Sage ERP X3 becomes Oracle Database Ready

Business software vendor, Sage Business Solutions’ ERP X3 solution has been granted Oracle Database Ready status through the Oracle Partner Network (OPN).

The announcement means that Sage has tested and supports ERP X3 on Oracle Database 11g Release 2, and at extension, Oracle Database Appliance. Results demonstrated smooth installation of ERP X3 application databases on Database Appliance.

The Oracle Database 11g Release 2 offers Sage industry leading performance, reliability, and scalability to power business critical applications.

Customer benefits of the solution are cost-effectiveness by lowering storage usage, reduced administration tasks, and enabling consolidation onto secure private database cloud environments.

Sage ERP X3 V6.3 is available on Oracle Database 11g Release 2.

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As reported at ARNet.

Consider the possibilities (especially now, in these challenging times)

A recent survey of published results by manufacturing and service companies[1] that have applied constraint management methods effectively shows:

• A mean reduction in lead time of 70%
• A mean reduction in manufacturing cycle times of 65%
• A mean improvement in due-date performance of 44%
• Mean inventory reductions of 49%
• A mean combined financial improvement (revenue, throughput, profit) of 76%

[1] Mabin, Victoria J. and Steven J. Balderstone, The World of the Theory of Constraints: A Review of the International Literature, St. Lucie Press, Boca Raton, FL, 2000

[Excerpt from Schragenheim, Eli and H. William Dettmer, Manufacturing at Warp Speed – Optimizing Supply Chain Financial Performance, St. Lucie Press, Boca Raton, FL, 2001]

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If you would like help getting started with apply constrain management to your business for rapid ROI and ongoing improvement, please contact me. Find me on LinkedIn.

What’s wrong with EOQ?

Economic Order Quantity (EOQ) EOQ is essentially an accounting formula that determines the point at which the combination of replenishment costs and inventory carrying costs are the least. The goal being to minimize both the ongoing costs of carrying inventory and the expenses involved with replenishing inventory.

The basic EOQ formula looks like this:

As you can see, this formula attempts to balance (simultaneously) the following factors related to the business expense linked to holding and replenishing inventory:

1. Usage rates – how many are sold or consumed over a period of time (one year in the basic formula)
2. Cost of replenishment – how much it costs the firm to replenish a single inventory item (SKU) from the point of recognizing the need for replenishment through putting the quantities back on the shelf
3. Carrying costs – all of the costs and expenses related to storing and handling of the inventory quantities held

Let us take a look at how these factors interact in a practical example:

In our example, we have an item that has a cost of $25 per unit, and the average daily demand is five (5) units. For this firm, the cost of replenishment is slightly above average—sitting at $30 per PO line processed for inventoried goods.

Observe what happens to the EOQ on this item as the cost of carrying inventory moves through the range from five percent (5%) to 40 percent.

When inventory carrying costs are very low compared to the cost of replenishment (five percent and $30, respectively), EOQ recommends big orders. In this case, each order would support more than 75 days of average demand.

On the other end of the spectrum, when carrying costs are quite high (40 percent) relative to the cost of replenishment, EOQ suggests smaller inventories (as the result of smaller orders) and the order cycle is slashed to almost one-third its former value (now, just over 26 days).

Underlying assumptions

The assumption being made in the construction of the EOQ formula is that the cost of carrying inventory is linear. That, at a five percent rate, a one dollar decrease in inventory on-hand will lead to a five cent reduction in carrying costs to the firm. Similarly, at a 40 percent carrying cost rate, a one dollar decrease in inventory on-hand will lead to a 40 cent decline in carrying costs.

Unfortunately, the linear relationship assumed by the EOQ formula simply does not exist.

When calculating the cost of carrying inventory, a large number of factors are generally considered:

1. Warehouse space rental (or equivalent)
2. Utilities expense
3. Property tax expense
4. Maintenance expenses on the warehouse and warehouse equipment
5. Inventory write-offs/write-downs
6. Other inventory shrinkage
7. Financing expenses for the warehouse, the equipment, and the inventory itself
8. Insurance expenses on the warehouse and the inventory
9. Labor expenses related to warehouse operations

When inventory is reduced $1,000 in a warehouse with a calculated 25 percent carrying cost, what are the likely real impacts on expenses for carrying inventory?

1. Warehouse space rental (or equivalent) – no change
2. Utilities expense – no change
3. Property tax expense – no change
4. Maintenance expenses -  no change
5. Inventory write-offs/write-downs – possibly some change, but not necessarily at the same “average” rate
6. Other inventory shrinkage – same as above
7. Financing expenses for the warehouse, et al -  no change
   Financing expense on the value of the inventory – some change possible
8. Insurance expenses on the warehouse, et al – no change
   Insurance expenses on inventory – some change
9. Labor expenses – no change

In short, only three of the nine items involved in calculating the cost of carrying inventory would likely change based on $1,000 reduction in inventory. That’s because increases or decreases in the volume and dollar amount of inventory held in a warehouse operations produce relatively large but non-linear changes operating expenses.

