Wednesday, December 3, 2008

Recession, Deflation: What is it to Supply Chains?

Here are some of the signs of our times.

  • Recession, we knew it all along except that now it is official!
  • Deflation, the stories are everywhere even though they don't add up. See this article for more information, though let us assume some deflationary pressures may actually exist and continue for some time.
  • Credit crunch, less said the better. This is probably the root cause of major financial failures for many a companies. Fed is throwing reams of dollars at the problem but it is still out there threatening businesses, large and small that need debt for short term and long term obligations.

What does this mean to retailers? Pretty much the same thing, as all of the above conditions result into low demand for goods and services, pressure on pricing power, and tight credit for consumers as well as retailers. This adds up to the following net results.

  • Top line impact. Low top line growth as consumers cut their spending due to recessionary pressures, job losses, uncertain financial conditions and tight consumer credit. All of these will result into low to no top line growth, and in some cases it may very well shrink.
  • Bottom line impact. Low profitability, fueled by increased competition, too little demand, too little disposable income that all adds up to pressure on cutting prices further to retain the sales, and hurts the bottom line.
  • COGS impact. Higher cost of operations, driven by the tight credit and higher cost of money when it is available. Add to that the volatility of demand, and the desire to maintain good inventory levels to service customers when they do step in and buy.

So what is a retailer to do?

There really are not many options. In good times, you could follow a top line growth strategy effectively, but when the sales tank due to depressed consumer demand, there is only one strategy that works: laser sharp focus on the Cost and Efficiency. For the financially inclined, COGS and Asset Turnover.

A good supply chain strategy can deliver on both these fronts.

In fact, supply chains are all about costs and efficiency. Anything you do better in your supply chain management is bound to affect either of the two. Take for example, inventory planning processes. If enhancements to this process results in lower inventory, your inventory turnover goes up affecting the Asset Turnover positively. Assuming that the process improvements result in better fulfillment but do not affect inventory levels, the cost of operations for fulfillment and cost of lost sales goes down through better inventory deployment. Either way you come out ahead. Take transportation optimization, you reduce the miles, and have a direct impact on cost of transportation and hence COGS. Take forecasting improvements to have higher accuracy, and once again you save the cost of lost sales through better planning and deployment of inventories.

In fact any supply chain process improvement whether it is in planning or execution, network design or supply planning, demand planning or warehousing; all lead to either direct cost savings affecting the COGS, or more efficient use of assets affecting the Asset Turnover.

The next question of course is which is more important? Cost or Efficiency? Well that really depends!

It depends on what does the retailer want to achieve? If the retailer has good operating cash flow (and hence no need to borrow funds from the market), your efforts should be more focused on direct cost savings that will translate into the bottom line gains. If operating cash flow is an issue; and, it is if your survival depends on it, then profitability is a secondary consideration, and efficient use of resources may make more sense specially in a market where credit is either not available or the cost of servicing credit is simply unacceptable.

Rather than closing all initiatives, corporations should analyze and understand the impact of each current supply chain initiative. Then they should re-prioritize using the analysis, and their current needs. For reprioritization, follow the steps below.

  • List all your current supply chain initiatives. Note where in their deployment life-cycle these initiatives are, what are the sunk costs, and what are the estimated costs to finish them?
  • Classify these initiatives into those affecting Costs, and those affecting Efficiency.
  • For those affecting costs, determine the impact on COGS; for those impacting efficiency, determine the impact on Asset Turnover.
  • Establish the immediate organizational priority between Costs & Efficiency.
  • Re-prioritize the current initiatives based on the above information.

Tuesday, November 25, 2008

Deflation: Should you Worry?

If you have tuned into the news during the last few days, you must have heard the coverage on deflation, and how spiraling deflation is getting out of control. But if the prices are really falling, it never felt like that at the grocery store, or at the departmental store, or toy store, or at the dentist. So why are the analysts worried sick? I believe they are trying to find news where none exists -- as yet.

While deflation may be a definite economic evil, the numbers so far do not add up to the extremes pointed out by the mainstream media or the analysts. But then, you could not blame them really -- an inflation report within normal limits won't make the news, would it?

Let us go to the source, and look at some numbers provided by the Bureau of Labor Statistics.

Here is the graph for Consumer Price Index - All Urban Consumers, Not Seasonally Adjusted, available at http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=CUUR0000SA0&output_view=pct_1mth.

CPI, All Urban Consumers, Not Seasonally Adjusted, for ALL ITEMS

The graph above shows data for ALL ITEMS. And that is a key difference because when you exclude the prices of food and energy, the prices actually rose as seen the picture below. This is available at http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=CUUR0000SA0L1E&output_view=pct_1mth. Now we have all seen the prices at the gas pump fall dramatically after their equally dramatic and unexplained rise in summer this year, and that is a big part of the deflationary data being touted as a real concern.

CPI, All Urban Consumers, Not Seasonally Adjusted, for Excluding Food and Gas

Again, if the prices are really falling, why are we not feeling any reprieve? Because year over year inflation is UP, by 3.7% even when you include the recent big drops in gas prices, (http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=CUUR0000SA0&output_view=pct_12mths). See below.

CPI - All Urban Consumers, 12 Months Percent Change for ALL ITEMS

Here is a break-up of inflation numbers, and these are really helpful in understanding where the problem lies, rather than painting a broad-brush picture of impending doom due to deflation. All numbers are available at: http://www.bls.gov/news.release/cpi.t07.htm. The numbers below are for based on chained Consumer Price Index for All Urban Consumers (C-CPI-U): U.S. city average, by expenditure category and commodity and service group, with December 1999=100.

Expenditure categoryUnadjusted Relative Unadjusted percent change to importance, indexes Oct. 2008 from-Oct, 2007Unadjusted Relative Unadjusted percent change to importance, indexes Oct. 2008 from-Sept, 2008
All Items

3.3

-0.8

Food & Beverages

5.9

0.5

Housing

3.0

-0.3

Apparel

0.1

1.0

Transportation (read: Cars and autos)

3.6

-4.5

Medical Care

2.7

0.2

Recreation

1.1

0.0

Education & Communication

2.7

0.1

Other goods and services

3.9

0.3

Energy (read: Gas)

11.4

-9.8

There you see the two categories that are causing the biggest deflationary pressures, cost of energy (we know gas is down), and cost of transportation (cars and autos). Neither of them is exactly news, sales of big ticket items is down, and is expected to be slower with the continuing credit crunch.

Should you worry? Decide for yourselves!

Wednesday, October 15, 2008

Retail Holiday Sales Myth

It is that time of the year again. Analysts are predicting bleak sales for retailers, and consequent hard times for these corporations financially.

One of the closely held beliefs is that that "the holiday sales can account for as much as 40-50% of merchants' annual revenues".

You can see that in this news story at CNN http://money.cnn.com/2008/10/13/news/economy/retail_shakeout/index.htm that states, "The November-to-December holiday gift-buying months can account for as much as 50% of merchants' annual profits and sales."

Or here at Bloomberg http://www.bloomberg.com/apps/news?pid=20601103&sid=axCzcG1d.YTg&refer=us which says, "Sales are slowing just as merchants prepare for the holiday selling season, which may account for as much as 35 percent of a retailer's revenue."

