Issue link: https://iconnect007.uberflip.com/i/1058015
78 SMT007 MAGAZINE I DECEMBER 2018 There are both technology and management options out there. The productivity paradox continues to thrive. At an event in Scandinavia recently, I showed a slide of how high-volume produc- tivity expectations of 80% or more are now replaced with 20–40% in today's higher mix environment. I was approached afterwards by someone saying that I had the numbers wrong, as they dreamed of being able to achieve 20%. The real number in many companies is far less than that today, which appears to support the German report. The mix of products in produc- tion continues to increase. Additionally, with a more volatile demand requirement, customers of manufacturing want the ability to change delivery quantities and times immediately whilst also not accepting the cost of holding additional buffer stock. Factories are paying the price of the effect of fluctuating customer demand directly into their automated produc- tion lines. To find the answer to this apparent conun- drum, we start by looking at what is report- ed in terms of metrics within factories. Targets are the critical drivers for manufacturing per- formance. Around the factory, we see reports and charts explaining targets and achievement related to things such as on-time delivery, pro- duction rate, materials scrap, quality issues, etc. Pretty much everything in the factory is measured to some extent based on those statis- tics. Management wants to see that each pro- cess is under control and improvements are being steadily made. To focus on the produc- tivity perspective, seeing what is posted in fac- tories appears rather optimistic compared to what would be expected when thinking about the productivity paradox. Any schoolboy math- ematician will quickly deduce that the met- rics within the factory are based on a differ- ent dataset than the German report, which is where the problem lies. The measurement of internal performance can be justifiably made in many different ways. Statistics can be made to show whatever specif- ic perspective is needed. This style of reporting started when many enjoyed high-volume pro- duction. Dedicated production lines were mak- ing products as fast as possible. The empha- sis was on getting more and more throughput from each square meter of line space. Perfor- mance was simply based on how many place- ments per hour could be achieved. Extreme effort went into the optimization of machine programs. However, to measure the machine performance accurately meant that machine downtime outside of machine responsibili- ty should be ignored. If the line could poten- tially make 2,000 products per day, this was the rate against which performance was mea- sured. It was extremely unlikely that the cus- tomer needed exactly 2,000 products per day. Even in those days, demand fluctuated. When the finished goods warehouse start- ed to fill to a bursting point, the line was tak- en down, unscheduled, and perhaps used this opportunity to perform maintenance. These times were excluded from the productivity cal- culation because it was an external, uncon- trolled variable from the point of view of the machine engineers. This was the start of bad habits that developed and broadened over the course of time. More and more exclusions were made to reflect specific narrow scopes of responsibility as product mix increased. Pro- ductivity and capacity calculations became far more complex as techniques to manage higher mix came into play. For example, the common setup of feed- ers on SMT placement machines was seen as a way to avoid the physical changing of loca- tions of materials on the machines between different products. If two products running consecutively required the same materi- Factories are paying the price of the effect of fluctuating customer demand directly into their automated production lines.