Optimizing Men and Material Movement in Plant Layout

In any food processing or manufacturing plant, people and materials are constantly on the move. Workers walk between stations, raw materials travel from storage to processing lines, and finished goods make their way to packaging. If these movements are chaotic or poorly planned, the result is wasted time, higher labour costs, and slower output. That’s why analysing the movement of men and materials is a fundamental step in designing an efficient plant layout. This analysis uses structured tools – specifically trip matrices, cost matrices, distance matrices, and the total cost matrix – to make data-driven decisions about where each section of a plant should be located.

Why movement analysis matters in plant layout 🔗

Every unnecessary metre a worker walks or a trolley of raw material travels adds cost. Over weeks and months, these small inefficiencies compound into significant expenses. Effective plant layouts are designed to minimise the total distance travelled by both employees and materials, which in turn reduces handling costs, shortens processing time, and boosts overall productivity.

In food processing specifically, rapid and efficient movement is even more critical. Perishable raw materials need to reach processing stations quickly. Finished products must move to cold storage or packaging without delay. Any bottleneck caused by a poorly placed section can lead to quality issues, spoilage, or non-compliance with food safety standards.

The analysis of men and material movement provides a quantitative basis for layout decisions. Rather than arranging sections based on guesswork or convenience, plant managers can use mathematical matrices to identify exactly which sections interact most frequently and which movements cost the most – and then position departments accordingly.

Understanding the key matrices 🔗

The movement analysis process relies on four interconnected matrices. Each one captures a different dimension of how people and materials move through the plant. Together, they give a complete picture of the cost of internal transportation.

The trip matrix (from-to chart) 🔗

The trip matrix – also called a from-to chart – is the starting point. It records the number of trips made between every pair of departments or sections over a defined period (a day, week, or month). Each row represents a “from” department and each column represents a “to” department.

For example, in a fruit canning plant with sections like receiving, washing, peeling, canning, sterilisation, labelling, and storage, the trip matrix would show how many times materials or workers move between each pair. If the peeling section sends material to the canning section 80 times per day, that number goes into the corresponding cell of the matrix.

The trip matrix immediately reveals which pairs of sections have the highest interaction frequency. Research on plant layout efficiency shows that sections with high trip counts between them should ideally be placed close together. This is the core insight that drives layout optimisation.

The distance matrix 🔗

The distance matrix records the physical distance between the centroids (centre points) of each pair of departments in the current or proposed layout. Distances are typically measured in metres or feet using the rectilinear distance method – which calculates distance along horizontal and vertical paths rather than straight lines, since people and trolleys usually move along aisles, not diagonally through equipment.

If receiving and washing are 10 metres apart, washing and processing are 15 metres apart, and receiving and storage are 50 metres apart, these values populate the distance matrix. The matrix is often symmetrical – the distance from A to B is the same as from B to A – unless one-way flow paths are enforced.

The distance matrix is essential because a high number of trips between two sections only becomes a problem if those sections are far apart. Conversely, even a moderate trip count can add up if the distance is large.

The cost matrix 🔗

The cost matrix captures the cost of moving one unit load over one unit distance between each pair of departments. This cost depends on the mode of material handling used. For instance, manual carrying by workers is more expensive per metre than using a conveyor belt or a forklift.

In a food processing plant, cost per unit distance might vary between sections depending on whether materials are moved by hand carts, gravity rollers, belt conveyors, or forklifts. A section that requires workers to manually carry heavy crates will have a higher unit cost than a section connected by an automated conveyor system.

If the cost matrix is uniform – say, the entire plant uses the same handling method at ₹0.50 per unit per metre – then it simplifies to a single constant. But in most real plants, handling methods differ across sections, making a full cost matrix necessary.

The total cost matrix 🔗

The total cost matrix brings everything together. For each pair of departments, the total cost is calculated as:

Total Cost (i, j) = Trips (i, j) × Distance (i, j) × Cost per unit distance (i, j)

This multiplication is done cell by cell, and the resulting total cost matrix shows the transportation cost associated with every department pair. The sum of all cells in this matrix gives the total material handling cost for the entire plant layout.

The goal of layout optimisation is to minimise this total sum. The total cost matrix makes it immediately clear which department pairs are the most expensive to maintain in their current positions. If the peeling-to-storage pair has a very high total cost because the sections are far apart and trips are frequent, the layout should be rearranged to bring them closer.

Step-by-step process of movement analysis 🔗

Here’s how plant engineers and food technologists typically carry out this analysis from start to finish.

Step 1: identify all departments and sections 🔗

List every functional area in the plant – raw material storage, preparation, processing, quality control lab, packaging, finished goods storage, utilities, offices, etc. Each one becomes a row and column in your matrices.

Step 2: collect trip data 🔗

Over a representative period, record the number of trips between each pair of sections. This can be done through direct observation, work study sheets, or data from automated tracking systems. The resulting data populates the trip matrix.

Step 3: measure or estimate distances 🔗

For the current layout, measure the actual distances between department centroids. For a proposed layout, estimate distances from the floor plan. Enter these into the distance matrix.

Step 4: determine unit transport costs 🔗

Calculate the cost of moving one load one metre between each pair. Factor in labour wages, fuel or electricity for equipment, and maintenance of handling devices. This creates the cost matrix.

Step 5: compute the total cost matrix 🔗

Multiply corresponding cells of the trip, distance, and cost matrices to get the total cost for each pair. Sum all values to obtain the grand total transportation cost.

Step 6: analyse and rearrange 🔗

Identify the department pairs with the highest total costs. Explore layout alternatives that bring high-interaction sections closer together. Recalculate the total cost for each alternative layout and select the one with the lowest total cost.