As inventory grows, changes like adding a second shift in the warehouse, acquiring additional warehouse space, or adding manpower to handle increased volumes happen incrementally. The EOQ formula has no way to account for these non-linear changes to operating expenses. Therefore, your EOQ decision-making my be entirely off the mark for success and increased profits.

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What’s the answer?

To manage your inventory quantities, I would highly recommend the application of Dynamic Buffer Management. [Click on the link and read the article there.]

To deal with non-linear changes in your enterprise—decisions that may lead to major changes in inventories (increases or decreases)—you need a broader formula that considers your system (your enterprise) as a whole. That would be this one:

Where,

• ROI = Return on Investment
• delta-T = Change in Throughput 
• delta-OE = Change in Operating Expenses
• delta-I = Change in Inventory or demand for other Investment

This formula would cover changes like adding a second shift (change in Operating Expenses) or building a new warehouse (change in Investment).

Think about. Contact me if you need further clarifications.

Getting started in Business Intelligence (BI) on a budget

This is a simple demonstration as to how you and your firm can get started turning the data that you already have into the information you desperately need using tools you already own. The task of turning data into information for decision-making is the essence of business intelligence (BI).

So, here we go.

Everybody has data

Everybody has data. Many companies are wallowing in data. What they are lacking is “information.”

Read my posts here and here for more about the differences between data, information and knowledge.

Quick! Take five or ten minutes to peruse the following table of data and write down everything that you see in these data to help make decisions about the firm’s future.

I will give you one hint: the column identified as ‘ARPAC’ is “Average Revenues per Active Customer.”

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Okay. Times up.

Hold on to your list.

Turning data into information—simply, easily, cheaply

In order to produce what follows, I used only Microsoft® Excel™ and its native ability to access databases to fetch and refresh data.

Here’s the first graph I produced:

This is nothing more than a simple bar graph of column “SOSales” (Sales Order Sales, as opposed to Invoiced Sales, for example) shown in the data above. I used Microsoft’s native capabilities to add a “trend line.”

By looking at this simple graph, several questions might come to mind that would bear further investigation:

1. Why have our monthly sales dropped from just over $8 million a month to an average of about $6 million per month over these 29 months?
2. Why or how were able to produce about $11 million in sales in July of 2008? What did we do differently? How can we build on what we learned in that experience?
3. Is my drop in sales related to lost customers?

The next graph that I produced looked like this:

This graph answered my question number three above—at least partially. Month-to-month our firm has stayed pretty steady in terms of the number of active customers served. The firm is hovering right in the 250-customers-per-month range.

On the one hand, that is good. It means the firm is steady in this regard, but it does provoke other questions that would need to be answered through further digging:

1. We are serving about 250 customer per month, but is the same 250 customers, or do I have high turnover rates for customers?
2. Are we constantly having to spend precious marketing resources to capture new customers, or do we have a high volume of repeat business?

But wait! If we are not loosing customers (at least in numbers), but our sales are falling off (in aggregate), what is that telling us?

The third graph I produced was “Average Sales per Active Customer” (month-to-month). This graph clearly shows that between January 2008 and May 2010, the firm’s average sale per active customer fell from about $32,000 per customer to under $25,000 per customer.

Here again, this graph immediately provides clues worthy of further, more detailed, investigation:

1. Are these different customers buying less product? Or, are we serving pretty much the same customers, but they are just buying less from us?
2. Either way, we should figure out why: Are they buying similar quantities, but our prices (and, perhaps, margins) have shrunk over this period? Or, are they buying smaller quantities of merchandise or services from us?
3. Either way, we should find out why: If they are buying smaller quantities, is some of that business going to our competitors?

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Next steps

As you can see, turning the data into information allows our mind to quickly digest it and move toward decision-making. In some cases—perhaps many cases, when you first start—the process will lead to further information gathering.

On the other hand, you will sometimes discover that tribal knowledge already present in your organization will help you take immediate steps to begin making more money tomorrow than you are making today. Frequently, those steps involve no investment at all. Sometimes all it take is understanding better what is happening. Other times, a simple policy change permits significant increases in Throughput and profits.

After all, isn’t that really what you want to do—not spending six-figures on a new business intelligence “solution”?

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Read more here about unlocking “tribal knowledge.”

