# User Guide

**1. General Information**

**2. Starting Up**

**3. Connecting data**

**4. Demand and Sales Forecasting**

**5. Inventory Planning**

**6. Reference**

**1. General Information**

**2. Starting Up**

**3. Connecting data**

**4. Demand and Sales Forecasting**

**5. Inventory Planning**

**6. Reference**

understanding-the-ir-calculation

In this article, we explain how Streamline calculates:

To explain the calculations we will use the example project.

To explain, how the **Order now qty** column is calculated, let’s show the **Demand forecast** section of the inventory report table. To do this, go to the report settings and check the **Demand forecast** option (see figure below).

Let us take a look at item **H8010**. The **Order cycle** and the **Lead time** are both two months. (see figure below).

The **On hand** is **105** units. That is enough to fulfill the first part of the lead time period (the forecast shows **94** units demand in **January**) but not enough to satisfy the last part of the lead time (the forecast is **91** units in **February**).

Streamline uses a color-coding for the **On hand** column and **Demand forecast** section of the report. In our case, a red background of the **On hand** cell means that there is currently insufficient inventory to cover the demand during the lead time period.

All we can do is to replenish the next order cycle period (**March** and **April**). Thus, Streamline suggests an order of **200** units currently to cover the demand during this period.

The formula for the Order now qty in Excel-like format is:

, (1)
**Order now qty** = MAX(CEILING(MAX(0, Order cycle demand [starting from **Lead time**] + **Safety stock** + **Pending sales orders** - *Remaining*), **Rounding**), **Min lot**)

where:

.
*Remaining* = MAX(0, MAX(0, **On hand**) + **In transition** - Lead time demand)

Past due **In transitions** compensate negative **On hand**. It means that if you have past due **In transition** of **100** units, **On hand** is **-20**, and **Lead time demand** is **40**, then *Remaining* = MAX(0, -20 + 100 - 40) = 40.

Let’s calculate the order quantity for our example. As we don’t have constraints for this item, the formula is simpler:

.
**Order now qty** = Order cycle demand [starting from lead time] + **Safety stock** + **Pending sales orders** – *Remaining*

In our case:

*Remaining* = MAX(0, 105 + 0 – (94 + 91)) = 0.

**Order now qty** = (102 + 94) + 4 + 0 – 0 = 200.

In this section, we describe:

- the horizon of Streamline’s purchase plan and projected inventory levels;
- the data required for calculation a purchase plan;
- how a purchase plan and projected inventory levels are calculated by Streamline.

Let’s go to the **Inventory report** tab and show the **Purchase plan**, **Projected inventory levels**, and **Demand forecast** sections. To do this, click the **Settings** button and check the options shown in the figure below.

As you see, the **Purchase plan** (and **Projected inventory levels**) horizon is less than the **Demand forecast** horizon – the **Horizon** parameter of Streamline (see figure below).

The **Purchase plan** (and **Projected inventory levels**) horizon is always *min*(**Lead time** + **Order cycle**) – 1 data aggregation period shorter than the **Demand forecast** plan. Where *min()* means the minimal from all of the items.

The **Purchase plan** section is calculated for each item separately. The ability to compute a purchase plan for an item depends on the availability of data, to be more precise, whether the forecast **Horizon** is a multiple of the lead time or not (see figure below).

As you see, Streamline can’t calculate the purchase plan for all the periods for the highlighted item (the **May** cell is empty). That is because the forecast **Horizon** doesn’t cover the last lead time period completely, consequently, Streamline can’t simulate the arrival of the last purchase order and calculate purchase plan for the periods by this arrival.

Streamline calculates purchase plan and projected inventory levels by simulating future consumption and replenishment events.

Let’s consider the same item **H8010**. Recall that the order cycle and the lead time for this item are both two months.

The suggested replenishment quantities are displayed in the first period of the order cycle in the **Purchase plan** row of the **Table**. These should be ordered by the first day of the period they are shown (see figure below).

Streamline recommends ordering **200** units currently (or by the beginning of **January** if the situation will not change). We have already explained how this suggestion was calculated. As we have a shortage (**On hand** is **105** units however the lead time demand is **94** + **91**), Streamline expects that we will have **200** units on hand at the end of **February**. This quantity intended to cover the demand in **March** and **April** (see figure below).

Taking into account the forecast for **March** of **102** units, the inventory level at the end of this month is expected to be **98** units (see figure below).

To fulfill the demand for the next order cycle (**May** and **June**), Streamline suggests to order **215** units by the beginning of **March** (see figure below).

This quantity is calculated using the formula (1):

**Order qty** = (107 + 108) + 4 + 0 – (200 - 102 - 94) = 215.

Then, taking into account the forecast for **April** of **94** units, order arrival of **215** units and inventory level at the end of **March** of **98** units, projected inventory level at the end of **April** is expected to be **219** units (see figure below):

**Inventory level** = 98 + 215 – 94 = 219.

And so on…

As you see, Streamline always keeps safety stock of **4** units on hand from period to period in our example.

The case we consider here is pretty simplified: there are no constraints on the lot size, and lead time and order cycle are multiple of one data aggregation period.

Let’s continue examining item **H8010**. As you see, safety stock is only **4** units in our example (see figure below).

Why is its value so small? It’s because of the way it’s currently being calculated. To find out the method used, open the report settings by clicking the **Settings** button on the **Inventory report** tab toolbar (see figure below).

We see that the calculation is based on the given **Service level** of 98% (meaning that we will have sufficient inventory to cover 98% of projected sales).

The exact Excel-like formula to calculate safety stock using this method is

,

where:

`Service level coefficient`

is determined by the given**Service level**unambiguously;`δ`

is unbiased standard deviation of the model on the learning set.

The **Order cycle** value is converted to the data aggregation periods before the calculation.

In our case, the model is pretty accurate - the Safety stock δ is just **1.16** (see figure below).

Therefore, we only need to maintain a small amount of safety stock:

**Safety Stock** = CEILING(2.05 * 1.16 * SQRT(2)) = 4.

If you find this estimation unreliable due to a small resultant amount, you can configure Streamline to take the demand of a given number of future periods as the safety stock. To do this:

- Go to the menu
**File**>**Settings**and then go to the**Inventory**tab or click the**Settings**button on the**Inventory report**toolbar. - Check the
**Demand of the future periods**option. - Enter the number of periods.
- Click
**OK**(see figure below).

The number of periods can be given as a fractional number.

As you see, Streamline has immediately recalculated the safety stock (and the entire inventory report) as we made the change. Now the safety stock is **215** units (see figure below).

understanding-the-ir-calculation.txt · Last modified: 2019/02/07 13:17 by admin