This is not new either. Check out the predictions for the last year at MSNBC http://www.msnbc.msn.com/id/21772138/ that claimed, "...Retailers typically make 40 percent of their annual profits in the final six weeks of the year."

So how true is that? Do the holiday sales really add that substantial a revenue to a retailer's top-line, or profits to their bottom-line. I decided to do some data hunting. Here is what I did. I picked up the revenues and EPS numbers on some of the well known retailers from MSN Money for the financial years 2007, and 2008. Then I applied the following logic to compute the holiday effect on revenues and EPS of these retailers.

  1. Compute the average of revenues for the first three quarters.
  2. Subtract the average in step 1 from the revenue in fourth quarter. This gives us the nominal holiday effect on revenues for quarter 4.
  3. Take the result from step 2, and compute this as the percent of annual revenues. This gives us the percent impact of holiday sales on the annual revenues.

Here is what I found (source of all financial data is MSM Money on 10/15/2008). For the eight well known retailers below comprising of departmental stores, soft-lines, hard-lines the highest holiday impact on revenues 11.95% for Macy's in 2007, and lowest on -4.67% for Lowes in 2008.

image

image

image

image

So the notion that a very substantial part of retailers revenues and profits come from holiday sales -- as much as 35 to 50% is plain WRONG!!! The fact is that holidays certainly affect the retailers but the impact is far lower than what is generally believed.

Thursday, September 18, 2008

Supply Chain Strategy Trend One: "Rise of the Rest"

In the previous post, I highlighted the two emerging trends that will shape the future of the supply chains. This article follows up on the first of these two main trends that affect us. Quoting from Zakaria, I called the trend "Rise of the Rest" as it really summarizes the impact of this trend on the supply chains for the future.

Rise of the Rest is based on the premise that the world as a whole is evolving in all possible aspects: politically, financially, socially, and culturally. While all these aspects of this evolution are important the one we are most interested in evaluating is the economic aspects of this evolution. Growing economies around the world affect how the corporate supply chains will emerge and grow with it. It affects all aspects of the supply chains including sourcing, purchasing, suppliers, logistics, assortments, and selling.

As economies grow, so does the consumption. The new demand-supply equation affect the costs till the equilibrium is reached again. This change in costs and their management is one the trends that I believe will affect the supply chains directly.

The two largest components of costs for retailers are the cost of purchase, and the cost of distribution. Right now both of them are trending up, as they have done for most part of this year and the last year. While supply chains have always focused on costs, and cost savings through more efficient planning and operations has been their core value proposition -- till now, supply chain costs have been evaluated in isolation. I believe that is about to change. Pioneers will start looking at a more holistic picture of cost of "doing business" rather than focusing on specific supply chain areas such as logistics. This broader view of the costs will cause evolution of processes that go across merchandising, supply chain and sales; and would provide a common sense of cost and profitability to the corporations of tomorrow.

While these changes could be driven from various points of view, driving them from the supply chain point of view may be most logical as the discipline already provides a framework for modeling costs, networks, processes and leverages mathematical optimization.

The Rising Costs of "doing business"

We mentioned above that the two largest components of costs for Retailers are the cost of merchandise, and cost of distribution. Cost of distribution is easier to understand as it is largely related to warehousing and transportation. The rising cost of energy has kept the focus sharp and clear on this part of the cost equation. The other cost, namely the cost of merchandise is the one that is defined in very narrow terms today, and needs to be expanded and evaluated with sharper focus.

Most corporations with advanced supply chain teams focus on both of these costs through optimizing various supply chains functions such as Sourcing, Inventory Planning, Replenishment Planning, Warehousing and Transportation. The current processes, however evaluate these costs in silos and even when optimized, the models do not provide any global evaluation of these costs or any ability to compare scenarios and predict long term effect of decisions.

I believe that this the next big opportunity for cost savings. The continuing pressure on costs with a weak economy and resulting inability to pass on costs to consumers are going to drive the companies to evolve these processes further to squeeze as much cost from their eco-systems as possible. Today there is almost no visibility or understanding of the "total costs" of the merchandise and in fact, most retailers lack the information, inclination and the capacity to do this.

To provide a better context, we will borrow from the concept of "customer life-time value" (CLV). The objective of determining the life-time value of a customer is to focus on long term customer service/satisfaction for an overall higher profits from the relationship, rather than maximizing short-term sales revenues. And putting such programs actually work -- ask Harley-Davidson.

Total Life-time Cost

So what is the "total life-time costs" of a product? The question may not sound as interesting as CLV though it is a pertinent question to ask. The life-time costs of a product will include not only the cost of purchase, but cost of planning the assortment, planning the demand, sourcing and evaluating the suppliers, actual purchase costs over the whole season or life of the product, cost of distribution, cost of marketing, cost of mark-downs, cost of returns, cost of customer relationships due to gaps in the customer expectations and product functions, etc. This is what I call the "total life-time cost" picture for the merchandise and it is this picture that will be enabled by the processes of the new supply chains. It is this picture that will then become the basis for product portfolio and profitability analysis that should ideally drive the assortment decisions for the retailers in future.

Let us again go back to the "cost of distribution" for a minute. This has attracted and retained put attention not because it is the largest part of the cost ion the equation but simply because it is easier to measure and control. Most companies can track what they pay to their carriers, and how much doers of cost them to run their warehousing operations. Anything that is easily understood and measured gets our attention. Even though the "actual" costs of distribution for a specific merchandise is debatable due to crude cost-allocation practices, we at least know the total costs across the enterprise related to distribution. As far the cost allocations go, that will be the subject of another article.

Now let us look at the "cost of merchandise". Is this the cost that is paid to the suppliers against the purchase orders? What about the cost of creating and processing the purchase orders? What about the cost of planning the replenishments? What about cost of inventory planning that determines the final replenishment numbers? What about the cost comparisons among suppliers that have contracts with price-breaks going over a few seasons, and affect the total costs over the life of the product? What about the cost of signing a contract that goes bad and needs re-negotiating or legal action? What about cost fluctuations due to currency variations for merchandise bought foreign suppliers, and transported by foreign carriers? What about customer returns, mark-downs, marketing, cost of bad assortment decisions, etc, etc. There are just too many pieces to this cost that are very fuzzily defined in today's cost accounting processes, and therefore fail to provide any sense of "what does it really cost me". These processes need to be defined better and tied together in the definition of "cost of merchandise" so that a merchant can actually make decisions that are objective and based on data rather than instinct and experience.

Understanding these costs will affect almost all processes -- the way we assess the product profitability, assortments, sourcing, replenishment strategies, inventory policies and of course the logistics.

New View of the World

Another aspect of life that changes for supply chain strategist as a result of the "rise of the rest" is the procurement strategy. For almost two decades now, China has been the focus for cheap manufactured goods. Prior to that it was Japan. Cost equation simply worked that way. The depressed wages, cheaper energy prices, an abundance of labor, and almost non-existent middle class to drive local consumption -- all of these factors favored China to be the manufacturing hub of the world.

Almost all these factors have changed in recent years with consistent trends. Refer to my previous post where I have provided links establishing the facts. Wages in China have doubled, energy prices have quadrupled, commodity prices have almost doubled, and China's middle class has emerged as a substantial "consumer" in its own right.