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How I did it step-by-step

1. Identify the data
2. Build a SQL Server view or query
3. Connect Microsoft Excel to the data
4. Build the graphs

Total time: about 2 to 2.5 hours

Business Intelligence for the coming year

Recently I was asked by a business writer for my recommendations for “BI New Year’s Resolutions.” I doubt my response was what the writer had hoped for, since many business blogs and publications garner support from advertisers. And, when you are doing that for a living, you really want to write things that are supportive of the kinds of products your advertisers supply. These days, since business intelligence (BI) is all the rage, there are a lot of dollars being proffered for advertising of upscale business intelligence solutions.

For better or worse, I don’t have to worry about that. (Of course, my income is smaller as a result.) But, here’s what I wrote—along with some other advice to round it out.

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BI New Year’s Resolution

RESOVED – I will never, ever, ever again undertake a BI project just because someone in my organization thinks “it might pay-off.” Instead, I will faithfully resolve to calculate—in advance—the expected ROI (return on investment) for the project.

I have learned my lesson: BI is not like an engine oil additive: departments can’t just “pour it in and expect the company to run smoother, faster, longer and get higher mileage” through some mystical power brought to them by the BI fairy.

In calculating the ROI, I will also remember that “approximately right” is fart better than “precisely wrong,” so will not waste my firm’s precious resources trying to hone a number to perfection before taking action—especially in this tough economy.

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The second question to which this writer asked me to reply regard “top BI trends” for 2012. Once again, I’m pretty sure I let her down. Here’s what I wrote:

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BI Trends for 2012

In 2012, an increasing number of small-to-mid-sized firms will discover that, to get started in BI, they do not need to make six-figure investment. In fact, they may not even need to make a five-figure investment.

If they can unlock “tribal knowledge” and begin to understand what to measure in order to make a real difference in the Throughput of their system (i.e., the whole firm), chances are they can make use of tools they already have like Microsoft® Excel™ to capture data from their ERP system directly via ODBC (open database connectivity) or OLEDB (object linking and embedding for databases). This may lead to insights, and those insights may lead them to market segmentation or other innovative profit-improvers. They do not need expensive software to build a simple, yet valuable, dashboard so they can start making more money sooner—rather than later.

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In the next post, I will provide a concrete example of how simple BI can be done using tools your firm probably already owns.

(c)2008, 2009, 2010, 2011 Richard D. Cushing

Technology Wars 2: The Search for More Profits

Almost a year ago I wrote an article entitled, “What does ‘demand-driven’ really mean?” in which I outlined a view of a supply chain driven end-to-end by real-time (or near real-time) demand feedback. My recollection of this writing was triggered today by an article that appeared today on the Financial Times website: “Technology: Smarter software helps minimise discounting.”

In the FT (Financial Times) article, Claer Barrett writes:

“As retailers grapple with falling consumer spending and rising costs, the smart use of technology is proving a valuable weapon.

“Creating a point-of-sale linked supply chain is the latest tactic that larger retailers are employing in order to manage inventories and minimise discounting.”

Among other things, Barrett discusses how the entire supply chain—from the retail all the way back to the manufacturer—is being forced to cope with greater and greater uncertainty. At the same time, Barrett correctly points out that today’s “consumer is more empowered than ever before” via online shopping and price-comparison options.

Barrett’s discussion of the matter leads directly to another topic on which I have written here a number of times—namely, market segmentation. [Click here for more.] Retailers everywhere are learning to collect and leverage high volumes of point-of-sale data, mostly through the proliferation of loyalty programs. [Note: I just checked my pockets. I must be a member a more than dozen loyalty programs ranging from pet supply stores to gas stations and more.]

Between a rock and hard place

Even with improved ability to segment the market and identify buying trends and patterns, the whole supply chain is still caught between the “opposing problems of excess inventory and stock shortages,” as Barrett puts it. Barrett, however, is far too gentle, I think. The horns of the dilemma should really be stated as

excess inventory versus stock-outs.

Almost everyone who has had responsibility for managing inventories of any kind knows exactly what I’m talking about. Being short on stock (low inventories) does not on whit of damage. But being out-of-stock means

1. Lost sales of the out-of-stock goods
2. Lost sales on other goods that may have been purchased by customers seeking the out-of-stock item(s)
3. Potentially, customers lost temporarily or even permanently to competitors

As I have stated elsewhere, the value of losses resulting from out-of-stock conditions—if calculated at all—is almost always vastly understated.

However, on the other end of the spectrum, even though the supply chain suffered out-of-stocks on (almost always) the most popular items, they are almost never able recoup the profits on those items for which they are overstocked.

No.

In fact, chances are they will have to liquidate their overstocked item at or below the price they paid for them. Hence, Barrett’s reference to finding ways to “minimise discounting.”

