A Furniture Industry Case Study

Manufacturing is a complex activity even for simple products like Furniture. For instance, a furniture manufacturer makes parts for 9 different furniture models using 4 different production machines.

The manufacturer requires each model parts to be made in different quantities before they are assembled into a final assembled model.

After the parts are made by the machines, they are separated into kits consisting of parts belonging to each model groups, before they are sent to be painted and then assembled together.

The table below describes the parts made by the 4 production machines, namely PB9, PB10, IRON and PB11 respectively, where the part cycle times for each machine and the quantities required from each machine differ from part to part.

The manufacturer faces some complexities in the production process, such as:

  1. Parts for each model must to be made in the quantities specified in the table above.
  2. Only after each part quantity target is met for a given model can all parts for that model move to the next step in the production process, which is painting.
  3. Different parts have different cycle times.
  4. Machines make parts for different models as shown in Table 1.
The first step in the furniture production process is to schedule each of the 4 machines to make the part quantities listed in the table above.

The manufacturer typically schedules each machine in a sequential manner based on the values in Table 1. He feels this to be a simple way to schedule the machines, as it meets his purpose to make the part quantities.

And he does not follow any special routine in scheduling the machines as long as all the part quantity targets are met for every model.

Therefore, the schedule for PB9 resembles the values in Table 1, which is exactly the same as the values listed for PB9 in Table 2. The cycle time values in Table 2 are indicated in minutes and have been converted from Table 1 values, which are in seconds. The type column contains the Furniture model number.

So PB9 machine starts by making 215 parts of a part for Model 1 followed by 52 parts of a part for Model 4 and so on and so forth. In short, PB9 makes parts of 7 parts of 6 models as it makes two parts of model 6 altogether.

Similarly, other machine schedules operate in a similar manner.

And all machines operate simultaneously, when the part production starts.

The outline of the factory line can be seen in the FlexSim model above.

The manufacturer notices that his part inventory increases dramatically. This is due to the condition that parts have to remain in the 9 model buffer areas, until part quantities are met for each model, before moving to the painting area.

Parts waiting in a buffer or WIP area is painfully expensive to the factory's cost of operations.

The manufacturer also faces a lead time problem, as considerable time is lost in parts waiting for completion of target quantities in each of the nine model buffers.

The manufacturer also notices that the amount of WIP in each of the 9 model buffer areas is directly related to how he schedules the 4 machines.

So, he asks the big question.

Can I intelligently schedule the 4 machines to reduce or minimize the WIP?

And the above objective requires optimization to minimize the WIP or inventory,

Optimization Process Step 1

The first step in the Optimization process is to add the current or initial schedule for the 4 machines inside the OptQuest window of the FlexSim model, as shown below:

Optimization Process Step 2
The second step is to define the Performance variable, which in our case is average inventory.

In this step the above schedule is used by the Optimization engine to output an optimized schedule that minimizes the average inventory. The optimization results for minimizing the average inventory is shown below.

We select the lowest value for average inventory to output the new schedule.

Optimization Process Step 3
The optimum schedule for the 4 machines reduces the average inventory.
The average inventory from the optimum schedule can now be compared with the average inventory from the initial schedule, as shown in the graph below.

The results show that the average inventory from the initial schedule is 1357 and the average inventory from the optimum schedule is inventory 853.

This results in an inventory reduction of 37%, which is a huge saving in operational costs.

The new schedule can now be exported into an Excel Spreadsheet to schedule the 4 machines with the optimum schedule and to derive the benefit of a lower average inventory of 853 parts and consequently a lower cycle time.

And this methodology can be used for different furniture models, where the bill of material is different and where part allocation to the 4 machines changes with every model, and where the cycle time to make a given part varies from part to part.

In short, we have technologies today to work more intelligently to save costs. And these kinds of analyses cannot be done using Excel Spreadsheets.

And the ROI from the use of advanced analytics like simulation and optimization is significantly higher than from other methods of analysis.