Key Differentiators

4X4 Matrix Instead of 3X3

An in-depth study of Inventory Management reveals often overlooked transactions that do not find a place in the regular Inventory classifications. Every known and popularly practiced Inventory Management Systems talk only about 3 levels of classification.

With the number of products increasing at a frantic pace in today’s business environment, identification of redundant products becomes increasingly difficult. A multi-dimensional inventory analysis is essential to an interesting perspective to understand the dynamics of supply and demand study.

The Magic Zer ‘0’

What makes this application the most efficient and most suitable for inventory management is the magic of Zer0 – the very concept that revolutionized the world of numbers eons back. Without 0, mathematics as we know it today would not have evolved. By tweaking the same Zer0 into day-to-day inventory management, your business can have better control over the entire product cycle right from procurement to storage to availability on demand.

This Magic Zer0 adds a fresh twist to the regular inventory analysis studies adopted for cost and inventory optimization. This multi-dimensional inventory analysis is your answer to identifying redundant products in your business and handling them efficiently to maximize profit and economize inventory management.

Introducing the new Zer0 field to the existing 3X3 inventory matrix results in

The 10th field, Blank-Blank is the key that converts this inventory matrix to a strategy matrix that helps identify your redundant products that may not be in demand anymore, yet often classified under the head Z or C in regular inventory management analysis.

The red fields indicate abnormal or exceptional transactions that are generally overlooked in existing 2-dimensional inventory analysis practices, which are generally classified into two heads.

2D X Y Z Zer'0'
A AX AY AZ --
B BX BY BZ --
C CX CY CB --
Zer'0' -- -- -- Zer'0'-Zer'0'

Define period of Analysis

SMART4-D gives the users the option to define the period of consolidation for data analysis. This period of consolidation can be on months. Let’s say you have data for a period of one year i.e. January 2015 to December 2016. Most systems recommend you to do the analysis for the entire year. SMART4-D allows user to define the period of analysis. If you select ‘6 Months’ as period of analysis, you can see how the product behaves every six months. The 4-dimensional product classifications for the selected periods of consolidation are derived automatically. 6-Months and 3-Months  period details are given below.

6 Months Consolidation
1 Jan-15 to Jun-15
2 Feb-15 to Jul-15
3 Mar-15 to Aug-15
4 Apr-15 to Sep-15
5 May-15 to Oct-15
6 Jun-15 to Nov-15
7 Jul-15 to Dec-15
3 Months Consolidation
1 Jan-15 to Mar-15
2 Feb-15 to Apr-15
3 Mar-15 to May-15
4 Apr-15 to Jun-15
5 May-15 to Jul-15
6 Jun-15 to Aug-15
7 Jul-15 to Sep-15
8 Aug-15 to Oct-15
9 Sep-15 to Nov-15
10 Oct-15 to Dec-15

Why is it important to define the Period of Analysis?

For any enterprise, if you consider shorter periods of analysis, the total number of active items may be very less and correspondingly lesser items get classified into different sub-groups. Similarly if you consider larger periods of data analysis, the total numbers of active items are relatively huge. Therefore more items get classified into different sub-groups. Consider this example of an enterprise with 330 SKUs in Finished Goods. The ABC classifications for different Periods of Analysis from 1-Month to 12-Months are tabulated below. For 1-Month period of analysis, the numbers of active items  are 76 and for 12-Month period, the numbers of active items increases to 220. This directly influences number of items on the sub-categories as shown in the table below.

Active %Diff A B C
1 Month 76 7 20 49
2 Months 117 35% 8 23 86
3 Months 132 11% 10 24 98
4 Months 154 14% 12 25 117
5 Months 165 7% 14 26 125
6 Months 170 3% 15 25 130
7 Months 182 7% 15 26 141
8 Months 185 2% 15 26 152
12 Months 220 12% 18 29 173

Therefore it is appropriate to allow the users to define the Period of Analysis. Also it gives an opportunity to study how products jump form one category to the other.

Parameter settings for ABC, XYZ, abc, xyz Percentages

SMART 4-D allows the users to define the distribution of various sub-categories. The process is designed in such a way that the total for each of the category is always 100. This option gives users to study the impact on the sub-categories when these percentages are varied.

A 80
B 15
C 5
Total 100
X 20
Y 30
Z 50
Total 100
a 80
b 15
c 5
Total 100
x 20
y 30
z 50
Total 100

Multi-Dimensional Analysis – Need and Advantages

Viewing Inventory data based on Value and Consistency in demand may not suffice. There may be other parameters or dimensions that inventory can have. Consistency of demand can also be computed based on number of orders and not just quantity. More number of orders mean items are more consistent and les number of orders mean items are less consistent. Also analysis based on number or orders are easy to compute compared to Standard Deviation and Co-efficient of Variation. Therefore it is easy to add two more dimensions based on count of orders and this may be called as ‘abc’ and ‘xyz’ analysis as against ‘ABC’ and ‘XYZ’.

ABC Total Order Values
XYZ Consistency of Order Quantities
abc Total Number of Order Lines
xyz Consistency of Orders