The changes in each of these factors will cause changes in the procurement strategies. These changes will make the cost equation more equitable, and allow more regions of the world to participate in global commerce. What that means to the supply chain strategist is that the supply chains will grow to many more regions of the world, bringing in more complexity in ocean routes, port management, trade terms, compliance, foreign currency planning, cultural factors, financing and settlement. The increased complexity then will cause companies to re-evaluate the tools of the trade and invest in applications that help them address and grow with the new set of supply rules.

The Integrated View of Costs

Now imagine a hypothetical application that actually can project all the cost components mentioned above over the useful life-cycle of a product. And think how this will change the process of evaluating and launching a product. Also think about how such a process will have an integrated view of the currently siloed processes of product life-cycle management, merchandising and supply chains. You might shorten a season just a week to reduce the cost of mark-downs; you might split your purchases just so to optimize your seasonal ramp-down efforts in north-east that start a few weeks prior to ramping-down in south-east; you could just predict the total cost of merchandise procurement over the whole season because you know your projected demand at various nodes and command suppliers to use preferred warehouses for supplies; you could buy a week ahead to avoid the projected currency exchange rates that favor your supplier.....and so on.

Jog your imagination and see for yourself the possibilities of the future supply chain strategies and decide how to evolve to that next level.

©2008; Vivek Sehgal

Monday, September 8, 2008

Supply Chain Strategy Trend Two: Environmental Consciousness

In the previous post, I highlighted the two emerging trends that will shape the future of the supply chains. This article follows up on the second of these two main trends that affect us. We will call this trend "Environmental Consciousness" as this trend primarily focuses on the changes happening in today's manufacturing, and distribution industries in response to the enhanced awareness of the impact of these activities on the environment.
While this trend has been in the making for some time, it has gained great momentum in the recent years. The rising awareness of the impact of the human activity on the environment is the subject of discussion in more and more political, social and economic forums. It is also the subject of numerous reports from World Bank's Environmental Sustainability to Human Development Report 2007/2008 from United Nations.
Manufacturing and Distribution are two activities that affect the environment on a large scale. Manufacturing needs raw materials that come from natural resources in a number of cases, and the manufacturing process invariably needs energy to convert these raw materials into the finished products. Along the way it may produce wastes that must be treated, if toxic, before it can be released back into the environment. Distribution needs energy to move the products from one place to another and is a direct contributor to green house gases and resulting warming.
Supply chains manage manufacturing and distribution processes. And that is what brings them into sharp focus from this point of view.
While there are not many regulatory requirements that constrain the supply chain processes directly at this time, the indicators suggest that such requirements will exist pretty soon. For a look into what the future may look like, review the proposed carbon labeling act in California, http://www.carbonlabelca.org/2.html. Carbon emissions trading is already a reality in EU, and there is active talk of this system as a mechanism to control and govern the environmental effects of the industrial activities in the US as well. (Note that the US has operated cap-and-trade systems for emissions of sulfur dioxide and nitrogen oxides for year now).
Both of the above systems, namely the carbon labeling as well as the trade-and-cap systems can directly contribute towards controlling the environmental effects of manufacturing and retailing activities. Both affect the supply chain functions and its future evolution. The first achieves it through direct consumer discrimination based on the consciousness and the second one achieves it through regulation that affects the competitiveness of enterprises that are less environmental friendly than others.
While some of these measures will be voluntary and others regulatory in nature, it is clear that such measures will effectively change how we as consumers behave and react to products we buy. For example, consider the nutrition labels that were required to show the Nutrition Facts, basic per-serving nutritional information, on foods under the Nutrition Labeling and Education Act of 1990. These were introduced in 1992, and since then it has become an important part of the consumer behavior. It is not uncommon to find people checking the nutrition information in the grocery stores prior to putting the merchandise in their carts. A similar concept for carbon labeling will undoubtedly affect consumer behavior, and hence the retailer's behavior in how these products are assorted, sourced, processed, distributed and sold.

Carbon Labeling

California's Carbon Labeling Act of 2008 proposes to "Establish a methodology for determining and communicating the carbon footprint of a consumer product. If feasible, the state
board shall establish standards and methodologies for determining and communicating to consumers on a product label whether a product has a lower carbon footprint than the average comparable product available in the state."
Chances are that such a methodology will include some measure of (1) energy consumed in the production of a product, and disposal of any harmful byproducts (2) energy consumed in the distribution of a product from the manufacturer to the retailer's facilities, and finally (3) recycling characteristics of the materials used in production. Most of this information can be collected from the manufacturer and the retailer, and standardized in a format that is easy to understand and discriminate. And such labels will in turn affect the consumer preferences that drive the merchandising, sourcing, purchasing, distribution and stocking processes.

Trade-and-cap

The trade-and-cap system will primarily affect the manufacturing costs and affect the overall price paid by the consumer. Environmentally unfriendly products, even if cheap, will still have some impact in the same way as the allegations of using child labor had in recent years. This combination of regulatory and voluntary pressures will affect the consumer behavior albeit in a slightly indirect manner than the carbon labels. Managing costs eventually affects the same supply chain processes as above: merchandising, sourcing, purchasing, distribution and stocking.
The decision parameters and the metrics that define and measure these processes will change in response to these changes. So far these were primarily back-end supply chain processes that were merely enabling getting the right product at the right place at the right time and quantity. In the new context, they become front and center processes whose decisions affect the ultimate profitability and success of the company.
How will these process emerge in the future? How should they emerge? That is the subject of supply chain evolution strategy that we will continue focusing in the coming weeks.
©2008; Vivek Sehgal

Tuesday, August 19, 2008

Trends that Will Define Future Supply Chain Strategies

The sands of supply chain strategy planning are shifting again. It has evolved a lot, and changed a lot; and, it is happening again. The imperatives driving the supply chain for the next few years are becoming visible and they will shape this phase of supply chain evolution.

Supply chain was MRP in the 80s, that evolved to constrained based planning in the 90s, giving way to an integrated view of planning and execution currently through corporate-wide visibility and rich analytics.

Still so far, only a handful of companies have treated supply chains as a core part of their corporate strategy. These corporations have seen ample rewards in doing so. But most others had just started to seriously consider investments in supply chain strategy, when the new ground rules seem to be emerging for the next generation of supply chain thinking.

In making these statements, I want to differentiate between the automation of supply chain execution versus a truly strategic thinking that reviews the corporate supply chain from a strategic point of view that drives business functions and decisions.

  • The automation of supply chain transactions simply provides for efficient operations. Its value lies in the productivity enhancements that such systems provide. The business transactions in this category are largely standard, unvaried, and are supported through a multitude of vendor solutions available for all budgets. A good example in this category is warehouse management systems. While they do have a good ROI, these systems do not necessarily provide any competitive edge. These systems are no more elite, but have rather migrated into the “required” category if you wish to do business.
  • The strategic view of the supply chain attempts to view the corporate supply chain as a business strategy that binds together the assortment, sourcing, demand and supply management, planning and operations as a “whole” rather than the sum of its parts (like managing a warehouse). This is where the visionary corporations are focused and should be investing. This is what drives Walmart to review Brazil as a major market, GE to invest in the middle-east, and Halliburton to move their HQ to Dubai.

