Master data: your 11 killer questions answered

How can you become a master data maestro (and save your business thousands in the process)?

master data

The slightest mention of master data sends shudders down the spine. “Please! Anything but the ‘m-word’” I hear you cry.

But we can’t just brush it under the carpet and pretend like the problem doesn’t exist. It's gone on for too long now. We need to overcome this stumbling block once and for all!

Almost all businesses have some form of master data. In fact, many organisations have lots of it. So what’s the problem then?

Well, it is a simple equation: Rubbish in = rubbish out

Sure, most business have lots of data. But this doesn’t mean it is correct or complete. Furthermore, just because the data is in place, this doesn’t mean that master data is used effectively to make informed supply chain decisions.

In fact, we typically see that around 50% of the businesses we help lack the core master data which are fundamental for ‘good’ inventory management.  It’s always the elephant in the room. But, as the inventory expert, Tony Wild, once highlighted: “inventory is the physical consequence of missing data”.

So, if master data is SO important, why is it always overlooked (or worse still, ignored completely)?

The answer is simple: keeping master data up to date and complete is hard work! But you don’t need to worry!

After working with thousands of businesses across the globe, we understand the challenge companies face. That is why we have put together this simple guide to master data to help you out!

Contents

Why do so many businesses struggle with master data?

What is master data anyway?

Who should ‘own’ the master data?

Why do we need master data for inventory management?

How can you use master data to determine the order level?

How can you use master data to determine the order quantity?

How can we use master data to determine the review period?

A practical example: fixed vs variable ordering

How can you use master data to optimise your inventory?

How can you correct your master data (the easy way)? And what if you missing the data?

Why do so many businesses struggle with master data?

We all know that master data is essential for making effective supply chain decisions. But equally, everyone knows that keeping master up to date is a painful undertaking. As a consequence, master data is often forgotten about...

But here is the bitter truth: if your master data is incomplete or out of date, you will make poor decisions. FACT.

“But the data was 100% correct when I imported it” you cry.

I hear you. But things change. Even if the data was perfectly correct when it was first imported into your system, data is easily deleted, corrupted or misinterpreted. Or maybe the data is just no longer a true reflection of the reality.

Imagine if you use a different supplier? Or maybe your supplier has changed the way they fufil your demand? What if your own storage costs have changed? Warehouse space isn’t as cheap as it was back in the good old days...

The point is simple: the quality of your master data erodes over time. And if you want to make effective inventory decisions, you need a robust basis of quality data.

Afterall, if you fail to keep this vital information up to date, this could cost your business millions in mistakes!

What is master data anyway?

According to Wikipedia, master data can be defined as:

"data about the business entities that provide context for business transactions"

But in the context of inventory management, master data is the fundamental bits of information that determine the ‘what’, ‘why’, ‘how’ and ‘who’ of everything to do with inventory.

To give a few examples, inventory master data covers the following areas:

  • specific details about the product in question (size, SKU number)
  • Information about the supplier (lead times, MOQs)
  • Details about currently inventory position (location, inventory level)
  • Details about the customer
  • Information around the past demand
  • As well as many other key data elements

Who should 'own' the master data?

Who should own the master data in your business?  Should it be the guys in IT, finance, operations, or even management?

This is a difficult question to ask. And many businesses don’t have a clear-cut answer. You probably think that it depends on the business in question…and to a degree, you would be correct!

Okay, there could well be a technological process or system that the IT team need to support. However, master data is everyone’s problem. From the demand planner to the CEO, we all have a duty to keep it clean. Afterall, everybody benefits!

But should management really be expected to spend their time updating data? True, this is unlikely to be the best use of their time. However, they must set the boundaries and ensure that all teams are doing their bit.

Why do we need master data for inventory management?

So, we all agree that master data is really important in a business. And it's clear that everybody needs to see master data as a fundamental part of their role. But do we really understand what this data is used for? Do we understand how master can help us to make better decisions around inventory management?

