23-Ind-A4 Production Management · December 2013
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Technical Examinations — December 2013 — 98-Ind-A4 Production Management. Three-hour, closed-book exam; Casio or Sharp approved calculators only. Format: seven questions, each worth 20 marks (sub-part weights as tabulated on the front page); only the first five questions appearing in the answer book are marked, so candidates effectively choose 5 of 7. All seven are solved below for completeness. The paper asks for point-form answers wherever possible; the solutions below use full working for clarity.
Reference texts: Nahmias & Olsen, Production and Operations Analysis (7th ed., Waveland/McGraw-Hill) — forecasting, inventory (EOQ) and aggregate planning; Sipper & Bulfin, Production: Planning, Control, and Integration — production-management systems; Hillier & Lieberman, Introduction to Operations Research (11th ed.) — LP formulation and project scheduling (CPM/PERT); Pinedo, Scheduling: Theory, Algorithms, and Systems (5th ed.) — parallel-machine scheduling, makespan and tardiness; Hopp & Spearman, Factory Physics (3rd ed.) — variability and production-system inefficiency; Niebel & Freivalds, Methods, Standards, and Work Design — division of labour and work-design history; ISO 9001:2015 and the Toyota Production System literature — quality management, 5S/lean and TPM.
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
Little's law states that, in any stable production or service system, the average number of items in the system (work-in-process, $L$) equals the average arrival/throughput rate ($\lambda$) multiplied by the average time an item spends in the system (flow time, $W$): $L=\lambda W$. Its significance is that it holds for essentially any queueing or flow system — a single machine, a whole factory, or a hospital emergency room — regardless of the arrival pattern, service-time distribution, or dispatching rule, as long as the system is in steady state. That generality makes it one of the few "free" quantitative levers a production manager has: if throughput $\lambda$ is fixed by demand, the only way to cut flow time $W$ (and hence lead time, a major driver of customer satisfaction and cost) is to reduce the amount of work-in-process $L$ sitting in the system. This is the theoretical justification behind WIP-capping systems such as kanban and CONWIP — by physically limiting $L$, a plant forces $W$ down without having to touch capacity. Little's law is also a cheap diagnostic: measuring any two of $L$, $\lambda$, $W$ on the shop floor immediately gives the third, exposing hidden inventory or excessive cycle time without a full simulation study.
5S (Sort — Seiri, Set in order — Seiton, Shine — Seiso, Standardize — Seiketsu, Sustain — Shitsuke) is a structured workplace-organization method from the Toyota Production System that removes clutter, gives every tool and part a labelled, visually obvious home, and keeps the standard through periodic audit and habit-building. Its significance is disproportionate to how simple it sounds: a disorganized workstation hides problems (a missing tool, a leaking machine, an out-of-spec part) inside visual noise, and searching for misplaced items is pure non-value-added time (muda). By making abnormalities visually obvious — a shadow board with an empty outline, a marked floor area that is not empty — 5S is usually the first step of any lean or TPM (total productive maintenance) rollout, because it builds workforce discipline and creates the visual baseline that later tools (kanban boards, andon signals, standardized work) depend on. Companies frequently find that safety incidents and minor stoppages drop measurably after a genuine 5S implementation, simply because hazards and defects that used to hide in clutter are now visible immediately.
Division of labour is the principle of breaking a complex production task into a set of simpler, narrower sub-tasks, each performed repeatedly by a different worker or work centre rather than by one person carrying a job from start to finish. Its classic illustration is Adam Smith's pin factory: ten specialized workers, each doing one or two of the eighteen steps of pin-making, could together produce thousands of times more pins per day than ten generalists each making a whole pin alone. The significance is threefold. First, specialization drives a steep learning-curve effect — a worker who repeats the same narrow motion thousands of times per shift becomes far faster and more accurate at it than someone switching between many different tasks. Second, it eliminates the setup and mental-switching time lost every time a worker changes tools, jigs, or skill context, which is a major hidden cost in craft-style production. Third, dividing labour makes it economical to design task-specific tooling and fixtures (since the tooling only ever needs to support one narrow operation), which is what ultimately enabled interchangeable-parts mass production and, later, the moving assembly line. The significance is not unqualified, however: extreme division of labour can produce monotony, disengagement, and repetitive-strain injury, and it makes the line fragile to absence or imbalance at any single narrow station — which is precisely why later work-design philosophies (job enlargement, job rotation, self-directed teams) exist as a counterbalance.
Total Productive Maintenance is a company-wide philosophy that makes equipment reliability everyone's responsibility, not just the maintenance department's, built around eight "pillars" (autonomous maintenance by operators, planned/preventive maintenance, quality maintenance, early equipment management, training, safety, administrative TPM, and focused improvement) and measured through Overall Equipment Effectiveness $OEE=\text{Availability}\times\text{Performance}\times\text{Quality}$. Its significance is the shift it represents from "fix it when it breaks" reactive maintenance — where unplanned downtime is treated as an unavoidable cost of running machinery — to a proactive discipline that attacks the "six big losses" (breakdowns, setup/adjustment, idling/minor stoppages, reduced speed, process defects, and reduced yield) at their root. By training machine operators to perform basic cleaning, inspection, and lubrication themselves (autonomous maintenance), TPM catches the small abnormalities — a loose bolt, an unusual vibration, a slow leak — long before they escalate into a major breakdown, while freeing skilled maintenance technicians to focus on the harder failure modes and on redesigning equipment so it is easier to maintain in the first place. TPM is a direct extension of 5S and is one of the pillars of lean manufacturing/the Toyota Production System, because unreliable equipment is itself a major source of the production variability discussed elsewhere on this paper (Question 5) — a machine with unpredictable uptime forces the same costly buffering (extra WIP, safety stock, slack capacity) as any other variability source.