Using CRAFT for layout optimisation 🔗

For plants with many departments, manual rearrangement becomes impractical. This is where computerised methods come in. The most well-known is CRAFT (Computerized Relative Allocation of Facilities Technique), an improvement-based algorithm that automates the layout optimisation process.

CRAFT takes the from-to chart, cost matrix, and an initial layout as inputs. It then systematically swaps pairs of departments, recalculates the total material handling cost after each swap, and keeps the arrangement that produces the greatest cost reduction. This process repeats until no further improvement is possible.

Studies in manufacturing have shown significant cost reductions through CRAFT. One case study in a wooden door panel factory reported a reduction in daily material handling costs of over 10%, along with a similar decrease in total material movement distance. While CRAFT does not guarantee a globally optimal solution, it consistently produces layouts that are significantly better than unoptimised arrangements.

Practical strategies for reducing movement costs 🔗

Beyond the matrix-based analysis, there are several practical strategies that food processing plants can apply to minimise movement costs.

Group related sections together 🔗

Sections that interact frequently should be adjacent. In a dairy processing plant, for instance, the pasteurisation unit, homogeniser, and filling station should be positioned in sequence. This is a direct application of the insights from the trip matrix – high-frequency pairs belong close together.

Design linear or U-shaped flow paths 🔗

A linear flow – where raw material enters at one end and finished goods exit at the other – minimises backtracking. A U-shaped layout achieves the same goal while also keeping the receiving and dispatch areas on the same side, which is useful when both share the same loading dock.

Minimise cross-traffic 🔗

When the paths of different material streams cross each other, it creates congestion and safety hazards. Thoughtful layout planning ensures that major flow paths do not intersect. This is especially important in food processing plants where raw and finished goods must remain separated for hygiene reasons.

Use appropriate material handling equipment 🔗

Replacing manual carrying with conveyors, gravity rollers, or automated guided vehicles (AGVs) reduces both the cost per trip and the time per trip. Automation technologies like AGVs and conveyor belts eliminate the need for manual transportation between stations, directly lowering the values in the cost matrix.

Revisit layouts periodically 🔗

Product lines change, volumes shift, and new equipment gets installed. A layout that was optimal five years ago may no longer be efficient. Regular movement analysis – even a simplified version – helps identify emerging bottlenecks before they become costly problems.

A worked example for a small food processing unit 🔗

Consider a small pickle manufacturing unit with four sections: A (raw material storage), B (washing and cutting), C (mixing and brining), and D (bottling and packaging).

Suppose the daily trip counts are: A→B = 40, A→C = 5, A→D = 2, B→C = 35, B→D = 5, C→D = 30. The current distances (in metres) are: A-B = 20, A-C = 40, A-D = 60, B-C = 25, B-D = 45, C-D = 20. Assume a uniform transport cost of ₹2 per trip per metre.

The total cost for each pair is: A-B = 40 × 20 × 2 = ₹1,600; A-C = 5 × 40 × 2 = ₹400; A-D = 2 × 60 × 2 = ₹240; B-C = 35 × 25 × 2 = ₹1,750; B-D = 5 × 45 × 2 = ₹450; C-D = 30 × 20 × 2 = ₹1,200. The grand total is ₹5,640 per day.

The B-C pair has the highest cost (₹1,750). If we rearrange the layout to bring B and C closer – say, reducing B-C distance to 10 metres – that pair’s cost drops to ₹700, and the new total becomes ₹4,590. That’s a saving of ₹1,050 per day, or roughly ₹31,500 per month, from a single rearrangement.

Tools and techniques for movement analysis 🔗

Several tools support this analysis beyond pen-and-paper matrices.

String diagrams are a classic method study tool where a thread is stretched on a scaled layout drawing to trace and measure actual movement paths. They help visualise and quantify the total distance travelled by workers and materials, making inefficiencies immediately visible.

Flow process charts document every step in a process – operations, transports, inspections, delays, and storage – along with the distance covered in each transport step. Combining flow process charts with the matrix approach gives both qualitative and quantitative insight into movement patterns.

Computerised tools like CRAFT, CORELAP, ALDEP, and modern simulation software (FlexSim, ProModel) allow engineers to model, test, and compare multiple layout alternatives before making physical changes. Recent advances combine simulation with AI-based optimisation to further refine layout decisions, achieving measurable improvements in logistics efficiency and material flow.

Benefits of systematic movement analysis 🔗

When done properly, the analysis of men and material movement delivers several tangible benefits for food processing plants. It reduces total transportation costs by positioning high-interaction departments close together. It cuts down processing cycle times because materials reach each station faster. It improves worker productivity by reducing the time employees spend walking rather than performing value-adding tasks. It enhances food safety by ensuring that raw and processed materials follow clearly defined, non-crossing paths. And it provides a documented, data-driven basis for layout decisions that can be revisited and updated as the plant evolves.

What do you think? How often does your food processing facility revisit its plant layout to account for changes in product lines or production volumes? Could a simple trip matrix reveal hidden inefficiencies in the way materials move through your plant?

References

1. https://crowengineering.com/engineering-design-services/optimal-plant-layout-design/
2. https://www.researchgate.net/publication/262182217_Efficiency_Improvement_of_a_Plant_Layout
3. https://slm.mba/mmpo-003/optimizing-plant-layout-with-craft/
4. https://www.researchgate.net/publication/296443457_Facility_Layout_Design_by_CRAFT_Technique
5. http://conf.e-jikei.org/ICTSS2018/proceedings/materials/proc_files/A006/Camera_ready_ICTSS2018_A006.pdf
6. https://sixsigmadsi.com/manufacturing-facility-layout/
7. https://umbrex.com/resources/industry-analyses/how-to-analyze-a-manufacturing-company/factory-layout-and-process-flow-optimization/
8. https://www.mdpi.com/2075-1702/12/6/390