FASE III: Comunicación de Resultados

In this chapter current planning activities in regard with the tactical planning are explained further. This is done to create a better understanding of the requirements SA has for a tactical workforce planning and the situation in which it has to operate. As with the planning type, Nefit uses no clearly defined model or planning method. For a better understanding of the workforce planning activities at Nefit, all planning activities will be discussed in light of the four step model of (Anderson, 2004). The model is thus not used by Nefit and the steps are not performed as clearly divided as in the model. For convenience the model, already shown as flowchart 2 in section 3.3, is shown again in flowchart 8.

Flowchart 8: The four step process of Martin Anderson (Anderson, 2004)

4.2.1 Supply analysis

SA uses several tools to measure the supply of capacity based on employee availability. These tools are described below, with the resulting values for the variables measured.

Skill availability

SA uses a skill matrix where the skills of all employees are listed; an example is given in appendix 3. As explained in paragraph 4.1.2 employees with highly perceived abilities are trained in more skills than other employees. For this reason the skill matrix is used to support decisions on who to offer a contract as well. This skill matrix is to be used as input in the workforce planning to indicate the number of employees available and what jobs they can perform.

Employee availability

To monitor the availability of employees, SA uses an employee planning. The employee planning is a document with a rolling horizon in which the absence of employees is planned and monitored. This document is used as input in determining which employees are available at any point in time. The historical information on absence levels and planned leave is used to set an estimate for future sickness leave. Current unplanned absence at SA is around 3%.

Throughput times

The main issue SA is struggling with is determining the throughput times for PO’s through the entire SA department and through every workstation. All 4000 different spare parts have their own routings and activities connected with them. The effect is that throughput times for most PO’s are unique. The throughput times are mainly depending on order size, the product itself and the workstations where the PO is handled. Therefore, each workstation may provide an upper boundary for production capacity for a certain range of products.

The current system used by SA to measure the throughput times is by registration of the start and finish times for every PO at every workstation. From this information an overview is created for every product, as shown in Appendix 4. The advantage of this method is that it can deliver a large quantity of data over a relative short period of time. The data created is also based on actual achieved production times instead of theoretical achievable times. The main disadvantage is that the method is not exact, since production times for the entire order are rounded off to full minutes for each workstation. The method of measuring allows noise to enter the data as well. It is possible that a fraction time between start en finish is not spend on production of that order.

The example given in graph 2 shows a typical relation between the production time and the production quantity. This line clearly shows that larger quantities require less production time per unit than smaller quantities. This is caused by setup times at workstations and other actions that have to be performed in equal measure for all production sizes. An example of this may be the walking time for order pickers to the pick location. Note that due to the labour intensive activities, production time is the larger part of the total production costs. For reliable estimation of the production time and costs it is therefore advisable to set a standard production quantity for each spare part. The forthcoming standardised production time can then be used as input in the workforce planning.

Graph 2: achieved production time per unit for each batch size.

4.2.2 Demand analysis

To keep track of actual demand at SA, the registration used to count available PO’s and the number of articles to produce as described in paragraph 4.1.2 is used. This registration method is used to count the PO’s that are released for production, for every day within the planning horizon. The same is done for the total number of articles that have to be produced. Because PO’s cannot yet be connected with a production time, this is the most reliable method available to establish the workload.

Graphs 3 and 4 show respectively the number of production orders released and the number of products released. A distinction is made between; backlog orders and orders that do not have to be finished before the date of measurement. A short discussion of the information found in these figures is given below.

Graph 3: Available workload over time measured in number of production orders

0 1 2 3 4 5 6 7 0 5 10 15 20 25 P rodu ct ion ti m e ( m inut e s)

Batch size (units)

Achieved production time per unit for each batch size

Time per unit trendline 0 100 200 300 400 500 600 w or kl oa d (# P O 's ) Date

Workload over time measured in number of production orders

Graph 4: Available workload over time measured in number of articles

In general it can be seen that the graphs show continuous change in the workload available at each point in time. This is mainly caused by PO’s which are released at pre-set times and not continuously. This is shown best by the sudden peaks shown around 27-2-2012 and 2-4-2012. Peaks like these are mainly caused by one of two reasons. First cause is orders coming from the warehouse in Lollar, ordering several products at the same time in large quantities. The second cause is delivery of large amount of purchase parts to SP, so that a large number of PO’s are to be released. Some days several small orders may be finished over a period. At other days one or two larger orders or no order at all may be finished during the same period.

Over time it can be seen that there is a wave movement in the graphs. For example, the period between 13-12-2011 and 16-01-2012 has high workload availability indicating a peak demand and possible capacity shortage over that period. The period between 20-1-2012 and 7-2-2012 however is a clear example of a period with low workload availability. This indicates where the capacity can easily keep up with demand. As employees will not be fired before SA is completely out of work, this situation occasionally occurs after peaks in demand.

Both graphs seem to indicate that SA suffers from facing backlog in production at all time during the measurement period. This is caused by an error in the production orders released, which assigns all PO’s to earlier finishing dates than when they are actually acquired. Even though the indicated delivery date may be passed, there might still be enough time for completion of a PO. This has great implications for the workforce planning, as unavailability of the actual delivery date makes it impossible to produce a reliable and efficient planning.

The fourth notable effect is that at no point in time there was absolutely no workload available. This is mainly due to two reasons. Firstly some orders may already be finished but not yet signed off as complete, thus still show up in the ERP system as available workload. Secondly some orders may be blocked from production due to errors in the order, the product or stock levels of required purchase parts. For these reasons the available workload is not likely to reach zero, even though SA has to send people home.

0 10000 20000 30000 40000 50000 60000 w or kl oa d (# uni ts ) Date

Workload over time measured in number of articles

Comparable with graph 3 and 4 above are the graphs 5 and 6 shown below. Graphs 5 and 6 show, respectively, the number of PO’s and the number of products that are not released for production yet. This workload has arrived in its production period, but no stock is available at that point in time. This explains the source of one of the main causes of sudden peaks explained earlier in this paragraph. These peaks are shown here as sudden declines, as can be found around 22-12-2011. Both graphs show a steady decline in workload, especially from the beginning of 2012. This is a clear sign of reducing demand while moving into the low season.

Graph 5: Expected workload over time measured in number of production orders

0 50 100 150 200 250 300 350 400 450 w or kl oa d (# P O 's ) Date

Expected workload over time measured in number of production

orders

Graph 6: Expected workload over time measured in number of articles

4.2.3 Gap Analysis

As indicated in paragraph 4.1.2, the supervisor generally establishes whether there is a difference between demand of production hours and the supply. For this step there is no method or tool used, that can be used to define the gap. This is an important weakness in the current planning method, as the gap is not quantified.

4.2.4 Solution Analysis

The solution analysis is currently made by the supervisor of SA, as is done with the GAP analysis. The skill matrix and employee planning described in paragraph 4.2.1 are used to establish the supply of production capacity. The production order registration described in paragraph 4.2.2 is used to establish the demand in capacity. The information from these three tools taken together is used in the process of determining how to cover the required demand. Although they function as input, they do not indicate how any possible capacity problems are to be solved. This is done by the supervisor according to the method shown in flowchart 7. The lack of a well defined solution analysis means that there is a great risk of deviating from standard procedures. If the supervisor is absent, the decision process is likely to be different, affecting the performance of SA.