In essence, master data is used for everything. And different teams will use the information in slightly different ways. But when it comes to supply chain management, correct and reliable master data is an absolute must for satisfying customer demand. Afterall, without this data, it would be impossible to know how much inventory you need to fulfil your customer’s demand.

To properly understand the importance of master data, we need to first define what ‘good’ inventory management looks like. The foundation of inventory management revolves around two key questions:

  • When should you place an order?
  • How big should your order be?

In the next couple of points, we will explore how you can use master data to determine order levels and order volume. But this is just the tip of the iceberg. From making assortment decisions to selecting the right supplier, master data is the back-bone of every supply chain decision!

master data

How can you use master data to determine the order level?

To begin with, we are going to focus on the first question: When is the right time to place an order?

Is it when you sell the last unit? Or is it when you start to run a bit low? Or should this decision be based on something else?

Ordering at the right time is vital. If you order too soon, your warehouse will become clogged up with excess stock you don’t need. But on the other hand, if you order too late, you will face costly stock outs. If the product in question has extensive lead-times, the problem is only exacerbated!

Thankfully, there is a simple formula for working out the order level:

Average demand X (lead time + review time) + safety stock

However, to apply this formula, we need three key pieces of master data:

  • The average demand you expect to see for the product in question during the supplier's lead time
  • The requirement for safety stock to cover volatility in demand and supply
  • The lead time & the review time (Note: we will dive into this later)

So, let’s break this down:

Working out the average demand during the lead time

Obviously, we need enough inventory to satisfy demand while we wait for our next delivery to arrive. This is called the cover period.

So, we therefore need to know what the typical lead time would be. And we also need to know what the demand we are anticipating during this period. Typically, we base this on historic demand.

Working out the requirement for safety stock

Safety stock is necessary to accommodate variations in demand and supply.  To anticipate the variation in supply, we need to look at the supplier’s track record for delivery reliability. Do they deliver when they promise or do deliveries always arrive late?

In terms of demand history and delivery reliability, we need a sufficient amount of data in order to make a good estimate.

You also need to understand the target service level.

Note: this is not a number that you can look up or calculate, but a criterion set by management to determine how high (or low) this should be.

Once the service level is clear, we can determine the requirement for safety stock in two main ways:

  1. The number of times a product is out of stock
  2. The number of units that are of out of stock

This safety stock criteria is therefore also a management variable, and to a large extent determines the level of risk.

Finally, the data to determine the order level is now visible! When the current stock falls below the order level, another order must be placed.

Once the service level is clear, we can determine the requirement for safety stock in two main ways:

  1. The number of times a product is out of stock
  2. The number of units that are of out of stock

This safety stock criteria is therefore also a management variable, and to a large extent determines the level of risk.

Finally, the data to determine the order level is now visible! When the current stock falls below the order level, another order must be placed.

master data
master data checklist

 

 

Practical example: determining when to order

Imagine you are the inventory manager at Pedal Cycling Ltd, the U.K’s fastest growing distributor of bikes and cycling accessories. You need to determine the optimal moment to place an order for SKU# 35647594945

What information do you need? In the checklist below, we demonstrate what master is required.

To make things simple, lets imagine that the demand is fairly consistent and suppliers typically deliver on time and in full (if only this were true in real life!)

So, in our simple example, as soon as our inventory falls below 510 units, we need to place an order!

How can you use master data to determine the order quantity?

We now know when we need to place an order, but how much stock should we order at this moment?

Again, it depends!

Regardless of the strategy, determining the order quantity is a trade-off between two types of costs. The first is the cost you incur related to the cost of holding products in stock. The later relate to the costs associated with placing an order.

Other data needed to determine the order quantity are the forecasted demand for the item (which we can base on the historical demand and the purchase price of the product.

When it comes to order quantities, there is typically huge potential for optimisation here. However, for the purposes of this article, we will keep things nice and simple. If you are interested in reading more, check out our guide to economic order quantities.

master data

How can we use master data to determine the review period?

