Consultants in Logistics

A MODEL OF EXCELLENCE - Designing Supply Chains For The Future

A MODEL OF EXCELLENCE - Designing Supply Chains For The Future

Dealing with uncertainty is nothing new for the logistics industry and logistics professionals have become adept at responding to an ever changing environment.  Key to success is the ability to envisage possible future scenarios and evaluate alternative supply chain solutions quickly, and cost effectively, so that when change does come the solutions have already been formulated and can be implemented.  Change includes current affairs, sales growth, changes in manufacturing location, changes in energy costs, changes in the way customers buy products and the service they demand -  and many, many more. All will influence the optimum supply chain solution in the years ahead.

Evaluating future supply chain solutions

For some the solutions are relatively simple to evaluate.  The operational requirement quickly dictates the solution.  For others, however, the solution is not so straightforward.

The most complex decisions are where a series of factors inter-relate with each other resulting in a large number of potential infrastructure solutions to be evaluated and assessed.  These might include:

  • Multiple manufacturing sites or ports of entry, each producing either similar or different product ranges
  • The effect of the location of manufacturing points on order lead time; a particular issue as more and more manufacturing moves abroad
  • The impact of inbound transport methods on order lead time and cost, e.g., sea freight versus air freight
  • The option of trading off larger production runs or order sizes with lower unit costs
  • The impact of different locations and numbers of stockholding locations on transport costs
  • The impact of different numbers of stockholding locations on delivery lead times

Of course, complex solutions don’t have to involve warehousing.  Transport networks can be equally complex with regard to the number and location of delivery depots versus sortation hubs.  Increasingly, retailers are replacing warehouses with cross-dock facilities as stockholding gets pushed back to the manufacturer or distributor.

Typically each potential solution has the following elements that will need to be determined:

  • Impact on warehousing or depot costs
  • Impact on inbound transport costs
  • Impact on outbound transport costs
  • Impact on stockholding costs
  • Impact on customer service levels

For example, a single European Distribution Centre could reduce warehousing, inbound transport and stockholding costs but is likely to increase outbound transport costs and could increase delivery lead times.  On the other hand multiple regional warehouses within a single country may allow same-day delivery but with a significant increase in warehousing and stockholding costs.  

What appears to be a relatively straightforward problem can quickly result in a large number of options to be evaluated, as a change in one factor impacts upon others, and it soon becomes apparent that conventional spreadsheet analysis will not suffice.  It is at this point that computer modelling comes into its own.

Computer Modelling

The primary benefit of computer modelling is that, once the model has been established, a large number of alternative solutions can be evaluated quickly, accurately, and at minimal cost.  This is particularly important as one set of answers quickly leads to a whole range of new questions.

The modelling process begins with a review of the existing operation, the identification of the range of solutions to be modelled, and the key variables that will need to be considered.  The second stage is the preparation of a data set that can be used in the modelling process.  This is likely to include:

  • Description of current infrastructure; location of sites, capacities, resources, etc
  • The logistics task; inbound movements, outbound movements, product range, volumetrics
  • Financial information; current operating costs, transport tariffs, stock value, capital employed
  • Service levels currently achieved

Ideally data should be provided at the most granular level possible, For example, real order data for a representative period, volumetric and financial information by SKU and cost of individual resources. The more accurate the data, the more accurate the solution.

The third stage is the construction of the model itself. Ideally, the model should be adapted to fit the problem rather than try to fit the problem to the model. 

The fourth stage is calibration, which involves loading the model with the current operational data to confirm that the output in terms of resources and costs is comparable with the current situation.

Once built, the model can then be used to evaluate a whole range of different options including:

  • Different manufacturing locations or ports of entry
  • Different warehouse locations and numbers
  • Different order sizes
  • Varying sales volumes
  • Different service levels, e.g., next day delivery versus two day delivery or the introduction of home delivery
  • The impact of future economic scenarios e.g. increased fuel costs, increased labour costs, etc

Davies & Robson Infrastructure Modelling

Davies & Robson has a long history of infrastructure modelling across a wide range of industries and companies.  Projects have included:

  • Determining when a major fast-food company’s current network would reach capacity and where the new warehouse should be located
  • Determining the optimum warehouse configuration following the merger of two paper merchants
  • Evaluating the impact of merging two freight networks
  • Evaluating the feasibility of introducing a European warehouse for an office products company
  • Evaluating alternative warehouse locations for a plastics manufacturer
  • Evaluating alternative infrastructure solutions for a pub company

Our success is based on the ownership of our own software, our ability to tailor the model to fit the problem and our ability to manage and utilise very large data files at a very detailed level

Customer & product profitability

A major benefit of using data at the lowest possible level is the ability, as part of the modelling process, to evaluate customer or product profitability.  This can be particularly useful in determining which markets to attack or whether different customer groups or product groups should be treated differently.  Possible options can include:

  • Rationalising the product range
  • Introducing minimum order sizes
  • Providing different transport services
  • Introducing named day delivery to remote areas
  • Supplying some products from a central warehouse or on a made-to-order basis rather than direct from local stock

In today’s fast moving and highly competitive world, companies have to make the right decisions quickly.  Modelling is a powerful tool to assist in the decision making process where the implications of different management decisions can be evaluated in the safety of the office before they are put into practice.

For further information on how Davies & Robson’s proven methodology can help you make the right decisions call 01327 349090.

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