And it is the latter (strategic view of the supply chain) that is going to undergo major transformation in the coming years as corporations adjust to the environmental changes underway for the last year or so. We will talk about the two overbearing trends that are driving this change. Over the next few weeks, we will go into the details of these trends and the shape of things to come.

Merchandise Costs: “Rise of the Rest”

This trend has been recently highlighted by Fareed Zakaria in his new book, “The Post-American World”. He makes various arguments and illustrates them in multiple ways, but at the core of this trend is the fact that living standards are rising all around the world. The developing countries are growing at a faster pace than ever. And the combination of growth and higher living standards is pushing the wages and cost of production specifically in these regions, and generally all over the world. People have argued that this growth is also pushing the prices of food, commodities, and energy everywhere.

This trend affects the cost basis for everything that is manufactured and distributed, through the increased cost of materials, cost of higher wages, and finally the cost of transportation. The changes in the cost basis will change the outsourcing equation in manufacturing.

The commodities index for all commodities has gone up by 44% from 1998 to 2008 (Source: Bureau of Labor Statistics, Jan 1998 versus Jan 2008). Over the same time period, the index for Metals and Metal Products moved up 65%, and Industrial Commodities by 45%. All the indices were still trending upwards for 2008 at the time of writing.

image

The wages in China have nearly doubled in past four years outpacing the growth of GDP. (See the full story at Forbes at http://www.forbes.com/markets/2007/07/02/china-wage-growth-markets-econ-cx_jc_0702markets1.html).

According to the Department of Energy, the cost of diesel fuel has almost quadrupled in the same time from 1998 to 2008, (see, http://www.supplychainmusings.com/2008/05/optimization-transportation-versus.html).

These changes are not isolated spikes in a stable data series anymore. These changes have become trends that will define the cost equations for the decades to come. And these new cost bases will define the sources of our goods and services in the next few years. The change may not be subtle, China may not be manufacturing capital of the world any more, and India may not remain the back-end services capital. Consider some of the recent changes on manufacturing front: BMW starting a manufacturing plant in the US, Inbev buying Anheuser Busch and Chinese investments in manufacturing coming to GA facilitated by the Georgia China Alliance.

Business Costs: Environmental Evolution

The second trend that will shape the supply chains of the future is the environmental awareness, and the social pressure to address the issues related to the environment. This can manifest itself through various legal and regulatory requirements, such as the carbon trading; or in more stringent ways that affect the whole chain of raw materials, manufacturing processes, disposal and recycling. There is talk of “carbon labeling” in the industry that would require the retailers not only to gather the information but also share it with the consumers (see http://www.carbonlabelca.org/). These changes, legislative and otherwise, will drive the companies to review their existing processes and enhance them to align with the changes in the external environment.

These changes in the environmental sensitivities have the potential of affecting almost all of the organizational supply chain processes. Some of the processes directly impacted will be assortment planning, sourcing, vendor selection, manufacturing processes, packaging, disposal, distribution.

Over the next few weeks we will dig deeper to find out how these two trends affect the supply chain strategy and planning for the corporations. Till then...

©2008; Vivek Sehgal

Thursday, July 31, 2008

Transportation Operations Effectiveness

Transportation operations are a big part of a retailer’s distribution functions. As I mentioned in an earlier post, AMR has estimated that these costs can be up to 20-30% of the total supply chain costs. Transportation costs have been brought back into focus with the cost of fuel as it is a large part of the overall transportation costs.

Managing the transportation operations will help manage the costs better. But what exactly do you manage? An old clich√© – you can’t improve what you can’t measure. So what do you measure to manage the transportation operations?

One of the difficulties in defining these metrics is that most companies define a more generalized set of metrics for distribution, and this generalization takes away the focus from transportation operations and dilutes it with metrics that measures warehouse operations, fulfillment rates, inventory efficiency, etc. While it is completely sensible to measure these metrics to keep a healthy distribution system humming along, the specific metrics mentioned below are more sharply focused on measuring the efficiency of transportation operations, and controlling costs.

To obtain the transportation efficiencies and reduce the costs, the emphasis should be on creating better loads, reducing miles through better route planning and route optimization, optimized carrier selection, and finally validating what you pay for freight.

Build Better Loads:

How many of your loads are TL versus LTL? What is the average capacity utilization of trucks or rail-cars or containers for each of the road/rail/ocean modes? You can measure these by some of the following indicators. If you are carrying too many LTL loads, there may be opportunities for consolidation and creating TL loads. If the average utilization is low, then you can improve on load-building, and review your ti-hi requirements and compliance to these requirements. Also keep an eye on the trending for these metrics as that can be a predictor of the efficiency of overall transportation operations.

  • Number of loads that were TL, LTL from the total number of loads
  • Average truck/container utilization that can be measured by calculating the total freight (weight and volume) carried during a time period divided by the total capacity (weight and volume) of the equipment used for the purpose

Reduce Miles through Optimized Routes:

Measure average number of miles for a ton of freight in your distribution system. For any given period of time, consolidate all tonnage carried, and all miles traveled. Divide latter by the former and study the trend. Are you driving more miles to deliver a ton of freight, or less? As companies grow so do their freight tonnage. But using better software to optimize the loads and routes, better efficiencies can be achieved so that this ratio does not have to grow in the same proportion.

  • Total miles traveled
  • Total tonnage carried
  • Average number of miles traveled per unit weight of freight

Optimize Carrier Selection:

Review your carrier selection during the transportation planning. How many carriers do you have in each mode? Do you have enough volume leverage for each mode? Are there lanes that are not covered by any carrier contracts? How does the carrier selection algorithm work for the shipments? Some of the metrics that can help you measure the efficiency of carrier selection process is as under.

  • Total number of carriers used by mode in a period
  • Total load carried by each carrier versus number of shipments
  • Total number of shipments carried with carriers with a contract versus general carriers
  • Total freight carried with carriers with a contract versus general carriers

Validate what You Pay:

Finally do you have a process in place that allows you to validate the freight invoices? Does your process allow you to estimate the cost of freight against the carrier invoice? What about the accessorial costs? Do you have a lot of claims? Some of the following metrics may be useful.

  • Accuracy of freight invoices, as invoices that were validated and found to be correct versus not
  • Freight invoice auto-approval, as invoices that were validated by the system and approved versus those that needed manual touch for any reason
  • Average cost of shipment per unit. If the freight units are a combination of multiple units of measurement, you may normalize this as the cost of a ton-mile of freight
  • Average cost of inbound freight (calculate same way as above)
  • Average cost of outbound freight
  • Total freight as percentage of COGS, further split by inbound freight and outbound freight costs
  • Total accessorial cost as percentage of total freight, same ratio for inbound and outbound freight
  • Total claims as a percentage of total freight costs

Friday, July 25, 2008

Increasing Fuel Costs Hit Corporate Bottom Lines

Last week Costco reported revised estimates for the wall-street citing higher energy costs. Higher merchandising costs, and higher freight costs were among the culprits identified. Here is the summary from the article cited above: Costco chief financial officer Richard Galanti mentioned that soaring energy inflation is affecting the cost of goods, leading suppliers to push higher prices onto Costco and other retailers at a faster and higher rate in the past six to eight weeks than before. Galanti also said that suppliers want increases of 5 percent to 10 percent and even more in recent weeks, compared to a range of 2 percent to 4 percent earlier in the fiscal year.

