron­ics surged at the start of the pan­dem­ic, chip foundries decid­ed to pri­ori­tise them over cars as the mar­gins were high­er, which led to automak­ers scram­bling for a lim­it­ed sup­ply. Due to the auto­mo­tive industry’s reliance on JIT, the major­i­ty didn’t have a buffer of com­po­nents to meet the demand for new vehi­cles. 

Although the chips were down for most, one automak­er that man­aged bet­ter than oth­ers was Toy­ota. In the third quar­ter of last year, for the first time the Japan­ese automak­er sold more cars in Amer­i­ca than US auto giant Gen­er­al Motors. 

How did Toy­ota achieve this? Part­ly by build­ing a stock­pile of chips. Although a pio­neer of JIT and waste man­age­ment, Toy­ota had seen its sup­ply chain dev­as­tat­ed by the Fukushi­ma earth­quake and tsuna­mi in 2011 and so set about iden­ti­fy­ing the weak points in its sup­ply chain. The com­pa­ny asked its semi­con­duc­tor sup­pli­ers to have sev­er­al months worth of chips to hand at any one time, so it could pro­cure them in the event of future major sup­ply chain con­straints. 

Connecting the dots

Ana­lyt­ics dash­boards, pow­ered by the inter­net of things (IoT), can help man­u­fac­tur­ers to get a bet­ter grasp on inven­to­ry man­age­ment. Enter­prise resource plan­ning (ERP) soft­ware can mon­i­tor and cap­ture data on com­po­nents or raw mate­ri­als as they move along the sup­ply chain. Man­u­fac­tur­ers can then decide whether they need to break from the JIT mod­el and build up a stock­pile, as Toy­ota did, to nav­i­gate poten­tial short­ages in the future. 

Even with ERP soft­ware in place, how­ev­er, man­u­fac­tur­ers can some­times strug­gle to iden­ti­fy the weak points in sup­ply chains. A major rea­son for this is because the soft­ware hasn’t been con­nect­ed and ful­ly syn­chro­nised with oth­er appli­ca­tions and data sources. Not inte­grat­ing the ERP soft­ware with oth­er sys­tems, data is under­utilised and the tech­nol­o­gy isn’t lever­aged to its full poten­tial. 

Fail­ing to con­nect the dots in man­u­fac­tur­ing oper­a­tions means not all sup­ply chain risks and poten­tial bot­tle­necks are iden­ti­fied and tracked ear­ly enough. This can result in unex­pect­ed delays and over­stock­ing, which can ulti­mate­ly result in high­er car­ry­ing costs and waste. 

Data-sharing and collaboration ideal

Waste man­age­ment is crit­i­cal in the fast-mov­ing con­sumer goods or FMCG sec­tor, espe­cial­ly when it comes to per­ish­able food man­u­fac­tur­ing. Per­ish­able goods depend on JIT man­u­fac­tur­ing because they can’t sit in stor­age for long peri­ods. 

ERP soft­ware can help food pro­duc­ers to keep sup­ply chains flow­ing. But more than that it enables improved oper­a­tional effi­cien­cy and reduced waste and car­bon foot­print. 

Food pro­duc­ers are con­tin­u­ous­ly advanc­ing their net-zero goals by low­er­ing the amount of ener­gy and water used. “They’re increas­ing­ly find­ing ways to see through the data fog to under­stand how they man­u­fac­ture the same vol­ume of prod­uct with few­er resources and adapt lines that are not work­ing to full capac­i­ty,” says Dean Grif­fin, senior ser­vices solu­tion man­ag­er at a pack­ag­ing giant Tetra Pak. 

With food pro­duc­ers look­ing to shift more prod­ucts using few­er mate­ri­als, the pack­ag­ing indus­try also has a role to play, opti­mis­ing pack­ag­ing designs so they can be man­u­fac­tured using much less ener­gy and water. This can low­er the amount of scope 3 emis­sions – the result of activ­i­ties from assets not owned or con­trolled by the report­ing organ­i­sa­tion – absorbed down­stream by end-users. 

In an ide­al world, all food pro­duc­ers and pack­ag­ing man­u­fac­tur­ers would be shar­ing data on ener­gy and water con­sump­tion. Stake­hold­ers in the food sup­ply chain would also be mon­i­tor­ing and track­ing the move­ment of raw mate­ri­als and prod­ucts in real time through pack­ag­ing and dis­tri­b­u­tion. This would reduce car­bon foot­print as well as enhance food qual­i­ty and safe­ty, low­er­ing the risk of sup­ply chain con­t­a­m­i­na­tion. 

“Indus­try 4.0 is an enabling lay­er for data man­age­ment. The data col­lect­ed isn’t always per­fect, or com­plete, but that’s fine. As they utilise the data bet­ter, com­pa­nies’ under­stand­ing improves, result­ing in faster deci­sion-mak­ing,” says Grif­fin. 

While there is leg­is­la­tion in place designed to pre­vent incor­rect labelling on pack­ag­ing, and food fraud and con­t­a­m­i­na­tion, the UK Food Stan­dards Agency has called for a “data trust frame­work” to pro­mote infor­ma­tion exchange. This tech­nol­o­gy would allow com­mer­cial­ly sen­si­tive data to be shared between stake­hold­ers. 

“By arm­ing a work­force with more data, they can make more informed deci­sions, work more pro­duc­tive­ly as a team and col­lab­o­rate on solu­tions that address issues like food secu­ri­ty and safe­ty,” Grif­fin adds.

The ben­e­fit of man­u­fac­tur­ers shar­ing data is it enables stronger sup­ply chain col­lab­o­ra­tions. But if an upstream stake­hold­er hasn’t embraced IoT tech­nol­o­gy, and isn’t util­is­ing data from dif­fer­ent sources and sys­tems to man­age risks, then this can weak­en sup­ply chains. The more stake­hold­ers there are in a sup­ply chain and the fur­ther down­stream they are, the fur­ther away they are from know­ing the true extent of sup­ply chain bot­tle­necks upstream.