Already, we have seen that eight different types of master data are required to determine the order quantity and order level. But we are not finished yet!

We also have to decide whether we want to order at fixed times (e.g. once a week) as well as whether we want to order in fixed or variable order quantities.

Variable review times and order quantities allows us to remain responsive to the changes to the market. However, if you have large assortment, this may not be most efficient approach for item.

The alternative is to review products at set intervals and order in fixed quantities. While not as responsive, this approach may be best for more stable products.

But how can we determine when it is suitable to adopt a fixed of variable approach. The answer to this depends on three things:

  1. The strategic importance of the product
  2. The volatility of demand
  3. The volatility of supply

For the last two points we can use data that we have already collected. For the volatility of the demand, you can use the historical demand data. For the volatility in supply, we can look at the historic supplier reliability.

For important products, it is important to react quickly and immediately as soon as they fall below the order level. For less important items, you can safely order at fixed times. 

A practical example: fixed vs variable ordering

master data

Let’s return to pedal cycling ltd. Imagine that you need to determine whether the review period and order quantity for two different products should be fixed or variable.

 

To the left are the two products in question.

Given that demand for product A is volatile, the risk of going out of stock outs is high. Furthermore, given that margins are strong, a stock out situation could cost you dearly in terms of lost sales. Finally, given that this product is bought by you most important customers; stock outs could also harm customer relationships. With all this in mind, you need to remain on top of this product. Therefore, it probably best to adopt a variable approach to both order quantities and review periods.

 

On the other hand, demand for product B is far more stable. Therefore, you can anticipate future demand with greater confidence. Furthermore, given that the margins are relatively low, the impact of stock outs is likely to be much less. Therefore, the goal should be around ensuring the process of ordering is as efficient as possible. For this reason, you may be inclined to order at set specific times (e.g. once a month) in set order quantities (e.g. 100 units or a full container’s worth).

Therefore, you can anticipate future demand with greater confidence. Furthermore, given that margins are strong, a stock out situation could cost you dearly in terms of lost sales. Finally, given that this product is bought by you most important customers; stock outs could also harm customer relationships. With all this in mind, you need to remain on top of this product. Therefore, it probably best to adopt a variable approach to both order quantities and review periods.

On the other hand, demand for product B is far more stable. Therefore, you can anticipate future demand with greater confidence. Furthermore, given that the margins are relatively low, the impact of stock outs is likely to be much less. Therefore, the goal should be around ensuring the process of ordering is as efficient as possible. For this reason, you may be inclined to order at set specific times (e.g. once a month) in set order quantities (e.g. 100 units or a full container’s worth).

Using master data to optimise your inventory?

Based on these essential master date elements, we can already begin to make three more interesting analysis for management. The first is the ABC analysis, which the management can use to determine which (and how many) products the company should pay attention to.

The second analysis is the so-called Incremental Margin Analysis, which provides management with insight into which products contribute positively to the net margin.

The third analysis is the Delivery Time Deviation Distribution. This is an instrument that the supply chain team can use to gain insight into the performance of suppliers.

 

The table below provides an overview of the 9 essential master data blocks that are required support for each analysis. The fields shaded in green are management variables, which the MT has direct or indirect influence on this.

master data table

How can you correct your master data (the easy way)? And what if you’re missing the data?

Across your assortment, you probably have millions (if not billions) of data points. To update all of these would be a huge undertaking. So where do you begin? Well, you need to start with the important products.

And to identify the important products, we suggest you turn to your trusty ABC Analysis and focus all of your time and energy on the “A” Items. After all, these are the products that make you money or the products that your customers demand. The benefits of getting master data for this small proportion will be felt immediately across the business. And once all of the A items have been updated, you can move on to the B items and then finally the C items.

Conclusion: How does your business stack up?

In this article we have explored the importance of master data. We hope blog has been insightful.

Now that you are an expert on master data, what other areas of your supply chain do you need to work on?

Take our inventory power quiz now & discover where you can gain performance improvements!

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