Higher fuel and energy costs seem to be one of the single largest factor affecting the upward cost spiral for retailers. The energy used in manufacturing processes cannot be reduced very quickly as any changes in manufacturing process reconfiguration are bound to have large capital and time outlays. The second largest energy cost is in the transportation. AMR estimates that transportation costs can amount to 20-30% of the total supply chain costs.

US Department of Transportation reported over 4,500 ton-miles of freight for the year 2005, and it has been trending upwards as you can see in the referenced report. The good news is that the GDP is growing faster than the VMT for the last few years. Like most complex scenarios, there will be many contributing factors to it but I would like to believe that one of the factors is the rising popularity of the Transportation Optimization among the corporations.

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Transportation Optimization/Management Systems can help you in many ways to address the rising cost of freight. Deploying these systems is not a quick fix, but they have a proven ROI, and have even better return in current times with fuel spiraling upwards steadily. Transportation Management Systems (TMS) can yield substantial results through reduction of miles, optimal load and long-haul profiles (shifting of LTL loads to more TL loads), increased use of multi-modal shipments to better utilize the transportation network, and finally by reducing the freight cost invoicing errors.

Load and Route Optimization: Reduce Miles, Enhance Long-haul Legs, Increase Cube Utilization

TMS applications create better loads, allowing more efficient use of the trailer, rail-car and container capacity. They can also plan better routes to create multi-stop routes, continuous moves, and by utilizing pool-points where such trans-shipments may reduce the cost of freight. These systems also have the ability to create multi-leg, multi-modal shipments that can leverage cheaper transportation modes. For example using rail for the long haul portion of the route, and road for the final delivery leg can substantially lower the cost of transportation. Line haul rates for rail, and ocean (where that makes sense) are much more favorable compared to road freight.

While the inter-modal transportation always adds some more process complexity, the TMS applications make this transition easier by allowing you to automate most of the interactions between the carriers, drayage services, and the warehouses. On-boarding the partners, and certifying such automated interaction will still pose a challenge, but the rewards more than pay for such an initiative.

Freight Invoice Audit: Look Before you Pay

TMS applications also typically help in auditing the carrier invoices by validating shipments, line-haul, and accessorial charges. These not only make sure that the freight invoices are paid only for the services, but also ensure that the freight is being calculated using the contractual rates, and that all the accessorial are valid. It also prevents overpayments to carriers thus avoiding collection fees to 3rd party debt collectors when carriers fail to respond to the debit notes.

Fleet Management: Enhance Utilization, Reduce Miles

Most distribution intensive business use dedicated or own fleets for delivery to their stores. TMS can help enhance the fleet utilization efficiency for dedicated fleets by reducing the fleet miles traveled through better route planning. This is primarily obtained through multi-stop routes while simultaneously constraining on the warehouse shipping and store receiving schedules. Every mile saved on the fleet not only saves on the direct fuel cost, but also increases the fleet life due to associated wear and tear.

Monday, June 23, 2008

Understanding The Analytic Spectrum

Reporting and analytic solutions have a wide footprint. A simple listing of orders that is due to be delivered today will pass as a reporting solution, as will the report on total corporate spend across a product category. However that is pretty much where the similarity ends. Almost everything else for these reports is significantly different, including the business process that each of these supports, the audience, frequency, and the process for producing the two reports. This complexity is generally hidden from the users, and frequently produces the frustration in the relationship between the IT, and business on long lead-times and large budgets in deploying the reporting solutions.

Below we present the analytic spectrum from a techno-functional point of view to add the business understanding to IT centric teams and the technology understanding to the business teams.

Operational Reporting is the lowest granularity of reporting. Its objective is to support day-to-day operations of a specified role. These reports need real-time data, and any exceptions need to be addressed immediately. These reports are frequently part of the application that supports the business function, and are directly queried from the underlying applications’ OLTP (on-line transaction processing) database or its mirror.

For example, take the Purchase Order Management function. An expediter may need the listing of POs that are late for delivery. This is simply a list of POs that fit the user specific date filter where the need date has passed and the status of the PO has not changed. An inventory analyst may need a list of all the POs that are expected to be received today to make allocation decisions, or a financial analyst may need a list of all POs received a day before for accruals. All the three reports are immediately produced from the Purchase Order Management application directly from its transaction data, and no data-processing is required for creating the report. The target audience for operational reporting is the people who manage daily operations of the supply chain functions like purchasing, receiving, shipping, etc.

Process Support Analytics is the next level of reporting where the data from Operational Reporting applications is consolidated, processed, and used to create process metrics. These process metrics typically point to inefficiencies in the processes, and help the managers tune them for better performance. These reports typically lose the individual transaction character present in the operational reporting. While an expediter needs the list of PO line-items due on a given day (operational), a manager may need information on the number of items that needed to be expedited from a given vendor in a month to establish if the process is operating normally or not.

This type of analysis typically needs information for a longer time horizon to compare and establish trend lines. The individual transaction information is consolidated and processed to produce counts, summaries, cumulative values and so on. The reports are typically produced by moving the transaction data from the application OLTP database to a process centric database that consolidates the information. For example you may have a purchase database where all purchase transactions from all purchase applications are brought together. In order to bring together data from disparate systems, the data may need standardization, cleansing and referencing. The data is not real-time, and typically brought over after the active life of the transaction is over, for example after the POs are “closed”. Such data stores are often called Operational Data stores (ODS).

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Decision Support Analytics finally not only consolidate data for a process, but actually combine it across the processes. The objective of the decision support analytics is to provide inputs for improving corporate efficiencies across processes though better planning and optimization. Combining data across the processes typically needs the companies to be able to harmonize all master data so that the transactions from different business processes can be consolidated with the same context.

For example producing a total spend for a given product category across all vendors means that the financial and purchasing systems either have a common vendor, items, currency, and item hierarchy; or must know the mutual references to produce the common context.

Deploying the Analytic Spectrum

While it is quite simple to provide the operational reporting from individual applications, the complexity of the analytic environments increases exponentially for the Process and Decision support analytics. The most difficult part of establishing good functioning analytic environments is to be able to create common reference master and organizing data. The common master data refers to the entities like items, vendors, customers, locations, time, etc. that is used by several systems. The common organizing data refers to the hierarchies for items, locations, organizations, locations, etc. that is used to process the data up or down the hierarchies, or groups that are used to create consolidated numbers.

Creation of common master and organizing meta-data is a pre-requisite for success, and requires clear leadership from business and IT teams. Business teams need to understand the need of having a common reference, and provide the rules for cleansing and harmonization of this data. The IT teams need to be able to elaborate the need, and establish data staging areas where such cleansing and harmonization can happen with proper error and exception handling strategies. Without such common reference data and active IT-business partnership, any enterprise-wide reporting and analytic initiative is bound to fail.

In a future article we will look at the above spectrum in the supply chain context to establish what a supply chain reporting and analytics environment would look like.

Friday, June 6, 2008

Supply Chain Analytics & Data Discovery

When planning supply chain analytics, plan for conventional warehouse based reporting and analytics; and more sophisticated data exploration environment for data mining and statistical analysis. While the conventional reporting and analysis helps track and report on the efficiency of the supply chain processes, the data exploration tools can actually enhance your ability to continuously optimize the process parameters to function at their best. The good news is that you can use the same data warehouse for both the purposes.