The key to creating more profits is a “demand-driven” supply chain

My article on a demand-driven supply chain suggests technology that is within the reach of almost every retailer today—not just the big-box merchants. But it requires management to seek two things that they are presently overlooking in far too great a degree;

1. The true cost of out-of-stocks to their operations and to the entire supply chain
   
2. The return-on-investment available to them for building a truly connected and collaborative supply chain

If you are a mid-market retailer, distributor, wholesaler or manufacturer, do not delay in pursuing the discovery of ways to create for yourself a sustainable competitive advantage even in a very challenging economy.

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Further reading: Dynamic Buffer Management (DBM)

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Richard D. Cushing is a senior solution architect at RKL eSolutions in Lancaster, PA.

Finding Common Ground Between CFO and COO–Part 10

[Continuation…]

 

Some key factors in creating Irrefusable Offers

What are some of the key elements that should be considered by the CFO and COO when creating “Mafia Offers”?

Well, if the CFO and COO have come to properly understand their market and market segmentation through analytics (simple is better), the next step is to unlock “tribal knowledge” within the organization so the customer’s experience and customer’s desired results are clearly understood for each market segment. If necessary, that may mean identifying a market segment constituted of only one customer.

Make the offer learning, anticipating or filtering

The irrefusable offer must be one that lightens the burden felt by the customer, improves the customer’s experience, and produces better results for the customer. Offers that are learning, anticipating and filtering are such offers.

Take a look at the offer give in Example 1 in Part 8 of this series. This offer lightened the burden on the customer by reducing the customer’s need to store and handle large quantities of inventory month after month. The billing method, in turn, improved the cash flow for the customer, as well.

The offer was, in fact, anticipating the customer’s needs and filtering the volume down to the quantities actually required while still providing the prices to which the customer was accustomed under the previous ordering practices.

This offer, however, could have been made even more learning, anticipating and filtering. Suppose they had offered to simply replace the quantity actually consumed each month, rather than a flat 50,000 units per month. This may have been even more appealing to the customer.

Make the offer customizable

Buyers ranging from individuals buying one-offs via the Internet to professionals buying for big-box merchants today are influenced by customizable offers. Sometimes it is the actual product that is customized (e.g., made to specification, personalized, color or style options). However, even typical commodity offers can be customized.

When the product itself cannot or is not offered as customized, that does prevent the offer itself from being customized. Offers may be customized around several parameters:

• Delivery method – online, next-day, same-day, free-freight, in-person, vendor-managed and so forth
• Delivery quantities – incremental deliveries, truck-load, on-demand quantities
• Payment terms – credit card, 90-days same as cash, consumption-based invoicing

The goal, of course, in any customization in the offer is to improve the customer’s experience and results.

Make the offer upgradable

Without renegotiating the whole deal, an upgradable offer allows the customer to add-on, extend, or improve upon an existing trading agreement. This is especially valuable where the market may be subject to significant changes—as are most markets today. I think most CFOs and COOs would agree that they would much rather keep a customer through an upgradable offer than to risk losing the customer because the customer feels that they must renegotiate “the whole deal” anyway.

Again, looking back to the offer give in Example 1 in Part 8 of this series, you can easily see that this offer was, indeed, upgradable. The offer could be extended to buy more of the same product, or additional products could be purchased under the same plan.

Make the offer online, interactive or one that provides near real-time feedback

Offers that lead to or involve sharing information in real-time or near real-time are also generally more able to be learning, anticipating and filtering. The close contact created by interaction between seller and buyer may also lead to valuable insights that could lead to more customized or upgradable offers.

Some time ago I had opportunity to discuss a new software purchase with the CFO a rapidly growing $350 million enterprise. I asked him what he thought about the software, but when he replied, he did not really talk about “the software,” at all. He said:

“The company has this great approach to support. They offer an online knowledgebase that is searchable and provides a wealth of helpful information. But what’s even better is that they monitor activity on the support site 24-hours a day. If it seems you are not finding the answer to your question after a couple of attempts, a dialog box pops-up and a live support person proactively offers to help you resolve your issue right then and there.”

Clearly, this buyer of “software” was far more impressed with online, interactive, real-time feedback from support than the software itself. This CFO has purchased “software,” but what he got was a better experience and improved results from his purchase.

Accompany the offer with anytime access and response

In a world where the typical customer is empowered by Internet access to so many options, providing an offer that includes anytime access or response is likely to improve the customer’s experience and results. This is proven by the previous example where the CFO mentioned the fact that the support site was proactively monitored “24-hours a day.”

In fact, I personally find that my clients feel much more “connected” with me when I give them my cellular phone number and assure that they are free to call me (literally) 24-hours a day if they need my assistance.

[To be continued…]