Let us expand on the two underlying concepts for clarity.

Reporting and Analytics

This is the more conventional, familiar and easier to understand area of analysis. Most companies have reporting environments that allow them to generate standard reports. Some also have analytical environments that allow users to interactively generate multi-dimensional views for dissecting the data as they need. Most such environments are based on data warehouses that pull planning and transaction data from the supply chain applications, and present this data with common master data references. Some well known characteristics of such environments are as under.

  • Pre-defined data models and dimensions: Data models are pre-defined and rigid. The dimensions are constructed based on the original data model design and any changes need IT effort. The metrics are generally a combination of pre-defined standard metrics, as well as user created ad-hoc metrics that are based on formulas and use the existing data in the warehouse.
  • Processed and harmonized data: The contextual data such as items, locations, time, vendors, customers, etc. (also known as master data) is pre-processed and harmonized across all reporting applications. This is important because very few corporations currently have enterprise master data management systems. Any enterprise level reporting needs consistent master data to pull together the information from various applications and geographies and present an enterprise view.
  • Tactical or strategic, but repetitive and consistent metrics: This is the defining attribute of these systems. The metrics are consistent and repetitive. It is this characteristic that makes the warehouse’s rigidly defined data models possible.
  • Ad-hoc analysis components do not equate to data discovery: While ad-hoc reporting may be available, it is limited to providing user driven metrics that are computed on-demand. The analytics may provide multiple data views by user selected dimensions, and even consolidate data/metrics as user selects a different view. However none of this provides a true data discovery function that we would talk about in the following section on data exploration.

The conventional reporting and analytics provides a great way to build and report metrics on several supply chain areas, such as inventory, supplier compliance and sales analysis.

Data Exploration & Discovery

A lot of supply chain applications leverage data patterns. As these data patterns change and emerge, these applications provide solutions that are less than optimal. To keep these solutions at their most optimal levels, a data exploration and discovery environment should be created along-side the supply chain data warehouse. Such capability can provide clues to power users when the underlying data patterns change and allow them to change application configurations pro-actively rather than react to such changes using a conventional reporting environment.

A data exploration/discovery environment has the following characteristics.

  • No pre-defied data models, or dimensions: The data discovery models typically have no pre-defined models. They thrive on raw data. The models for discovery are created by the power users with a specific problem in mind. These models could be retained for future simulations, or thrown away after the target problem has been resolved, or the underlying reasons have been discovered.

As an example, consider a product profitability profiling model using data mining techniques. Once defined using the historical data on profitability, this model can be reused to “predict” the profitability profile of new merchandise before introducing the new products.

On the other hand an inventory profiling model may change from one season to another as the underlying parameters that drive such profiles change with time.

  • Involves data discovery: These models use raw data and discovery algorithms to find new and hidden patterns in data. The user does not have to “know” the data before using it, rather the system “discovers” the relationships, similarities, profiles, etc, that exist in the data and provides the output of such “discovery” to the user for review and decision support.

For example if you wish to create a profile of poorly performing stores, the user does not need to know what parameters to look for. Rather the discovery algorithms can group the poorly performing stores by exploring the historical data, and “discover” the parameters that are most relevant for such profiling.

  • Uses raw data: Unlike the reporting environments, the data discovery algorithms need raw, unprocessed data to be most effective.
  • Helps in setting up decision parameters for downstream processes: The data discovery and exploration tools are decision support applications that help the power users analyze data and make decisions on how best to run other related processes. For example demand forecasting, seasonal planning, strategic sourcing processes can all benefit from such analysis by detecting changes in data patterns through discovery.
  • Relatively long term and strategic in nature.

There are more supply chain processes that can benefit from data exploration and discovery. Some of these are flow path optimization, store cluster optimization for determining merchandise assortments, and targeted marketing etc. In fact any process that can benefit from the following functions is a good example of where data exploration and discovery can be leveraged.

  • Processes that depend on statistical analysis such as inventory planning, demand planning, supply planning
  • Processes that can leverage data mining and clustering techniques such as creating inventory groups for maintaining inventory policies, store groups for assortments, merchandise groups for profitability
  • Processes that can leverage simulation such as inventory planning, allocations, etc.

While most corporations plan and invest large amounts of capital on the conventional reporting solutions, the high-end data exploration and discovery solutions are comparatively rare. Part of the reason is lack of understanding on how these tools can be used as well as lack of people skilled in such tools who also understand the business of supply chain.

Most of the techniques mentioned above in the context of data discovery and exploration are part of a larger discipline known as predictive modeling and analysis. The predictive modeling techniques are used for projecting and managing risks and are quite well adapted in the financial and insurance industries. However their use for modeling and managing supply chains is still emerging.

Wednesday, May 28, 2008

Optimization: Transportation versus Inventory

In talking to a senior executive from a supply chain solutions company I heard an interesting comment last week – that more and more companies are looking at transportation optimization in the face of rising fuel costs. Of course there is a direct link between rising costs (see picture), and the desire to do something about it, but it also got me thinking about what other factors may be driving the interest in transportation optimization.

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Inventory

Traditionally the inventory has been the biggest focal point for supply chain managers. That makes sense as inventory consumes a substantial amount of operating cash flow for retailers. Assuming an inventory turns value of 7 for example, a retailer with $10B in revenues would have locked almost $1.5B of operating cash flow in the inventory. Therefore any reduction in the inventory results in more money available to other functions. However that is a double edged sword: achieve overly enthusiastic reduction in inventory and you will lose sales (and hence revenues) due to stock-outs, but under achievement results in bloated inventory that will eventually require clearance and pull down the margins.

Finding that golden “optimal” level that balances the two sides (service and excess) is hard to establish and harder to maintain as demand patterns evolve and change. Inventory joins the demand with supply – and it is this inherent position that makes it dependent on the supply and demand planning processes. In fact this dependence is very critical. The accuracy, stability and consistency of demand and supply planning processes affects the efficacy of the inventory planning. And that directly impacts the inventory levels and ability to service demand. Inventory planning typically uses demand, supply and lead-time for determining the optimal inventory levels to maintain a specific service level.

All the above makes it almost necessary to review these processes together to have any appreciable impact on the inventories. The combined impact of demand planning, inventory optimization, and supply planning processes can result in huge savings through reduced inventory in the system, lower clearance costs and better financial efficiencies. However it is a large effort and it impacts a large number of users in an enterprise. It also requires good clean master data and large amounts of historical transactional data, both of which need additional effort to obtain. This generally makes it a little more complex and requires a clear consistent strategy to successfully deploy. The rewards are bigger, but so is the effort leading up to it.

Let us also examine how the savings in inventory affect the company financials.

Inventories exist as assets (current assets to be specific) on the balance sheet. Any reduction in inventories therefore reduces the total assets and impacts the asset turnover ratio (see picture). A higher asset turnover ratio basically means that the corporation is able to generate the same revenue by deploying fewer assets than before. Assuming all else remains same, it results into higher ROA (return on assets) that can do wonderful things for a corporation like raise its share price, enable it to pay higher dividends, ability to expand or do any of the other things that spare change can do. Reduced inventories also reduce the inventory carrying costs (hence COGS), but the impact is relatively small because the fixed costs remain the same and only variable costs are reduced.

The financial impact largely makes the corporation more efficient in using the available resources.

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Transportation

Compared to the inventory optimization, transportation is a different story. It is almost an opportunity for the taking. No matter how the replenishment was decided, eventually what has been ordered needs to be moved from the suppliers to the retailer’s warehouses, and from then on to the stores. The transportation optimization just does not depend on other processes like the inventory optimization does. And almost all the data that this solution needs such as routes, rates, lanes, carriers, purchase orders, and weights/volumes of items is deterministic and largely available in the enterprise already. The only exception to this required data may be the volumetric and weights for the items but that too can be obtained in collaboration with suppliers. For an average retailer, shipping costs used to add up to 1.5 to 3% of the revenues (that is, when the oil was still not trading in the stratosphere, it would reasonably be higher now). Assuming a 2% rate, it is still a cool $200M for a $10B retailer. And unlike the inventory savings that indirectly improves ROA, any savings in transportation are immediately visible to the bottom line.

Here is the financial side of the story.

Shipping costs reduce the cost (hence COGS, cost of goods sold). With all else remaining same, it directly impacts the profitability or margin. First and foremost, profitability directly shows up in the bottom line, and provides extra cash for any other priorities. Then improved margin also improves the ROA (see picture) and allows the companies to do all the wonderful things we mentioned above that can be done with spare change.

Finally it looks like addressing inventory improves overall health but requires a broader approach to reviewing the business processes involved in supply and demand planning, but addressing freight costs is a quick solution to a specific problem of runaway fuel costs. And just as fortifying your diet with vitamins will make you stronger and healthier; an impending infection can only be cured with a strong dose of antibiotics. For now, inventory looks more like the vitamins while transportation is the strong dose of antibiotics. Take your pick!

Friday, May 16, 2008

Replenishment Policies and Inventory Planning

The two processes of replenishment and inventory are closely related. The inventory planning process establishes the optimal inventory levels that must be maintained to meet expected service levels for demand fulfillment. What does that exactly mean? To understand we need to explore the replenishment (or re-ordering) process. In doing so, we will also establish the decision parameters an inventory planning process provides for the replenishment to work at its most optimal levels.

Replenishment or Reordering

Reordering or replenishment process needs to define review period for reordering, and an ordering quantity. Then it needs the inventory parameters to determine whether an order for replenishment should be placed at the time of review or not. Based on how the review period and order quantities are defined, there are a few options to drive the reordering.

Continuous Review and Periodic Review

These terms refer to the frequency of review to determine when orders must be placed for replenishment.

In the continuous review process, the inventory levels are continuously reviewed, and as soon as the stocks fall below a pre-determined level (usually called, reorder point, or reorder level), replenishment order is placed. As more and more companies start using sophisticated IT systems to track their inventories in real-time, the continuous review method becomes a viable and optimal way to plan for replenishment.

Under periodic review, the inventory levels are reviewed at a set frequency. At the time of review, if the stock levels are below the pre-determined level, then an order for replenishment is placed, otherwise it is ignored till the next cycle. This method provides a viable process alternative to the continuous review by segmenting the merchandise into review buckets. This makes it easier to manage when the process is manual, or the number of items involved is extremely large, or when constraints on ordering-day exist.

Order Quantity and Order up-to Level

These terms refer to the process that is used to determine how much is ordered when a replenishment order is placed.

In the first process, the “order quantity” is fixed. If the review determines that an order should be placed, then the order for a pre-defined quantity for that item-location combination is placed for replenishment. The order quantity for all replenishment orders is fixed in this method, though order day may vary or may be fixed depending on the review method.

The second process defines a pre-determined “order up-to level” instead. The actual order quantity is determined as the difference between the on-hand stock on the review day, and the pre-determined “order up-to level”. The order quantity in this process will differ from one order to another depending on the on-hand quantity on the day of the review.

Between these two sets of parameters, four basic reordering process options become available.

Options for Re-ordering Process

Based on the above two parameters, the reordering process can be deployed in the four basic ways. The diagrams below depict these variations of the process.

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Inventory Planning

The two key inputs to optimally run the reordering processes above are the inventory safety stock and reorder levels. These parameters control two of the most critical factors in a supply chain, the amount of inventory, and the ability to maintain favorable service levels.

And both of these are defined by the inventory planning process. As the demand and supply patterns change, the optimal inventory levels required to guarantee imagedesirable service levels also change.

Due to inherent variability in the demand and supply streams at any supply chain node, the ability to service demand directly depends on the safety stock. The relationship between the two is exponential that means that a 100% guarantee to fulfill demand will, in theory, require an infinite amount of safety stock to be maintained.

A good inventory planning process helps define these levels, discriminating between products that require higher service levels versus those that don’t. It helps in maintaining user defined service levels that guarantee desirable fill-rates to fulfill the demand. It also reviews them frequently to make changes to the safety stock recommendations to adjust to the new demand/supply picture.

Here is a quick synopsis of the inventory planning or optimization process that determines the optimal inventory levels to meet a desired service level.

Inputs to Inventory Optimization

As shown in the picture, the inventory planning process takes the following inputs.

  • Desired Service Level -- this is normally a user provided input. The desired service level depends on the item in question, its sales attributes, demand, profitability and associative relationship to the other items. Users normally define groups of items that have similar attributes to define and manage the service levels.
  • Demand -- this is the historical and projected demand for the item at the location. Note that the demand at a location like store will be the POS (point of sale) history, while demand at a distribution center is simply the requests that stores placed on the DC. If the store requests on the DC are not available, one could use the outbound shipments as an approximation of such demand.
  • Supply -- this is the historical and projected supply of the item at the location. The supply at a location like store will be the shipments history from DC, and/or vendors; and supply at a distribution center is generally the inbound shipments from vendors.
  • Supply Lead-time -- this is the historical lead-time of the supplies. The lead-time may vary for every PO/transfer order that is fulfilled even for the same item/vendor/distribution center combinations. This time-series data provides the variability of such lead-time and helps the inventory optimization engine to determine the probability that a specific projected supply will be realized on the need date. image
Process of Inventory Optimization

The inventory planning process determines the variability of the historic data to determine the optimal inventory levels. Most algorithms uses statistical methods, and are therefore computationally intensive.

  • The process pre-processes the time-series data for demand, supplies, and supply lead-time to compute the mean and standard deviation of these series.
  • It then computes the optimal inventory levels (safety stocks) that will be enough to guarantee the target service levels.
Outputs of the Inventory Optimization

The process can recommend the following decision parameters that are then the inputs to the reordering process itself.

  • Recommended safety stock levels.
  • Reorder levels,
  • OUTL (order up-to levels), and
  • Order quantities for the fixed order quantity scenarios above.

Saturday, May 3, 2008

Distribution Options for Retailers

When it comes to distribution, Retailers have many options. These are Direct to Store, conventional Warehouse based “stock and distribute”, or Cross-dock (or flow-through) models. Each of these options affects the supply chain efficiencies and costs. In a previous article on cross-docking, we discussed the process, benefits, and some of readiness issues for cross-docking. The current discussion is targeted to review the costs associated with each of the three options, and a decision support methodology to select the optimal model for distribution.

Distribution Models for Retail

Understanding the costs and having processes to measure these costs provides an objective way to evaluate the impact of selecting these options. Most real-life situations will be complex enough to demand simultaneous deployment of all the three options for differing set of products and locations. But the cost analysis still helps to know what these sets should ideally be.

Following cost elements exist at various nodes and the arcs in the diagram.

Cost Elements Relevant to Distribution

The table below shows the cost elements related to each of the above distribution models. Not all elements exist at all the nodes or supply chain arcs. The table shows where these elements exist, and should be considered in costing the above options.

Some of these costs are harder to measure than others, but almost all of them can be objectively estimated for a good comparison among the available options for the selected set of products.

Cost Element

Direct-to-store

Warehouse (stock & distribute)

Cross-dock (flow-through)

Demand Planning

At the store

At the store

At the Warehouse

At the store

Replenishment Planning

At the store

At the store

At the Warehouse

At the store

Demand Consolidation

NA

NA

Aggregate at cross-dock

Ordering

At the store

At the Warehouse

At the cross-dock

Receiving

At the store

At the Warehouse

At the store

At the cross-dock

At the store

Disposition (after receiving)

NA

At the Warehouse

At the cross-dock

Put-away

NA

At the Warehouse

NA

Storage

NA

At the Warehouse

NA

Transfer Orders or Allocations

NA

At the store (pull based)

At the cross-dock for stores

Transfer Order Fulfillment Planning (waves) or Allocations

NA

At the Warehouse (order waves)

At the cross-dock (distribute using original demand, or allocate)

Picking

NA

At the Warehouse

NA

Packing

NA

At the Warehouse

At the cross-dock

Staging

NA

At the Warehouse

At the cross-dock

Shipping

Supplier to Store

Supplier to Warehouse

Warehouse to Store

Supplier to Cross-dock

Cross-dock to Store

Cost of Inventory

NA (common to all)

At the Warehouse

NA (assuming pure cross-dock, no stocking)

While the cost elements provide good data points for decision making, there is more to be considered.

We will look at the impact of these options on the twin supply chain parameters of lead-time and inventory. It is important to understand the interaction of costs with these two core parameters. For example, a shorter lead-time to replenish the store demand helps in better fulfillment decisions and specially helps when the demand is volatile. It also leads to smaller safety stocks at the stores. But it may also require more frequent and smaller shipments that affect the shipping and receiving operational costs.

Finally we will look at the supply chain processes, and how they get affected in each of the options.

Together this methodology provides a thorough decision support mechanism for selecting the optimal distribution models.

Lead-time and Inventory

Lead-time

We will assume that the lead-time in this context is the total time between the creation of demand at the store and its replenishment. From this point of view, the “warehouse” and the “cross-dock” models provide comparable lead-time efficiencies for the store.

However the “warehouse” model provides an inherent advantage. By maintaining the warehouse inventory, it helps in absorbing the variability in the demand and supply processes; and spares the stores from the effects of demand volatility.

The same is not quite true in the “cross-dock” model as there is no inventory to buffer such effects. Cross-docking models can distribute to stores using the original demand make-up, or by looking at the latest demand and re-allocating merchandise as it is received. While the latter provides a little respite by matching the latest demand against the supply, it still cannot address any system-wide demand surge, or drops.

The lead-time in “direct-to-store” delivery models gets adversely affected due to various factors. Among them are the mixed orders that suppliers need to fill to optimize transportation on a single store demand; locations of supplier warehouses; and the shipping lead-times.

Inventory

In each of the flow-paths above, inventories are required to be maintained either at the store, or at the warehouse, or at both the places.

In the “direct-to-store” delivery models, the inventories are required only at the stores. However the longer lead-time in this mode of replenishment requires that higher inventories be maintained at the stores for maintaining the desired service level.

In the “warehouse” model, the inventory is required largely at the warehouse with a small safety stock at the store. Low lead-times and guaranteed service levels (along the supply chain arc from warehouse to the store) almost take out the need to maintain sizable safety stock in the stores. This is especially suitable for items with low or intermittent demand as centralized inventory in the warehouse provides optimal inventory deployment scenario.

In the “cross-dock” model, the inventories are likely to be lowest. There are no inventories at the cross-docking facility; and stores need to maintain just enough till the next delivery from the warehouse. The demand planning ordering cycles can be tuned to optimize the inventory requirements.

Supply Chain Processes

Demand Planning

We will define demand planning in this context as demand forecasting, inventory planning, and replenishment planning to produce the final need quantities.

In a “direct-to-store” model, the demand planning process is relatively straightforward. The historical sales at the stores are the clear demand stream that can be directly used for future projections. Also this is a single echelon supply chain model with no requirements for demand propagation, or time-phased planning.

The “warehouse” model primarily targets the replenishment at the warehouses. To calculate the demand at the warehouse, one can use the historical shipments to stores, transfer order requests from stores, or the actual store sales propagated and time-phased at the warehouse echelon of the supply chain. The last one is the most desirable, but also most expensive to model and compute.

The “cross-dock” model can actually combine the best of both worlds for demand planning. It allows the demand to be planned at the store level, but consolidates this demand at the warehouse level so that consolidated orders to the suppliers can be created. This adds some complexity to the process; and may also require that the original store demand be maintained for final disbursement of merchandise at the flow-through center. Alternately dynamic allocations can be carried out at the time of receipt at the cross-docking facility.

Ordering

The process of ordering itself does not change much in the three scenarios. However the number of orders in the system varies widely. As each order incurs a processing cost, the higher number of orders to be processed can become overwhelming.

In a “direct-to-store” model, the purchase orders are created at the store level. This naturally results in a larger number of the purchase orders to be processed. In addition, the ordering constraints like order minimums can further make the process inefficient as the stores have to wait till they have enough demand to reach the order minimum, or artificially inflate the demand that may result into unwanted inventory. The upside though, is that there are no internal transfer orders to plan and execute as the orders are directly delivered at the store.

The “warehouse” model consolidates the store demand at the warehouse. As each warehouse may be fulfilling demand for several stores, it substantially reduces the number of orders to be managed. However there are internal transfer orders to be fulfilled that add to the costs.

The “cross-dock” model also consolidates the individual store demand to create aggregated orders for the suppliers. However this model requires that either the original store demand is retained for distributing the receipts, or an additional process of allocation is deployed to disburse the merchandise at the flow-through center.

Logistics

There are considerable differences in the logistics processes in all the above scenarios.

The “direct-to-store” model can result in a large number of LTL shipments. As orders are created for direct delivery to stores, the opportunity to consolidate orders to create TL shipments gets reduced pushing up the transportation costs. However there are warehouse tasks of receiving, putting-away and fulfillment that get eliminated in this approach.

In the “warehouse” model, the inbound transportation can be greatly optimized through consolidation of orders, and shipments bound for the warehouse. However the warehouse adds its own layer of activities and costs, before the merchandise reaches its final destination at the store. These activities consist of receiving, disposition, put-away, storage, order-wave, picking, packing, staging, and shipping at the warehouse.

The “cross-dock” model provides the same inbound transportation optimization opportunities as above. However it does add some of the warehouse activities and costs. The flow-through requires the merchandise to be received, sometimes broken-up, staged, re-palletized and shipped. However it does save on put-away, storage, order-wave and picking activities at the warehouse.